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Presents a multiple‐objective mathematical programming model to co‐ordinate logistics decisions with those on the interface between the production and marketing departments. The model can help decide on an overall budget to request from senior management for logistics and these interfaces, and in systematically allocating the funds between transport, inventory and production. In so doing, this multi‐period model specifies the timing and quantity of raw‐material purchases, and the location and timing of production activities and distribution flows. The budget for expenditures on logistics and its interface activities is taken as an objective to minimise, instead of as a given dollar level to be satisfied. A second objective is to maximise the profit of logistics and its related interfaces. Trade‐offs between these two conflicting aims yields the decision maker′s “best compromise” solution.

Globalization speeds up competition among nations in various sectors. In terms of multinational and transnational phenomena, countries are seen as inescapable from competition, thus the linking of the term global with “competitiveness.” The research described here explores the relationship between the competitiveness of a country and its implications for human development. For this purpose, using data envelopment analysis (DEA) and cluster analysis, 44 selected countries were evaluated. An output-oriented super-efficiency model where global competitiveness indicators are taken as input variables with human development indicators as output variables is utilized. Then cluster analysis depending on the competitiveness and human development indicators is conducted by using self-organizing maps to specify the development levels of the countries. Both analyses are repeated for years between 2005 and 2007. Finally, the relationship between the super efficiency scores and the development levels is analyzed.

The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector.

This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as “top 20 per cent” and “bottom 80 per cent” profitable.

Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership-related indicators, are also significant factors.

Organisations in the banking sector have to persuade their customers to use certain routine risk-bearing transaction-based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product-related variables directly increase the value of customers.

The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry-specific method which is based on easily measurable and objective indicators.

The primary objective of this paper is to explore how the companies in Turkey plan, manage, carry out and improve their logistics processes and, thus, provide a preliminary analysis to explore the current status of logistics in Turkey. For this purpose, a structured‐disguised survey was conducted with the top 250 firms of Istanbul Chamber of Commerce. The aims were: to specify the organizational, financial and managerial significance of logistics activities; to articulate the sourcing/ purchasing feature, customer service and order processing, to understand the changes in the number of suppliers and customers and to identify the features of activities and tools aimed to improve the quality/productivity in these systems; and to investigate the impact of the general characteristics on the first and second subcriteria for each firm.