Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/modelling-the-supply-chain-perception-gaps/
This study applies the research of perception gap analysis to supply chain integration and develops a generic model, the 3-Level Gaps Model, with the goal of contributing to harmonization and integration in the supply chain. The model suggests that significant perception gaps may exist among supply chain members with regards to the importance of different performance criteria. The concept of the model is conceived through an empirical and inductive approach, combining the research discipline of supply chain relationship and perception gap analysis. First hand data has been collected through a survey across a key buyer in the motor insurance industry and its eight suppliers. Rigorous statistical analysis testified the research hypotheses, which in turn verified the validity and relevance of the developed 3-Level Gaps Model. The research reveals the significant existence of supply chain perception gaps at all three levels as defined, which could be the root-causes to underperformed supply chain.
Industrial Benchmarking through Information Visualization and Data Envelopmen...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/industrial-benchmarking-through-information-visualization-and-data-envelopment-analysis-a-new-framework/
We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.
Rule-based expert systems for supporting university studentsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/rule-based-expert-systems-for-supporting-university-students/
There are more than 15 million college students in the US alone. Academic advising for courses and scholarships is typically performed by human advisors, bringing an immense managerial workload to faculty members, as well as other staff at universities. This paper reports and discusses the development of two educational expert systems at a private international university. The first expert system is a course advising system which recommends courses to undergraduate students. The second system suggests scholarships to undergraduate students based on their eligibility. While there have been reported systems for course advising, the literature does not seem to contain any references to expert systems for scholarship recommendation and eligibility checking. Therefore the scholarship recommender that we developed is first of its kind. Both systems have been implemented and tested using Oracle Policy Automation (OPA) software.
Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data En...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/benchmark-study-using-data-envelopment-analysis/
This study presents a comprehensive benchmarking study of the top 100 U.S. Universities. The methodologies used to come up with insights into the domain are Data Envelopment Analysis (DEA) and information visualization. Various approaches to evaluating academic institutions have appeared in the literature, including a DEA literature dealing with the ranking of universities. Our study contributes to this literature by the extensive incorporation of information visualization and subsequently the discovery of new insights.
DEA-Based Benchmarking Models In Supply Chain Management: An Application-Orie...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/data-envelopment-analysis/
Data Envelopment Analysis (DEA) is a mathematical methodology for benchmarking a group of entities in a group. The inputs of a DEA model are the resources that the entity consumes, and the outputs of the outputs are the desired outcomes generated by the entity, by using the inputs. DEA returns important benchmarking metrics, including efficiency score, reference set, and projections. While DEA has been extensively applied in supply chain management (SCM) as well as a diverse range of other fields, it is not clear what has been done in the literature in the past, especially given the domain, the model details, and the country of application. Also, it is not clear what would be an acceptable number of DMUs in comparison to existing research. This paper follows a recipe-based approach, listing the main characteristics of the DEA models for supply chain management. This way, practitioners in the field can build their own models without having to perform detailed literature search. Further guidelines are also provided in the paper for practitioners, regarding the application of DEA in SCM benchmarking.
Efficiency of Women’s Technical Institutions By Using Bcc Model Through Dea A...IOSR Journals
This paper conducts an application of the DEA Methodology in the assessment of the performance of JNTUH Colleges the indicators included the Faculty, Students, Infrastructure and Placements of the technical Institutions. The results reveal those institutions that more efficiently carry out these activities. The proposed method has been used for selection of quality attributes in technical education setting the performance of an institute is likely to be influenced by quality of teacher, quality of students, infrastructure administration, extent of training and placement and many others. It is felt that quality and performance evaluation is necessary not only for appraisal but it is also required to improve overall service quality. Finally we discuss about the existence of differences in the strengths and weaknesses between the technical institutions.
Service Quality, Patient Satisfaction, Word Of Mouth, and Revisit Intention i...YogeshIJTSRD
This study investigates the relationship between service quality, patient satisfaction, word of mouth WOM , and revisit intention among dental patients in a clinic, Thailand. The research employed a quantitative approach in data collection for statistical analysis. Quota sampling equally among four age groups was used, and 352 completed copies of self administered questionnaires were returned. The proposed theoretical framework was identified the model adopting PLS SEM. Findings reveal that patient satisfaction is a mediator between service quality and its outcomes of WOM and revisit intention. Referring to elements of service quality, empathy is the highest factor influencing patient satisfaction Beta=0.411, p 0.001 , followed by reliability Beta=0.183, p 0.05 , tangibles Beta=0.119, p 0.05 , assurance Beta=0.077, p 0.05 , and responsiveness, Beta=0.053, p 0.05 at R square 0.556. Revisit intention can be predicted by patient satisfaction by 53.4 percent Beta=0.731, p 0.001,R2=0.534 , and WOM can be explained by patient satisfaction by about 42.9 percent Beta=0.655, p 0.001, R2=0.429 . The study was limited to private dental practice a dental clinic . Thus, the extension to clinics around this area should be considered. Moreover, the researcher suggested comprehensive coverage of other predictors in further research. The implications are managers would emphasize healthcare service quality management to satisfy their patients because it creates positive word of mouth and a revisit intention among dental clinic’s patients. Supaprawat Siripipatthanakul "Service Quality, Patient Satisfaction, Word-Of-Mouth, and Revisit Intention in A Dental Clinic, Thailand" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43943.pdf Paper URL: https://www.ijtsrd.com/management/marketing/43943/service-quality-patient-satisfaction-wordofmouth-and-revisit-intention-in-a-dental-clinic-thailand/supaprawat-siripipatthanakul
Industrial Benchmarking through Information Visualization and Data Envelopmen...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/industrial-benchmarking-through-information-visualization-and-data-envelopment-analysis-a-new-framework/
We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.
Rule-based expert systems for supporting university studentsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/rule-based-expert-systems-for-supporting-university-students/
There are more than 15 million college students in the US alone. Academic advising for courses and scholarships is typically performed by human advisors, bringing an immense managerial workload to faculty members, as well as other staff at universities. This paper reports and discusses the development of two educational expert systems at a private international university. The first expert system is a course advising system which recommends courses to undergraduate students. The second system suggests scholarships to undergraduate students based on their eligibility. While there have been reported systems for course advising, the literature does not seem to contain any references to expert systems for scholarship recommendation and eligibility checking. Therefore the scholarship recommender that we developed is first of its kind. Both systems have been implemented and tested using Oracle Policy Automation (OPA) software.
Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data En...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/benchmark-study-using-data-envelopment-analysis/
This study presents a comprehensive benchmarking study of the top 100 U.S. Universities. The methodologies used to come up with insights into the domain are Data Envelopment Analysis (DEA) and information visualization. Various approaches to evaluating academic institutions have appeared in the literature, including a DEA literature dealing with the ranking of universities. Our study contributes to this literature by the extensive incorporation of information visualization and subsequently the discovery of new insights.
DEA-Based Benchmarking Models In Supply Chain Management: An Application-Orie...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/data-envelopment-analysis/
Data Envelopment Analysis (DEA) is a mathematical methodology for benchmarking a group of entities in a group. The inputs of a DEA model are the resources that the entity consumes, and the outputs of the outputs are the desired outcomes generated by the entity, by using the inputs. DEA returns important benchmarking metrics, including efficiency score, reference set, and projections. While DEA has been extensively applied in supply chain management (SCM) as well as a diverse range of other fields, it is not clear what has been done in the literature in the past, especially given the domain, the model details, and the country of application. Also, it is not clear what would be an acceptable number of DMUs in comparison to existing research. This paper follows a recipe-based approach, listing the main characteristics of the DEA models for supply chain management. This way, practitioners in the field can build their own models without having to perform detailed literature search. Further guidelines are also provided in the paper for practitioners, regarding the application of DEA in SCM benchmarking.
Efficiency of Women’s Technical Institutions By Using Bcc Model Through Dea A...IOSR Journals
This paper conducts an application of the DEA Methodology in the assessment of the performance of JNTUH Colleges the indicators included the Faculty, Students, Infrastructure and Placements of the technical Institutions. The results reveal those institutions that more efficiently carry out these activities. The proposed method has been used for selection of quality attributes in technical education setting the performance of an institute is likely to be influenced by quality of teacher, quality of students, infrastructure administration, extent of training and placement and many others. It is felt that quality and performance evaluation is necessary not only for appraisal but it is also required to improve overall service quality. Finally we discuss about the existence of differences in the strengths and weaknesses between the technical institutions.
Service Quality, Patient Satisfaction, Word Of Mouth, and Revisit Intention i...YogeshIJTSRD
This study investigates the relationship between service quality, patient satisfaction, word of mouth WOM , and revisit intention among dental patients in a clinic, Thailand. The research employed a quantitative approach in data collection for statistical analysis. Quota sampling equally among four age groups was used, and 352 completed copies of self administered questionnaires were returned. The proposed theoretical framework was identified the model adopting PLS SEM. Findings reveal that patient satisfaction is a mediator between service quality and its outcomes of WOM and revisit intention. Referring to elements of service quality, empathy is the highest factor influencing patient satisfaction Beta=0.411, p 0.001 , followed by reliability Beta=0.183, p 0.05 , tangibles Beta=0.119, p 0.05 , assurance Beta=0.077, p 0.05 , and responsiveness, Beta=0.053, p 0.05 at R square 0.556. Revisit intention can be predicted by patient satisfaction by 53.4 percent Beta=0.731, p 0.001,R2=0.534 , and WOM can be explained by patient satisfaction by about 42.9 percent Beta=0.655, p 0.001, R2=0.429 . The study was limited to private dental practice a dental clinic . Thus, the extension to clinics around this area should be considered. Moreover, the researcher suggested comprehensive coverage of other predictors in further research. The implications are managers would emphasize healthcare service quality management to satisfy their patients because it creates positive word of mouth and a revisit intention among dental clinic’s patients. Supaprawat Siripipatthanakul "Service Quality, Patient Satisfaction, Word-Of-Mouth, and Revisit Intention in A Dental Clinic, Thailand" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43943.pdf Paper URL: https://www.ijtsrd.com/management/marketing/43943/service-quality-patient-satisfaction-wordofmouth-and-revisit-intention-in-a-dental-clinic-thailand/supaprawat-siripipatthanakul
MINING DISCIPLINARY RECORDS OF STUDENT WELFARE AND FORMATION OFFICE: AN EXPLO...IJITCA Journal
Data mining is the process of analyzing large datasets, understanding their patterns and discovering useful
information from a large amount of data. Decision tree as one of the common algorithm of data mining is a
tree structure entailing of internal and terminal nodes which process the data to eventually produce a
classification. Classification is the process of dividing a dataset together in a high-class set such that the
members of each set are nearby as expected to one another, and different groups are as far as expected
from one another, where distance is measured with respect to the specific variable(s) you are trying to
predict. Data Envelopment Analysis is a technique wherein the productivity of a unit is evaluated by
equating the volume/amount of output(s) in relation to the volume/amount of input(s) used. The
performance of a unit is calculated by equating its efficiency with the best-perceived performance in the
data set. In this study, a model for measuring the efficiency of Decision Making Units will be presented,
along with related methods of implementation and interpretation. DEA assesses and evaluates the
efficiency of a unit dubbed as Decision-Making Units or DMU. There are many classification techniques
and algorithms but the research study used decision tree using CHAID algorithms. Classification decision
tree algorithm using CHAID as data mining technique identifies the relationship between the demographic
profile of the students and the category of offenses. Cross tabulation is a tool used to analyze categorical
data. It is a type of table in a matrix format that shows the multivariate occurrence dissemination of the
variables and delivers a basic picture of the interrelation between two variables. Both CHAID algorithm
and cross tabulation obtained the same results implying that higher percentage of students commit minor
offenses regardless of college, gender, year level, month and course. The CHAID algorithm used in a
software application Student Offenses Remediation System (STORES) serves as remediation plan for the
university. Further studies should be conducted to identify the effectiveness of the remediation plan by
conducting an empirical investigation on the rule set and/or implement another algorithm to determine the
program efficiency.
Strategy for Technology Transfer and Research Results Commercialization in Un...YogeshIJTSRD
In the globalization stage, there has been an increasing interest in the determinants and outcomes of successful technology transfer and commercialization of research results. In this study, An evaluation framework which crosses technology transfer services and research results commercialization in University has been created. We found that research based business idea generation increase at a faster rate for professors with private sector work experience who have more time for research in their positions. The article ends with a discussion of our empirical findings and its implications for support activities related to technology transfer and commercialization of research results. Dr. Le Nguyen Doan Khoi "Strategy for Technology Transfer and Research Results Commercialization in University" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44945.pdf Paper URL: https://www.ijtsrd.com/management/randd-management/44945/strategy-for-technology-transfer-and-research-results-commercialization-in-university/dr-le-nguyen-doan-khoi
SUPPLIER SELECTION AND EVALUATION – AN INTEGRATED APPROACH OF QFD & AHPIJESM JOURNAL
In current scenario strong competitive pressure forces several organizations to available their products and services, cheaper, faster and improved than the rivals to their valuable customer. Managers have come to comprehend that they cannot do it individually without suitable vendors. Supply Chain Management empower the flows of material, information and funds in a association consisting of customers, suppliers, manufacturers and distributors, which beings raw materials, maintain by internal operations complete with distribution of finished goods. In the continually changing world, assortment of appropriate vender is facilitating in supply chain management, selection of right vendor is extremely useful part of purchasing department. This paper seeks to propose a methodology to integrate the Analytical Hierarchy Process (AHP) for right supplier selection and evaluation and Quality Function Deployment (QFD) analysis to enhance the effectiveness of outsourcing decisions. A selection that combines the subjective factors and objective factors and attitude of the decision maker decide the best supplier in the supply chain management system. The proposed integrated model could be used for supplier selection, which involves several quantitative and qualitative factors. Also could be used to determining the optimum order quantity. The propose method is a group decision making approach which shadows the traditional approaches of supplier selection.
Supply Chain and Production Cost of Brewing Plants in South East, NigeriaYogeshIJTSRD
The study focused on Supply Chain and Production Cost of Brewing plants in South East, Nigeria. The study sought to ascertain the nature of relationship between Supply Chain and Production Cost of the Brewing plants in the South East, Nigeria. The study had a population size of 1528, out of which a sample size of 431 was obtained using Cochran’s formula at 5 error tolerance and 95 level of confidence. Primary data were collected through structured questionnaire and observation and secondary data were obtained through textbooks, and journal materials. Out of 431 copies of the questionnaire that were distributed, 401 copies were returned while 30 copies were not returned. The hypothesis was tested using Pearson Product Moment Correlation Coefficient. Finding revealed that there was a significant positive relationship between supply chain and production cost of Brewing plants in South East, Nigeria. r = 0.866 . The study concluded that supply chain practices is a set of activities carried out in any organization to promote effective management of its supply chains in order to improve production cost. The study recommended that brewing plants in South East Nigeria need to ensure that their supply chain concentrates on the most important member, the customer who should be kept satisfied at all costs, thus helping to boost customer services and also put in place a well managed supply chain that removes disruptions and obstacles in their business activities. Nwatu Chukwuemeka "Supply Chain and Production Cost of Brewing Plants in South-East, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44965.pdf Paper URL: https://www.ijtsrd.com/management/strategic-management/44965/supply-chain-and-production-cost-of-brewing-plants-in-southeast-nigeria/nwatu-chukwuemeka
Inventory management system which is helpful for the business operators, where shopkeeper keep the records of purchase and sales. This inventory management system will have the ability to track sales and available inventory, tells a shopkeeper when its time to reorder and how much to purchase. K Ravi Sai | Dr. P. Jaya Rami Reddy "A Study on Inventory Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44968.pdf Paper URL: https://www.ijtsrd.com/management/accounting-and-finance/44968/a-study-on-inventory-management/k-ravi-sai
Evaluation of E-Learning Web Sites Using Fuzzy Axiomatic Design Based ApproachGurdal Ertek
High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is
applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations
for future research are offered.
http://research.sabanciuniv.edu.
A NALYSIS O F S UPPLIER’S P ERFORMANCE T HROUGH F PIR /F NIR A ND M EM...csandit
In today’s highly competitive business environment,
evaluation of suppliers is the prime function
of the purchasing department of the organization. I
t is due to the fact that high percentage of the
material cost for manufacturing of a product is inv
olved. Identification of decision criteria and
methods for supplier evaluation are appearing to be
the important research area in the
literature. In this paper, hybrid methodology of Fu
zzy positive Ideal rating /Fuzzy Negative
Ideal rating and Membership Degree Transformation-
M (1, 2, 3) is proposed for evaluation of
supplier’s performance. A wide literature review is
made and six selection criteria namely:
Cost, Quality, Service, Business performance, Techn
ical Capability and Delivery performance
are considered for evaluation. A detailed applicati
on of the proposed methodology is illustrated.
The proposed methodology is useful not only to judg
e the overall performance of the supplier
but also to know which criteria/sub-criteria need t
o be improved
A SURVEY OF EMPLOYERS’ NEEDS FOR TECHNICAL AND SOFT SKILLS AMONG NEW GRADUATESijcseit
ABSTRACT
Motivated by concern about the ability of graduates to succeed in the workforce, universities frequently conduct surveys of local and regional employers, to understand those companies’ expectations. These can uncover specific needs not being addressed. Following a similar line of inquiry, prior research at Oregon State University interviewed employers, with the aim of identifying skills of concern. The current paper takes this research another step further by presenting a survey-based study aimed at quantifying the prevalence and level of employers’ desire for workers who have these identified skills. Although all skills were rated as moderately useful or better, most soft skills scored higher than most technical skills. Nonetheless, three technical skills (source code versioning, testing and agile methods) scored approximately as well as the soft skills; these three technical skills, like soft skills, were cross-cutting and applicable to more than one software development context. Further survey questions revealed that employers preferred that, to the extent that students focus on building technical skill, these learning experiences ideally should involve creating software that students can use as evidence of their qualifications.
Study of Performance Appraisal System for Faculty Members in Selected Managem...ijtsrd
"Performance Appraisal provides a periodic review and evaluation of an individual’s job performance. Although the appraisal forms may only be completed once a year, the job of performance appraisal is continuous – sometimes daily and requires effective communication on both the part of the supervisor and the Respondent. The supervisor is ultimately responsible to make sure these conversations actually take place and are documented. It is essential that the supervisor hold all performance discussions and documentation in complete confidence. Every organization is having an objective towards optimum performance and the Respondent is the key in achieving it. It is necessary that the Respondents performance should reach optimality for the success of the organization. Many organizations are having performance appraisal systems to evaluate the effectiveness and efficiency of their Respondent using linguistic labels to their performance. In a production unit, Respondent performance is proportional to the quality and quantity of production, where as in case of educational institute there is no such direct tool available to evaluate the productivity of its faculty members. In judging efficiency of faculty members, often the institute deals with vague or imprecise data resulting to an inconsistence performance evaluation. This study has been particularly taken by the researcher to understand the present scenario of the institute performance appraisal system in these colleges. Researcher wants to find out the weather appraisal is really helping Respondents for the better future or not. Mr. Santosh V. Hasure | Mr. Viraj V. Jadhav ""Study of Performance Appraisal System for Faculty Members in Selected Management Institutes Affiliated to Shivaji University Kolhapur"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23069.pdf
Paper URL: https://www.ijtsrd.com/management/hrm-and-retail-business/23069/study-of-performance-appraisal-system-for-faculty-members-in-selected-management-institutes-affiliated-to-shivaji-university-kolhapur/mr-santosh-v-hasure"
Implementation of AHP-MAUT and AHP-Profile Matching Methods in OJT Student Pl...Gede Surya Mahendra
ABSTRACT
To improve the quality and quality of employment, OJT is very much needed by Monarch Bali students, but the process, which is still manual, makes decisions that are taken less fast, accurate, effective and efficient. In line with the roadmap of Monarch Bali, it is necessary to develop an automation system to be able to improve the performance of decision making for OJT student placement by making a DSS. The method used in this research is AHP-MAUT and AHP-PM. The decision makers in this study were 3 people, and out of a total of 500 OJT students, 8 OJT students for F&B class, 12 OJT students for Housekeeping class, 13 OJT students for Catering class, and 17 OJT students for Food Management class with a total of 50 OJT students. Implementation of AHP-MAUT, OJT students from the F&B class with the code StudentD04 have the highest preference value of 0.5724, and OJT students from the beverage class with the code StudentA02 have a preference value of 4.1155 calculated using AHP-PM, each being ranked first.
Keywords:
Analytical Hierarchy Process, Multi-Attribute Utility Theory, Profile Matching,
CRISP-DM,
On the Job Training
Effective Marketing Science Applications: Insights from the ISMS-MSI Practice...Felipe Affonso
From 2003 to 2012, the ISMS-MSI Practice Prize/Award competition has documented 25 impactful projects,
with associated papers appearing in Marketing Science. This article reviews these papers and projects, examines
their influence on the relevant organizations, and provides a perspective on the diffusion and impact of
marketing science models within the organizations. We base our analysis on three sources of data—the articles,
authors’ responses to a survey, and in-depth interviews with the authors. We draw some conclusions about
how marketing science models can create more impact without losing academic rigor while maintaining strong
relevance to practice.
We find that the application and diffusion of marketing science models are not restricted to the well-known
choice models, conjoint analysis, mapping, and promotional analysis—there are very effective applications across
a wide range of managerial problems using an array of marketing science techniques. There is no one successful
approach, and although some factors are correlated with impactful marketing science models, there are a number
of pathways by which a project can add value to its client organization. Simpler, easier-to-use models that
offer robust and improved results can have a stronger impact than academically sophisticated models can. Organizational
buy-in is critical and can be achieved through recognizing high-level champions, holding in-house
presentations and dialogues, doing pilot assignments, involving multidepartment personnel, and speaking the
same language as the influential executives. And we find that intermediaries often, but not always, play a key
role in the transportability and diffusion of models across organizations.
Although these applications are impressive and reflect profitable academic–practitioner partnerships, changes
in the knowledge base and reward systems for academics, intermediaries, and practitioners are required for
marketing science approaches to realize their potential impact on a much larger scale than the highly selective
sample that we have been able to analyze.
Online payment portals are a powerful tool that makes our life simple and gives the luxury to make all required payment transactions around any part of the World. The advancement of internet and logistics systems, now it is possible for anybody to shop any product around the world and get it shipped to his\her. The main objectives are to study the problems faced through the online payment system. To study the factors influencing the online payment system.
Tutkimuksessa tarkastellaan työmarkkinoiden polarisoitumista Suomessa. Aiemmat tutkimukset tarkastelevat kehitystä koko talouden, toimialojen tai alueiden tasolla. Tutkimuksessa tarkastellaan työpaikkojen rakennemuutosta yritystasolla. Tulokset osoittavat, että rutiiniluonteiset työtehtävät ovat vähentyneet merkittävästi suomalaisissa yrityksissä. Palkkajakauman keskivaiheilla olevien rutiiniluonteisten työtehtävien väheneminen kytkeytyy informaatio- ja kommunikaatioteknologian käyttöönottoon yrityksissä.
Ph.D Public Viva Voce - PPT - Thesis - New Product Development Strategy and Analysis: A Study With Special Reference to Fabrication Engineering Industries in Chennai
Perception gap and its impact on supply chain performanceGurdal Ertek
The main purpose of this paper is to frame the perception differences between the buyer and supplier on the supply chain’s operational delivery, and to investigate their causal relation to the overall supply chain performance. A conceptual three-level model is developed to theorise the structural existence of the perception gaps in primarily a dyadic buyer-supplier setting. Using the primary data gathered through a major survey exercise, confirmative factor analysis and structural equation modelling were conducted to test the
hypotheses on the significance and relevance of the perception gaps in supply chain management. This study provides a better conceptual understanding of the perception differences on the required as well as achieved operational
deliveries within the supplier-buyer dyad, and reveals their significant and negative causal impact on the overall supply chain performance.
http://ertekprojects.com/gurdal-ertek-publications/
bit.ly/1PXM70m
http://www.inderscienceonline.com/doi/abs/10.1504/IJBPSCM.2015.069919?jour
nalCode=ijbpscm
Operation Management Strategies 1
LITERATURE REVIEW 7
Literature Reviewin Operation Management Strategies
Qualitative cases in operation management
The study inspects the condition of subjective research endeavors in operation administration. Five fundamental operation administration diaries are incorporated for their effect on the field. The subjective detailed analyzes picked were distributed somewhere around 1993 and 2008. With an expanding pattern to utilizing more subjective research endeavors, there have been significant and huge commitments to the operation administration department, particularly in the territory of hypothesis building.
In a significant number of the subjective detailed analyzes they explored, sufficient points of interest in research outline, information accumulation, and information investigation were absent. For example, there are studies that don't offer examining rationale or a portrayal of the investigation through which research draws results. Further, researches conventions for doing inductive detailed analyze are much better created contrasted with the research conventions for doing deductive careful investigations. Thusly, there is an absence of reliability in how the case technique has been connected. As subjective researchers, they offer recommendations on how we can enhance what have been done and raise the level of thoroughness and consistency (Mei, 2011).
Buyer perceptions of supply disruption risk
Scott argues that as supply chains get to be more minds boggling, firms face expanding dangers of supply interruptions. The process through which purchasers settle on decisions notwithstanding these dangers, nonetheless, has not been investigated. In spite of research highlighting the criticalness of behavioral methodologies to risk, there is restricted research that applies these perspectives of danger in the supply chain writing. This paper addresses this crevice by drawing on behavioral danger hypothesis to examine the causal connections among circumstance, representations of danger, and choice making inside the buying area.
They investigate the relationship between greatness of supply disturbance, likelihood of supply interruption, and general supply disturbance risk. Furthermore, they attract on trade hypotheses to distinguish item and business sector figures that effect purchasers' impression of the likelihood and extent of supply interruption. At last, they take a gander at how representations of danger influence the choice to look for options wellsprings of supply. The model was tested utilizing information gathered from 223 obtaining administrators and purchasers of immediate materials. The results demonstrate that both the likelihood and the size of supply disturbance are paramount to purchasers' general view of supply interruption risk (Ellis, 2010).
Examining supply chain relationships
Gilbert finds out that firm.
Fuzzy Logic Framework for Qualitative Evaluation of Supply Chain Responsivenesstheijes
Fuzzy logic can be a powerful tool for managers to use instead of traditional mathematical models when measuring the of supply chains responsivess. The flexibility of the model allows the decision maker to introduce vagueness, uncertainty, and subjectivity into the evaluation system. Responsiveness measurement represents a critically important decision that often involves subjective information. Fuzzy logic models provide a reasonable solution to these common decision situations. After extensive exploration of the literature, we recommend an outcome of developing a Fuzzy logic framework in measuring qualitative aspects of supply chain responsiveness. In this paper, responsiveness as one of the important factors of measuring qualitative performance is discussed and a fuzzy logic framework is developed to measure supply chain responsiveness.
MINING DISCIPLINARY RECORDS OF STUDENT WELFARE AND FORMATION OFFICE: AN EXPLO...IJITCA Journal
Data mining is the process of analyzing large datasets, understanding their patterns and discovering useful
information from a large amount of data. Decision tree as one of the common algorithm of data mining is a
tree structure entailing of internal and terminal nodes which process the data to eventually produce a
classification. Classification is the process of dividing a dataset together in a high-class set such that the
members of each set are nearby as expected to one another, and different groups are as far as expected
from one another, where distance is measured with respect to the specific variable(s) you are trying to
predict. Data Envelopment Analysis is a technique wherein the productivity of a unit is evaluated by
equating the volume/amount of output(s) in relation to the volume/amount of input(s) used. The
performance of a unit is calculated by equating its efficiency with the best-perceived performance in the
data set. In this study, a model for measuring the efficiency of Decision Making Units will be presented,
along with related methods of implementation and interpretation. DEA assesses and evaluates the
efficiency of a unit dubbed as Decision-Making Units or DMU. There are many classification techniques
and algorithms but the research study used decision tree using CHAID algorithms. Classification decision
tree algorithm using CHAID as data mining technique identifies the relationship between the demographic
profile of the students and the category of offenses. Cross tabulation is a tool used to analyze categorical
data. It is a type of table in a matrix format that shows the multivariate occurrence dissemination of the
variables and delivers a basic picture of the interrelation between two variables. Both CHAID algorithm
and cross tabulation obtained the same results implying that higher percentage of students commit minor
offenses regardless of college, gender, year level, month and course. The CHAID algorithm used in a
software application Student Offenses Remediation System (STORES) serves as remediation plan for the
university. Further studies should be conducted to identify the effectiveness of the remediation plan by
conducting an empirical investigation on the rule set and/or implement another algorithm to determine the
program efficiency.
Strategy for Technology Transfer and Research Results Commercialization in Un...YogeshIJTSRD
In the globalization stage, there has been an increasing interest in the determinants and outcomes of successful technology transfer and commercialization of research results. In this study, An evaluation framework which crosses technology transfer services and research results commercialization in University has been created. We found that research based business idea generation increase at a faster rate for professors with private sector work experience who have more time for research in their positions. The article ends with a discussion of our empirical findings and its implications for support activities related to technology transfer and commercialization of research results. Dr. Le Nguyen Doan Khoi "Strategy for Technology Transfer and Research Results Commercialization in University" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44945.pdf Paper URL: https://www.ijtsrd.com/management/randd-management/44945/strategy-for-technology-transfer-and-research-results-commercialization-in-university/dr-le-nguyen-doan-khoi
SUPPLIER SELECTION AND EVALUATION – AN INTEGRATED APPROACH OF QFD & AHPIJESM JOURNAL
In current scenario strong competitive pressure forces several organizations to available their products and services, cheaper, faster and improved than the rivals to their valuable customer. Managers have come to comprehend that they cannot do it individually without suitable vendors. Supply Chain Management empower the flows of material, information and funds in a association consisting of customers, suppliers, manufacturers and distributors, which beings raw materials, maintain by internal operations complete with distribution of finished goods. In the continually changing world, assortment of appropriate vender is facilitating in supply chain management, selection of right vendor is extremely useful part of purchasing department. This paper seeks to propose a methodology to integrate the Analytical Hierarchy Process (AHP) for right supplier selection and evaluation and Quality Function Deployment (QFD) analysis to enhance the effectiveness of outsourcing decisions. A selection that combines the subjective factors and objective factors and attitude of the decision maker decide the best supplier in the supply chain management system. The proposed integrated model could be used for supplier selection, which involves several quantitative and qualitative factors. Also could be used to determining the optimum order quantity. The propose method is a group decision making approach which shadows the traditional approaches of supplier selection.
Supply Chain and Production Cost of Brewing Plants in South East, NigeriaYogeshIJTSRD
The study focused on Supply Chain and Production Cost of Brewing plants in South East, Nigeria. The study sought to ascertain the nature of relationship between Supply Chain and Production Cost of the Brewing plants in the South East, Nigeria. The study had a population size of 1528, out of which a sample size of 431 was obtained using Cochran’s formula at 5 error tolerance and 95 level of confidence. Primary data were collected through structured questionnaire and observation and secondary data were obtained through textbooks, and journal materials. Out of 431 copies of the questionnaire that were distributed, 401 copies were returned while 30 copies were not returned. The hypothesis was tested using Pearson Product Moment Correlation Coefficient. Finding revealed that there was a significant positive relationship between supply chain and production cost of Brewing plants in South East, Nigeria. r = 0.866 . The study concluded that supply chain practices is a set of activities carried out in any organization to promote effective management of its supply chains in order to improve production cost. The study recommended that brewing plants in South East Nigeria need to ensure that their supply chain concentrates on the most important member, the customer who should be kept satisfied at all costs, thus helping to boost customer services and also put in place a well managed supply chain that removes disruptions and obstacles in their business activities. Nwatu Chukwuemeka "Supply Chain and Production Cost of Brewing Plants in South-East, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44965.pdf Paper URL: https://www.ijtsrd.com/management/strategic-management/44965/supply-chain-and-production-cost-of-brewing-plants-in-southeast-nigeria/nwatu-chukwuemeka
Inventory management system which is helpful for the business operators, where shopkeeper keep the records of purchase and sales. This inventory management system will have the ability to track sales and available inventory, tells a shopkeeper when its time to reorder and how much to purchase. K Ravi Sai | Dr. P. Jaya Rami Reddy "A Study on Inventory Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44968.pdf Paper URL: https://www.ijtsrd.com/management/accounting-and-finance/44968/a-study-on-inventory-management/k-ravi-sai
Evaluation of E-Learning Web Sites Using Fuzzy Axiomatic Design Based ApproachGurdal Ertek
High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is
applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations
for future research are offered.
http://research.sabanciuniv.edu.
A NALYSIS O F S UPPLIER’S P ERFORMANCE T HROUGH F PIR /F NIR A ND M EM...csandit
In today’s highly competitive business environment,
evaluation of suppliers is the prime function
of the purchasing department of the organization. I
t is due to the fact that high percentage of the
material cost for manufacturing of a product is inv
olved. Identification of decision criteria and
methods for supplier evaluation are appearing to be
the important research area in the
literature. In this paper, hybrid methodology of Fu
zzy positive Ideal rating /Fuzzy Negative
Ideal rating and Membership Degree Transformation-
M (1, 2, 3) is proposed for evaluation of
supplier’s performance. A wide literature review is
made and six selection criteria namely:
Cost, Quality, Service, Business performance, Techn
ical Capability and Delivery performance
are considered for evaluation. A detailed applicati
on of the proposed methodology is illustrated.
The proposed methodology is useful not only to judg
e the overall performance of the supplier
but also to know which criteria/sub-criteria need t
o be improved
A SURVEY OF EMPLOYERS’ NEEDS FOR TECHNICAL AND SOFT SKILLS AMONG NEW GRADUATESijcseit
ABSTRACT
Motivated by concern about the ability of graduates to succeed in the workforce, universities frequently conduct surveys of local and regional employers, to understand those companies’ expectations. These can uncover specific needs not being addressed. Following a similar line of inquiry, prior research at Oregon State University interviewed employers, with the aim of identifying skills of concern. The current paper takes this research another step further by presenting a survey-based study aimed at quantifying the prevalence and level of employers’ desire for workers who have these identified skills. Although all skills were rated as moderately useful or better, most soft skills scored higher than most technical skills. Nonetheless, three technical skills (source code versioning, testing and agile methods) scored approximately as well as the soft skills; these three technical skills, like soft skills, were cross-cutting and applicable to more than one software development context. Further survey questions revealed that employers preferred that, to the extent that students focus on building technical skill, these learning experiences ideally should involve creating software that students can use as evidence of their qualifications.
Study of Performance Appraisal System for Faculty Members in Selected Managem...ijtsrd
"Performance Appraisal provides a periodic review and evaluation of an individual’s job performance. Although the appraisal forms may only be completed once a year, the job of performance appraisal is continuous – sometimes daily and requires effective communication on both the part of the supervisor and the Respondent. The supervisor is ultimately responsible to make sure these conversations actually take place and are documented. It is essential that the supervisor hold all performance discussions and documentation in complete confidence. Every organization is having an objective towards optimum performance and the Respondent is the key in achieving it. It is necessary that the Respondents performance should reach optimality for the success of the organization. Many organizations are having performance appraisal systems to evaluate the effectiveness and efficiency of their Respondent using linguistic labels to their performance. In a production unit, Respondent performance is proportional to the quality and quantity of production, where as in case of educational institute there is no such direct tool available to evaluate the productivity of its faculty members. In judging efficiency of faculty members, often the institute deals with vague or imprecise data resulting to an inconsistence performance evaluation. This study has been particularly taken by the researcher to understand the present scenario of the institute performance appraisal system in these colleges. Researcher wants to find out the weather appraisal is really helping Respondents for the better future or not. Mr. Santosh V. Hasure | Mr. Viraj V. Jadhav ""Study of Performance Appraisal System for Faculty Members in Selected Management Institutes Affiliated to Shivaji University Kolhapur"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23069.pdf
Paper URL: https://www.ijtsrd.com/management/hrm-and-retail-business/23069/study-of-performance-appraisal-system-for-faculty-members-in-selected-management-institutes-affiliated-to-shivaji-university-kolhapur/mr-santosh-v-hasure"
Implementation of AHP-MAUT and AHP-Profile Matching Methods in OJT Student Pl...Gede Surya Mahendra
ABSTRACT
To improve the quality and quality of employment, OJT is very much needed by Monarch Bali students, but the process, which is still manual, makes decisions that are taken less fast, accurate, effective and efficient. In line with the roadmap of Monarch Bali, it is necessary to develop an automation system to be able to improve the performance of decision making for OJT student placement by making a DSS. The method used in this research is AHP-MAUT and AHP-PM. The decision makers in this study were 3 people, and out of a total of 500 OJT students, 8 OJT students for F&B class, 12 OJT students for Housekeeping class, 13 OJT students for Catering class, and 17 OJT students for Food Management class with a total of 50 OJT students. Implementation of AHP-MAUT, OJT students from the F&B class with the code StudentD04 have the highest preference value of 0.5724, and OJT students from the beverage class with the code StudentA02 have a preference value of 4.1155 calculated using AHP-PM, each being ranked first.
Keywords:
Analytical Hierarchy Process, Multi-Attribute Utility Theory, Profile Matching,
CRISP-DM,
On the Job Training
Effective Marketing Science Applications: Insights from the ISMS-MSI Practice...Felipe Affonso
From 2003 to 2012, the ISMS-MSI Practice Prize/Award competition has documented 25 impactful projects,
with associated papers appearing in Marketing Science. This article reviews these papers and projects, examines
their influence on the relevant organizations, and provides a perspective on the diffusion and impact of
marketing science models within the organizations. We base our analysis on three sources of data—the articles,
authors’ responses to a survey, and in-depth interviews with the authors. We draw some conclusions about
how marketing science models can create more impact without losing academic rigor while maintaining strong
relevance to practice.
We find that the application and diffusion of marketing science models are not restricted to the well-known
choice models, conjoint analysis, mapping, and promotional analysis—there are very effective applications across
a wide range of managerial problems using an array of marketing science techniques. There is no one successful
approach, and although some factors are correlated with impactful marketing science models, there are a number
of pathways by which a project can add value to its client organization. Simpler, easier-to-use models that
offer robust and improved results can have a stronger impact than academically sophisticated models can. Organizational
buy-in is critical and can be achieved through recognizing high-level champions, holding in-house
presentations and dialogues, doing pilot assignments, involving multidepartment personnel, and speaking the
same language as the influential executives. And we find that intermediaries often, but not always, play a key
role in the transportability and diffusion of models across organizations.
Although these applications are impressive and reflect profitable academic–practitioner partnerships, changes
in the knowledge base and reward systems for academics, intermediaries, and practitioners are required for
marketing science approaches to realize their potential impact on a much larger scale than the highly selective
sample that we have been able to analyze.
Online payment portals are a powerful tool that makes our life simple and gives the luxury to make all required payment transactions around any part of the World. The advancement of internet and logistics systems, now it is possible for anybody to shop any product around the world and get it shipped to his\her. The main objectives are to study the problems faced through the online payment system. To study the factors influencing the online payment system.
Tutkimuksessa tarkastellaan työmarkkinoiden polarisoitumista Suomessa. Aiemmat tutkimukset tarkastelevat kehitystä koko talouden, toimialojen tai alueiden tasolla. Tutkimuksessa tarkastellaan työpaikkojen rakennemuutosta yritystasolla. Tulokset osoittavat, että rutiiniluonteiset työtehtävät ovat vähentyneet merkittävästi suomalaisissa yrityksissä. Palkkajakauman keskivaiheilla olevien rutiiniluonteisten työtehtävien väheneminen kytkeytyy informaatio- ja kommunikaatioteknologian käyttöönottoon yrityksissä.
Ph.D Public Viva Voce - PPT - Thesis - New Product Development Strategy and Analysis: A Study With Special Reference to Fabrication Engineering Industries in Chennai
Perception gap and its impact on supply chain performanceGurdal Ertek
The main purpose of this paper is to frame the perception differences between the buyer and supplier on the supply chain’s operational delivery, and to investigate their causal relation to the overall supply chain performance. A conceptual three-level model is developed to theorise the structural existence of the perception gaps in primarily a dyadic buyer-supplier setting. Using the primary data gathered through a major survey exercise, confirmative factor analysis and structural equation modelling were conducted to test the
hypotheses on the significance and relevance of the perception gaps in supply chain management. This study provides a better conceptual understanding of the perception differences on the required as well as achieved operational
deliveries within the supplier-buyer dyad, and reveals their significant and negative causal impact on the overall supply chain performance.
http://ertekprojects.com/gurdal-ertek-publications/
bit.ly/1PXM70m
http://www.inderscienceonline.com/doi/abs/10.1504/IJBPSCM.2015.069919?jour
nalCode=ijbpscm
Operation Management Strategies 1
LITERATURE REVIEW 7
Literature Reviewin Operation Management Strategies
Qualitative cases in operation management
The study inspects the condition of subjective research endeavors in operation administration. Five fundamental operation administration diaries are incorporated for their effect on the field. The subjective detailed analyzes picked were distributed somewhere around 1993 and 2008. With an expanding pattern to utilizing more subjective research endeavors, there have been significant and huge commitments to the operation administration department, particularly in the territory of hypothesis building.
In a significant number of the subjective detailed analyzes they explored, sufficient points of interest in research outline, information accumulation, and information investigation were absent. For example, there are studies that don't offer examining rationale or a portrayal of the investigation through which research draws results. Further, researches conventions for doing inductive detailed analyze are much better created contrasted with the research conventions for doing deductive careful investigations. Thusly, there is an absence of reliability in how the case technique has been connected. As subjective researchers, they offer recommendations on how we can enhance what have been done and raise the level of thoroughness and consistency (Mei, 2011).
Buyer perceptions of supply disruption risk
Scott argues that as supply chains get to be more minds boggling, firms face expanding dangers of supply interruptions. The process through which purchasers settle on decisions notwithstanding these dangers, nonetheless, has not been investigated. In spite of research highlighting the criticalness of behavioral methodologies to risk, there is restricted research that applies these perspectives of danger in the supply chain writing. This paper addresses this crevice by drawing on behavioral danger hypothesis to examine the causal connections among circumstance, representations of danger, and choice making inside the buying area.
They investigate the relationship between greatness of supply disturbance, likelihood of supply interruption, and general supply disturbance risk. Furthermore, they attract on trade hypotheses to distinguish item and business sector figures that effect purchasers' impression of the likelihood and extent of supply interruption. At last, they take a gander at how representations of danger influence the choice to look for options wellsprings of supply. The model was tested utilizing information gathered from 223 obtaining administrators and purchasers of immediate materials. The results demonstrate that both the likelihood and the size of supply disturbance are paramount to purchasers' general view of supply interruption risk (Ellis, 2010).
Examining supply chain relationships
Gilbert finds out that firm.
Fuzzy Logic Framework for Qualitative Evaluation of Supply Chain Responsivenesstheijes
Fuzzy logic can be a powerful tool for managers to use instead of traditional mathematical models when measuring the of supply chains responsivess. The flexibility of the model allows the decision maker to introduce vagueness, uncertainty, and subjectivity into the evaluation system. Responsiveness measurement represents a critically important decision that often involves subjective information. Fuzzy logic models provide a reasonable solution to these common decision situations. After extensive exploration of the literature, we recommend an outcome of developing a Fuzzy logic framework in measuring qualitative aspects of supply chain responsiveness. In this paper, responsiveness as one of the important factors of measuring qualitative performance is discussed and a fuzzy logic framework is developed to measure supply chain responsiveness.
Supply Chain Metrics That Matter: A Focus on the High-Tech Industry - 2015Lora Cecere
Executive Summary: Current State of the High-Tech Industry
Globalization. Commodity inflation. Margin squeeze. Economic uncertainty. Warranty issues. Shortening product life cycles. Recalls. Labor arbitrage and outsourcing. The list of market pressures could go on and on, but one thing is clear: the high-tech industry was redefined over the course of the last decade. In Table 4 we show the progress of discrete industries for the periods of 2006-2014 and 2011- 2014. Notice there is more red (lack of progress) than green (progress) in the industry trends.
Table 4. Supply Chain Performance by Industry within the Discrete Industries
High-tech companies have the most advanced practices for inventory management, planning and analytics. They are just treading water (keeping slightly ahead of the market dynamics). The rate of change drives innovation. Within this industry there are more supply chain innovators taking a hard look and driving the adoption of prescriptive analytics and canonical value network infrastructures.
Taking a closer view at the value chain of the sub-industries within high-tech, i.e. consumer electronics, B2B Electronics, and semiconductor industries, the impact of the industry drivers and the importance of supply chain performance becomes clearer.
Table 5. Supply Chain Performance by Industry within the High-Tech Sector
The entire value chain is struggling to maintain margins and improve inventory turns. For consumer electronics and B2B electronics, growth is down, operating margins are degrading and inventory turns worsening. Supply chain matters more than ever.
A new fuzzy dematel todim hybrid method for evaluation criteria of knowledge ...ijmvsc
Knowledge management (KM) adoption in the supply chain network needs a good investment as well as
few changes in the culture of the entire SC. Knowledge management is the process of creating,
distributing and transferring information. The goal of this study is to Rank KM criteria in supply chain
network in Iran which is important for firms these days. Criterion used in this paper were extracted from
the literature review and were confirmed by supply chain experts. The proposed approach for ranking and
finding out about these criterion is hybrid fuzzy DEMATEL-TODIM, with using fuzzy number as data for
our studies we could avoid uncertainty. The data was gathered from PhD. And Ms. Students in industrial
engineering of Kharrazmi university of Tehran and PhD. And Ms. Students of the management department
of Semnan university. A new hybrid approach was used for achieving the results of this study. This new
hybrid approach ranks data criteria respect to each other, then by using TODIM for ranking respect to
the best situation (gains), the rates of criterion were determined which is a very important advantage.
The Influence of Supply Chain Integration on the Intrapreneurship in Supply C...IJERA Editor
These days, SMEs pay a lot of attention to concept of Supply Chain Management (SCM) in order to achieve
competitiveness. The logic behind such act is integrating the activities of value creation within any kind of
organizational context. Such integrity would collaborate with managers to accomplish the competitive edge that
they are aiming to achieve. The goal of current research is to identify scopes of a unique construct which is
known as Entrepreneurial Supply Chain Management competency. Therefore, the notions of SCM and
entrepreneurship are being aligned together for evaluating the organizational performance. The outcomes
demonstrate that SCM in fact is a critical issue that can alter the organizational performance, thus, through
consideration of SCM, we should focus on supply chain integration and its impacts on intrapreneurship and
innovation of an organization. In order to be successful in such competitive context, SMEs need to provide
novel competences which are not imitable and to increase their application in supply chain and also to improve
their total performance.
This Research deals with the supply chain
management (SCM) provide us a high practical rapidity flow of
high quality, significant information that will assist suppliers to
provide a constant and specifically timed flow of resources to
customers. However, unplanned demand oscillations, including
those caused by stock outs, in the supply chain performance
development produce distortions. There are numerous causes,
often in combination that will cause these supply chain
distortions to start what has become known as the “Bullwhip
Effect”.
While the devil is generally hidden in the details, as is the
case here, the most common drivers of these demand distortions
are: Customers, Promotions, Sales, Manufacturing Policies,
Processes, Systems and Suppliers. The “Bullwhip Effect” has in
the past been recognized as normal, and in fact, thought to be a
predictable part of the order-to-delivery cycle. In this paper we
propose a novel effective approach to find the MSE (Mean square
error) with the help of MAMDANI Fuzzy logic.
Supply Chain Metrics That Matter: A Focus on the High-Tech Industry - 2016Lora Cecere
Executive Overview
High-Tech supply chains serve global markets with regional preferences. They include some of the most advanced processes and strongest supply chain leadership across all industries. As a result, the value chain made more progress than others in the course of the last decade.
Unlike other value chains, all four segments of this value chain improved inventory turns. It was through hard work, network design, and a focus on planning. While other industries implemented supply chain planning and then turned to spreadsheets, this industry got good at managing inventories. The stakes were higher. As inventories sit in the channel for the High-Tech industry, prices fall. As a result, this industry has developed some of the best inventory practices across all industries.
On the flip-side, the lack of growth and the declining margins of the Contract Manufacturing industry is a risk for this value chain. Within the High-Tech value chain, Contract Manufacturing is the weak link.
The industry will drive the autonomous supply chain. These leaders will make the digital pivot first. With some of the earliest technology adopters, and with more to gain from the adoption of technology, look for companies like Apple, Cisco, Dell, EMC, Emerson, Intel, and Samsung to drive cloud-based computing, cognitive computing, the Internet of Things (IoT), sensor development, and prescriptive analytics. The industry is also driving a shift through wide adoption and use of Open Source code from the Apache Software Foundation. These manufacturing leaders will pave the way for others. Their ability to lead will drive cross-industry demand and growth agendas.
We hope that this report is a useful guide for companies in other industries to understand the impact of technology adoption on supply chain excellence.
Managing Material and Logistics Embeddedness: Material Buyers' PerspectiveRuss Merz, Ph.D.
Because an organization's visibility and decision-making abilities in a supply network is limited by its embeddedness, managing the embedded activities may be affected by non-contractual forms of governance and capability. Whatever the organization cannot see, it can't efficiently control. In this paper, the authors have studied non-contractual governance, dependence, and reliance in a manufacturer-vendor dyad in light of logistics, spill-over customer-centric service, and performance. Relational norms (information sharing and flexibility), trust, commitment, and bilateral dependence were hypothesized to explain manufacturers' logistics capability and customer-centric services. Using SEM-PLS (Structural Equation Modeling using Partial Least Squares) approach, all the hypothesized paths were proven with adequate R2 explained for each construct; R2 for financial performance was low.
This study pursued to investigate the effects of supply chain management practices on organizational
performance in the food complex industries in Asella town. A cross-sectional survey research design was
employed in this study. The population of interest comprised of all suppliers, employees, customers, retailers
were involved and multistage sampling was employed and 158 sample
Supply Chain Metrics That Matter: A Focus on Chemical, and Oil & Gas Companie...Lora Cecere
Executive Overview
Chemical supply chains serve global markets and multiple industries at varying levels of maturity. Over the last decade, no company stands out as a leader. The industry is stuck unable to make significant improvement on margin, inventory and asset utilization. The facts run counter to traditional beliefs. In most companies, there is a pervasive belief that Chemical and Oil and Gas companies implemented new technologies, and evolved processes to drive improved balance sheet results. As will be shown in this report, this is not true.
Why did this happen? The focus of the chemical companies remains functional and inside-out. The industry is slow to build adaptive networks and even slower to adopt demand-driven processes. This is in sharp contrast to an industry like consumer electronics where the thrusts and changes were swift and direct. To survive, these companies adopted new processes and technologies at a quicker rate than those in the Chemical, and Oil and Gas industries.
BASF wins the Supply Chains to Admire award while Statoil becomes a finalist. To help the industry to understand the current state and benchmark current processes, here we share insights.
The Race for Growth
The chemical industry experienced a post-recessionary boom with growth rates of 11% in the period of 2010-2012. In the recent three years, the growth rate has slowed to -1%. These recent growth rates were greatly affected by the boon and slowing of the Chinese markets and by the ups and down in crude. Over the period, AgroSciences and Specialty chemicals experienced the highest growth rates of the sector.
With the dramatic impact of the economy of growth and industry sector performance, one would think that the supply chain leaders of this sector would be aggressively pursuing market-driven supply chain practices to forecast based on market indicators and translate channel demand to supply. This is not the case. These processes remain very supply-centered with no chemical company driving market-driven programs.
Consultants' Voice on Supply Chain Excellence - 20 August 2012Lora Cecere
This report is the second report in a two-part series. The first report published in May 2012 and represents the Supply Chain Executives’ voice and perspectives on supply chain excellence. This report is a companion report reflecting the views of consulting partners working on supply chain across multiple industries. In this report, we contrast the two views while sharing insights from the Consultants’ Aggregate Voice on supply chain excellence.
A TWO-STAGE HYBRID MODEL BY USING ARTIFICIAL NEURAL NETWORKS AS FEATURE CONST...IJDKP
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simpleneural network structure as the new feature construction tool in the firststage, thenthe newly created features are used asthe additional input variables in logistic regression in the second stage. The modelis compared with the traditional onestage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under ROC curve, andKS statistic. By creating new features with theneural network technique, the underlying nonlinear relationships between variables are identified. Furthermore, by using a verysimple neural network structure, the model could overcome the drawbacks of neural networks interms of its long training time, complex topology, and limited interpretability.
Supply Chain Metrics That Matter: A Focus on the Retail Industry - 16 FEB 2017Lora Cecere
Report Details: This report is based on analysis of financial balance sheet and income statement data within the Retail industry, for the period of 2006-2015. The data is collected from YCharts.
Objective: To use financial balance sheet and income statement data to better understand the state of Grocery Retailers' and Mass Merchants' supply chains and to determine which companies’ supply chains did the best on the delivery of a portfolio of metrics over the last decade.
Highlight: During the Great Recession retailers faced strong declines in spending. It was a critical time, but for many it was an opportunity to emerge stronger. Those who redefined their stores for the dollar-conscious customer or built new and innovative formats while driving supply chain innovation, drove strong balance sheet results. Others learned that doing traditional retail more efficiently was not enough.
Optimizing the electric charge station network of EŞARJertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/optimizing-the-electric-charge-station-network-of-esarj/
In this study, we adopt the classic capacitated p-median location model for the solution of a network design problem, in the domain of electric charge station network design, for a leading company in Turkey. Our model encompasses the location preferences of the company managers as preference scores incorporated into the objective function. Our model also incorporates the capacity concerns of the managers through constraints on maximum number of districts and maximum population that can be served from a location. The model optimally selects the new station locations and the visualization of model results provides additional insights.
Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysisertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/risk-factors-and-identifiers-for-alzheimers-disease-a-data-mining-analysis/
The topic of this paper is the Alzheimer’s Disease (AD), with the goal being the analysis of risk factors and identifying tests that can help diagnose AD. While there exists multiple studies that analyze the factors that can help diagnose or predict AD, this is the first study that considers only non-image data, while using a multitude of techniques from machine learning and data mining. The applied methods include classification tree analysis, cluster analysis, data visualization, and classification analysis. All the analysis, except classification analysis, resulted in insights that eventually lead to the construction of a risk table for AD. The study contributes to the literature not only with new insights, but also by demonstrating a framework for analysis of such data. The insights obtained in this study can be used by individuals and health professionals to assess possible risks, and take preventive measures.
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/text-mining-with-rapidminer/
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Competitive Pattern-Based Strategies under Complexity: The Case of Turkish Ma...ertekg
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This paper aims to augment current Enterprise Architecture (EA) frameworks to become pattern-based. The main motivation behind pattern-based EA is the support for strategic decisions based on the patterns prioritized in a country or industry. Thus, to validate the need for pattern-based EA, it is essential to show how different patterns gain priority under different contexts, such as industries. To this end, this chapter also reveals the value of alternative managerial strategies across different industries and business functions in a specific market, namely Turkey. Value perceptions for alternative managerial strategies were collected via survey, and the values for strategies were analyzed through the rigorous application of statistical techniques. Then, evidence was searched and obtained from business literature that support or refute the statistically-supported hypothesis. The results obtained through statistical analysis are typically confirmed with reports of real world cases in the business literature. Results suggest that Turkish firms differ significantly in the way they value different managerial strategies. There also exist differences based on industries and business functions. Our study provides guidelines to managers in Turkey, an emerging country, on which strategies are valued most in their industries. This way, managers can have a better understanding of their competitors and business environment, and can develop the appropriate pattern-based EA to cope with complexity and succeed in the market.
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Visual and analytical mining of transactions data for production planning f...ertekg
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implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework.
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A Framework for Visualizing Association Mining Resultsertekg
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Association mining is one of the most used data mining techniques due to interpretable and actionable results. In this study we pro-pose a framework to visualize the association mining results, speci¯cally frequent itemsets and association rules, as graphs. We demonstrate the applicability and usefulness of our approach through a Market Basket Analysis (MBA) case study where we visually explore the data mining results for a supermarket data set. In this case study we derive several
interesting insights regarding the relationships among the items and sug-gest how they can be used as basis for decision making in retailing.
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The Bullwhip Effect In Supply Chain Reflections After A Decadeertekg
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A decade has passed since the publication of the two seminal papers by Lee, Padmanabhan and Whang (1997) that describes the “bullwhip effect” in supply chains and characterizes its underlying causes. The bullwhip phenomenon is observed in supply chains where the decisions at the subsequent stages of the supply chain are made greedily based on local information, rather than through coordination based on global information on the state of the whole chain. The first consequence of this information distortion is higher variance in purchasing quantities compared to sales quantities at a particular supply chain stage. The second consequence is increasingly higher variance in order quantities and inventory levels in the upstream stages compared to their downstream stages (buyers). In this paper, we survey a decade of literature on the bullwhip effect and present the key insights reported by researchers and practitioners. We also present our reflections and share our vision of possible future.
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Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
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Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
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Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
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As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
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Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
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1. 1
Lu, D., Ertek, G., Betts, A. (2014) “Modelling the supply chain perception gaps”. The
International Journal of Advanced Manufacturing Technology, 71(1-4), 731-751.
Note: This is the final draft version of this paper. Please cite this paper (or this final
draft) as above. You can download this final draft from the following websites:
http://research.sabanciuniv.edu
http://ertekprojects.com/gurdal-ertek-publications/
Modelling the supply chain perception gaps
Dawei Lu
WMG, University of Warwick, Coventry, UK
Gurdal Ertek1
Faculty of Engineering and Natural Sciences, Sabancı University,
Istanbul, Turkey
Alan Betts
HT2 Ltd., Wheatley, Oxfordshire, UK
Abstract This study applies the research of perception gap analysis to supply chain
integration and develops a generic model, the 3-Level Gaps Model, with the goal of
contributing to harmonization and integration in the supply chain. The model suggests
that significant perception gaps may exist among supply chain members with regards
to the importance of different performance criteria. The concept of the model is
conceived through an empirical and inductive approach, combining the research
discipline of supply chain relationship and perception gap analysis. First hand data has
been collected through a survey across a key buyer in the motor insurance industry
1
Corresponding author. Gürdal Ertek. Address: Faculty of Engineering and Natural Sciences, Sabancı University,
Orhanlı, Tuzla, 34956, Istanbul, Turkey. Email: ertekg@sabanciuniv.edu Tel: +90(216)483-9568. Fax: +90(216)483-9550.
2. 2
and its eight suppliers. Rigorous statistical analysis testified the research hypotheses,
which in turn verified the validity and relevance of the developed 3-Level Gaps Model.
The research reveals the significant existence of supply chain perception gaps at all
three levels as defined, which could be the root-causes to underperformed supply
chain.
Keywords: 3-Level Gaps Model; perception gaps; supply chain management; supply
chain integration; supplier evaluation.
1. Introduction
Over the last two decades, supply chain integration (SCI) has become increasingly important
across all industrial sectors [1]. However, delivering and sustaining it in a real-world supply
chain turns out to be a serious management challenge [2,3]. Integration across the supply
chain has also been seen as the driver towards better performance and a source of
competitive advantage [4-7]. Nevertheless, increased supply chain complexity, market
dynamics, and technological disruptiveness have made it a challenging endeavour [8].
One of the major impediments in SCI is the perception gap – predominantly the
underlying differences of views and expectations between supplier and buyer towards key
performance criteria. Perception gap is not immediately visible and has not been measured
so far in the literature. Perception gap exists where different parties see the world differently
[9]. In the context of supply chain management (SCM), it often results in and is commonly
exhibited as the expectations differences. It represents the differences of tacit knowledge
between different people or groups of people on the same object. The persistent presence of
the perception gaps can severely undermine the business relationships and the
products/services delivery standards of a supply chain. Since perception gaps between
supplier(s) and buyer(s) are often the root cause to many problems, they must be made
explicitly visible and subject to the management scrutiny [10].
The fundamental research problem therefore can be identified as follows: We know a
notional and plausible existence of perception gaps arising within a supply chain, but do not
necessarily know the precise degree of severity of its existence, nor are we clear about the
different types of the gaps. Furthermore, not knowing precisely the where-about of its
existence has made it impossible for supply chain managers to take effective measures to
mitigate the potential negative impacts of the perception gap. This becomes a legitimate
problem because perception gap self-evidently relates to supply chain performance and
especially the level of cohesiveness and integration. Performance measurement is an essential
concept in SCM, and is used not only for supplier evaluation, but also for supplier selection
3. 3
[11-14]. However, there does not exist a research that frames and measures the perception
gap regarding the importance of the various performance criteria.
Perception gap and its behaviour are not new concepts, but their implications with regards
to understanding the supply chain relationships and SCI have not been thoroughly explored
[15,16]. Even strategic information exchange, which is much simpler than SCI, can enhance
supply chain performance [17]. Communication of perception gaps should be part of strategic
information exchange, and one would expect better supply chain performance when
perception gaps are eliminated. Slack et al. [9] identified the operational principle that
“unsatisfactory supplier relationships can be caused by requirements and fulfilment
perception gaps”. The main model describing the differing perceptions across the dyadic
supply chain, as described by Slack et al. [9], is illustrated in Figure 1. Perceptions can play
not only a direct, but also an indirect role on the performance of the supply chains, through
the attitudes and actions that the managers take based on their perceptions. Ho et al. [18]
illustrate this phenomenon for the case of SCM system adoption in enterprises.
It might seem that the model can be applied to all the dyadic links in a supply chain, with
specific focus on the requirements perception gaps and the fulfilment perception gaps. Yet,
fundamental research questions (RQ) still remain to be answered:
RQ1. Could significant levels of perception gaps exist in supply chains?
RQ2. Would it be helpful to identify them theoretically with a model, in order to reveal the
root causes of the problems in SCM?
RQ3. Could the understanding and the measurement of the perception gaps provide
guidance to the strategic supply chain performance improvement?
Fig. 1 Supplier perception gaps (Slack et al., 2009)
4. 4
In this paper, we carry out thorough statistical hypothesis testing using real-world case
data, for addressing research question RQ1. Yet, before that, we present an extensive
exploratory discussion, engaging relevant body of literatures, to propose a conceptual
framework that integrates the supply chain perception gaps into the 3-Level Gaps Model,
which in effect addresses RQ2. RQ3 will be left for a more extrapolated discussion at the end
of the paper. Part of RQ3 is meant to be provocative and may not be fully answered, and thus
may have to be left for future research.
The key objective of this paper, therefore, is to ascertain the significance of perception
gaps from a SCM perspective and to develop a conceptual model. The developed model will
frame the three types of perception gaps and their relevance in the context of improving SCI.
The novelty of the paper is mainly in theory. Yet, there is also a rigorous and
methodological statistical analysis to test the novel theory. Since the novel theoretical model
of 3-Level Perception Gaps has not been offered in literature before, the analysis approach,
including the mathematical formalism, the selection of the statistical procedures, and the
presentation of the statistical results, is also novel.
The remainder of the paper is organized as follows: Section 2 presents a literature review
on supply chain relationships, integration and provides theoretical background on perception
and expectation. Section 3 introduces the 3-Level Gaps Model and its hypotheses. Section 4
explains how the data were collected and how the statistical methods were employed for the
data analysis regarding a major Motor-Insurer company. Section 5 presents further analysis,
results, and the managerial implications. Finally, Section 6 summarizes the key conclusions
and outlines further work.
2. Literature review
The purpose of the literature review here is to establish the relevance of the concept of
perception gaps and the highly concerned SCM issues including relationships, integration,
and performance. This will then lead to a better understanding of why it is necessary to have
a framework of perception gaps before any of those issues can be addressed more effectively.
Furthermore, the review also shows a gap in the literature where the topic could be more
extensively discussed in the context of SCM.
Starting with the issue of SCI, over the last few decades the importance of supply
relationships has been discussed extensively amongst the academics and practitioners alike.
Those discussions have led to converged findings on the critical success factors for developing
an appropriate portfolio of supply relationships [19-21]. More in-depth explorations were
also seen to be carried out on the interaction of those factors [22-25]. Somewhat conclusively,
5. 5
those researches have all pointed out the significant implication of people’s anticipation and
expectation to the effectiveness of SCI. The issues of perception gaps between the suppliers
and buyer as a negative factor has been highlighted in some of the mentioned studies, but
only implicitly.
The nature of the buyer-supplier relationship plays a pivotal role in SCI. Some researchers
[26,27] further underpin the strategic decision-making role of relationship in SCI in terms of
supply chain design and configuration. Other researchers discuss the critical role that supply
relations played in obtaining competitive advantage in today’s fast changing business
environment [28,29].
As a broad development trend, it can be observed that over the years, the main focus of
relationship management has shifted away from predominantly discrete transaction-based
exchanges towards continuous relationship-based exchanges [30,31]. This trend was also
seen to be alongside with the shift from operational to process-oriented SCI. However, buyer-
supplier relationship development is not the ultimate objective for SCI. It is only the means
to achieve better SCI and better supply chain performance. Lee [32] suggests three primary
dimensions of SCI: organisational relationship linkages, information integration, and co-
ordination & resource sharing. Handfield and Nicols [33] define the three principal elements
of SCI as relationship management, information systems, and management of material flows.
Van Donk and van der Vaart [34] also propose similar concepts of SCI. Thus, relationship
management delivers the implementation-end of SCI, whilst SCI is the extent that
organisations are integrated with their supply chain [35]. To this end, it is safe to observe that
the issues of perception gaps in the context of SCM are deeply intertwined in the concept and
practice of SCI and supply chain relationship management.
Given the increasing trend of global supply chain competition, integration is regarded as
one of the key prerequisites for sustained supply chain success [36,37]. The underlying
concept of SCI originated from a system perspective, in which the optimised whole will
always have more value-adding than any sub-systems. SCI can be characterised by
cooperation, collaboration, information sharing, trust, partnerships, joint new product
introduction, process alignment, as well as other traits [38]. Benefits and advantages of
integration have long been demonstrated via its impact on supply chain performance
[39,40,41,42]. It is therefore also logical to make extrapolated causal links from perception
gaps to supply chain performances, although how significant this causal link might be is a
very much a research agenda. Thus understanding perception gaps is important due to its
potential impact on supply chain performance.
Customer behaviour theories (including relationship marketing, personalized marketing,
customer retention) consistently state that buyer’s psychological factors, such as individual
perception, expectation, motivation, attitude, and belief play pivotal role in determining the
6. 6
level of satisfaction, preferences and the associated consequential behaviours such as
purchasing decisions and loyalty [43,44]. Customer behaviour theories also stipulate that
understanding and cultivating the right customer expectation is the centre piece for achieving
customer satisfaction and effective supply chain intermediation [45,46,47]. One can
understand that the buyer’s perception is based on its evaluation of the product or service
received. When perceived performances are lower than expectations, it is a sign of poor
service or product quality by the suppliers; and the reverse indicates good quality and service
standard. The perception or the perceived quality is an overall judgment on the supplied
products or services [48,49,50]. However, prior to their service experience, buyers create
expectations against which the supplier’s performance is evaluated [51]. Consequently, the
images of perception involves the subjective responses of people and are therefore highly
likely inconsistent with the reality or with each other [52]. All these observations from the
literature serve as the empirical evidences of the undeniable existence of “perception gaps”
and their implications to SCI.
Customer perceptions and expectations are central to supply relationship. Studies by
Oliver & DeSarbo [53] and Andreassen [54] found a theoretical support for the effects of
perception on the customer satisfaction or dissatisfaction. They stated that the perception-
based expectations cause an assimilation effect, while discrepancy between perception and
reality results in a contrast effect. According to the assimilation theory, people tend to
respond according to their expectations because they are reluctant to admit wide
discrepancies [55].
Our literature review clearly shows that some limited theories on perception gaps may
have already been documented. Yet, studies in how do they affect the SCM and supply chain
performance remain scarce. This vacuum in the literature is one of the main motivations for
the research.
3. Three-Level Gaps Model
Based on the literature review in the field of perception gaps and the general knowledge of
SCM, we take a view that the perception gaps in the supply chain can occur at three different
levels. At each level the perception gaps are formed from very different factors and can have
very different managerial implications. To theorize the perception gaps and their managerial
implications, we frame and propose a conceptual model—“3-Level Gaps Model” as shown in
Figure 2. The model illustrates the positions and the inter-relations of all possible perception
gaps at the three levels between any two tiers of a supply chain. The model as a conceived
idea will only be accepted methodologically as a meaningful contribution to the body of
knowledge if it is tested and verified using appropriate methods. Thus, as a research
approach, we propose three hypotheses regarding each of the specific perception gaps, and
7. 7
then apply the appropriate statistical methods to test them. The data collection described in
Section 4 and data analysis in Section 5 are intended to show that the perception gaps not
only do exist at all three different levels in the Motor-Insurer’s supply chain case, but also
with a convincing statistical significance.
Fig. 2 Perception gaps between stages (Level-1), within a single stage (Level-2), and within
each of the entities in a stage (Level-3).
Logically and structurally there are three levels in the supply chain, where the perceptions
can be compared: between two companies of the two adjacent tiers; between companies
within the same (supplier or buyer) tier; between individual people within any firm of the
supply chain.
The Level-1 gaps are the perception differences between the two adjacent tiers of a
supply chain, and reflect the gaps between the suppliers and buyer’s perceptions as a
collective view of the organisation on the performance criteria (or fulfilment standards).
Level-1 gaps therefore represent the major impediment to SCI, which is the original
motivation for Slack’s model [9]. Level-1 perception gaps often imply the need for
organizational level communication, openness in sharing information across supply chain
(between organisations) [56], closer alliances in setting strategic goals [57], supplier
development [58], and defining market positioning. Furthermore, Level-1 gaps may also
suggest the need for coordination mechanisms, such as the appropriate design of the
8. 8
contracts between the buyer and supplier [59,60] or coordinated inventory planning [61],
which can significantly increase supply chain performance.
Hypothesis 1: Level-1 perception gaps exist at a significant level between the buyer and
its suppliers; and the contents and significance of the perception gaps varies with
different suppliers.
The Level-2 gaps are the perception variations, also with a collective view of
organisation, but between the different suppliers (or buyers) within the same tier. These
variations reflect the unique business nature of specific suppliers and how they might factor-
in to the understanding of the performance objectives for the buyer. Level-2 gaps analysis
often implies that there is a need to manage and coordinate with different types of suppliers
in a customized way in order to achieve consistent performance across the supply base. “One-
size fit all” approach to different suppliers could be the cause of the Level-2 perception gaps.
Hypothesis 2: Level-2 perception gaps do exist at a significant level between the
suppliers in the same tier. For each supplier-supplier pair the gaps may differ for each
performance criteria for which the perceptions are measured.
The Level-3 gaps are the perception gaps between the individual people or functions
within one supplier or buyer, which is mainly due to the different views between the
individual respondents. If a high degree of variance is in presence, it could be the result of a
lack of internal communication or the lack of the processes regarding internal
communication. The lack of internal coherence of views within an organization is surely a
critical but negative measure of capability. The cause could be down to the ways the
employees are trained. It may also relate to the organizational culture. The Level-3 gaps could
be a source of motivation or lack of it for improving the company’s personnel management
and employee training. Our analysis in Section 5.4 provides specific guidance on how internal
communication might be improved.
Hypothesis 3: Level-3 perception gaps (within a supplier or a buyer) do exist at a
significant level between the individuals, who may have different views in connection
with their roles or positions in the company. The significance of this gap may vary for
each supplier or buyer.
9. 9
Table 1 The 3-Level Gaps Model and its implications
Levels Where Descriptions Implications Remedies
Level-1 Between
buyer and
suppliers
SC
Requirement
and fulfilment
gaps
Impediments to
supplier
development and
SC integration
Long term, close
partnership;
information sharing;
joint planning
Level-2 Between
different
suppliers in
the same tier
Suppliers
differentiation
gaps
Hinders the
optimisation of
consistent quality
and cost
Tailored relationship
and bespoke processes
and KPI to each type of
supplier
Level-3 Between
people who
may or may
not have
different
roles.
Role based
perspective
gaps
Barriers to internal
operational
coordination
Internal
communication;
adequate employee
training;
empowerment.
The above model has hopefully advanced our understanding of the perception gaps
beyond the scope covered by the current literatures. The quantitative measures of these gaps
can be observed through proper data collection and data analysis. The result can be used
separately to guide the specific management effort in different levels, which hopefully may
harmonize the understanding of performance objectives and consequently help managing the
resources to tackle the areas that are most in need. Looking across the three different levels
together, the comparison of the measures can reveal a pattern of “gaps profile”. This profile
offers a brief overview and can be used to guide the managers to tackle the most needed
levels in terms of “action economy”. In conjunction with the diagramming model shown in
Figure 2 above, a summary of the 3-Level Gaps Model can also be given in Table 1 with more
emphasis on their managerial implications and remedies.
In order to argue the validity of the above model, one must first verify the significance of
the existence of the three gaps, not just their existence, which may be taken as obvious.
Secondly, it must also show that the model is theoretically acceptable in terms of the
independence between the gaps at the three levels, and consequently each of them may
impact upon entirely different aspects of the supply chain measures.
In answering the RQ2, it becomes evident that the above model described in Table 1 is
theoretically helpful in identifying the three independent perception gaps embedded in a
supply chain, because it helps to map out each perception gap with the problems often
encountered in SCM. This model, thus, can serve as look-up table for managers to identify
10. 10
the possible root causes of the problem. Knowing full well that the problems listed in the
model may have more-than-one causes, it is arguable that the model does give managers a
clear guidance for streamlining the problems to their different categories of perception gaps
as an additional theoretical dimension to already existed ones. It can also be argued that each
of the causal linkages mapped out in the model between the perception gap and the possible
problems it caused is not necessarily counterintuitive as such, but putting them together
symmetrically as a framework does elevate our understanding at a higher theoretical level.
Table 2 The suppliers and the services they provide
Supplier Service
1 Motor Dealer
2 Motor Dealer
3 Body shop
4 Body shop
5 Body shop
6 Accident
repairer
7 Accident
repairer
8 Electrical testing
4. Verifying the model
4.1. Survey and data collection
Working with the senior management team of the Motor-Insurer, we identified a group of
eight key suppliers plus the buyer (the Motor-Insurer itself) as the respondent-base. The
suppliers are coded as Supplier 1,…,8 to mask their real identity. The services and products
provided by the suppliers are listed in Table 2. These suppliers were selected based on the
highest relevance and appropriateness for the research questions: following a Pareto pattern
[62], their size and relationship to the buyer made them the crucial first tier suppliers.
Our key contacts at the eight suppliers and the buyer were asked to instruct their staff at
all levels of the organisation to complete a simple on-line questionnaire. Altogether 120
participants from the eight suppliers and 87 respondents from the buyer were identified and
they all dutifully responded to the questionnaire. The respondents are coded as illustrated in
Figure 3. All the participants were asked to identify their role in the company being one of the
front line staff, team leaders, managers, senior managers or others. The purpose for this
stratification was to allow for in-depth investigation into the connections between the roles
they play internally and the views they behold.
11. 11
Fig. 3 Perception of the respondents at the suppliers and the buyer regarding the
performance measures
The first question in the survey is the key question analysed in this paper (other survey
questions are used in separate researches). It asked the respondents to allocate 100 points
between the eight performance criteria (coded from ‘A’ to ‘H’) below in terms of the
importance to Motor-Insurer’s business:
A. Service with a real "wow" factor being prepared to go the extra mile
B. Innovative products or services
C. Low price/charges
D. Fast response to your requests
E. Being on time
F. Not making mistakes
G. Personal touch
H. Dealing well with problems and queries
12. 12
This question represents the suppliers’ and buyer’s perceived weight or priorities of the
importance on those eight criteria of the supplier’s performance measures. These criteria
were developed through the synthesis of the five performance objectives (quality, speed,
dependability, flexibility and cost) identified by Slack et al. [9] and the model of Service
Excellence by Johnston & Clark [63], which identified four factors of service (deliver the
promise, deal with problems and queries, provide a personal touch and go the extra mile).
Since it is not the main interest of this paper to determine how appropriate this set of
supplier performance criteria is, we will not extend the discussion of the criteria in this paper.
Understandably, the content coverage of these performance criteria may have the effect on
the performance management, but will not affect the methodological validity for testing the
hypotheses.
The correctness of the data has been systematically achieved based on the taxonomy of
dirty data by [64]. The supplier names, the buyer name, and the names of the respondents in
each company have been masked with unique identifying codes. When needed, the codes can
be tracked back to their originals through lookup tables.
Table 3 The vectors/matrices and the mathematical expressions
Vector/Matrix
Title
Value in
Vector/Matrix
Vector/Matrix
Title
Value in
Vector/Matrix
Average1 (Matrix) ( ) Average11 (Matrix) ( )
Average2 (Vector) ( ( )) Average12 (Vector) ( ( ))
Average3 (Vector) ( ( )) Average13 (Vector) ( ( ))
Average4 (Matrix) ( ) Average14 (Matrix) ( )
Average5 (Vector) ( ( )) Average15 (Vector) ( ( ))
Average6 (Vector) ( ( )) Average16 (Vector) ( ( ))
Average7 (Matrix) ( ) Average17 (Matrix) ( )
Average8 (Vector) ( ( )) Average18 (Vector) ( ( ))
Average9 (Vector) ( ( )) Average19 (Vector) ( ( ))
4.2. Mathematical formalism
In this section, we introduce a mathematical notation to represent the collected data and
analysed results. This notation is essential for the succinct calculations used in the summary
tables, and for easy communication of the statistical analysis. The notation consists of the
sets, parameters, and functions. The vectors and matrices in the summary tables are then
expressed in terms of this notation. Table 3 presents the titles for the vectors/matrices
presented throughout the paper and in the Appendix..
13. 13
We also define the following:
Sets
: set of suppliers
: set of respondents; ( )
: set of respondents at the buyer (who can weigh the performance measures for more than
one supplier)
: set of respondents at supplier (who can weigh the performance measures only for their
company)
: set of performance measures/criteria (same for all suppliers)
Parameters
: weight given by respondent at the buyer for performance measure of supplier
: weight given by respondent at supplier for performance measure of supplier
Functions
( ): average of values over the values of index , where is matrix
( ): standard deviation of values over the values of index , where is matrix
( ): coefficient of variation (CV) of values over the values of index , where is
matrix; ( ) ( ) ( )
4.3. Statistical analysis
Summary statistics computed for the data include the average, standard deviation (stdev)
and coefficient of variation (stdev/mean) for the subsamples. Sample average is an estimate
of the population mean, which is a measure of central tendency in data. While standard
deviation and coefficient of variation (CV) are both the measures of variability (spread) in
data, the latter is a more reliable measure, since it scales the variability with respect the
magnitude of the central value (average).
Throughout the study, perception gaps have been identified and tested through repeated
application of formal statistical tests, whose references are given in Appendix H. A
fundamental issue is the selection of the appropriate statistical tests for measuring the
statistical significance of the hypothesized differences in the weight values [65]. The most
basic decision to be made is whether parametric (t-test, ANOVA) or nonparametric tests
(Mann-Whitney, Kruskal-Wallis) should be applied. When applicable, parametric tests are
preferred due to their power, that is, their requirement for smaller sample sizes to draw
conclusions with the same degree of confidence. However, parametric tests are applicable
14. 14
only when the data follows parameterized distributions, such as the requirement of normal
distribution for the t-test. Nonparametric test such as Mann-Whitney and Kruskal-Wallis, on
the other hand, use the rank data to compute the test statistics, and do not require the data to
come from a particular distribution [65].
For deciding on the selection of the test type (parametric vs. nonparametric) Shapiro-Wilk
test has been applied to test normality of data subsamples. The parametric t-test has been
applied for comparing differences among two random samples that both follow normal
distribution. When any of the distributions were not following normal distribution, the
nonparametric Mann-Whitney test has been applied instead of the t-test to test differences
between two samples. The nonparametric Kruskal-Wallis test has been applied for comparing
differences among three or more samples. The parametric ANOVA test would have been
applied for comparing differences among three or more samples if all followed normal
distribution [65]; however, the conditions for the application of this test were not satisfied in
the study.
5. Results and implications
5.1. Survey and data collection
One of the goals of this paper is to identify whether perception gap exists with respect to the
importance of SC performance criteria. Table 4 presents the averages of the weights for each
performance criterion (‘A’ through ‘H’) for each supplier, as perceived the suppliers. Table 5
presents the same averages as perceived by the buyer. The differences in value suggest the
existence of perception gaps, and that will have to be investigated and tested through
appropriate statistical tools.
Table 4 Matrix Average1 of averages of the weights for each performance criterion (A
through H) for each supplier, as perceived from the supplier’s side
Average1
Supplier A B C D E F G H Average2 Count
1 13.41 7.59 28.29 9.24 12.24 7.12 6.65 15.47 12.50 17
2 10.39 9.61 14.50 9.67 14.28 12.56 11.11 17.89 12.50 18
3 9.39 9.89 22.61 10.00 7.83 10.61 6.22 23.44 12.50 18
4 27.86 10.05 8.77 13.55 8.55 7.86 13.09 10.27 12.50 22
5 28.26 8.54 11.49 11.17 10.66 9.20 8.94 11.74 12.50 35
6 11.67 8.33 18.00 12.00 2.00 20.00 9.00 19.00 12.50 3
7 24.00 13.00 13.00 10.67 9.67 9.67 9.67 10.33 12.50 3
8 34.25 5.25 17.00 10.00 14.00 8.00 4.00 7.50 12.50 4
Average3 20.25 9.04 15.88 10.90 10.48 9.62 9.15 14.68 Total: 120
15. 15
Table 5 Matrix Average11 of averages of the weights for each performance criterion (A
through H) for each supplier, as perceived by the buyer
Average11
Supplier A B C D E F G H Average12 Count
1 16.46 4.63 15.04 15.21 11.00 10.71 6.33 20.63 12.50 24
2 13.57 3.00 5.71 12.57 12.71 14.00 7.14 31.29 12.50 7
3 20.38 1.88 6.25 18.00 13.25 10.13 7.38 22.75 12.50 8
4 11.69 3.88 12.19 12.75 11.38 14.81 7.06 26.25 12.50 16
5 17.88 1.76 9.20 13.20 8.72 15.76 7.56 25.92 12.50 25
6 18.63 3.37 8.00 12.96 13.04 12.30 9.41 22.30 12.50 27
7 8.00 4.00 6.00 10.00 10.00 5.00 3.00 54.00 12.50 5
8 16.52 4.29 8.38 13.29 10.29 12.62 10.38 24.24 12.50 21
Average13 16.37 3.41 9.76 13.61 11.11 12.70 7.89 25.15 Total: 133
Table 6 p-values for the Level-1 gaps for each (supplier, criterion) pair for each of the
performance criterion
A B C D E F G H
1 0.8526 0.0225 0.1101 0.1815 0.9041 0.2740 0.7573 0.8944
2 0.5153 0.0043 0.0125 0.2359 0.7010 0.3442 0.2303 0.5051
3 1.0000 0.0319 0.0825 0.3375 0.2275 0.9555 0.7764 0.5955
4 0.0353 0.0562 1.0000 0.4110 0.2808 0.0824 0.0357 0.0025
5 0.0232 0.0000 0.1234 0.3514 0.1520 0.0575 0.1749 0.0023
6 1.0000 0.1502 0.1152 0.9720 0.0373 0.2366 0.7801 1.0000
7 0.0148 0.0101 0.0734 1.0000 0.9480 0.1685 0.0314 0.0336
8 0.8526 0.6142 0.1313 0.4994 0.5550 0.2636 0.0800 0.0343
Table 7 p-values for the Level-1 gaps for each criterion, over all suppliers
First Vector Second Vector Test Employed p-value Test Result
Average1.A Average11.A Wilcoxon 0.3828
Average1.B Average11.B Wilcoxon 0.0078 *
Average1.C Average11.C Wilcoxon 0.9453
Average1.D Average11.D Wilcoxon 0.0391 *
Average1.E Average11.E Wilcoxon 0.7422
Average1.F Average11.F Sign test 0.7266
Average1.G Average11.G Wilcoxon 0.4609
Average1.H Average11.H Sign test 0.0703 *
5.2.Level-1 gaps
The first sets of statistical tests are aimed at revealing the Level-1 gaps between two
neighbouring supply chain tiers. These gaps are revealed through the identification of
16. 16
statistically significant differences in the means of the weight values. To this end, the
parametric t-test and the nonparametric Mann-Whitney test have been applied for
measuring the significance of the differences between the means of two random samples: The
weight values of the supplier and the buyer, regarding each (supplier, buyer) pair. The
selection of the appropriate test on each mean of the weight value depends on the normality
of the samples’ distribution, and the process of the selection is documented in Appendix B.
Table 6 presents the p-values (whose lower values denote higher statistical significance) for
the Level-1 gaps for each (supplier, criterion) pair. The statistically significant differences for
p0=0.10 are shown in bold.
Having observed the existence of the Level-1 gaps for “supplier- criterion” pairs, the next
question is whether the gaps for at least some of the criteria are statistically significant
enough. To this end, Wilcoxon test and sign test were applied to compare the means of two
paired samples: the average weight values of the supplier (Average1) against that of the buyer
(Average11) for each criterion. The selection of the appropriate test methods again depends
on the normality of the samples, and this information is given in Appendix B.
Table 7 presents the p-values, whose smaller values denote higher statistical significance)
for the Level-1 gaps for each criterion between two neighbouring supply chain tiers. The
results in Table 6 and 7 support Hypothesis 1, showing that perception gaps exist for criteria
B, D, and H with credible significance.
Fig. 4 Implication of Level-1 perception gaps
The implication of the Level-1 gaps can be profound to SCI. To begin with, the literature
on the key success factors fall short of addressing the existence and critical role the
perceptions gaps play in achieving seamlessly integrated supply chain in terms of
information flow and material value-adding flow. The model has been verified from a
17. 17
supplier to buyer link, but it could also be extrapolated to the supply chain to consumer link
(or supplier to consumer link). Notwithstanding that it has not been explicitly tested as such
in this research, empirical experiences and many studies have already alluded that the
perceptions gaps or expectation discrepancies also exist in the supplier-consumer link.
Theoretically the authors do admit that the measures of the Level-1 perception gaps in the
supplier-buyer link are different to those in the supplier-consumer links. In fact every link is
different in their measures for perception gaps. However, it remains the authors’ proposition
that the model of Level-1 gaps do cover the entire supply chain theoretically as shown in
Figure 4.
The Level-1 perception gaps are thus a generalised theoretical concept that covers all the
supply-buyer It can be observed that:
Both gaps are in the same flow direction as shown in the Figure 4
Both gaps are at the highest supply chain level, not within a specific tier or within an
organisation
Both gaps address the discrepancies on the measures of supply and demand.
Although this research is based on the case of supplier-buyer perception gaps, studies
show strong evidence of similar cases between suppliers and consumers [16,66]. Hence, a
conjectural implication would be that the Level-1 perception gap not only models the
supplier-buyer integration but also the supply chain–consumer integration. In other words,
understanding the perceptions gaps throughout the supply chain not only helps the better
integration of within the supply chain but also beyond the supply chain to consumer-
integration; the impact of the perception gaps is as critical to the buyer-supplier as to the
supply chain–consumer. Nevertheless, the actions to narrow down the perception gaps may
have to be very different due to the difference of purchasing behaviour differences.
5.3.Level-2 gaps
This type of gap is within a supply chain tier. As in the Level-1 gaps, the parametric t-test and
the nonparametric Mann-Whitney test have been applied for measuring the significance of
the differences between the means of two random samples. This time, however, the samples
were the weight values of two suppliers, which (without loss of generality) we will refer to as
First-Supplier and Second-Supplier for each criterion.
18. 18
Table 8 Statistically significant Level-2 gaps (marked with T)
Supplier Pair A B C D E F G H Count of T
1-2 T T T F T T T T 7
1-3 T T T F T T F T 6
… … … … … … … … … …
7-8 F F F F F F T F 1
Count of T 17 14 15 7 20 17 19 19 Total: 128
Table 8 presents a summary of the statistical significance of the Level-2 gaps, and the full
Table is given in Appendix C. In Table 8, T (True) denotes that the difference is statistically
significant at p=0.10 (one-sided), whereas F (False) denotes that the difference is not
significant. A considerable percentage (57%) of the table cells contains the value T, thus
proving the Hypothesis 2.
When the number of gaps are observed for each criterion (the bottom row in Table 8),
criteria E, G, and H have the highest values, suggesting that significant gaps exist among an
overwhelming percentage of the supplier pairs for these criteria. The selection of the
appropriate test depends on the normality of the samples, and the process is documented in
Table 15 of the Appendix. The results for Level-2 gaps also suggest that the gaps within the
supplier tier is largely independent to that of the Level-1 as shown in criteria E and G, as
opposed to B and D for Level-1.
5.4.Level-3 gaps
The Level-3 gaps are within a supplier or buyer. Tables 9 displays the coefficient of
variations (CV) of the weights for each (supplier, performance criterion) pair, as perceived
by the supplier. Table 10 presents the same statistics for the weights perceived by the buyer.
The values in these tables are obtained through the division of the standard deviation values
(in Appendix A by the average values in Tables 4 and 5.
19. 19
Table 9 Matrix (Average7) of coefficient of variations (CV) of the weights for each
performance criterion (A through H) for each supplier, as perceived at the supplier tier (the
highest three and lowest two values in the matrix are shown in bold)
Average7
Supplier A B C D E F G H Average8 Count
1 0.76 0.75 1.23 0.71 0.72 0.63 0.67 0.73 0.78 17
2 0.49 0.47 0.83 0.66 0.33 0.46 0.40 0.45 0.51 18
3 0.82 1.04 1.16 0.86 0.74 0.97 0.84 1.21 0.95 18
4 0.86 1.45 1.41 1.53 0.80 0.94 0.77 0.98 1.09 22
5 0.95 0.74 0.86 0.81 0.56 0.62 0.71 0.60 0.73 35
6 0.78 0.80 0.59 0.52 0.87 0.50 0.87 0.61 0.69 3
7 0.08 0.20 0.08 0.11 0.16 0.16 0.16 0.06 0.13 3
8 1.29 1.18 0.70 0.82 0.78 0.78 0.74 0.77 0.88 4
Average9 0.75 0.83 0.86 0.75 0.62 0.63 0.64 0.68 0.72 Total: 120
These matrices Average 7 and Average 17 in Tables 9 and 10 suggest that coefficient of
variability are not uniform. The highest CV on the supplier side (Table 8) is observed within
Supplier 4, especially regarding criteria D (CV=1.53), B (CV=1.45), and C (CV=1.41),
indicating a large Level-3 perception gap. This means that the weights given by the 22
respondents within Supplier 4 for D, B, and C have the highest variability when compared
with other (supplier, criterion) pairs. The lowest CV values on the supplier side are observed
for Supplier 7. Since the number of respondents for Suppliers 6, 7, and 8 are very few, we
focus on the other suppliers, and observe that Supplier 2 has the least CV values overall,
especially regarding E (CV=0.33) and G (CV=0.40). This means that the weights given by the
18 respondents within Supplier 2 are very consistent, indicating low levels of the Level-3
perception gaps.
On the buyer side, highest CV values is for (Supplier 5, B) with CV=2.47, which is much
higher than the next highest CV value (CV=1.91). Thus, the 25 respondents at the buyer
(Motor-Insurer) have great variability with respect to how much weight they give to criterion
B for Supplier 5. The smallest CV value on the buyer side is with regards to the importance of
D for Supplier 8 (CV=0.58).
A formal statistical test has been carried out (Appendix D), yielding statistically significant
Level-3 gaps within all the suppliers. Hence Hypothesis 3 has been tested positive.
20. 20
Table 10 Matrix (Average17) of coefficient of variations (CV) of the weights for each
performance criterion (A through H) for each supplier, as perceived at the buyer (the highest
and lowest values in the matrix are shown in bold)
Average17
Supplier A B C D E F G H Average18 Count
1 0.94 1.91 1.44 0.94 0.85 0.99 0.96 1.31 1.17 24
2 0.87 1.22 1.90 0.75 0.78 1.36 1.06 1.01 1.12 7
3 1.44 1.79 0.88 0.95 0.86 0.69 1.00 1.43 1.13 8
4 0.71 1.52 1.69 0.67 0.74 0.90 0.89 0.87 1.00 16
5 1.27 2.47 1.36 0.74 1.13 0.87 1.23 1.00 1.26 25
6 1.42 1.29 1.06 0.91 0.69 0.84 0.75 0.81 0.97 27
7 1.14 1.05 1.09 1.06 1.06 1.22 0.91 0.78 1.04 5
8 0.67 1.60 0.98 0.58 0.67 0.74 0.79 0.92 0.87 21
Average19 1.06 1.60 1.30 0.83 0.85 0.95 0.95 1.02 1.07 Total: 133
The next analysis is to establish the positioning of the individual respondents with respect
to each other, and to identify the subgroups of consistent respondents. For this purpose,
hierarchical clustering and multi-dimensional scaling methods from the machine learning
discipline [67] have been employed. The resulting analysis gives us the clue to what can be
done to close the Level-3 perception gap, and to achieve consistency throughout the
company. These results are provided in the Appendices E, F, and G.
5.5. Findings and discussion
As has started above, close range research of the perception gaps and their impact on SCI is a
rich, sophisticated and penetrating exploration of epistemological issues concerning the deep
rooted causes of many management shortfalls. Thus, it pushes one step further towards
making some fundamental claims regarding to academic understanding of roles of perception
gaps in SCM and empirical guidance to deliver some tangible benefits.
Academic implications
Learned from the above analysis and results, we are now in a lot more confident position to
address the research questions set forth in Section 1.
For the RQ1, statistical analysis of the survey data reveals the statistical significance of
perception gaps between the collective views of supplier-buyer pairs and supplier-supplier
pairs, as well as within groups of individual respondents. Thus the answer becomes clearly
straightforward that the perceptions gaps do exist at all three levels at a significant level in a
supply chain. The consequence of dissatisfaction from both buyers and customers, or even
the complete broken down supply chain intermediation function can now be approached
from a perception gaps’ perspective.
21. 21
For the RQ2, as a conceptual framework discussed in Table 1, the 3-Level Gaps Model can
be helpful in identifying not only the sources but also the locations of the perception gaps.
The statistical data analysis and hypotheses testing have demonstrated the independence of
the three types of perception gaps, thus verified the category validity of the model. This shows
that the gap profile against defined measures can vary from one level to another.
Theoretically, each perception gap at a specific level has now been related to a corresponding
supply chain problem. The model basically defined the three categorised sources of
perception gaps and mapped them to their corresponding SCM problems, namely: Level-1
gaps are linked to buyer supplier coordination; Level-2 gaps are linked to the rationalisation
within a single supply base – achieving consistency and harmony in between suppliers
horizontally; Level-3 gaps links to the participating organisation’s internal congruence and
communication effectiveness. In a reverse direction, the model provided guidance from
problems to the possible root causes arising from perception gaps. In short, the answer to
RQ2 is that the model developed in this research is helpful to identify the types of perception
gaps in order to track down the root causes of SCM problems, albeit they may not be the only
root causes.
For RQ3, as discussed in Section 1, part of RQ3 is meant to be provocative and may not be
fully answered. Surely a better understanding of the perception gaps will aid the supply chain
strategic decision making in the context of improving SCM to achieve better performances. In
fact the hidden question could be “has the developed model provided any such better
understanding?”. To answer this, there are three positive arguments we can draw.
First, the data analysis shows a significant level of the perception gaps in existence,
providing a new quantitative understanding on the severity of the perception gaps. Second,
moving from a terminology to a defined framework revealing all the relevant perception gaps
and their locations of existence in a supply chain structure; this development provides a new
understanding in terms of their portfolio and embedding structure in a supply chain. Third,
the model enables a possible causal relation from the perception gaps to some of the SCM
problems, adding a new understanding of its managerial relevance and implication of the
issue. Hence, the answer is that the model will help supply chain performance improvement.
In another words, the model is theoretically helpful in categorising and streamlining the
performance delivery “mechanisms”.
Supply chain performances can only be delivered, measured and improved through a
specific “mechanism”. The Level-1 pair of supplier-buyer is one of the mechanisms that
deliver the “supplier performance” in the eyes of the buyer. The Level-2 is the mechanism
that delivers the supplier base capabilities, including reliable standard and potential synergy
of the supply network. Toyota’s Keiretsu system is precisely the mechanism that delivers such
performance. Level-3 is the mechanism that embodies the performance capability at the
22. 22
individual “cells” level. The participating organisation’s internal performances such as
capacity, flexibility, knowledge management, skill training are examples. With the 3-Level
Gaps model, the performances are now dovetailed to their delivery mechanism, and
consequently the perception gaps become the ratchet within, that help or hamper the
function of the mechanism.
Managerial implications
Not to overstate any promising practical benefits, we believe a further research may be
required to investigate explicitly the impact of perception gaps on the supply chain
performances. But for now, some practical implications may still be plausible.
First practical implication is that it puts new measures into the supply chain’s health-
check. Measures of perception gaps do not always appear on the measurement list, nor
do they replace any existing ones, but only to add-on and to complement them. The
measures can be coded as: PG-1, PG-2 and PG-3, corresponding to the perception gaps
at each of the 3 levels.
Secondly, when the supply chain performance falls short of what is expected, with the
3-Level Gaps Model, managers can map-out from the performance measures to the
delivery mechanisms and finally to the specific perception gaps.
To summarise, there are three key practical implications of the perception gaps to the
SCM. First, understanding and measuring the Level-1 gaps facilitates the SCI by making sure
what suppliers deliver is what buyers really want; closing the Level-2 gaps will help to
harmonise the consistency in quality and cost across the supplier base; managing the Level-3
gaps will help the supplier internal communication and congruence.
6. Conclusions
Overall, the research reviewed the literature on the perception gaps in the context of SCM.
Our first hand data collection and subsequent thorough statistical analysis on the perception
gaps revealed a significant level of existence in the chosen supply chain case. The research
finds that the perception gaps do exist at the three defined levels of a supply chain, instead of
just one level (Level-1) as suggested in Slack’s model. The perception gaps at the Level-2 and
Level-3 as defined in the 3-Level Gaps Model have also shown some distinct implications to
the supply chain performance management over and above what has been discovered at
Level-1. The 3-Level Gaps Model has been created to represent and map out the co-existence
of the three types of perception differences. It could be suggested to the future researchers
that despite the abundance of literature, supply chain integration (SCI) could have taken a
completely different but perhaps more effective approach, starting from discovering the
perception gaps as one of the underlying causes to many performance shortfalls.
23. 23
The study has industrial implications and applications. Immediate likely users of the
model are companies involved in the multiple-supplier single-buyer supply chain
relationship. Such companies, knowing for sure that there can be a significant gap along the
supply chain regarding the importance of different performance criteria, can take the pre-
cautions and establish the communication needed to eliminate the perception gaps. This also
calls for a methodological testing of the existence of significant perception gaps, which is also
described in this paper. Our paper also describes the sources and remedies for the perception
gaps at the three different levels, which serves as a reference for all supply chain practitioners
in industry. This type of a recipe, framed according to the levels at which the perception gaps
occur, is provided for the first time in the literature.
Further research could involve the mapping of inter-connections of the perception gaps
with many operational factors, in order to understand their influence on the supply chain
relationship and supply chain performances. It is the authors’ planned next research to look
into the perception gaps and their direct impact on supply chain performances by using
statistical techniques on the text data gathered in the survey.
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Appendix A. Standard deviations of the weight values
Table 11 Matrix Average4 of standard deviations of the weights for each of the performance
criterion (A through H) for each supplier, as perceived at the supplier stage.
Average4
Supplier A B C D E F G H Average5 Count
1 10.25 5.68 34.90 6.57 8.76 4.50 4.49 11.29 15.48 17
2 5.10 4.51 11.99 6.34 4.76 5.80 4.48 8.02 7.18 18
3 7.74 10.28 26.14 8.56 5.78 10.30 5.20 28.48 16.27 18
4 23.91 14.56 12.39 20.66 6.86 7.38 10.04 10.09 15.42 22
5 26.77 6.32 9.83 9.09 5.92 5.73 6.34 7.02 13.04 35
6 9.07 6.66 10.58 6.24 1.73 10.00 7.81 11.53 9.23 3
7 2.00 2.65 1.00 1.15 1.53 1.53 1.53 0.58 4.83 3
8 44.18 6.18 11.94 8.21 10.98 6.27 2.94 5.74 17.87 4
Average6 21.45 8.64 19.66 11.20 6.81 7.08 6.82 14.12 Total: 120
Table 12 Matrix Average14 of standard deviations of the weights for each of the
performance criterion (A through H) for each supplier, as perceived at the buyer stage.
Average1
4
Supplier A B C D E F G H
Average1
5 Count
1 15.55 8.85 21.65 14.24 9.30 10.57 6.06 26.93 16.13 24
2 11.86 3.65 10.87 9.47 9.91 19.10 7.56 31.57 16.47 7
3 29.39 3.36 5.52 17.09 11.37 6.96 7.41 32.54 18.03 8
4 8.36 5.89 20.54 8.59 8.47 13.28 6.30 22.93 14.31 16
5 22.75 4.34 12.49 9.81 9.82 13.65 9.30 25.86 16.39 25
6 26.36 4.33 8.45 11.83 8.95 10.36 7.05 18.14 14.54 27
7 9.08 4.18 6.52 10.61 10.61 6.12 2.74 42.04 21.96 5
8 11.03 6.86 8.19 7.77 6.92 9.34 8.22 22.26 12.26 21
Average1
6 19.04
5.8
9
14.1
2
11.1
0
9.0
0
11.5
7
7.4
4
25.3
4 15.46
Total:
133
29. 29
Appendix B. Selecting the statistical test for Level-1 Gaps
Table 13 p-values obtained in the Shapiro-Wilk normality tests at the Supplier stage. The
tests analyze whether the weights given by each supplier for each criterion show normal
distribution or not. p-values less than 0.10 (shown in bold) suggest statistical evidence that
the underlying distribution is not normal.
A B C D E F G H
1 0.0989 0.4565 0.0000 0.1242 0.2169 0.2800 0.3138 0.4685
2 0.1578 0.8146 0.0000 0.0001 0.9431 0.8310 0.8051 0.9020
3 0.0914 0.0197 0.0008 0.0998 0.2217 0.0361 0.0020 0.0000
4 0.0046 0.0000 0.0000 0.0000 0.1027 0.0320 0.0684 0.0039
5 0.0000 0.0271 0.0005 0.0000 0.1423 0.0129 0.2451 0.1677
6 0.3172 0.1436 0.3631 0.4633 0.0000 1.0000 0.1224 0.8564
7 1.0000 0.3631 1.0000 0.0000 0.6369 0.6369 0.6369 0.0000
8 0.0272 0.3954 0.2793 0.9086 0.8027 0.9571 0.7335 0.2725
Table 14 p-values obtained in the Shapiro-Wilk normality tests at the Buyer stage. The tests
analyze whether the weights given by the buyer for each supplier-criterion combination
show normal distribution or not. p-values less than 0.10 (shown in bold) suggest statistical
evidence that the underlying distribution is not normal.
A B C D E F G H
1 0.0115 0.0000 0.0000 0.0043 0.0116 0.0005 0.0063 0.0000
2 0.5714 0.0878 0.0003 0.2940 0.3391 0.0102 0.1074 0.0038
3 0.0105 0.0007 0.3393 0.2515 0.0264 0.7821 0.1283 0.0014
4 0.0907 0.0003 0.0000 0.1699 0.1269 0.0919 0.0925 0.0005
5 0.0000 0.0000 0.0000 0.3084 0.0015 0.0107 0.0002 0.0000
6 0.0000 0.0001 0.0005 0.0000 0.1439 0.0266 0.0398 0.0000
7 0.2538 0.3140 0.4211 0.4677 0.4677 0.1458 0.0065 0.0148
8 0.6054 0.0000 0.0218 0.2467 0.2033 0.2656 0.0304 0.0000
30. 30
Table 15 The statistical test that should be selected to test the statistical significance of the
Level 1 gaps for each supplier-criterion combination. The parametric t-test (t) is selected if
both samples (supplier weights and buyer weights for this combination) follow normal
distribution. The non-parametric Mann-Whitney test (M-W) is selected if one or both the
samples do not follow normal distribution.
A B C D E F G H
1 M-W M-W M-W M-W M-W M-W M-W M-W
2 t M-W M-W M-W t M-W t M-W
3 M-W M-W M-W M-W M-W M-W M-W M-W
4 M-W M-W M-W M-W t M-W M-W M-W
5 M-W M-W M-W M-W M-W M-W M-W M-W
6 M-W M-W M-W M-W M-W M-W M-W M-W
7 t t t M-W t t M-W M-W
8 M-W M-W M-W t t t M-W M-W
Table 16 The results of the Shapiro-Wilk normality tests for the vectors in Tables 2-7. Under
p=0.10, the vectors marked with * and the columns/rows whose names are written next to
the matrices are not normally distributed (the Shapiro-Wilk p-value is less than the threshold
p=0.10). Cells with denote normally distributed vectors; empty cells indicate the
irrelevancy of the test.
Vector/Matrix Normality Test
Result
Vector/Matrix Normality Test
Result
Average1
(Matrix)
Column F
Rows 1, 3, 4, 5, 7, 8
Average11
(Matrix)
Column H
Rows 2, 7
31. 31
Appendix C. Level-2 Gaps
Table 17 Statistical significance of Level-2 gaps
Supplier
Pair A B C D E F G H
Count of
T
1-2 T T T F T T T T 7
1-3 T T T F T T F T 6
1-4 T F T T T T T T 7
1-5 T T T T F T T T 7
1-6 F F F T T T T T 5
1-7 T T F F T T T T 6
1-8 T T F F T F T T 5
2-3 F T F F T T T T 5
2-4 T T T T T T F T 7
2-5 T T T T T T T T 8
2-6 F F T T T T F F 4
2-7 T T F T T T F T 6
2-8 T T F F F T T T 5
3-4 T F T F F F T T 4
3-5 T F T F T F T T 5
3-6 F F F F T T T F 3
3-7 T T F F F F T T 4
3-8 T T F F T F T T 5
4-5 F F T F T T T T 5
4-6 T F T F T T F T 5
4-7 F T T F F F F F 2
4-8 F F T F T F T F 3
32. 32
5-6 T F T F T T F T 5
5-7 F T F F F F F F 1
5-8 F F T F F F T F 2
6-7 T F F F T F F F 2
6-8 F F F F T T T F 3
7-8 F F F F F F T F 1
Count of T 17 14 15 7 20 17 19 19
Total:
128
33. 33
Appendix D. Level-3 Gaps Analysis
Table 18 Statistical significance of Level-3 gaps
Organization p
Within Supplier 1 0.0033
Within Supplier 2 0.8523
Within Supplier 3 0.0000
Within Supplier 4 0.0000
Within Supplier 5 0.0000
Within Supplier 6 0.0701
Within Supplier 7 0.0000
Within Supplier 8 0.0000
Within the Buyer for Supplier 1 0.0000
Within the Buyer for Supplier 2 0.0000
Within the Buyer for Supplier 3 0.0000
Within the Buyer for Supplier 4 0.0000
Within the Buyer for Supplier 5 0.0000
Within the Buyer for Supplier 6 0.0000
Within the Buyer for Supplier 7 0.0000
Within the Buyer for Supplier 8 0.0000
34. 34
Appendix E. Machine learning model
Machine learning is the sub branch of artificial intelligence within computer science,
concerned with the design and development of algorithms that allow computers to learn
autonomously from empirical data, either supervised to model input-output relations, or
unsupervised to model only input data (Alpaydın, 2009). The unsupervised machine learning
methods employed in this study are distance maps, based on correlation, dendrograms that
visualize the results of hierarchical clustering, and multi-dimensional scaling (MDS) graphs
that visualize the proximity of a set of observations on a 2-D plane based on their similarities.
A machine learning model was constructed in the Orange data mining software for
unsupervised learning (Figure 5). The unsupervised machine learning model aims at
identifying subgroups of similar respondents and subsets of similarly weighted performance
measures (questions). The model incorporates distance calculation based on Pearson
correlation, distance map visualization superimposed with hierarchical clustering, and multi-
dimensional scaling graph. Given the perceived weights for a given set of performance
measures and for a group of respondents, the model initially computes the distances between
the respondents. Then, distance map and the dendrogram are drawn based on the distance
matrix. In Figure 6, the distances are computed based on Pearson correlation between
perceived weight vectors for Supplier 1. Next, a multi-dimensional scaling is carried out, and
the respondents are mapped with respect to each other (Figure 7).
Figures 6 and 7 help us to answer a fundamental question: Which respondents’ weights are
similar? Dendrogram (Figure 6) and MDS graph (Figure 7) both give us the proximity
information, but in different ways: Dendrogram gives the proximity information in a
hierarchical context, whereas MDS gives it in a geographical context. In the dendrogram
(Figure 6), the respondents are connected under the same umbrella, such as respondent
R108 and R104 and R112 and R116, whilst the MDS graph (Figure 7) represents the
respondents that are close to each other in the map. The two mentioned pairs of respondents
are close to each other in Figure 7, where the point (respondent) pairs that behave similarly
(closes to each other) are linked by lines. In Figure 7, the colors of the points denote the roles
of the respondents within that company. It shows that the Senior Managers are closer to each
other, whereas the Front Line Staff is more dispersed in opinions.
The above analysis may have given us the clue to what can be done to close the Level-3
perception gap, and to achieve consistency throughout the company. One possible solution
can be to first match the consistent individuals with each other to enable them to understand
why they behave similarly, and then group them with the subgroups and individuals farthest
from them. This way, the reasons for the largest gaps can be revealed through group
meetings, and consistency can be improved.
35. 35
Figures 6 and 7 can be drawn for a supplier not only based on the evaluation on the supplier
side, but also based on the evaluations on the buyer side. Yet another analysis for each
supplier could be the visualization of the respondents from the supplier and buyer combined.
Such visualization would reveal not only Level-3 gaps, but also Level-2 gaps. Hence,
regarding the weights of the criteria for each supplier, three dendrograms and three MDS
graphs can be created, totaling to 48 graphs for the 8 suppliers. In this paper, the
visualizations are given only for Supplier 1, based on the weights given at the supplier
(Supplier 1). The same analysis was carried out based on the weights given by the
respondents at the buyer and at both. These additional analyses are presented in the
Appendices F and G.
It is evident that the analysis in Figures 6 and 7 can lead to identify the gaps between
individuals for not only Level-3 analysis, but also for Level-1 and Level-2 analysis. For
example, the combined analysis of the data for respondents for two suppliers through
dendrogram and MDS graph can help in identifying the gaps at the much more detailed
individual level, as opposed to the gaps at the company level.
Cross-communication is an indispensable part of remedy to close the gaps, and the machine
learning techniques, as employed in the analysis, demonstrate how and where such
communication can be improved.
Figure 5 Unsupervised machine learning model for identifying subgroups of similar
respondents and subsets of similarly weighted performance measures (questions). The model
is constructed in the Orange data mining software.
36. 36
Figure 6 Dendrogram and distance map for the respondents (R101, …, R117) within Supplier
1
38. 38
Appendix F. Level-3 Gaps at the buyer, regarding the weights for Supplier 1
Figure 8 Dendrogram and distance map for the respondents at the buyer stage
40. 40
Appendix G. Combined analysis of Level-1 and Level-3 Gaps, regarding the
weights for Supplier 1
Figure 10 Dendrogram and distance map for the respondents at the supplier and buyers
stages combined
42. 42
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