A BENCHMARK MODEL FOR INTERNAL ASSESSMENT OF INDUSTRY USING FUZZY TOPSIS APPR...ijmech
Internal assessment is one of the most important factor of an industry, needs appropriate improvement
planning between the departments with development of Benchmark model among them. The proposed study
applies Fuzzy Technique for Order Preference by Similarity to Ideal Solution to rank different alternatives.
The preliminary results indicate that the proposed model is capable of determining appropriate
competition between departments which are Human Resource, Finance, Production, Quality Assurance. To
remove the subjectivity, the linguistic data about the attributes is converted into a crisp score by using
fuzzy numbers and then the different alternatives are evaluated based on attributes by TOPSIS approach to
find the best alternative according to the industry’s requirement. Thus the endeavor has been made by the
authors to give a simple model for the evaluation of internal assessment of an industry
Selection of Equipment by Using Saw and Vikor Methods IJERA Editor
Now a days, Lean manufacturing becomes a key strategy for global competition. In this environment the most important process is the selection of the equipment. Equipment selection is a very important issue for effective manufacturing companies due to the fact that improperly selected machines can negatively affect the overall performance of manufacturing system. The availability of large number of equipments are more hence, the selection of suitable equipment for certain operation/ product becomes difficult. On the other hand selecting the best equipment among many alternatives is a Multi-criteria decision making ( MCDM ) Problems. In this Paper an approach which employs SAW, VIKOR Methods proposed for the equipment selection problem. The SAW and VIKOR is used to analyze the structure of the equipment selection problem and to determine weights of criteria and to obtain Final Ranking
AN INTEGRATED APPROACH FOR ENHANCING READY MIXED CONCRETE SELECTION USING TEC...A Makwana
The use of Ready Mixed Concrete (RMC) by the construction industry in most industrialized
countries is now well established. With the help of going over expertise of experts and their relevant
specialized literature, effective Criterias in Ready Mixed Concrete (RMC) selection and the Criterias
which will be used in their evaluation is extracted. For TOPSIS, the computations were carried out
using Microsoft Excel 2013. The weight of the Criterias is calculated first through Analytic
Hierarchy Process (AHP) and then it is analyzed by Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) method. The respondents were selected from various construction
occupancy mainly Ready Mixed Concrete (RMC) Plant Managers, Consultants and Contractors.
Total 100 Survey Questionnaires were distributed to Respondents in Anand, Nadiad, Vadodara,
Ahmedabad, from which 60 Responses were collected as per sample size calculation, in that 21 were
from Ready Mixed Concrete (RMC) Plant Managers, 26 were from consultants and 13 were from
contractors.The problem solution result shows: Respondent no. 3 (R3) is best because of its largest
weight age and Respondent no. 25 (R25) is worse because of its smallest weightage.
A BENCHMARK MODEL FOR INTERNAL ASSESSMENT OF INDUSTRY USING FUZZY TOPSIS APPR...ijmech
Internal assessment is one of the most important factor of an industry, needs appropriate improvement
planning between the departments with development of Benchmark model among them. The proposed study
applies Fuzzy Technique for Order Preference by Similarity to Ideal Solution to rank different alternatives.
The preliminary results indicate that the proposed model is capable of determining appropriate
competition between departments which are Human Resource, Finance, Production, Quality Assurance. To
remove the subjectivity, the linguistic data about the attributes is converted into a crisp score by using
fuzzy numbers and then the different alternatives are evaluated based on attributes by TOPSIS approach to
find the best alternative according to the industry’s requirement. Thus the endeavor has been made by the
authors to give a simple model for the evaluation of internal assessment of an industry
Selection of Equipment by Using Saw and Vikor Methods IJERA Editor
Now a days, Lean manufacturing becomes a key strategy for global competition. In this environment the most important process is the selection of the equipment. Equipment selection is a very important issue for effective manufacturing companies due to the fact that improperly selected machines can negatively affect the overall performance of manufacturing system. The availability of large number of equipments are more hence, the selection of suitable equipment for certain operation/ product becomes difficult. On the other hand selecting the best equipment among many alternatives is a Multi-criteria decision making ( MCDM ) Problems. In this Paper an approach which employs SAW, VIKOR Methods proposed for the equipment selection problem. The SAW and VIKOR is used to analyze the structure of the equipment selection problem and to determine weights of criteria and to obtain Final Ranking
AN INTEGRATED APPROACH FOR ENHANCING READY MIXED CONCRETE SELECTION USING TEC...A Makwana
The use of Ready Mixed Concrete (RMC) by the construction industry in most industrialized
countries is now well established. With the help of going over expertise of experts and their relevant
specialized literature, effective Criterias in Ready Mixed Concrete (RMC) selection and the Criterias
which will be used in their evaluation is extracted. For TOPSIS, the computations were carried out
using Microsoft Excel 2013. The weight of the Criterias is calculated first through Analytic
Hierarchy Process (AHP) and then it is analyzed by Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) method. The respondents were selected from various construction
occupancy mainly Ready Mixed Concrete (RMC) Plant Managers, Consultants and Contractors.
Total 100 Survey Questionnaires were distributed to Respondents in Anand, Nadiad, Vadodara,
Ahmedabad, from which 60 Responses were collected as per sample size calculation, in that 21 were
from Ready Mixed Concrete (RMC) Plant Managers, 26 were from consultants and 13 were from
contractors.The problem solution result shows: Respondent no. 3 (R3) is best because of its largest
weight age and Respondent no. 25 (R25) is worse because of its smallest weightage.
Methodology of thesis 'research barriers in the implementation of reverse log...Irfan iftekhar
These are the barriers in the implementation of reverse logistics due to lack of interest and knowledge of top decision makers of the organizations. Besides when an entity's organizational capabilities are strong, it can progress smoothly, but when they are weak it can find it difficult to get the job done, making errors due to underestimating the problems. An organization's capability is its ability which can win over the barriers in the implementation of RL practices. This consists of the strategy of a company, its strategic plans, its commitment, employees' hiring and skills development, a working system of performance appraisal and supporting programs.
For more course tutorials visit
www.newtonhelp.com
QNT 565 Week 1 Individual Assignment Business Research Case Study
QNT 565 Week 1 DQ 1
QNT 565 Week 1 DQ 2
QNT 565 Week 2 Learning Team Assignment Research Proposal Part I
This is a presentation from video on 'Introduction to Operations Research' available at the end of this presentations and directly at https://youtu.be/PSOW3_gX2OU
Topics like Organisations of Operations Research, History of Operations Research Role of Operations Research(OR), Scope of Operations Research(OR), Characteristics of Operations Research(OR), Attributes of Operations Research(OR).
This video also talks about Models of Operations Research
• Degree of abstraction
o Mathematical models
o Language models
o Concrete models
• Function
o Descriptive models
o Predictive models
o Normative models
• Time Horizon
o Static models
o Dynamic models
• Structure
o Iconic or physical models
o Analog or schematic models
o Symbolic or mathematical models
• Nature of environment
o Deterministic models
o Probabilistic models
• Extent of generality
o General model
o Specific models
Ph.D Public Viva Voce - PPT - Thesis - New Product Development Strategy and Analysis: A Study With Special Reference to Fabrication Engineering Industries in Chennai
This ppt will explain you the Defintion ,detailed explanation of phases with necessory diagrams, Applications ,Limitations and scope of Operations Research
Application of Fuzzy Analytic Hierarchy Process and TOPSIS Methods for Destin...ijtsrd
Destination selection is one of the most become an extremely popular. Sometimes the terms tourism and tourism are used pejoratively to indicate a shallow interest in the societies or islands that traveler's tour. This system presents the use of fuzzy AHP and TOPSIS for deciding on the selection of destination as like the selection of island. In this system, eight countries that include in South East Asia Thailand, Singapore, Malaysia, Indonesia, Philippine, Vietnam, Cambodia, Brunei are used. At first, the user can choose the specific country to decide the island of these countries and their preferences attraction, environment, accommodation, transportation, restaurant, activity, entertainment and other facilities are taken as inputs and then display the list of alternatives that matched with user's preferences. Fuzzy analytic hierarchy process is used in determining the weight of criteria and alternatives. Technique for Order Preference by Similarity to Ideal Solution TOPSIS method is used for determining the final ranking of the alternatives. Finally, this system shows the list of destinations depend on user's preferences. Hnin Min Oo | Su Hlaing Hnin "Application of Fuzzy Analytic Hierarchy Process and TOPSIS Methods for Destination Selection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27975.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-processing/27975/application-of-fuzzy-analytic-hierarchy-process-and-topsis-methods-for-destination-selection/hnin-min-oo
Unit I (8 Hrs)
Introduction to Linear Programming – Various definitions, Statements of basic
theorems and properties, Advantages Limitations and Application areas of Linear
Programming, Linear Programming -Graphical method, - graphical solution
methods of Linear Programming problems, The Simplex Method: -the Simplex
Algorithm, Phase II in simplex method, Primal and Dual Simplex Method, Big-M
Method
Unit II (8 Hrs)
Transportation Model and its variants: Definition of the Transportation Model
-Nontraditional Transportation Models-the Transportation Algorithm-the Assignment
Model– The Transshipment Model
Unit III (8 Hrs)
Network Models: Basic differences between CPM and PERT, Arrow Networks,
Time estimates, earliest completion time, Latest allowable occurrences time,
Forward Press Computation, Backward Press Computation, Representation in
tabular form, Critical Path, Probability of meeting the scheduled date of completion,
Various floats for activities, Critical Path updating projects, Operation time cost trade
off Curve project,
Selection of schedule based on :- Cost analysis, Crashing the network
Sequential model & related problems, processing n jobs through – 1 machine & 2
machines
Unit IV (8 Hrs)
Network Models: Scope of Network Applications – Network definitions, Goal
Programming Algorithms, Minimum Spanning Tree Algorithm, Shortest Route
Problem, Maximal flow model, Minimum cost capacitated flow problem
Unit V (8 Hrs)
Decision Analysis: Decision - Making under certainty - Decision - Making under
Risk, Decision
under uncertainty.
Unit VI (8 Hrs)
Simulation Modeling: Monte Carlo Simulation, Generation of Random Numbers,
Method for
Gathering Statistical observations
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
What is Strategy - Thinking like a StrategistAmit Kapoor
What is Strategy? Strategy is a very young concept. Lets explore a little more about strategy and then go down the journey of understanding how to think like a strategist.
Methodology of thesis 'research barriers in the implementation of reverse log...Irfan iftekhar
These are the barriers in the implementation of reverse logistics due to lack of interest and knowledge of top decision makers of the organizations. Besides when an entity's organizational capabilities are strong, it can progress smoothly, but when they are weak it can find it difficult to get the job done, making errors due to underestimating the problems. An organization's capability is its ability which can win over the barriers in the implementation of RL practices. This consists of the strategy of a company, its strategic plans, its commitment, employees' hiring and skills development, a working system of performance appraisal and supporting programs.
For more course tutorials visit
www.newtonhelp.com
QNT 565 Week 1 Individual Assignment Business Research Case Study
QNT 565 Week 1 DQ 1
QNT 565 Week 1 DQ 2
QNT 565 Week 2 Learning Team Assignment Research Proposal Part I
This is a presentation from video on 'Introduction to Operations Research' available at the end of this presentations and directly at https://youtu.be/PSOW3_gX2OU
Topics like Organisations of Operations Research, History of Operations Research Role of Operations Research(OR), Scope of Operations Research(OR), Characteristics of Operations Research(OR), Attributes of Operations Research(OR).
This video also talks about Models of Operations Research
• Degree of abstraction
o Mathematical models
o Language models
o Concrete models
• Function
o Descriptive models
o Predictive models
o Normative models
• Time Horizon
o Static models
o Dynamic models
• Structure
o Iconic or physical models
o Analog or schematic models
o Symbolic or mathematical models
• Nature of environment
o Deterministic models
o Probabilistic models
• Extent of generality
o General model
o Specific models
Ph.D Public Viva Voce - PPT - Thesis - New Product Development Strategy and Analysis: A Study With Special Reference to Fabrication Engineering Industries in Chennai
This ppt will explain you the Defintion ,detailed explanation of phases with necessory diagrams, Applications ,Limitations and scope of Operations Research
Application of Fuzzy Analytic Hierarchy Process and TOPSIS Methods for Destin...ijtsrd
Destination selection is one of the most become an extremely popular. Sometimes the terms tourism and tourism are used pejoratively to indicate a shallow interest in the societies or islands that traveler's tour. This system presents the use of fuzzy AHP and TOPSIS for deciding on the selection of destination as like the selection of island. In this system, eight countries that include in South East Asia Thailand, Singapore, Malaysia, Indonesia, Philippine, Vietnam, Cambodia, Brunei are used. At first, the user can choose the specific country to decide the island of these countries and their preferences attraction, environment, accommodation, transportation, restaurant, activity, entertainment and other facilities are taken as inputs and then display the list of alternatives that matched with user's preferences. Fuzzy analytic hierarchy process is used in determining the weight of criteria and alternatives. Technique for Order Preference by Similarity to Ideal Solution TOPSIS method is used for determining the final ranking of the alternatives. Finally, this system shows the list of destinations depend on user's preferences. Hnin Min Oo | Su Hlaing Hnin "Application of Fuzzy Analytic Hierarchy Process and TOPSIS Methods for Destination Selection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27975.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-processing/27975/application-of-fuzzy-analytic-hierarchy-process-and-topsis-methods-for-destination-selection/hnin-min-oo
Unit I (8 Hrs)
Introduction to Linear Programming – Various definitions, Statements of basic
theorems and properties, Advantages Limitations and Application areas of Linear
Programming, Linear Programming -Graphical method, - graphical solution
methods of Linear Programming problems, The Simplex Method: -the Simplex
Algorithm, Phase II in simplex method, Primal and Dual Simplex Method, Big-M
Method
Unit II (8 Hrs)
Transportation Model and its variants: Definition of the Transportation Model
-Nontraditional Transportation Models-the Transportation Algorithm-the Assignment
Model– The Transshipment Model
Unit III (8 Hrs)
Network Models: Basic differences between CPM and PERT, Arrow Networks,
Time estimates, earliest completion time, Latest allowable occurrences time,
Forward Press Computation, Backward Press Computation, Representation in
tabular form, Critical Path, Probability of meeting the scheduled date of completion,
Various floats for activities, Critical Path updating projects, Operation time cost trade
off Curve project,
Selection of schedule based on :- Cost analysis, Crashing the network
Sequential model & related problems, processing n jobs through – 1 machine & 2
machines
Unit IV (8 Hrs)
Network Models: Scope of Network Applications – Network definitions, Goal
Programming Algorithms, Minimum Spanning Tree Algorithm, Shortest Route
Problem, Maximal flow model, Minimum cost capacitated flow problem
Unit V (8 Hrs)
Decision Analysis: Decision - Making under certainty - Decision - Making under
Risk, Decision
under uncertainty.
Unit VI (8 Hrs)
Simulation Modeling: Monte Carlo Simulation, Generation of Random Numbers,
Method for
Gathering Statistical observations
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
What is Strategy - Thinking like a StrategistAmit Kapoor
What is Strategy? Strategy is a very young concept. Lets explore a little more about strategy and then go down the journey of understanding how to think like a strategist.
A Topic Model of Analytics Job Adverts (Operational Research Society Annual C...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
A Topic Model of Analytics Job Adverts (The Operational Research Society 55th...Michael Mortenson
This presentation presents recent research into definitions of analytics through analysis of related job adverts. The results help us identify a new categorisation of analytics methodologies, and discusses the implications for the operational research community.
How to conduct a literature review: A literature review on knowledge manageme...Roberto Cerchione
Guidelines for writing a literature review applied to the topic of Knowledge Management in SMEs.
This paper provides a systematic review of the literature on knowledge management (KM) in small and medium enterprises (SMEs) and SME networks. The main objective is to highlight the state-of-the-art of KM from the management point of view in order to identify relevant research gaps. The review highlights that in recent years the trend of papers on the topic is growing and involves a variety of approaches, methodologies and models from different research areas. The vast majority of papers analysed focus on the topic of KM in the SME while there are only few papers analysing KM in networks populated by SMEs. The content analysis of the papers highlights six areas of investigation from which were derived ten research questions concerning three perspectives: the factors affecting KM; the impact of KM on firm’s performance; the knowledge management systems.
to cite this paper: Cerchione, R., Esposito, E., Spadaro, M.R. A literature review on knowledge management in SMEs (2016) Knowledge Management Research and Practice, 14 (2), pp. 169-177.
to link to this paper: doi:10.1057/kmrp.2015.12
Current labs can greatly benefit from a digital transformation.
FAIR data principles are crucial in this process.
Laying a solid data governance foundation is an invaluable long-term move.
Modeling Framework to Support Evidence-Based DecisionsAlbert Simard
Describes a framework for modelling in a regulatory environment founded on sound scientific and knowledge management concepts. It includes 1) demand (isue-driven) and supply (model driven) approaches to modelling, 2) balancing modeler, manager, and user perspectives, 3) documentation to demonstrate due diligence, and a 700-term glossary.
QSO 510 Final Project Guidelines and Rubric Overview .docxmakdul
QSO 510 Final Project Guidelines and Rubric
Overview
The final project for this course is the creation of a statistical analysis report.
Each day, operations management professionals are faced with multiple decisions affecting various aspects of the operation. The ability to use data to drive
decisions is an essential skill that is useful in any facet of an operation. The dynamic environment offers daily challenges that require the talents of the operations
manager; working in this field is exciting and rewarding.
Throughout the course, you will be engaged in activities that charge you with making decisions regarding inventory management, production capacity, product
profitability, equipment effectiveness, and supply chain management. These are just a few of the challenges encountered in the field of operations management.
The final activity in this course will provide you with the opportunity to demonstrate your ability to apply statistical tools and methods to solve a problem in a
given scenario that is often encountered by an operations manager. Once you have outlined your analysis strategy and analyzed your data, you will then report
your data, strategy, and overall decision that addresses the given problem.
The project is divided into two milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final
submissions. These milestones will be submitted in Modules Three and Seven. The final project is due in Module Nine.
In this assignment, you will demonstrate your mastery of the following course outcomes:
Apply data-based strategies in guiding a focused approach for improving operational processes
Determine the appropriate statistical methods for informing valid data-driven decision making in professional settings
Select statistical tools for guiding data-driven decision making resulting in sustainable operational processes
Utilize a structured approach for data-driven decision making for fostering continuous improvement activities
Propose operational improvement recommendations to internal and external stakeholders based on relevant data
Prompt
Operations management professionals are often relied upon to make decisions regarding operational processes. Those who utilize a data-driven, structured
approach have a clear advantage over those offering decisions based solely on intuition. You will be provided with a scenario often encountered by an operations
manager. Your task is to review the “A-Cat Corp.: Forecasting” scenario, the addendum, and the accompanying data in the case scenario and addendum; outline
the appropriate analysis strategy; select a suitable statistical tool; and use data analysis to ultimately drive the decision. Once this has been completed, you will
be challenged to present your data, data analysis strategy, and overall decision in a concise report, justifying your analysis.
Specifically, the ...
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
"Protectable subject matters, Protection in biotechnology, Protection of othe...
PhD Thesis Igor Barahona July 26th of 2013
1. The level of adoption
of analytical tools.
Igor BARAHONA
Barcelona, Spain. July 26th of 2013
2. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
Contents.
2
3. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
3
4. Processor
Dhrystone
MIPS
Cost Year
UNIVAC I
0.002 MIPS at
2.25 MHz
$11.500.000.
USD
1951UNIVAC I
Computers performance.
1951
IBM
System/370 mod
el 158-3
IBM
System/370 mo
del 158-3
1 MIPS at
8.69 MHz
$2,248,550.
USD
1971 1971
INTEL 286 Intel 286
2.66 MIPS at
12.5 MHz
$3,500. USD 1982 1982
Intel
Pentium III Intel Pentium III
2,054 MIPS at
600 MHz
$2,000. USD 1999 1999
Intel Core i7
2600K
128,300 MIPS
at 3.4 GHz
$1,000. USD 2011 2011
Intel
Core i7
2600K
5. If computers are more powerful...........
Lots of data but not information
Powerful computers but
unstructured problems
Difficulties of getting fast and
accurate information.
Make sense of “data tsunami”
that is hitting modern industries
5
What does it
imply?
Burby & Atchison (2007)
Kaushilk (2011)
6. The business environment...................
More complexity requires analyzing
real-time-data and for making
better decisions.
Reduction on differentiation
points due to the globalization of
markets.
Customers better informed with
more alternatives.
Data has to be converted into
“Information” that triggers
managerial action.
6
How is it
changing?
Stubbs (2011)
McDonough (2009)
7. Extensive utilization of
data, information and
quantitative models.
Understand past / present
performance
Reduce uncertainty
Predict future results
Making better decisions based on
quantitative evidence
OUTPUTS
ADDED VALUE
INPUTS
7
It can be defined in terms of inputs and outputs
What is an analytical tool?
Davenport & Harris (2007)
8. • Using analytics
– Finding the best customers, and charging
them the right price
– Minimizing inventory in supply chains
– Allocating costs accurately and
understanding how financial performance is
driven.
Using analytical tools is good.......
8
But It is better competing with them...
• Competing with analytics.
– Making analytics and fact-
based decisions a key
element of strategy and
competition
Davenport, Harris & Morrison (2010)
9. Thesis Objectives
1. Propose a theoretical scale to measure the level of adoption of
analytical tools in companies.
2. Design a reliable and valid instrument to collect data from a
sample of companies located in Barcelona, Spain.
3. Analyze data collected from the surveyed companies, in order to
draw conclusions about the level of adoption of analytical tools in
Barcelona by applying the Statistical Engineering approach.
4. Rank the sampled companies in the scale by applying the
Evidential Reasoning approach.
5. Conduct in-depth interviews with managers, consultants and
academics with the purpose of finding out soft and unstructured
aspects about the level of adoption of analytical tools in
Barcelona by applying the Laddering Methodology.
6. Based on results generated, provide practical guidelines to
stakeholders who are interested in expanding the use of analytical
tools in companies and creating competitive advantages.
The level of
adoption of
analytical
tools.
9
10. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
10
11. Systemic
thinking
Management
support
Removing obstacles
Human, technical and
financial resources.
.
Encouraging staff
involved in the project
Emergency
Hierarchy
Communication
Control
1
4 key-drivers for LAAT expansion.
2
Deming, (2000)
Deming, (2000)
Hahn et al (2000)
Yeo (1993)
Tort-Martorell et al
(2011)
Hoerl & Snee (2010)
Checkland (1999)
11
12. DB. Competitive
advantage
Communication
outside the
company.
Lower price / cost
Market niche
Differentiation
Privileged location.
Customer Relationship Managers (CRM)
Trust
Long term relationships
3
4
Supply chain Managers (SCM)
4 key-drivers for LAAT expansion.
Langfield-Smith &
Greenwood (1998)
Davenport, Harris &
Morrison (2010)
Porter (1990)
Poon &Wagner
(2001)
Blanchard (2010)
12
13. 13
Theoretical model and 4 key drivers.
LAAT
MANAGEMENT
SUPPORT
COMMUNICATION
OUTSIDE
SYSTEMIC
THINKING
DB. COMPETITIVE
ADVANTAJE
13
14. 1. We proposed a 5 level
scale
2. At level 1 we find
companies that do not use
any analytical tool.
3. At level 5 we find
companies that use
analytical tools as a
strategic support for their
competitive advantage.
4. At levels 2, 3 and 4 we find
companies that are
improving on using
analytical tools
Five level scale
14
15. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
15
16. Analytical tools on finance.
S – Strength
W – Weaknesses
O – Opportunities
T – Threats.
Score Cards
Financial benchmarking
Predictive analytics
applied to Ratios analysis,
Balance sheets and
income statements .
Janis (2008) Xu DL (2012) Morris et al. (2002)
16
17. Analytical tools on manufacturing
Design of experiments
(DOE)
Six Sigma.
Statistical Process Control.
(SPC)
The seven management
tools
Surface response
Hoerl et al (1993) Ishikawa (1988)
Futami (1986) Deming (2000)
17
18. Analytical tools on R&D
Clinical trials
Control groups
Survival analysis
Stochastic process
Multivariate analysis
Davenport, Harris &
Morison (2010)
Liu et al (2008)
18
19. Analytical tools on Human Resources
Multivariate regression
Assess intangible assets
MCDA methods
Correspondence analysis
Decisions trees
Harris, Craig & Egan (2009)
Lewis (2003)
Armstrong (2012)
19
20. Analytical tools on Marketing
Time series
General linear models
MCDA methods
Multivariate analysis
Survey research methods
Customer relationships
management
Armstrong (2012)
Deming (2002)
Burby & Atchison (2007)
20
21. Analytical tools with suppliers
Decisions trees
MCDA methods
Multivariate analysis
Supply chain management
Petroni & Braglia (2000)
Verma & Pullman (1998)
Nydick & Hill (1992)
21
22. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
22
23. Questionnaire design.
• A 7-step methodology was
adapted to design and
validate the scale.
• In the first part the issue is
to provide valid and
reliable items
• The second part is focused
on the validity and
reliability of the scales
23
Menor & Roth (2007)
24. Theoretical domain (1/7)
• Four theoretical constructs
were investigated
• A total of 17 items were
derived from the
theoretical constructs
• Each item was associated
to a five level Likert scale.
24
Bryman (2012)
Menor & Roth (2007)
Michie et al (2005)
25. Item generation (2/7)
The “degree of
understanding” was
calculated in order to
ensure each item is
understandable and easy
to read. (Kappa index for
multiple raters)
ITEM Judge1 Judge2 Judge3 Judge4 Judge5 Judge6 Judge7 Judge8
DB-CA1 4 4 4 4 4 4 4 4
DB-CA2 4 4 4 4 4 4 4 4
DB-CA3 4 5 4 5 4 5 4 5
DB-CA4 5 5 4 4 4 4 4 4
DB-CA5 4 4 4 4 4 4 4 4
MS-DA1 4 4 4 4 4 4 4 4
MS-DA2 4 4 4 4 4 4 4 4
MS-DA3 4 4 4 5 4 5 4 4
MS-DA4 4 4 4 4 4 4 4 4
MS-DA5 4 4 4 4 4 4 4 4
MS-DA6 5 5 5 5 5 5 5 5
SYS1 5 5 5 5 5 5 5 5
SYS2 5 5 5 5 5 5 5 5
SYS3 4 5 5 5 5 5 4 5
SYS4 5 5 5 5 5 5 5 5
SYS5 5 5 5 5 5 5 5 5
COMOUT 5 5 5 5 5 5 5 5
A total of four constructs
were operationalized in 17
items. All the questions were
designed in a Likert scale
from 1 to 5
Grade Kappa
Standard
Error
z Prob>Z
4 0.77980 0.045835 170132 <.0001
5 0.77980 0.045835 170132 <.0001
Overall 0.77980 0.045835 170132 <.0001
Item refining (3/7)
25
Good level of
understanding.
Fleiss (1971) Cohen (1960)
26. Questionnaire development (4/7)
Section
Number
of items
Categorical questions 3
Data Based Competitive Advantage 5
Management Support Data Analysis 6
Systemic Thinking 5
Communication outside the company 1
Total 20
Structure of the first draft
of questionnaire
1
Two steps on the pilot test
It was shared in social
networks
2 It was sent to 300
companies members of
the UPC-Alumni
31 responses
were obtained
Improving the
order of questions
Reviewing
features of the
cover letter
Final writing of
questions
26
28. Survey data collection (5/7)
602.161
41.152
86.094,00
474.915
Total Indústria Construcció Serveis
6,064 companies were invited by
sending it electronically
255 responses were obtained.
Analytics diagnostic free of charge.
Open to share results.
28
IDESCAT (2013)
30. Reliability (6/7)
Cronbach Alpha
ITEM Response
We apply analytical tools in all decisions we
make
strongly agree
completely agreeWe exploited and analyzed plenty of data
during the last year
The use of statistics is useless to build
competitive advantages in our company
completely agree
Alphas are
helpful to
identify these
type of
incoherence
Formulation and outputs
K= Items of the section
Si= standard deviation of the item
St= standard deviation of the section
Subsection Alpha
Data Based Competitive Advantage. (DB-
CA)
0.8884
Management Support in Data Analysis.
(MS-DA)
0.8025
Systemic Thinking (SYS) 0.7761
Communication Outside the Company
(COM-OUT)
1.0000
30
Cronbach (1951) Streiner (2003)
31. Reliability (6/7)
Interclass correlation coefficient
row /company-effect
column/ item-effect
Source of variation
Sum of
Sq
D.F
Mean of
Sq
F-
Value
Pr(>F)
Between Companies (row-effect) 1734.138 153 11.334
Within
Companies
(item-effect)
Within
Companies
817.168 15 54.478 55.768 .000
Residuals 2241.894 2295 .977
Total 3059.063 2310 1.324
Formulation and outputs
Intra- class Correlation Coefficient (ICC)
Two-way
Random Effect
Model
ICC
95.00% C.I
Lower Upper
Average
Measure
(Within effect)
.887 .851 .915
Shrout & Fleiss (1979) Tian (2005)31
32. Item and scale refinement (7/7)
Explanatory factor analysis (EFA)
Quetionnaire ITEM Factor1 Factor2 Factor3 Factor4
Understanding benetifs DB_CA1 0.757
Product Improvement DB_CA2 0.756
Statistics Support DB_CA3 0.831
Statistics Importance DB_CA4 0.806
Statistics Encouragement DB_CA5 0.659
Staticstics Training MS_DA1 0.826
New knowledge implementation MS_DA2 0.723
Data collection process MS_DA3 0.527
Budget for projects MS_DA4 0.837
Technological resources MS_DA5 0.622
Competitor's Investigation MS_DA6 0.561
Efforts recognition SYS1 0.595
Mission understanding SYS2 0.693
Communication openness SYS3 0.571
Teamwork culture SYS4 0.764
Reinforcement on data usage SYS5 0.534
Communication suppliers/customers COM_OUT 0.852
DB-CA. Data-Based
Competitive
Advantage
MS-DA. Management
Support on Data
Analysis
SYS. Systemic Vision
of the business
COM-OUT.
Communication
Outside company.
(clients and suppliers)
In order to validate our questionnaire, the 17 items were clustered on the first 4 factors
using the loadings as classification criteria
32
Krzanowski (2000) Long (1983) Kaiser (1958)
33. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
33
34. Statistical Engineering case of study
Don't focus on the introduction of new theories but rather
how they might be best utilized for practical benefit
Strategic:
Statistical
Thinking.
Tactical:
Statistical
Engineering.
Operational:
Statistical Methods
and Tools.
Statistical
Theory.
Statistical
Practice.
How to utilize the principles and techniques of statistical
science for benefit of humankind.
How to best utilize statistical concepts, methods, and tools
and integrate them with information technology and other
relevant sciences to generate improved results.
Systemic thinking
Variance reduction
Holistic approach
Statistical Methods
Specialized software
Skilled staff
34
Hoerl & Snee (2010) Anderson-Cook et al (2012) Hoerl & Snee (2012)
35. Statistical Engineering case of study
Data collection2
Confirmatory analysis3
Relationship
between companies
4
Relationship between key
drivers
5
Conclusions
Understanding project’s
scope
1
Flowchart
Questionnaire
Survey
Datasheet
Correspondence analysis (CA)
Factor Analysis
Logistic Regression (LR)
Correlation Matrix (CM)
illustrates the relation between statistical thinking
and statistical methods.
35
Seven statistical tools were wisely integrated in a
five step process to accomplish a unique objective.
36. Data collection (2/5)
6,064 companies
were invited by sent
it electronically
255 responses
were obtained.
Confirmatory analysis (3/5)
Quetionnaire ITEM Factor1 Factor2 Factor3 Factor4
Understanding benetifs DB_CA1 0.757
Product Improvement DB_CA2 0.756
Statistics Support DB_CA3 0.831
Statistics Importance DB_CA4 0.806
Statistics Encouragement DB_CA5 0.659
Staticstics Training MS_DA1 0.826
New know ledge implementation MS_DA2 0.723
Data collection process MS_DA3 0.527
Budget for projects MS_DA4 0.837
Technological resources MS_DA5 0.622
Competitor's Investigation MS_DA6 0.561
Efforts recognition SYS1 0.595
Mission understanding SYS2 0.693
Communication openness SYS3 0.571
Teamw ork culture SYS4 0.764
Reinforcement on data usage SYS5 0.534
Communication suppliers/customers COM_OUT 0.852
ITEMS
36
37. 37
The 255 responses were discomposed
and represented at the 2 biggest factors
Correspondence analysis (4/5)
39. 39
Level 1 is close from Micro Size.
Level 4 is close from Middle Size
40. 40
Services Companies are more suitable to be
analytical oriented
Products Companies are more related with level 1
and Micro size
41. 41
Middle size companies are closer to “better and
different” strategies.
There is a group for Micro-size, Products, Level 1
and No Competitive Advantage
41
42. COMMUNICATION
OUTSIDE
COMPANY
DB.
COMPETITIVE
ADVANTAGE
SYSTEMATIC
THINKING
MANAGEMENT
SUPPORT. DA
C.M allows us to understand and quantify relationships
between the AVERAGES of the Key Drivers
0.702
0.648
0.300
Pearson Correlation Coefficients
DBCA MSDA SYS COMOUT
DBCA. Data Based
Competitive Advantage
1.000 0.70243 0.69484 0.05246
MSDA. Management
support data analysis
1.000 0.64852 -0.03397
SYS. Systematic Thinking 1.000 0.30036
COMOUT. Communication
Outside Company
1.000
0.695 These
correlations
were
calculates with
the AVERAGES
of ITEMS.
Correlation Matrix (4/5)
42
Hair, et al (2006)
Krzanowski (2000)
43. To predict if on a set of 255 Spanish companies, either a company has analytics aspirations
or not. (Level=>4)
Level 4 is the starting point of the use of analytical tools as a distinctive competence in the
industry
RESPONSE VARIABLE:
0Y
1Y
If the company does not has analytical aspirations. (Level<4)
If the company has analytical aspirations. (Level>=4)
NOANALYTICAL
ASPIRATIONS. (LEVEL
1 , 2AND 3)
ANALYTICAL
ASPIRATIONS
(LEVEL 4 AND 5)
TOTAL
186 69 255
73% 27% 100%
PREDICTORS
G1 Understanding the benefits of Statistics
G2 Statistics builds the Comp. Adv
G3 There is one mission and vision
G4 Communication with clients and suppliers
The predictors were
taken from the
questionnaire ITEMS
Logistic regression (5/5)
43
Philip and Teachman (1998)
44. )(43210
1
ijkllkji GGGG
P
P
Ln
THE MODEL
have p-values less than 0.05,
indicating that there is
sufficient evidence that the
coefficients are not zero using
an alfa level of 95%
The goodness-of-fit tests,
with p-value equal to 1.000.
Indicate that there is
insufficient evidence to
claim that the model does
not fit the data adequately.
1. UNDERSTANDING THE BENEFITS OF APPLIED STATISTICS BUSINESS.
2. BUILDING A COMPETITIVE ADVANTAGES BY DATA ANALYSIS.
3. ESTABLISHING A MISSION AND VISION STATEMENTS ON THE COMPANY
4. STIMULATING COMMUNICATION OUTSIDE COMPANY.
Coefficients for
these variables
are not cero.
Logistic Regression Table
Odds 95% CI
Predictor Coef SE Coef Z P Ratio Lower Upper
Constant -17.8045 3.13596 -5.68 0.000
DB_CA1 1.65439 0.313537 5.28 0.000 5.23 2.83 9.67
DB_CA3 0.723906 0.271505 2.67 0.008 2.06 1.21 3.51
SYS2 1.12321 0.273354 4.11 0.000 3.07 1.80 5.25
COM_OUT 1.54055 0.382019 4.03 0.000 4.67 2.21 9.87
Goodness-of-Fit Tests
Method Chi-Square DF P
Pearson 105.652 111 0.625
Deviance 72.350 111 0.998
Hosmer-Lemeshow 4.405 8 0.819
44
45. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
45
46. Evidential Reasoning case of study
A generic evidence-based multi-criteria decision analysis (MCDA) approach
for dealing with problems having both quantitative and qualitative criteria
under various uncertainties including ignorance and randomness.
Main documented applications:
• Environmental impact assessment,
• Organizational self-assessment
• Portfolio investments
• Prioritizing voices of customers.
The ER approach is implemented in a software called Intelligent
Decision System.
The Belief Decision Matrix allows us more realistic assessments than
traditional Decisions Matrix.
Accepts data of different formats with various types of uncertainties as
inputs, such as single numerical values, probability distribution, and
subjective judgments with belief degrees.
Main features in
the ER approach
Xu & Yang (2001) Yang & Singh (1994)
46
47. Main ER assumptions
Main
assumptions.
Exclusiveness of grades: A grade Hn is assumed to be mutually exclusive of
another Hn+1
Completeness of criteria: Suppose an overall criterion is assessed through
m sub-criteria. These m sub-criteria are said to be collectively exhaustive
or complete.
Weight as degree of importance: The weight of a sub-criterion yi,
denoted by wi, is a degree of importance of yi in the assessment of the
overall criterion.
Distribution assessment.
Generation of the overall belief
Linear algorithm Non -linear algorithm
Xu & Yang (2002) Yang (2001)
Yang & Singh
(1994)
47
48. Model definition2
Relate father and bottom
attributes.
3
Assigning weights.4
Assigning belief degree.
5
Calculate assessments.
6
Data collection1
Implement the set of rules
Pair-wise comparison
Define uncertainty
Sensitivity test
Overall performance
Compare alternatives
Transform means-values to degree
of belief
Conclusions
Evidential reasoning case of study
A six-step methodology was adapted for this case of
study48
Apply ER algorithm to extract relevant conclusions
about which attributes clearly contribute to the
expansion of LAAT and therefore to reach
competitive advantages.
49. Model definition (2/6)
Model summary Grades
Numer of parent attributes: 4 u(H1):= u (Analytic ignorance) =0.00
u(H2):= u (Local applications) = 0.25
Numer of bottom atributes: 17 u(H3):= u (Analytical aspirations) =0.50
Selected method for relating parent and
bottom attributes: RULE-BASED
APPROACH (Yang 2001)
u(H4):= u (Analytics as a systems) = 0.75
u(H5):= u(Analytics as competitive advantage) = 1.00
49
50. Relate father and bottom attributes. (3/6)
Relating attributes is defined as how the assigned
grades are converted to the ones of their parents
If MSDA is worst =0.00
Then Overall Performance is Analytical
Ignorance=100%
If MSDA is poor=0.25
Then Overall Performance is Local
Focus=100%
If MSDA is average=0.50
Then Overall Performance is Analytical
aspirations=100%
If MSDA is good=0.75
Then Overall Performance is Analytics
as System=100%
If MSDA is excellent=1.00
Then Overall Performance is Analytics
as Comp. Advantage=100%
In similar way, the attributes
• SYS
• COM-OUT
• DBCA
were related to their parent attributes50
Yang (2001)
51. Assigning weights . (4/6)
0.22
0.18
0.21 0.21
0.17
0.14 0.15 0.16
0.12
0.16
0.28
0.16
0.20 0.18
0.23 0.22
1.00
Data-Based competitive
advantage
Management support on data
analysis
Systemic Thinking Communication outside the
company
MS-DA6 was the most important. It refers to whether the top
management promotes the use of data to evaluate how the
competitors are evolving
SYS4 refers to whether there is a teamwork culture in the
company.
51
52. Assigning belief degree. (5/6)
The degree of belief represents the extent to which an answer is believed to be true.
The following expression was utilized for
assigning belief degrees
For SYS4 µ= 3.80
The belief
structure is
{(“Worst” with β=0.00),
(“Poor” with β =0.00),
(“Average” with β =0.20),
(“Good” with β =0.80),
(“Best” with β =0.00)}.
In this way the mean-value was transformed into 5
values, which describe the scenario more accurately
The transformation was applied to the 17 attributes
of the model
52
53. Calculate assessments. (6/6)
The overall performance
Middle companies are slightly more
analytical oriented than big
This result is coherent with CA of the
slide 42.
In the CA, middle companies
are closest to the Level 5.
Distance among Big and
Middles is also small.
53
55. Sensitivity test
Calculate assessments. (6/6)
Micro Company Small Company
Middle company Big Company
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Averagescore
Average scores for the Overall PerformanceAverage scores for the Overall Performance
Weight of Systematic Thinking
Givenweight
Micro Company Small Company
Middle company Big Company
0%
20%
40%
60%
80%
100%
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Averagescore
Average scores for the Overall PerformanceAverage scores for the Overall Performance
Weight of Communication Outside the company
GivenweightSensitivity to changes in the weight of
SYS
For lower weights on SYS sensitivity
increases
For higher weights, the sensitivity
decreases
Sensitivity to changes in the weight of COM-
OUT
For higher weights, the sensitivity
increases
55
For lower weights on COM-OUT, the
sensitivity decreases
56. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
56
57. 57
How customers translate the attributes of products
into meaningful associations with respect to
self, following Means-End Theory
Understand how customers underlying personal
motivations with respect to any given product or
service
Higher level distinctions provides a perspective of
how the attributes are processed from a motivational
perspective
Investigate about the connections between the
attributes and personal motivations.
Reynolds & Gutman 1988, Gutman 1982
The Laddering technique
57
58. How ladders are built?
ATTRIBUTES
CONSEQUENCES
VALUES
Reynolds & Gutman 1988; Gutman 1982
The Laddering technique
Full-bodied taste/
less alcohol
Avoid getting drunk
(wasted) / Socialize
Sense of belonging /
Responsibility to family
59. Respondent Industry Length
1 Project Manager Consultancy Services 50 min
2 CEO Entertainment Services 55 min
3 Head of department Government 1:15 hr
4 Owner / Founder Marketing Research 56 min
5 CEO
Plastic packages
manufacturer
45 min
6 Professor / Researcher Academy 36 min
7 Professor / Researcher Academy 35 min
8 Business consultant Consultancy Services 50 min
9 Analytics consultant Consultancy Services 48 min
10 Professor / Researcher Academy 52 min
The persons who responded the interview.
The designed script was used
in all interviews
The interviews took place on
respondent ’s office
There are digital records for
each one interview
The script’s structure follows
the 5 key drivers
The Laddering technique
59
60. The Laddering technique
60
One example of ladder
ATTRIBUTES
CONSEQUENCES
VALUES
A Improve the knowledge of data
A Goal setting
Ladder taken from the
interview with a CEO of
Packaging
Manufacturer
C Lower cost
C Continuous learning
60
V Serving the society
V Add value to stakeholders
61. Implication Matrix
Data is accessible and
supports decisions (1)
Improve D. Analysis
(21) 17 times
Improve D. Analysis
(21) add value to
stakeholders (29)
18 times
62. Hierarchy Value Map (HMV)
HVM is a way to graphically represent the most
dominant connections. It is a representation of the
linkages across levels of abstraction, starting with
attributes and finishing with values
It should include ladders with 4
or more direct relations. (A total
of 84 in this case)
The main purpose is to highlight
meaningful connections between
(A)-(C)-(V)
Obtained by the cumulative
frequency of direct relations.
Reynolds & Gutman 1988; Gutman 1982
62
63. (1)
Data is
accessible
and supports
decisions
(2)
Data
online
(5)
High
skilled
staff
(6)
Enough
support
(7)
High
tech
(4)
standar
dized
proced
ures
(12)
the most
efficient
structure
(15)
innovate
products
and
services
(8)
Commun
ication
with C&S
(10)
informati
on
outside
(11)
Market
research
(9)
Creativit
y to new
ideas
(14)
Respond
more
quickly
(13)
Flexibility
(3)
Goal
Setting
17 7 13115 6 3 58 7 46 563
(21)
Improve
data
analysis
(28)
staff
efficiency
and
motivation
(16)
Analyze
data from
market
(24)
Knowledge
of data
(19)
Exceeding
customer
exp
(20)
Good
image of
the
company
10
8
(29)
add value
to stake
holders
(30)
Being a
leader
(31)
Communic
ation and
trust
(33)
Passion, Qu
ality and
Excellence
(25)
Long term
relationships
7
8
11
14
12
(22)
improvin
g process
(23)
Improvin
g results
4 7
(27)
More
money (17)
Continuous
learning
(18)
Distinctive
competence
(26)
Lower
cost 4
65
14
11
4
4
6
13
12
(34)
serving
the
society
(32)
honesty
and
credibility
6 7 9 6
14
10
18
8
Hierarchy Value Map (HMV)
63
64. Summary table
The 10 attributes which have the biggest number of relations.
They concentrate the 80% of the total relations .
This table allow us to identify the attributes which have the biggest
impact on values.
64
65. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
65
66. LEVEL 1
A small interest on using
analytical tools shows up.
LEVEL
2
Small and local success brings the
attention of the Senior
Management.
Final point. Analytical
initiatives did not reach
expectations from the senior
management.
Having the leadership in the
market though the use of
analytical tools.
“Prove-it”
Strategy
LEVEL 3
LEVEL 4
LEVEL 5
The first attempt to broad an
analytical project. Define an
analytical vision “Plan your work” .
Embedding the strategic and
critical process with the Analytical
Vision. “Work your plan”
Analytical
Ignorance
Analytical
Focus
Analytical
Aspirations
Analytical
Engineering
Analytics as
competitive
advantage
Road Map for upgrading the scale
Keep working on:
Diagnostic Actions Diagnostic.
Until the highest level in the scale is
reached.
66
68. 1. Introduction.
2. The level of adoption of analytical tools. A theoretical
perspective.
3. The analytical tools in different areas of the company.
4. The questionnaire design.
5. The cases of study.
• Statistical Engineering.
• Evidential Reasoning.
• The laddering technique.
6. Conclusions and guidelines to stakeholders.
7. Further lines of research.
68
69. Scale aggregation
Overall assessment. Level of
Adoption of Analytical Tools.
(LAAT)
Questionnaire
Data-Based
competitive
advantages
In-depth
interviews
Operative
attributes
Systematic
thinking
Management
support
Communication
outside
Tactical
features
Organizational
values
17 attributes on a five-level scale 33 concepts on a three-level scale
How aggregate them
while losing or
distorting information
is prevented?
69
70. Scale aggregation
The research presented on:
Yang, J. B., Xu, D. L., Xie, X., & Maddulapalli, A. K. (2011). Multicriteria
evidential reasoning decision modelling and analysis-prioritizing voices of
customer.Journal of the Operational Research Society, 62(9), 1638-1654.
Will be adapted to investigate this aggregation and
accomplish the following objectives:
• Investigate the scales from questionnaires and in-depth
interviews in order to aggregate them into a unique framework.
• Apply the evidential reasoning approach for calculating the
overall performance of the level of adoption of analytical tools.
• Offer relevant guidelines to organizations that are interesting
in improving their analytical capabilities.
70
71. Publications
Conference papers
Submitted for publication
Barahona, I., & Riba, A. (2012). Applied Statistics on Business at Spain: A Case of
Statistical Engineering. In ASA (Ed.). In 2012 Joint Statistical Meetings. Vol. Book of
abstracts, pp. p 246). San Diego CA:(ASA).
Barahona Igor, & Alex, R. (2013). The level of adoption of analytical tools in
Barcelona, Spain. In JIPI (Ed.). In Jornada d'Investigadors Predoctorals
Interdisciplinària[February 7th of 2013]. Vol. Book of abstracts, pp. Page 7.).
Barcelona, Spain:(Universitat de Barcelona).
Igor, B., & Alex, R. (2011a). Applied statistics as competitive advantage. In ENBIS (Ed.).
In11th Annual ENBIS Conference. Vol. Book of abstracts, pp. P. 67).
Coimbra, Portugal:(ENBIS).
Igor, B., & Alex, R. (2011b). La estadística aplicada a la gestión como una ventaja
competitiva. In S. d. e. aplicada (Ed.). In I Jornades de Consultoria Estadística i Software.
pp. p. 16-17). Barcelona, Spain:(Servei d'estadística aplicada).
Barahona, Igor., Riba Alex & Yang, Jian-Bo. “The level of adoption of analytical tools in
Spain. An empirical study based on the evidential reasoning approach”. Decisions Support
Systems. Ref. No: DECSUP-D-13-00247
Barahona, Igor & Riba Alex. “The level of use of statistical tools. A case of statistical
Engineering”. Quality Engineering. Ref. No: LQEN-2013-0088.