An analysis of supply chain related graduate programmes in Europe

This is the post-print version of the Article. The official published version of the Article can be accessed from the links below. Copyright @ 2013 Emerald Group Publishing Limited.Purpose – Motivated by a lack of studies in graduate level supply chain education, this research aims to explore trends in supply chain-related graduate programmes in Europe and to propose a framework for designing such programmes. Design/methodology/approach – The authors determine “knowledge” and “skills” areas applicable to supply chain management (SCM) education and analyse supply chain-related graduate programmes published by the European Logistics Association in 2004. They revisit the same programmes in 2011 to determine the recent situation and the trends. The authors use cluster analysis to reveal the similarities and differences among these programmes. Findings – The authors find two distinct clusters: focused and diversified. Focused programmes offer modules in knowledge and skills areas apart from SCM at a negligible level and place more emphasis on SCM in 2011 when compared to 2004. Diversified programmes show a similar increase in the emphasis on SCM with more variety in the knowledge and skills areas. Research limitations/implications – The authors' findings are based on SCM programmes delivered in Europe and over two discrete time periods. Future research should seek to extend this analysis to other continents with larger samples and incorporate the industry perspective to determine the potential gap between what programmes offer and what industry requires. Practical implications – SCM-related graduate programmes continue to redefine themselves. Clustering predominantly serves the universities in re-assessing and re-engineering their programmes, helps prospective graduates in their selection process and assists managers in their recruitment practices. Originality/value – This paper establishes a baseline for assessing SCM-related graduate programmes with respect to the knowledge and skills they offer and introduces a framework that may serve as a starting point for the design and positioning of such programmes

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

A Decision Support on Planning Retail Tenant Mix in Shopping Malls

AbstractConsumption and shopping patterns are subject to changes for a variety of product categories. Shopping malls are becoming new places that consumers visit where the shopping itself has changed meaning and focus. Retailers are now concerned to find ways of presenting themselves inside these modern buildings and adapt to rapidly changing consumer expectations. Shopping malls, on the other hand, should focus on planning the appropriate retail tenant mix that will contribute their image and attract more visitors and shoppers. Although tenant mix selection has been a concern for shopping mall managers, no best way or strategy is offered as solving this problem. In this study, a multicriteria decision making framework is developed to prioritize the factors related with planning retail tenant mix within shopping malls. Shopping mall managers and experts are interviewed in accordance with the iterative steps of the decision making process. First of all, the factors are identified by the respondents. Then they are requested to indicate the relationships among factors. Analytical Network Process (ANP) is selected to construct the framework because of the dependencies and feedback relationships among the factors. Finally, the relative strengths of direct influences of factor pairs with respect to the third affected factors are revealed based on the pairwise comparison questions. As a result, the priorities of the factors are computed. Retail store's creating variety in shopping mall with its product offer, satisfying the needs of the shopping mall's target consumers, sales revenue per square meter, awareness of the retail store brand, being an anchor store for the shopping mall are found as the important factors. The priorities of factors can give guidance to shopping mall management team as a rating tool. The total rating score of a candidate retailer can be computed as the weighted average of individual contributions of the store with respect to each factor where weights are the computed priorities. This score can be used as a resulting acceptance/rejection decision of including a retail store into tenant mix. The proposed framework can also be utilized for open-air centers and hybrid centers. The priorities of the factors found at the present study can be compared with those of further studies conducted in different countries and cultures

ANP application for evaluating Turkish mobile communication operators

The proposed decision model, based on Analytic Network Process (ANP), has been developed in order to determine the dynamics of the Global System for Mobile Communications (GSM) market. In accordance with the sequence of ANP, first of all, the problem was structured and modeled. At the next step, a quantitative analysis was conducted with the customers in order to assess the importance of the related factors and forecast the market shares of the Turkish mobile communication operators. The forecasted market share values were compared with actual ones for the validation of the decision model. The assessed importance of the factors in a valid model would be a potential competitive advantage for the mobile operators

ANP application for evaluating Turkish mobile communication operators

The proposed decision model, based on Analytic Network Process (ANP), has been developed in order to determine the dynamics of the Global System for Mobile Communications (GSM) market. In accordance with the sequence of ANP, first of all, the problem was structured and modeled. At the next step, a quantitative analysis was conducted with the customers in order to assess the importance of the related factors and forecast the market shares of the Turkish mobile communication operators. The forecasted market share values were compared with actual ones for the validation of the decision model. The assessed importance of the factors in a valid model would be a potential competitive advantage for the mobile operators

A DECISION SUPPORT SYSTEM TO IMPROVE SERVICE QUALITY IN MULTIMODAL RAPID RAIL SYSTEMS: A BAYESIAN PERSPECTIVE

Presentation of the work in the 41st International Conference on Computers and Industrial Engineerin

A decision support system for proactive failure prevention: a case in a leading automotive company

Purpose: In the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company. Design/methodology/approach: Initially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data. Findings: To the best of authors’ knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations. Originality/value: High level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS