Demet Bayraktar

An integrated approach for supplier selection

Competitive international business environment has forced many firms to focus on supply chain management to cope with highly increasing competition. Hence, supplier selection process has gained importance recently, since most of the firms have been spending considerable amount of their revenues on purchasing. The supplier selection problem involves conflicting multiple criteria that are tangible and intangible. Hence, the purpose of this study is to propose an integrated model for supplier selection. In order to achieve this purpose, supplier selection problem has been structured as an integrated lexicographic goal programming (LGP) and analytic hierarchy process (AHP) model including both quantitative and qualitative conflicting factors. The application process has been accomplished in a food company established in Istanbul, Turkey. In this study, the model building, solution and application processes of the proposed integrated model for supplier selection have been presented.

An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information

Supplier evaluation and selection are critical decision making processes that require consideration of a variety of attributes. Several studies have been performed for effective evaluation and selection of suppliers by utilizing several techniques such as linear weighting methods, mathematical programming models, statistical methods and AI based techniques. One of the successful evaluation methods proposed for this purpose is data envelopment analysis (DEA), that utilizes techniques of mathematical programming to evaluate the performance of a set of homogeneous decision making units, when multiple inputs and outputs need to be considered. It is often complicated, costly and sometimes impossible to acquire all necessary information from all potential suppliers to attain a reasonable set of similar input and output values which is an essential for DEA. The purpose of this study is to explore a novel integration of neural networks (NN) and data envelopment analysis for evaluation of suppliers under incomplete information of evaluation criteria.

An Application of Expert System Approach for Supplier Evaluation and Selection

The conditions of global competition are becoming more difficult in all the industries. All companies are competing with global and local rivals. Suppliers have played a crucial role for companies to outperform their rivals in competitive markets. This increases the importance of the supplier evaluation and selection process which is a multi-dimensional decision making process containing various variables, quantitative and qualitative criteria, heuristics and experiences of managers. Development of expert systems simulating this multidimensional problem solving process of a human being, has gained importance in the past years. The aim of this study is to point out the importance of supplier evaluation and selection in the buying process and also to develop an expert system for supplier evaluation and selection. The proposed expert system is called as ESforSES (An Expert System for Supplier Evaluation and Selection). ESforSES was applied in a large-scale electronic company. The results of the application shows that ESforSES is a reliable and objective system for evaluating and selecting of the suppliers and it may be utilized by small and medium sized companies by making some modifications and improvements according to their requirements and strategies

A knowledge-based expert system approach for the auditing process of some elements in the quality assurance system

Abstract The purpose of this study is to propose a knowledge-based expert system approach which has the guidance attributes for the auditing process of “contract review”, “purchasing”, and “handling, storage, packaging, and delivery” quality system elements in the ISO 9000 quality assurance system. In regard to the proposed system called expert system approach in the quality assurance system (ESAQAS), the application of the knowledge-based expert system approach in the quality assurance system was studied. In this connection, the development and the application process of ESAQAS are presented.

Multi Criteria Classification for Spare Parts Inventory

Maintenance operations directly influence the performances of railway vehicles and play a crucial role in railway services to provide uninterrupted and high quality service to passengers. With the exception of preventive activities, the demand of spare parts for maintenance tasks is usually random; hence, the fast and secure management of the spare parts inventory is an important factor for the successful execution of the maintenance process. The purpose of this research is to extend the classical ABC analysis by developing a multi-criteria inventory classification approach for supporting the planning and designing of a maintenance system. Relevant classification criteria and control characteristics of maintenance spare parts are identified and selected and discussed in terms of their effects on maintenance operations, purchasing characteristics, positioning of materials, responsibility of control, and control principles.

A knowledge-based expert system for technology acquisition in small and medium scale manufacturing organizations

Abstract Consistent and reliable decision making for technology acquisition in small and medium scale manufacturing organizations is vitally important since these firms are the backbone of national economies, both in developed and developing countries. Because of their flexibility, small and medium scale firms are successful in adopting new technologies. However, a careful analysis should be conducted in technology acquisition decisions. Since these decisions require special type of knowledge and expertise, expert system, as an important tool of computerized decision making, can overcome these multidimensional difficulties. This paper proposes a knowledge-based approach making use of issues such as sales, processes, costs and general policies, in decision processes for technology acquisition by small and medium scale manufacturing organizations in the developing environments.

Light rail passenger demand forecasting by artificial neural networks

The success of strategic and detailed planning of public transportation highly depends on accurate demand information data. Short-term forecasting is the key to the success of transportation operations planning such as time-tabling and seat allocation. This study adopts neural networks to develop short-term passenger demand forecasting models to be used in operational management of light rail services. A multi-layer perceptron (MLP) model is preferred due to not only its simple architecture but also proven success of solving approximation problems. For eliminating the significant seasonality in time slots, each time slot is handled independent of the others, and an artificial neural network based on daily data is developed for each. Regarding to the 74 different time slots, 74 different neural networks are trained by history data. Three illustrative examples are demonstrated on one of the time slots and performance of the forecast models are evaluated based on mean square errors (MSE) and mean absolute percentage errors (MAPE).

Improvement of capacity utilization in a subcontracting small scale manufacturing company

In this study, a goal programming model is proposed for the improvement of capacity utilization in a subcontracting small scale manufacturing company. This is a suburban Istanbul manufacturing company that produces certain final and intermediate products for a few large scale animal health companies. The company operates two production lines and produces different kinds of veterinary drugs and feed additives. Because this is a small scale company, it has the flexibility of reducing production costs as low as possible and also operating at a low profitability level. The utilization of production capacity is low. The company aims to increase profitability by increasing the production capacity to higher utilization levels.

An Expert System Approach for Improving Service-Levels between Buyer and Supplier

Service-level requirements emerge among the alternative strategies for supply chain firms that have already harvested the benefits of adopting ever low system wide costs. Though, the subjective and ever changing nature of services related understandings creates a challenge. In an attempt to fill this gap, our recent work presents an expert system approach, ESSER (Expert System Application for Suppliers to Improve Service-Levels) as a decision support mechanism for helping supplier firms to tailor their service offerings in order to meet updated service level requirements of their individual buyers.

Integrated Approaches for Determining the Turkish Naval Force Structure by Using the Analytic Hierarchy Process and Goal Programming Methods

Determining the naval force structure is a complicated decision making process, since it requires taking into consideration several conflicting factors. The purpose of this study is to propose two integrated models for determining the ship types combination for the TNSG by considering cost, force level, manpower, and the required potential factors needed to meet threat’s power in different warfare areas. Accordingly, we propose two integrated AHP and GP models. The comparison of the real cases is made with the results of the proposed two models. Thus, this study aims to supply a decision support system to the DM during the decision-making process for determining the naval force structure.