Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-and-analytical-mining-of-sales-transaction-data-for-production-planning-and-marketing/
Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist of filtering, clustering and comparison steps
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.
To Development Manufacturing and Education using Data Mining A Reviewijtsrd
In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas. Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the extraction of information from huge volume of data or set through the use of various data mining techniques. The data mining techniques like clustering, classification help in finding the hidden and previously unknown information from the database. In addition, data mining also important role and educational sector. Educational Data Mining EDM is a field of analysis and research where various data mining tools and techniques are used to optimize the applications in education sector. The paper aims to analyze the enormous data from the education sector and provide solutions and reports for specific aspects of education sector such as student's performance and placements. Moreover, this paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. Aye Pwint Phyu | Khaing Khaing Wai "To Development Manufacturing and Education using Data Mining: A Review" 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/ijtsrd27910.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/27910/to-development-manufacturing-and-education-using-data-mining-a-review/aye-pwint-phyu
Toko Rejeki Celular is a shop that sells a variety of telecommunications electronic equipment, one of which is a handphone. The store manager is required to be able to make the right decisions in determining the sales strategy. In order to do this, further analysis is needed regarding data from the sale of mobile phones and the needs of customers. The purpose of this study was to apply data mining techniques to the Rejeki Celular Shop in Merauke Regency. The results of the study are expected to provide information in the form of classifications of sales of mobile phones that are most popular with customers and are less popular (best sales and normal sales). The data mining method used is the decision tree method, where the algorithm used is the C45 algorithm. As for the attributes are the type of mobile phone, price range, battery size and screen size. The data sample used is 21 data which is the sales data for mobile phones for 1 month. The results of this study are in the form of a system built using the PHP programming language and MySQL database. The highest factor affecting the purchase of mobile phones at Toko Rejeki Celular is the type of mobile attribute with the highest Gain, which is equal to 0.21687. The next factor is the price range attribute. As for the battery capacity factor and screen size it has no effect in producing a decision tree.
Data Mining of Project Management Data: An Analysis of Applied Research Studies.Gurdal Ertek
Data collected and generated through and posterior to projects, such as data residing in project management software and post project review documents, can be a major source of actionable insights and competitive advantage. This paper presents a rigorous
methodological analysis of the applied research published in academic literature, on the application of data mining (DM) for project management (PM). The objective of the paper is to provide a comprehensive analysis and discussion of where and how data mining is applied for project management data and to provide practical insights for future research in the field.
https://dl.acm.org/citation.cfm?id=3176714
https://ertekprojects.com/ftp/papers/2017/ertek_et_al_2017_Data_Mining_of_Project_Management_Data.pdf
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
Linking Behavioral Patterns to Personal Attributes through Data Re-Miningertekg
Download Link >https://ertekprojects.com/gurdal-ertek-publications/blog/linking-behavioral-patterns-to-personal-attributes-through-data-re-mining/
A fundamental challenge in behavioral informatics is the development of methodologies and systems that can achieve its goals and tasks, including be-havior pattern analysis. This study presents such a methodology, that can be con-verted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, through the re-mining of colored association graphs that represent item associations. The methodology is described and mathematically formalized, and is demonstrated in a case study related with retail industry.
Clustering Prediction Techniques in Defining and Predicting Customers Defecti...IJECEIAES
With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several ecommerce sites and compare their competitors‟ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-RecencyFrequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macroaveraging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers‟ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.
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.
To Development Manufacturing and Education using Data Mining A Reviewijtsrd
In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas. Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the extraction of information from huge volume of data or set through the use of various data mining techniques. The data mining techniques like clustering, classification help in finding the hidden and previously unknown information from the database. In addition, data mining also important role and educational sector. Educational Data Mining EDM is a field of analysis and research where various data mining tools and techniques are used to optimize the applications in education sector. The paper aims to analyze the enormous data from the education sector and provide solutions and reports for specific aspects of education sector such as student's performance and placements. Moreover, this paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. Aye Pwint Phyu | Khaing Khaing Wai "To Development Manufacturing and Education using Data Mining: A Review" 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/ijtsrd27910.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/27910/to-development-manufacturing-and-education-using-data-mining-a-review/aye-pwint-phyu
Toko Rejeki Celular is a shop that sells a variety of telecommunications electronic equipment, one of which is a handphone. The store manager is required to be able to make the right decisions in determining the sales strategy. In order to do this, further analysis is needed regarding data from the sale of mobile phones and the needs of customers. The purpose of this study was to apply data mining techniques to the Rejeki Celular Shop in Merauke Regency. The results of the study are expected to provide information in the form of classifications of sales of mobile phones that are most popular with customers and are less popular (best sales and normal sales). The data mining method used is the decision tree method, where the algorithm used is the C45 algorithm. As for the attributes are the type of mobile phone, price range, battery size and screen size. The data sample used is 21 data which is the sales data for mobile phones for 1 month. The results of this study are in the form of a system built using the PHP programming language and MySQL database. The highest factor affecting the purchase of mobile phones at Toko Rejeki Celular is the type of mobile attribute with the highest Gain, which is equal to 0.21687. The next factor is the price range attribute. As for the battery capacity factor and screen size it has no effect in producing a decision tree.
Data Mining of Project Management Data: An Analysis of Applied Research Studies.Gurdal Ertek
Data collected and generated through and posterior to projects, such as data residing in project management software and post project review documents, can be a major source of actionable insights and competitive advantage. This paper presents a rigorous
methodological analysis of the applied research published in academic literature, on the application of data mining (DM) for project management (PM). The objective of the paper is to provide a comprehensive analysis and discussion of where and how data mining is applied for project management data and to provide practical insights for future research in the field.
https://dl.acm.org/citation.cfm?id=3176714
https://ertekprojects.com/ftp/papers/2017/ertek_et_al_2017_Data_Mining_of_Project_Management_Data.pdf
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
Linking Behavioral Patterns to Personal Attributes through Data Re-Miningertekg
Download Link >https://ertekprojects.com/gurdal-ertek-publications/blog/linking-behavioral-patterns-to-personal-attributes-through-data-re-mining/
A fundamental challenge in behavioral informatics is the development of methodologies and systems that can achieve its goals and tasks, including be-havior pattern analysis. This study presents such a methodology, that can be con-verted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, through the re-mining of colored association graphs that represent item associations. The methodology is described and mathematically formalized, and is demonstrated in a case study related with retail industry.
Clustering Prediction Techniques in Defining and Predicting Customers Defecti...IJECEIAES
With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several ecommerce sites and compare their competitors‟ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-RecencyFrequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macroaveraging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers‟ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.
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.
Practices and ideas of supply chain management evolve and change fast. Modern information
and communication, for instance. The study is based on SCM's analysis as a business and industry. This
study provides a comprehensive investigation of attitudes, practises and designs based on the categories.
In order to handle supply chain management, we are exploring particular questions about SCD
integration, the instrument for planning and control and communication. The following are the key
results. To what extent SCM strategy and controls are used to improve suppliers and customers. The key
probity of SCM is cost efficiency, volume as well as delivery speed. It is also considered as an essential
input to the selection process of supply chain partners, now businesses want us to speed up the SC
operation through technology usage
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Selection of the Best Proposal using FAHP: Case of Procurement of IT Master P...IJECEIAES
IT master plan, which allows planning and managing the development of the computer systems, derives its importance in the central role of the computer systems in the functioning of organizations. This article focuses on the use of FAHP method for analysis and evaluation of tenders during the awarding of contracts of IT master plan’s realization. For those purposes, a painstaking work was realized for making an inventory of criteria and sub-criteria involved in the evaluation of tenders and for specifying the degrees of preference for each pair of criteria and sub-criteria. To find a provider for the IT master plan’s realization, organizations are increasingly using tendering as the mode of awarding contracts. This paper is an improvement of a previous published paper in which AHP method was used. The goals of this work are to make available to members of tenders committee a decision support tool for evaluating tenders of IT master plan’s realization and endow the organizations with effective IT master plans in order to increase their information systems’ performance.
A Study on Impact of Designation & Employment Role Consumption of Multi-Funct...inventionjournals
MFP leaders have been busy aggressively working on various strategies in order to retain leadership positions and break through the clustering in the leadership zone. MFP leaders need to focus on certain important characteristics that appeals to customers. This article is a maiden attempt to study some certain usage characteristics of individual customers in view of their working properties like designation level and vertical they belongs to. The study was done on 80 employees who belongs to supervisory level in their respective organizations. The data so collected through market survey analyzed through chi-square analysis and the study found that the study characteristics are independent of workplace characteristics in the companies.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
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A NEW MULTI CRITERIA DECISION MAKING METHOD : APPROACH OF LOGARITHMIC CONCEPT...ijaia
The primary aim of the study is to introduce APLOCO method which is developed for the solution of multicriteria
decision making problems both theoretically and practically. In this context, application subject of
APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey.
The secondary purpose of the study is to identify the independent variables affecting the factories in the
operating phase and to estimate the effect levels of independent variables on the dependent variable in the
organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to
mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as
the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also
used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is
the best alternative in term of the investment potential and other alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the
study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation
research as well as MLP used in determination of weights.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
Thesis - Mechanizing optimization of warehouses by implementation of machine ...Shrikant Samarth
Task: As Research Project is part of a postgraduate course it is also required that students employ and
develop their research knowledge and skills in an applied fashion. The Research Project must
involve the identification, generation, or collation of relevant primary or secondary data and the
ability to analyze them in a meaningful and critical manner.
Approach: Data was taken from a working organization to resolve the issue regarding the space optimization of the warehouse which results into losses to the company.
Findings: Ada boosting algorithm works best for identification of the blowout products beforehand which would help warehouse manager to apply strategies on the products which would take time to sell. So that, the losses associated to such products can be avoided.
Tools: Python programming, Excel visualizations, Overleaf latex
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.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Impact and Implications of Operations Research in Stock Marketinventionjournals
The motivation of this article is to advocate the administrative routine of settling on choices construct in light of instinct, as well as instinct combined with quantitative investigation. Operations Research (OR) is one of the main administrative choice science instruments utilized by benefit and charitable, for example, stock market. Gauging stock return is an important financial subject that has attracted researchers' consideration for a long time. It includes a supposition that basic data openly accessible in the past has some prescient connections to the future stock returns. This review tries to help the financial specialists in the stock market to choose the better planning for purchasing or offering stocks based on the information extricated from the chronicled costs of such stocks. The choice taken will be founded on choice tree classifier which is one of the Operations Research techniques.
Visual Mining of Science Citation Data for Benchmarking Scientific and Techno...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-mining-of-science-citation-data-for-benchmarking-scientific-and-technological-competitiveness-of-world-countries/
In this paper we present a study where we visually analyzed science citation data to investigate the competitiveness of world countries in selected categories of science. The dataset that we worked on in our study includes the number of papers published and the number of citations made in the ESI (Essential Science Indicators) database in 2004. The dataset lists these values for practically every country in the world. In analyzing the data, we employ methods and software tools developed and used in the data mining and information visualization fields of the Computer Science. Some of the questions for which we look for answers in this study are the following: (a) Which countries are most competitive in the selected categories of science? (i.e. Engineering, Computer Science, Economics & Business) (b) What type of correlations exist between different categories of science? For example, do countries with many published papers in the field of Engineering science also have many papers published on Computer Science or Economics & Business? (c) Which countries produce the most influential papers? This analysis is needed since a country may have many papers published but these papers may be cited very rarely. (d) Can we gain useful and actionable insights by combining science citation data with socioeconomic and geographical data?
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Simulation Modeling For Quality And Productivity In Steel Cord Manufacturingertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/simulation-modeling-for-quality-and-productivity-in-steel-cord-manufacturing/
We describe the application of simulation modeling to estimate and improve quality and productivity performance of a steel cord manufacturing system. We describe the typical steel cord manufacturing plant, emphasize its distinguishing characteristics, identify various production settings and discuss applicability of simulation as a management decision support tool. Besides presenting the general structure of the developed simulation model, we focus on wire fractures, which can be an important source of system disruption.
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/depolama-sistemleri/
Depolar, ürünlerin dağıtımı sırasında kullanılan geçici stok noktalarıdır. Depolar, tedarik zincirlerinin hedeflenen amaçlar doğrultusunda çalışmasına ve lojistik faaliyetlerinin etkin yürütülmesine önemli katkıda bulunurlar. Depolar, üretim tesislerinin içinde veya yanında bulunabileceği gibi, ayrı, özel olarak inşa edilmiş yapılar halinde de kurulabilirler. Şekil 4.1’de, tipik bir deponun genel görünüşü sunulmaktadır. Malzeme/ürünler, bu tipik depoda raflarda depolanmakta, malzeme giriş çıkışları depo rampaları üzerinden gerçekleşmekte, yükleme/boşaltma işlemleri forklift olarak adlandırılan araçlar kullanılarak gerçekleştirilmektedir. Deponun yönetimi, Depo Yöneticisi (Warehouse Manager) ya da Depo Müdürüunvanını taşıyan bir lojistik uzmanı tarafından yürütülmektedir.
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.
Application of local search methods for solving a quadratic assignment proble...ertekg
Ertek, G., Aksu, B., Birbil, S. E., İkikat, M. C., Yıldırmaz, C. (2005). “Application of local search methods for solving a quadratic assignment problem: A case study”, Proceedings of Computers and Industrial Engineering Conference, 2005. Istanbul, Turkey.
Practices and ideas of supply chain management evolve and change fast. Modern information
and communication, for instance. The study is based on SCM's analysis as a business and industry. This
study provides a comprehensive investigation of attitudes, practises and designs based on the categories.
In order to handle supply chain management, we are exploring particular questions about SCD
integration, the instrument for planning and control and communication. The following are the key
results. To what extent SCM strategy and controls are used to improve suppliers and customers. The key
probity of SCM is cost efficiency, volume as well as delivery speed. It is also considered as an essential
input to the selection process of supply chain partners, now businesses want us to speed up the SC
operation through technology usage
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Selection of the Best Proposal using FAHP: Case of Procurement of IT Master P...IJECEIAES
IT master plan, which allows planning and managing the development of the computer systems, derives its importance in the central role of the computer systems in the functioning of organizations. This article focuses on the use of FAHP method for analysis and evaluation of tenders during the awarding of contracts of IT master plan’s realization. For those purposes, a painstaking work was realized for making an inventory of criteria and sub-criteria involved in the evaluation of tenders and for specifying the degrees of preference for each pair of criteria and sub-criteria. To find a provider for the IT master plan’s realization, organizations are increasingly using tendering as the mode of awarding contracts. This paper is an improvement of a previous published paper in which AHP method was used. The goals of this work are to make available to members of tenders committee a decision support tool for evaluating tenders of IT master plan’s realization and endow the organizations with effective IT master plans in order to increase their information systems’ performance.
A Study on Impact of Designation & Employment Role Consumption of Multi-Funct...inventionjournals
MFP leaders have been busy aggressively working on various strategies in order to retain leadership positions and break through the clustering in the leadership zone. MFP leaders need to focus on certain important characteristics that appeals to customers. This article is a maiden attempt to study some certain usage characteristics of individual customers in view of their working properties like designation level and vertical they belongs to. The study was done on 80 employees who belongs to supervisory level in their respective organizations. The data so collected through market survey analyzed through chi-square analysis and the study found that the study characteristics are independent of workplace characteristics in the companies.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
Selection of Articles using Data Analytics for Behavioral Dissertation Resear...PhD Assistance
Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention .
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A NEW MULTI CRITERIA DECISION MAKING METHOD : APPROACH OF LOGARITHMIC CONCEPT...ijaia
The primary aim of the study is to introduce APLOCO method which is developed for the solution of multicriteria
decision making problems both theoretically and practically. In this context, application subject of
APLACO constitutes evaluation of investment potential of different cities in metropolitan status in Turkey.
The secondary purpose of the study is to identify the independent variables affecting the factories in the
operating phase and to estimate the effect levels of independent variables on the dependent variable in the
organized industrial zones (OIZs), whose mission is to reduce regional development disparities and to
mobilize local production dynamics. For this purpose, the effect levels of independent variables on dependent variables have been determined using the multilayer perceptron (MLP) method, which has a wide use in artificial neural networks (ANNs). The effect levels derived from MLP have been then used as
the weight levels of the decision criteria in APLOCO. The independent variables included in MLP are also
used as the decision criteria in APLOCO. According to the results obtained from APLOCO, Istanbul city is
the best alternative in term of the investment potential and other alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and Antalya, respectively. Although APLOCO is used to solve the ranking problem in order to show application process in the paper, it can be employed easily in the solution of classification and selection problems. On the other hand, the
study also shows a rare example of the nested usage of APLOCO which is one of the methods of operation
research as well as MLP used in determination of weights.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
Thesis - Mechanizing optimization of warehouses by implementation of machine ...Shrikant Samarth
Task: As Research Project is part of a postgraduate course it is also required that students employ and
develop their research knowledge and skills in an applied fashion. The Research Project must
involve the identification, generation, or collation of relevant primary or secondary data and the
ability to analyze them in a meaningful and critical manner.
Approach: Data was taken from a working organization to resolve the issue regarding the space optimization of the warehouse which results into losses to the company.
Findings: Ada boosting algorithm works best for identification of the blowout products beforehand which would help warehouse manager to apply strategies on the products which would take time to sell. So that, the losses associated to such products can be avoided.
Tools: Python programming, Excel visualizations, Overleaf latex
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.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Impact and Implications of Operations Research in Stock Marketinventionjournals
The motivation of this article is to advocate the administrative routine of settling on choices construct in light of instinct, as well as instinct combined with quantitative investigation. Operations Research (OR) is one of the main administrative choice science instruments utilized by benefit and charitable, for example, stock market. Gauging stock return is an important financial subject that has attracted researchers' consideration for a long time. It includes a supposition that basic data openly accessible in the past has some prescient connections to the future stock returns. This review tries to help the financial specialists in the stock market to choose the better planning for purchasing or offering stocks based on the information extricated from the chronicled costs of such stocks. The choice taken will be founded on choice tree classifier which is one of the Operations Research techniques.
Visual Mining of Science Citation Data for Benchmarking Scientific and Techno...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-mining-of-science-citation-data-for-benchmarking-scientific-and-technological-competitiveness-of-world-countries/
In this paper we present a study where we visually analyzed science citation data to investigate the competitiveness of world countries in selected categories of science. The dataset that we worked on in our study includes the number of papers published and the number of citations made in the ESI (Essential Science Indicators) database in 2004. The dataset lists these values for practically every country in the world. In analyzing the data, we employ methods and software tools developed and used in the data mining and information visualization fields of the Computer Science. Some of the questions for which we look for answers in this study are the following: (a) Which countries are most competitive in the selected categories of science? (i.e. Engineering, Computer Science, Economics & Business) (b) What type of correlations exist between different categories of science? For example, do countries with many published papers in the field of Engineering science also have many papers published on Computer Science or Economics & Business? (c) Which countries produce the most influential papers? This analysis is needed since a country may have many papers published but these papers may be cited very rarely. (d) Can we gain useful and actionable insights by combining science citation data with socioeconomic and geographical data?
Financial Benchmarking Of Transportation Companies In The New York Stock Exc...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/financial-benchmarking-of-transportation-companies-in-the-new-york-stock-exchange-nyse-through-data-envelopment-analysis-dea-and-visualization/
In this paper, we present a benchmarking study of industrial transportation companies traded in the New York Stock Exchange (NYSE). There are two distinguishing aspects of our study: First, instead of using operational data for the input and the output items of the developed Data Envelopment Analysis (DEA) model, we use financial data of the companies that are readily available on the Internet. Secondly, we visualize the efficiency scores of the companies in relation to the subsectors and the number of employees. These visualizations enable us to discover interesting insights about the companies within each subsector, and about subsectors in comparison to each other. The visualization approach that we employ can be used in any DEA study that contains subgroups within a group. Thus, our paper also contains a methodological contribution.
Simulation Modeling For Quality And Productivity In Steel Cord Manufacturingertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/simulation-modeling-for-quality-and-productivity-in-steel-cord-manufacturing/
We describe the application of simulation modeling to estimate and improve quality and productivity performance of a steel cord manufacturing system. We describe the typical steel cord manufacturing plant, emphasize its distinguishing characteristics, identify various production settings and discuss applicability of simulation as a management decision support tool. Besides presenting the general structure of the developed simulation model, we focus on wire fractures, which can be an important source of system disruption.
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/depolama-sistemleri/
Depolar, ürünlerin dağıtımı sırasında kullanılan geçici stok noktalarıdır. Depolar, tedarik zincirlerinin hedeflenen amaçlar doğrultusunda çalışmasına ve lojistik faaliyetlerinin etkin yürütülmesine önemli katkıda bulunurlar. Depolar, üretim tesislerinin içinde veya yanında bulunabileceği gibi, ayrı, özel olarak inşa edilmiş yapılar halinde de kurulabilirler. Şekil 4.1’de, tipik bir deponun genel görünüşü sunulmaktadır. Malzeme/ürünler, bu tipik depoda raflarda depolanmakta, malzeme giriş çıkışları depo rampaları üzerinden gerçekleşmekte, yükleme/boşaltma işlemleri forklift olarak adlandırılan araçlar kullanılarak gerçekleştirilmektedir. Deponun yönetimi, Depo Yöneticisi (Warehouse Manager) ya da Depo Müdürüunvanını taşıyan bir lojistik uzmanı tarafından yürütülmektedir.
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.
Application of local search methods for solving a quadratic assignment proble...ertekg
Ertek, G., Aksu, B., Birbil, S. E., İkikat, M. C., Yıldırmaz, C. (2005). “Application of local search methods for solving a quadratic assignment problem: A case study”, Proceedings of Computers and Industrial Engineering Conference, 2005. Istanbul, Turkey.
Optimizing Waste Collection In An Organized Industrial Region: A Case Studyertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/optimizing-waste-collection-in-an-organized-industrial-region-a-case-study/
In this paper we present a case study which involves the design of a supply chain network for industrial waste collection. The problem is to transport metal waste from 17 factories to containers and from containers to a disposal center (DC) at an organized region of automobile parts suppliers. We applied the classic mixed-integer programming (MIP) model for the two-stage supply chain to the solution of this problem. The visualization of the optimal solution provided us with several interesting insights that would not be easily discovered otherwise.
Design Requirements For a Tendon Rehabilitation Robot: Results From a Survey ...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/design-requirements-for-a-tendon-rehabilitation-robot-results-from-a-survey-of-engineers-and-health-professionals/
Exoskeleton type finger rehabilitation robots are helpful in assisting the treatment of tendon injuries. A survey has been carried out with engineers and health professionals to further develop an existing finger exoskeleton prototype. The goal of the study is to better understand the relative importance of several design criteria through the analysis of survey results and to improve the finger exoskeleton accordingly. The survey questions with strong correlations are identified and the preferences of the two respondent groups are statistically compared. The results of the statistical analysis are interpreted and insights obtained are used to guide the design process. The answers to the qualitative questions are also discussed together with their design implications. Finally, Quality Function Deployment (QFD) has been employed for visualizing these functional requirements in relation to the customer requirements.
Teaching Warehousing Concepts through Interactive Animations and 3-D Modelsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/teaching-warehousing-concepts-through-interactive-animations-and-3-d-models/
Teaching Warehousing Concepts through Interactive Animations and 3-D Models
Encapsulating And Representing The Knowledge On The Evolution Of An Engineeri...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/encapsulating-and-representing-the-knowledge-on-the-evolution-of-an-engineering-system/
This paper proposes a cross-disciplinary methodology for a fundamental question in product development: How can the innovation patterns during the evolution of an engineering system (ES) be encapsulated, so that it can later be mined through data analysis methods? Reverse engineering answers the question of which components a developed engineering system consists of, and how the components interact to make the working product. TRIZ answers the question of which problem-solving principles can be, or have been employed in developing that system, in comparison to its earlier versions, or with respect to similar systems. While these two methodologies have been very popular, to the best of our knowledge, there does not yet exist a methodology that reverse-engineers, encapsulates and represents the information regarding the application of TRIZ through the complete product development process. This paper suggests such a methodology that consists of mathematical formalism, graph visualization, and database representation. The proposed approach is demonstrated by analyzing the design and development process for a prototype wrist-rehabilitation robot and representing the process as a graph that consists of TRIZ principles.
The Bullwhip Effect In Supply Chain Reflections After A Decadeertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/the-bullwhip-effect-in-supply-chain-reflections-after-a-decade/
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.
Application Of Local Search Methods For Solving A Quadratic Assignment Probl...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/application-of-local-search-methods-for-solving-a-quadratic-assignment-problem-a-case-study/
This paper discusses the design and application of local search methods to a real-life application at a steel cord manufacturing plant. The case study involves a layout problem that can be represented as a Quadratic Assignment Problem (QAP). Due to the nature of the manufacturing process, certain machinery need to be allocated in close proximity to each other. This issue is incorporated into the objective function through assigning high penalty costs to the unfavorable allocations. QAP belongs to one of the most difficult class of combinatorial optimization problems, and is not solvable to optimality as the number of facilities increases. We implement the well-known local search methods, 2-opt, 3-opt and tabu search. We compare the solution performances of the methods to the results obtained from the NEOS server, which provides free access to many optimization solvers on the internet.
Re-Mining Item Associations: Methodology and a Case Study in Apparel Retailingertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/re-mining-item-associations-methodology-and-a-case-study-in-apparel-retailing/
Association mining is the conventional data mining technique for analyz-ing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analy-sis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analy-sis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.
Impact of Cross Aisles in a Rectangular Warehouse: A Computational Studyertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/impact-of-cross-aisles-in-a-rectangular-warehouse-a-computational-study/
Order picking is typically the most costly operation in a warehouse and traveling is typically the most time consuming task within order picking. In this study we focus on the layout design for a rectangular warehouse, a warehouse with parallel storage blocks with main aisles separating them. We specifically analyze the impact of adding cross aisles that cut storage blocks perpendicularly, which can reduce travel times during order picking by introducing flexibility in going from one main aisle to the next. We consider two types of cross aisles, those that are equally spaced (Case 1) and those that are unequally spaced, which respectively have equal and unequal distances among them. For Case 2, we extend an earlier model and present a heuristic algorithm for finding the best distances among cross aisles. We carry out extensive computational experiments for a variety of warehouse designs. Our findings suggest that warehouse planners can obtain great travel time savings through establishing equally spaced cross aisles, but little additional savings in unequally-spaced cross isles. We present a look-up table that provides the best number of equally spaced cross aisles when the number of cross aisles (N) and the length of the warehouse (T) are given. Finally, when the values of N and T are not known, we suggest establishing three cross aisles in a warehouse.Order picking is typically the most costly operation in a warehouse and traveling is typically the most time consuming task within order picking. In this study we focus on the layout design for a rectangular warehouse, a warehouse with parallel storage blocks with main aisles separating them. We specifically analyze the impact of adding cross aisles that cut storage blocks perpendicularly, which can reduce travel times during order picking by introducing flexibility in going from one main aisle to the next. We consider two types of cross aisles, those that are equally spaced (Case 1) and those that are unequally spaced, which respectively have equal and unequal distances among them. For Case 2, we extend an earlier model and present a heuristic algorithm for finding the best distances among cross aisles. We carry out extensive computational experiments for a variety of warehouse designs. Our findings suggest that warehouse planners can obtain great travel time savings through establishing equally spaced cross aisles, but little additional savings in unequally-spaced cross isles. We present a look-up table that provides the best number of equally spaced cross aisles when the number of cross aisles (N) and the length of the warehouse (T) are given. Finally, when the values of N and T are not known, we suggest establishing three cross aisles in a warehouse.
Statistical Scoring Algorithm for Learning and Study Skillsertekg
İndirmek için Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/statistical-scoring-algorithm-for-learning-and-study-skills/
This study examines the study skills and the learning styles of university students by using scoring method. The study investigates whether the study skills can be summarized in a single universal score that measures how hard a student works. The sample consists of 418 undergraduate students of an international university. The presented scoring was method adapted from the domain of risk management. The proposed method computes an overall score that represents the study skills, using a linear weighted summation scheme. From among 50 questions regarding to learning and study skills, the 30 highest weighted questions are suggested to be used in the future studies as a learning and study skills inventor. The proposed scoring method and study yield results and insights that can guide educators regarding how they can improve their students’ study skills. The main point drawn from this study is that the students greatly value opportunities for interaction with instructors and peers, cooperative learning and active engagement in lectures.
A Framework for Automated Association Mining Over Multiple Databasesertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-framework-for-automated-association-mining-over-multiple-databases/
Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance.
Supplier and Buyer Driven Channels in a Two-Stage Supply Chainertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/supplier-and-buyer-driven-channels-in-a-two-stage-supply-chain/
We explore the impact of power structure on price, sensitivity of market price, and profits in a two-stage supply chain with single product, supplier and buyer, and a price sensitive market. We develop and analyze the case where the supplier has dominant bargaining power and the case where the buyer has dominant bargaining power. We consider a pricing scheme for the buyer that involves both a multiplier and a markup. We show that it is optimal for the buyer to set the markup to zero and use only a multiplier. We also show that the market price and its sensitivity are higher when operational costs (namely distribution and inventory) exist. We observe that the sensitivity of the market price increases non-linearly as the wholesale price increases, and derive a lower bound for it. Through experimental analysis, we show that marginal impact of increasing shipment cost and carrying charge (interest rate) on prices and profits are decreasing in both cases. Finally, we show that there exist problem instances where the buyer may prefer supplier-driven case to markup-only buyer-driven and similarly problem instances where the supplier may prefer markup-only buyer-driven case to supplier-driven.
Re-mining Positive and Negative Association Mining Resultsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/re-mining-positive-and-negative-association-mining-results/
Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, sepa-rately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been han-dled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the as-sociation rules, are characterized and described through the second data mining stagere-mining. The applicability of the methodology is demon-strated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue.
Visual and analytical mining of sales transaction data for production plannin...Gurdal Ertek
Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in
processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information
out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The
primary phases of the proposed framework consist of filtering, clustering and comparison steps 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.
http://research.sabanciuniv.edu.
A machine learning model for predicting innovation effort of firmsIJECEIAES
Classification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process.
DESIGN, DEVELOPMENT & IMPLEMENTATION OF ONTOLOGICAL KNOWLEDGE BASED SYSTEM FO...IJDKP
Dynamism and uncertainty are genuine threats for current high technology organisations. Capability to change is the crux of sustainability of current large organisations. Modern manufacturing philosophies, including agile and lean, are not enough to be competitive in global market therefore a new emerging paradigm i.e. reconfigurable manufacturing systems is fast emerging to complement the application of lean
and agile manufacturing systems. Product, Process and Resource (PPR) are the core areas in an engineering domain of a manufacturing enterprise which are tightly coupled with each other. Change in one (usually product) affects the others therefore engineering change management activity has to tackle
PPR change effects. Current software applications do not provide an unequivocal infrastructure where PPR can be explicitly related. It follows that reconfigurable techniques can be further complemented with the help of knowledge based systems to design, engineer, manufacture, commission and change existing processes and resources against changed products.
Software plays a critical role in businesses, governments, and societies. To improve
performance and quality of the software are important goals of software engineering. Mining
data has recently emerged as a promising means to meet this goal due to two main trends:
The increasing abundance of such data and its demonstrated helpfulness in solving numerous
real-world problems. Poor performance costs the software industry millions of money
annually in the form of lost revenue, hardware costs, damaged customer relations and
decreased productivity. Performance analysis and evaluation through data mining technique
will result performance improvement suggestions for software developers.
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
Rule-based Information Extraction for Airplane Crashes ReportsCSCJournals
Over the last two decades, the internet has gained a widespread use in various aspects of everyday living. The amount of generated data in both structured and unstructured forms has increased rapidly, posing a number of challenges. Unstructured data are hard to manage, assess, and analyse in view of decision making. Extracting information from these large volumes of data is time-consuming and requires complex analysis. Information extraction (IE) technology is part of a text-mining framework for extracting useful knowledge for further analysis.
Various competitions, conferences and research projects have accelerated the development phases of IE. This project presents in detail the main aspects of the information extraction field. It focused on specific domain: airplane crash reports. Set of reports were used from 1001 Crash website to perform the extraction tasks such as: crash site, crash date and time, departure, destination, etc. As such, the common structures and textual expressions are considered in designing the extraction rules.
The evaluation framework used to examine the system's performance is executed for both working and test texts. It shows that the system's performance in extracting entities and relations is more accurate than for events. Generally, the good results reflect the high quality and good design of the extraction rules. It can be concluded that the rule-based approach has proved its efficiency of delivering reliable results. However, this approach does require an intensive work and a cycle process of rules testing and modification.
Rule-based Information Extraction for Airplane Crashes ReportsCSCJournals
Over the last two decades, the internet has gained a widespread use in various aspects of everyday living. The amount of generated data in both structured and unstructured forms has increased rapidly, posing a number of challenges. Unstructured data are hard to manage, assess, and analyse in view of decision making. Extracting information from these large volumes of data is time-consuming and requires complex analysis. Information extraction (IE) technology is part of a text-mining framework for extracting useful knowledge for further analysis.
Various competitions, conferences and research projects have accelerated the development phases of IE. This project presents in detail the main aspects of the information extraction field. It focused on specific domain: airplane crash reports. Set of reports were used from 1001 Crash website to perform the extraction tasks such as: crash site, crash date and time, departure, destination, etc. As such, the common structures and textual expressions are considered in designing the extraction rules.
The evaluation framework used to examine the system's performance is executed for both working and test texts. It shows that the system's performance in extracting entities and relations is more accurate than for events. Generally, the good results reflect the high quality and good design of the extraction rules. It can be concluded that the rule-based approach has proved its efficiency of delivering reliable results. However, this approach does require an intensive work and a cycle process of rules testing and modification.
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.
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.
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.
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/
The goal of this chapter is to introduce the text mining capabilities of RAPIDMINER through a use case. The use case involves mining reviews for hotels at TripAdvisor.com, a popular web portal. We will be demonstrating basic text mining in RAPIDMINER using the text mining extension. We will present two different RAPIDMINER processes, namely Process01 andProcess02, which respectively describe how text mining can be combined with association mining and cluster modeling. While it is possible to construct each of these processes from scratch by inserting the appropriate operators into the process view, we will instead import these two processes readily from existing model files. Throughout the chapter, we will at times deliberately instruct the reader to take erroneous steps that result in undesired outcomes. We believe that this is a very realistic way of learning to use RAPIDMINER, since in practice, the modeling process frequently involves such steps that are later corrected.
Competitive Pattern-Based Strategies under Complexity: The Case of Turkish Ma...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/competitive-pattern-based-strategies-under-complexity-the-case-of-turkish-managers/
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.
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-tutorial-on-crossdocking/
In crossdocking, the inbound materials coming in trucks to the crossdock facility are directed to outbound doors and are directly loaded into trucks that will perform shipment, or are staged for a very brief time period before loading. Crossdocking has a great potential to bring savings in logistics: For example, most of the logistics success of Wal-Mart, the world’s leading retailer, is attributed to crossdocking.In this paper,the types of crossdocking are identified, the situations and industries where crossdocking is applicable are explained, prerequisites, advantages and drawbacks are listed, and implementation issues are discussed. Finally a case study that describes the crossdocking applications of a 3rd party logistics firm is presented.
Demonstrating Warehousing Concepts Through Interactive Animationsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/demonstrating-warehousing-concepts-through-interactive-animations/
In this paper, we report development of interactive computer animations to demonstrate warehousing concepts, providing a virtual environment for learning. Almost every company, regardless of its industry, holds inventory of goods in its warehouse(s) to respond to customer demand promptly, to coordinate supply and demand, to realize economies of scale in manufacturing or processing, to add value to its products and to reduce response time. Design, analysis, and improvement of warehouse operations can yield significant savings for a company. Warehousing science can be considered as an important field within the industrial engineering discipline. However, there is very little educational material (including web based media), and only a handful of books available in this field. We believe that the animations that we developed will significantly contribute to the understanding of warehousing concepts, and enable tomorrow’s practitioners to grasp the fundamentals of managing warehouses.
A Framework for Visualizing Association Mining Resultsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-framework-for-visualizing-association-mining-results/
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.
Application of the Cutting Stock Problem to a Construction Company: A Case Studyertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/application-of-the-cutting-stock-problem-to-a-construction-company-a-case-study/
This paper presents an application of the well-known cutting stock problem to a construction firm. The goal of the 1Dimensional (1D) cutting stock problem is to cut the bars of desired lengths in required quantities from longer bars of given length. The company for which we carried out this study encounters 1D cutting stock problem in cutting steel bars (reinforcement bars) for its construction projects. We have developed several solution approaches to solving the company’s problem: Building and solving an integer programming (IP) model in a modeling environment, developing our own software that uses a mixed integer programming (MIP) software library, and testing some of the commercial software packages available on the internet. In this paper, we summarize our experiences with all the three approaches. We also present a benchmark of existing commercial software packages, and some critical insights. Finally, we suggest a visual approach for increasing performance in solving the cutting stock problem and demonstrate the applicability of this approach using the company’s data on two construction projects.
Benchmarking The Turkish Apparel Retail Industry Through Data Envelopment Ana...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/benchmarking-the-turkish-apparel-retail-industry-through-data-envelopment-analysis-dea-and-data-visualization/
This paper presents a benchmarking study of the Turkish apparel retailing industry. We have applied the Data Envelopment Analysis (DEA) methodology to determine the efficiencies of the companies in the industry. In the DEA model the number of stores, number of corners, total sales area and number of employees were included as inputs and annual sales revenue was included as the output. The efficiency scores obtained through DEA were visualized for gaining insights about the industry and revealing guidelines that can aid in strategic decision making.
An Open Source Java Code For Visualizing Supply Chain Problemsertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/an-open-source-java-code-for-visualizing-supply-chain-problems/
In this paper, we decribe an open source Java class library for visualizing supply chain problems within a geographical context. The highly competitive markets and recent technological advances make the use of such supply chain network visualizations critical in both strategic and tactical levels. The most important characteristic of our work is its easy integration with any Java application. Our software differs from any other commercial and open source supply chain visualization tool by its simple structure, easy adoption and implementation and high compatibility. The main motivation of our study was to develop a simple – yet effective – library that would not require to learn and apply complicated visualization tools and data structures such as Geographical Information Systems (GIS). In this study, we illustrate the use of our visualization tool through maps of Turkey, Europe, North and South America, the United States and the NAFTA. We believe that ease of visualization offered by our open source tool will contribute to a multitude of projects in supply chain design, as well as increasing productive communication among practitioners, especially involved in strategic level decision making processes. We foresee that our supply chain visualization tool will fill a gap in this area with its simple but effective structure.
This paper discusses fundamental issues in dairy logistics in a tutorial format. We summarize findings of more than twenty student groups who carried out independent literature surveys and interviewed professionals in the industry. The critical issues in carrying out dairy products logistics, the logistics strategies that are employed by dairy producers in the world and some newly introduced products in the industry and in what ways the introduction of these new products changes the logistics operations are pointed out. The importance of hygiene, cooling, time, humidity, cost, distance, flexibility and meeting the demand is emphasized under the subtitle of critical issues. Except those critical issues, there are some others like short shelf life, quality, emulsion, pasteurization, UHT which depend on the characteristics of the milk and milk products. Logistics strategies in dairy industry are studied by dividing it into two subtitles: the ones that are used in the world and the ones in Turkey. A benchmarking between Turkey and the world is also included at the end. As the variety of milk and milk products increase day by day, the new ingredients of new products also affects the transportation plans. Those impacts are also discussed as a part of our paper. Some descriptive drawings and figures are also embodied. Throughout this paper, only the production, warehousing and transportation of milk, cheese, yoghurt, and similar dairy products are discussed. Ice-cream especially is set out of the scope as it completely differs from actual dairy products as milk, cheese and yoghurt in the means of production and distribution.
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/a-taxonomy-of-logistics-innovations/
In this paper we present a taxonomy of supply chain and logistics innovations, which is based on an extensive literature survey. Our primary goal is to provide guidelines for choosing the most appropriate innovations for a company, such that the company can outrun its competitors. We investigate the factors, both internal and external to the company, that determine the applicability and effectiveness of the listed innovations. We support our suggestions with real world cases reported in literature.
Innovation in Product Form And Function: Customer Perception Of Their Valueertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/innovation-in-product-form-and-function-customer-perception-of-their-value/
The goal of product design is to obtain the maximum effect with minimum cost in functionality and aesthetic beauty. Consumers are attracted to the designs that reflect their use behaviors and psychological responses more than they are to the simple visual representations. When product functions and qualities are similar across products, customers make their purchasing decision upon aesthetic form. Form presents a significant competitive factor that improves the value of a product. Overall, the purpose of this study is to examine the most important product design factors that affect the market share trends of mobile phone companies. Study uses product characteristics for 1,028 mobile phones released between 2003 and 2008 as a case study. The multiple linear regression analysis is used to select highly correlated variables that influence the market share, and Mallow's Cp method is used to determine the best-fitting model. The Partial Regression Coefficients are used to evaluate the relative importance of design criteria. The nine mobile phone design features that affect the market share were identified, and the block form style is determined as the most important design factor. Using these approaches, this study demonstrates how investments should be directed in the next mobile phone design process.
Developing Competitive Strategies in Higher Education through Visual Data Miningertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-data-mining-for-developing-competitive-strategies-in-higher-education/
Information visualization is the growing field of computer science that aims at visually mining data for knowledge discovery. In this paper, a data mining framework and a novel information visualization scheme is developed and applied to the domain of higher education. The presented framework consists of three main types of visual data analysis: Discovering general insights, carrying out competitive benchmarking, and planning for High School Relationship Management (HSRM). In this paper the framework and the square tiles visualization scheme are described and an application at a private university in Turkey with the goal of attracting bright-est students is demonstrated.
Kimya Sanayinde Su Tasarrufu İçin Karar Destek Sistemi ertekg
İndirmek çin Bağlantı > https://ertekprojects.com/gurdal-ertek-publications/blog/kimya-sanayinde-su-tasarrufu-icin-karar-destek-sistemi/
Bu bildiride, Türkiye’nin sanayileşmiş bölgelerinden Gebze’de bulunan bir temizlik kimyasalları fabrikası için geliştirdiğimiz ve 7 ay boyunca kullanılarak test edilen bir Karar Destek Sistemi (KDS) tanıtılacak ve yapılan çalışma özetlenecektir. Üretim planlamadan sorumlu fabrika çalışanları bu yeni sistemi uygulamaya aldıktan sonra firma haftada 1 tona yaklaşan su tasarrufu sağlamıştır. Su tasarrufunun yanısıra maliyet, enerji ve işgücü kazançları da gözlemlenmiştir. Temizlik kimyasallarının üretiminin planlamasında faydası ve kullanılabilirliği kanıtlanan bu sistem, ürünlerarası geçişin ürün karakteristiklerine göre yıkama gerektirdiği boya, tekstil, gıda ve diğer kimya sanayilerinde de kullanılabilme potansiyeline sahiptir.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
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Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
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This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
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⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
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"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
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Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
The Tata Group, a titan of Indian industry, is making waves with its advanced talks with Taiwanese chipmakers Powerchip Semiconductor Manufacturing Corporation (PSMC) and UMC Group. The goal? Establishing a cutting-edge semiconductor fabrication unit (fab) in Dholera, Gujarat. This isn’t just any project; it’s a potential game changer for India’s chipmaking aspirations and a boon for investors seeking promising residential projects in dholera sir.
Visit : https://www.avirahi.com/blog/tata-group-dials-taiwan-for-its-chipmaking-ambition-in-gujarats-dholera/
Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s Dholera
Visual and analytical mining of transactions data for production planning for production planning and marketing
1. Ertek, G., Kuruca, C., Aydin, C., Erel, B.F., Dogan, H., Duman, M., Ocal, M., and Ok, Z.D. (2004).
"Visual and analytical mining of sales transaction data for production planning and marketing.”
4th International Symposium on Intelligent Manufacturing Systems, Sakarya, Turkey.
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 http://research.sabanciuniv.edu.
Visual and analytical mining of transactions data
for production planning for production planning
and marketing
Gurdal Ertek, Can Kuruca, Cenk Aydin,
Besim Ferit Erel, Harun Dogan, Mustafa Duman,
Mete Ocal, Zeynep Damla Ok
Sabanci University
Istanbul, Turkey
3. Visual and analytical mining of transactions data for production planning and marketing
Gurdal Ertek, Can Kuruca, Cenk Aydin, Besim Ferit Erel, Harun Dogan, Mustafa Duman, Mete
Ocal, Zeynep Damla Ok
Abstract
Recent developments in information technology paved the way for the collection of large
amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in
processing these massive amounts of raw data gathered turns out to be the effective
management of data with the ultimate purpose of deriving necessary and meaningful information
out of it. The following paper presents an attempt to illustrate the combination of visual and
analytical data mining techniques for planning of marketing and production activities. The
primary phases of the proposed framework consist of filtering, clustering and comparison steps
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.
Submission areas: Decision support systems, data mining, information technologies
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4. Introduction
Widespread use of information technology has resulted in massive collections of data regarding
most aspects of an enterprise. The amount of data on the marketing side has exploded due to
widespread usage of barcode systems, accounting and Enterprise Resorce Planning (ERP)
software and also due to collection of Business-to-Consumer (B2C) and Business-to-Business
(B2B) electronic commerce data. The amount of data that comes from manufacturing processes
has also exploded, due to application of Computer Integrated Manufacturing (CIM) systems,
barcode and radio frequency technology, which provide bulky amounts of real time data.
Effective collection, management, reporting, interpretive analysis and mining of enterprise data
can help in establishing effective control of manufacturing activities, achieving effective
production planning and increased sales, and consequently increasing the firm’s profitability.
Data mining can also serve the purpose of increasing customer satisfaction by offering and
timely delivering them products that they are willing to purchase. Keeping existing customers is
typically much more profitable than trying to acquire new customers. This observation is indeed
the underlying concept of Customer Relationship Management (CRM) systems (Shaw et al.,
2001).
As suggested earlier, data from various areas of an enterprise is now widely available; however,
in this paper, only sales transactions data is considered. The reason for choosing this particular
data type is that sales transactions data is collected and archived in almost every firm and it is
actually the essential input to two very critical aspects of enterprise planning, namely marketing
and production planning. A framework for the analysis of this type of data is proposed and
implemented in the software developed, namely CuReMa. The main contribution of the
framework and the prototype software is the integration of visual and analytical data mining
techniques for marketing and production planning. Kreuseler and Schumann (2002) present a
similar approach combining visual and analytical data mining techniques without focusing on
particular enterprise data.
The paper starts by offering a brief review of the literature concerned with visual and analytical
data mining techniques. In the following sections, we present the framework we propose and
explain how it is implemented in CuReMa. Before concluding, the applicability of the proposed
framework is demonstrated with a sample analysis session with the software.
Literature Review
Analytical data mining techniques are widely used and implemented (Han and Kamber, 2001). In
recent years, visual mining of data has also gained importance. The traditional exploratory
graphical data analysis methods such as scatter plots, box plots (Chambers et al., 1983) have
been enriched with a wide array of new visual representations and methods (de Oliveira and
Levkowitz, 2003). The field of computer science involved in such representations and methods is
referred to as information visualization. The number of journal articles in information visualization
has shown significant increase from 1990’s to present (Chen, 2002), indicating that the field is a
promising branch of computer science.
Work related to this paper can be grouped in two categories, based on their scope:
a) Data mining for marketing:
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5. Shaw et al. (2001) provide a recent survey of data mining applications for marketing.
They present a taxonomy of data mining tasks and provide five broad categories:
Dependency analysis, class identification, concept description, deviation detection, and
data visualization. Our study suggests a framework that relates to all but the first of these
categories.
Keim et al. (2002) develop a visualization technique named “pixel bar charts” for analysis
of very large multi-attribute data sets and demonstrate applicability of their approach by
analyzing real-world e-commerce data sets.
b) Data mining for manufacturing:
Applications of information visualization in the domain of manufacturing include data
representation for engineering design and data analysis for predicting product failure
rates (Spence, 2001, p28—30 and p60—61, respectively).
Dabbas and Chen (2001) present an integrated relational database approach for
semiconductor wafer fabrication, which resulted in improved manufacturing performance.
They describe how they combined multiple data sources and various reports under the
integrated approach.
Data mining tools that can handle fairly large amounts of data and allow derivation of insights
are numerous. Spotfire, Miner3D and XLMiner1 are among successful commercial products that
can be used to analyze data from a variety of domains. Software libraries which allow building
customized visual interfaces to domain-specific applications are also available: For instance,
Eick (2000) presents ADVIZOR as “a flexible software environment for visual information
discovery” that allows creating visual query and analysis applications. Our implementation has
some similarities to these products: For example, one of the similarities between CuReMa,
Spotfire and Miner3D is that all of them allow the user to conduct a query on products or
customers within a given range through the use of sliders.
Proposed Framework
An approach that integrates visual data mining methods with analytical methods for mining sales
transaction data is proposed to perform the three critical functions listed below, and to answer
the given questions as well as many other unlisted ones:
1) Filtering
This initial step includes filtering of data in different dimensions and displaying and analyzing the
filtered data. In this stage of the analysis, the products or the customers within a cluster or a
group are represented with pie charts. This representation allows the identification of significant
items and outliers in the cluster or the group. The answers to the following questions can be
obtained as a result of filtering:
• What are the total sales of top-selling n products for the top-purchasing c customers
within time interval (t1, t2) and what is the share of each product?
• What are the total sales of n slowest-moving products for a given set of customers
(selected from a list sorted based on sales) within time interval (t1, t2) and what is the
share of each product?
• How much sales were generated for each product represented in the pie chart?
1
The information regarding these products can be found on http://www.spotfire.com,
http://www.miner3d.com, and http://www.resample.com/xlminer/ respectively.
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6. • What are the total purchases of top-purchasing c customers from a given set of products
(selected from a list sorted based on sales) within time interval (t1, t2), and what is the
share of each customer?
• How many purchases were made by each of the customers represented in the pie chart?
2) Clustering
The second step involves clustering products and customers with respect to selected measures
and generating related reports. This phase provides the answer for the questions such as:
• How can the products be grouped with respect to seasonal sales patterns?
• How can the market be segmented with respect to seasonal purchasing patterns of
customers?
Many other useful statistics about each cluster may be acquired after clustering. These statistics
include the revenue generated by each product, the product with the highest sales level, and the
product purchased by the greatest number of customers.
3) Comparison
Finally, clusters are compared with respect to selected measures. One of the questions
answered through comparison would be the following:
• How do the products differ from each other with respect to seasonal sales patterns? Are
there significant differences among given clusters?
The questions above focus on customer and product clusters, yet CuReMa can answer the
same questions based on customer and product groups, which could come with the dataset. The
sample analysis session will demonstrate how some of these questions are answered using
CuReMa.
An Implementation of the Framework: CuReMa
The data mining framework has been implemented as a prototype decision support system to
demonstrate the viability of the proposed approach. Real world data from a regional distributor of
automotive spare parts, covering approximately 18 months of sales transactions, has been
analyzed with the developed software. The software, named CuReMa after Customer
Relationship Management, allows mining of the dataset for marketing and production planning
purposes.
The implementation has been done in Java programming language (Doke et al., 2002) using the
Eclipse Integrated Development Environment (IDE)2 under Microsoft Windows operating system.
CuReMa was built based on a 3-tier design, separating data access, interface and business
classes (Doke et at. 2002). MySQL is employed as the database server and MySQL Control
Center3 is used in constructing and maintaining the database. Java and MySQL were selected
primarily due to their platform independence, which allows porting the developed system to
various operating systems, such as Linux and MacOS. Java language also has the advantages
of being purely object oriented and having extensive libraries that allow rapid prototyping. Being
interpreted at runtime -as opposed to being executed as native code- makes Java programs run
2
The information on Eclipse IDE can be retrieved from http://www.eclipse.org
3
MySQL Control Center can be accessed through http://www.mysql.com/products/mysqlcc/
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7. slower than programs written in some other languages. However, implementation of distributed
shared memory and remote method invocation (not implemented in CuReMa) can enable
programmers to build scalable Java programs (Kielmann et al., 2001).
The relational database in MySQL contains a number of tables. The table “transactions”, (shown
in Figure 1) contains the sales transactions data and forms the source of all the other tables.
Figure 1. Snapshot of the database, illustrating the fields of the “transactions” table
We now present how each function proposed in the framework is implemented:
Filtering: Interactive Visual Querying
In CuReMa, the user can perform visual query of the dataset using pull down menus and sliders.
The sliders enable filtering by time, by customer and by product name. The customers and the
products are sorted in decreasing order of total sales from the left to the right. Positions of the
sliders are translated into SQL (Structured Query Language) (Elmasri and Navathe, 1994, Ch7)
statements and reflected on the pie charts in real time. One such statement (that corresponds to
the filtering illustrated in Figure 2) is the following:
SELECT ItemName AS NAME,
SUM(TOTAL) AS TOT FROM deneme3.reference
WHERE DATE <='2003.6.6' AND DATE >= '2003.3.5'
AND CustomerNo>= 1 AND 173 >= CustomerNo
AND 1 <= ItemNo AND 259 >= ItemNo
GROUP BY NAME ORDER BY TOT DESC
Pie charts display the share of each customer/product within the selected ranges.
Clustering: Analytical Data Mining
The analytical data mining method implemented in CuReMa is K-Means clustering (Han and
Kamber, 2001, Ch8). This method starts with a set of entities, accompanied with given attribute
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8. values, and proceeds by clustering the entities into a specified number of clusters. The attribute
values are the average sales in each month of the year.
K- Means algorithm initially selects random values as the K-cluster means. Then, it examines
each object pertaining to the dataset and assigns the object to the closest cluster possible,
whose proximity is measured by the aggregate distance between the cluster mean and the
object's actual values. An epoch consists of N iterations where N is the number of elements in
the dataset. As the new objects are added to the clusters for various epochs, the means are
dynamically updated. If the total number of moves of all data objects from a cluster to another is
zero at the end of an epoch, the algorithm ceases and the partitioning is completed.
The algorithm may end up with fewer clusters than specified: For instance, while exploration of
the automotive spare parts dataset in CuReMa, it has been observed that a customer clustering
run with a desired number of 7 clusters resulted in only 3 clusters.
Comparison: Visual Data Mining
Parallel coordinate plots (Inselberg and Dimsdale, 1990) are used for comparison of
product/customer clusters, and are implemented in Mondrian and XMDV4 software. Parallel
coordinate plot “maps a k-dimensional data or object space onto the 2D display by drawing k
equally spaced axes in parallel” (de Oliveira and Levkowitz, 2003). In the plot, each axis
corresponds to an attribute, and each line corresponds to an element of the dataset. In CuReMa,
customer purchases and product sales can be compared in a parallel coordinate plot based on
monthly sales.
Sample Analysis Session
In this section we present how the user can interact with CuReMa in an analysis session.
4
Mondrian and XMDV can be accessed from http://www.theusrus.de/Mondrian/index.html and
http://davis.wpi.edu/~xmdv/ respectively.
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10. The analysis begins with Filtering, where the user selects slicing based on products from the
pull-down menus in the Graph Frame (Figure 2). The user selects the data for the last 3 months
for all customers (with sales in the last 3 months) and top-selling 100 products from the sliders.
The top-selling product is observed to be “MOT.YAG” (motor oil) by selecting the largest slice in
the pie chart.
Next, the user performs clustering of the products from the previous analysis by clicking the
“Cluster” button on the Graph Frame (Figure 3). Since products are displayed, the check box
next to Product is selected. The user also selects clustering criteria, number of clusters, and the
time range.
Clustering is completed in approximately 2 minutes on a PC with Intel Pentium IIIE 933 MHz
processor and 384 MB of RAM. Then the user selects the item named
“Top100ProductsInLast3Months” in the Query Frame, “Parallel Coordinate Plot” from Graphics
menu and clicks “Draw Charts” button to obtain the plot in Figure 4. In this plot, products in
clusters 2 and 3 are observed to exhibit almost constant sales throughout the selected months
whereas products in cluster 1 have the largest amount of sales, but also a greater variance as
compared to others. A report can be generated inside Reports Frame by clicking “Generate
Report” button in the left bottom corner.
Figure 4. Comparison of product clusters
The user can also analyze customers, clustering them into similar-behaving groups. Figure 5
offers the result of one such clustering. In this stage of the analysis, all the customers are
selected at all the times, and clustering is performed. It can be observed that cluster 1 shows
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11. stable sales, with minimal variability. Trying to increase the sales volume to these customers can
be a profitable strategy, since these customers do not cause big fluctuations in production
schedules. Clusters 2 and 3 are observed to have very high levels of sales in March, while
clusters 0 and 4 exhibit high sales volumes in August and September respectively. These
fluctuations should be further investigated by examining the reports generated and by filtering
the sales transaction data for these months.
Figure 5. Comparison of customer clusters
Conclusions and Future Work
In this paper a framework is presented for the analysis of sales transaction data using visual and
analytical data mining techniques. The framework suggests applying filtering, clustering and
comparison through interactive pie charts, K-Means method and parallel coordinate plots,
respectively. The framework is implemented in a software to demonstrate how the analysis can
be carried out. A sample session is exhibited.
The framework proposed in this paper can be extended to answer a greater range of questions.
Visual metaphors reported in information visualization survey papers (e.g. de Oliveira and
Levkowitz, 2003) can be adapted to the current framework, allowing observation and query of
other aspects regarding the data. From an analytical point of view, other data mining techniques
such as deriving association rules for revealing patterns (Changchien and Lu, 2001) and
performing cross-tabulation analysis (Verhoef et al., 2002) can be made part of the framework
and implemented in CuReMa.
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12. CuReMa can be tested and used with a variety of transactional datasets coming from different
industries (e.g. from an e-commerce website) and the scope of the program can be broadened
to include other data fields, such as ages and income levels of customers, and marginal profits
of the products. On the methodological side, clustering algorithms other than K-Means can be
built into the software and their performance can be compared to the performance of K-Means.
One such method is SOM (Self Organized Maps), which is implemented in coherence within a
visual data mining framework by Kreuseler and Schumann (2002). The clustering function in the
software can be extended by taking into consideration other attributes besides monthly sales
averages. One such attribute can be recency of purchases by customers and by products:
Verhoef et al. (2002) report that recency and frequency of purchases are among the most
popular variables used for clustering while carrying out market segmentation analysis.
Data analyzed can have domain-specific considerations. For example, in many countries around
the world, inflation is a real-world fact that cannot be neglected if the analyst attempts to perform
a valid and insightful analysis. Adding tables into the database that keep track of the inflation
indices on a monthly basis and adjusting calculations based on these indices would be pretty
valuable for the dataset analyzed in such a study. Another domain-specific consideration would
be the effects of holidays. One could take into account religious holidays in Turkey which turn
out to be around ten days earlier from the previous year. This and similar considerations pose
significant challenges.
One possible area of future work is to investigate the implemented software from the point of
view of human-computer interaction (Shneiderman, 1998). One can investigate the performance
of various visual components and their arrangements by formal usability tests on groups of users
and analyze results.
Acknowledgement
The authors would like to thank Ender Yalcin for providing the sales transaction data and for
explaining the processes involved in decision making. The authors would also like to thank Selim
Balcisoy, Pelin Gulsah Canbolat and the anonymous referees for their valuable suggestions and
remarks.
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