Browsing by Author "Selim, H"

Now showing 1 - 7 of 7
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    Integrating simulation modelling and multi criteria decision making for customer focused scheduling in job shops
    Güçdemir, H; Selim, H
    Today, customer centricity is an important strategy in business-to-business markets and manufacturing companies need decision support systems that provide adequate information for customer centric applications. This study proposes an integrated decision support system that combines simulation modelling and multi-criteria decision making. More specifically, job shop lot streaming problem is dealt with, and it is aimed to determine the best dispatching rules to schedule batches on machines. To this aim, three renowned performance-oriented criteria; (i) mean flow time, (ii) percentage of tardy orders, (iii) makespan and one customer-oriented criterion; (iv) mean percentage deviation from the customer expectations are considered. Effect of different classical and customer-oriented dispatching rules on these performance criteria are investigated. The performance criteria are weighted using analytical hierarchy process by considering the level of bottleneck resource utilization and customer importance weights. The results reveal that customer-oriented dispatching rules provide better outcomes in case of high level of bottleneck resource utilization and high fluctuation amongst the customer importance weights.
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    Dynamic dispatching priority setting in customer-oriented manufacturing environments
    Güçdemir, H; Selim, H
    In today's competitive environment, customer-oriented view is essential in gaining sustainable competitive advantage. This study aims to reflect the customer-oriented view to production planning and control decisions. To this aim, a simulation optimization-based approach is developed for job shop systems with dynamic order arrivals. Product-type-based lot splitting is applied in order to improve the flow time, and machine-based dispatching rules are utilized for sublot scheduling to realize dynamic scheduling. Multiple customer segments with different importance weights and their expectations and penalties on order completion rate on due date, tardiness, and earliness are considered. A customer satisfaction-based objective function is defined. Customer-oriented dispatching rules are proposed in this study to ensure the prioritization of orders from the key customers in order fulfilling. In order to prevent customer losses by providing a balanced structure between the customer segments in terms of the satisfaction levels, weight setting functions that dynamically compute the weights in the proposed dispatching rules are proposed. It is aimed to determine the near-optimal values of the segment-based parameters of the related weight setting functions. To this aim, a differential evolution algorithm-based simulation optimization approach is proposed. To confirm its viability, the proposed approach is applied to a realistic job shop system.
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    Integrating multi-criteria decision making and clustering for business customer segmentation
    Güçdemir, H; Selim, H
    Purpose - The purpose of this paper is to develop a systematic approach for business customer segmentation. Design/methodology/approach - This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely Ward's method, single linkage and complete linkage, and a partitional clustering algorithm, k-means, are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. Findings - Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as best, valuable, average, potential valuable and potential invaluable according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. Research limitations/implications - The success of the proposed approach relies on the availability and quality of customers' data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment. Practical implications - The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers. Social implications - The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies. Originality/value - This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.
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    ANALYSIS OF THE DETERMINANTS OF CUSTOMER SATISFACTION IN AN INTERNATIONAL MANUFACTURING FIRM
    Selim, H; Selim, S; Eroglu, S
    Today, applying an effective customer relationship management, which is one of the key processes of supply chain management, becomes a must to provide a sustainable competitive advantage. Customer relationship management is a customer-focused strategy, and it is based on collection, assessment and use of customer data. Under the implementation of customer relationship management in an international firm in food sector, factors that affect satisfaction levels of retailers and distributors, which take place in the supply chain, are investigated in this study. The results of the analysis, which are carried out by using factor analysis method and ordered probit model, shed light on the customer-related strategies and decisions of the firm by revealing the factors that affect customers' satisfaction levels as well as the level of importance of these factors.
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    Simulation Optimization Approach for Customer Centric Lot Streaming Problem in Job Shops
    Güçdemir, H; Selim, H
    In today's competitive market, time based strategies become important to gain sustainable competitive advantage. Therefore, manufacturing companies need advanced production planning and control (PPC) techniques in order to respond quickly to demand. In addition, customer centricity becomes the key success factor for manufacturing industry. In this regard, integration of customer relationship management (CRM) and PPC decisions is necessary in order to satisfy customers with efficient use of manufacturing capabilities. The aim of this study is to analyze the effect of customer centricity on lot streaming (LS) problem. To this aim, LS problem in job shop environment is dealt with in this study, and traditional LS problem is extended by including preferences of multiple customer segments. It is intended to find a lot splitting policy for each customer segment-product type combination. The objective function is defined as minimization of total weighted percentage deviation from the preferences of customer segments. Simulation optimization approach is used to solve the problem. The results reveal that the proposed approach supports order splitting decisions, and can effectively be used in practice.
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    Customer centric production planning and control in job shops: A simulation optimization approach
    Güçdemir, H; Selim, H
    Today, customer centricity becomes the key success factor for the manufacturing companies in gaining sustainable competitive advantage. In this regard, they need advanced production planning and control (PPC) techniques to be more customer focused. This study aims to integrate customer relationship management (CRM) and PPC approaches in order to use manufacturing resources of job shops more effectively in satisfying customers. To this aim, a simulated annealing based simulation optimization approach is proposed. To confirm the viability of the proposed approach, it is applied to a realistic job shop system. In order to accelerate the flow of production, product type based lot splitting is applied. In the scheduling phase, dynamic scheduling is implemented by machine-based dispatching rules. Multiple customer segments with different importance weights, and their expectations and penalties on order completion rate on due date, tardiness and earliness are considered. The aim of the proposed approach is to make near optimal policy decisions regarding the machine-based dispatching rules and number of equal sublots for the products. In this regard, four well-known dispatching rules and five modified versions of these rules which are proposed in this study are employed. Computational experiments are performed by using different dominance relationships between the customer segments, inter-arrival times and level of due date allowance factor. Results of the experiments reveal that integration of CRM and PPC approaches in job shop systems provides more effective use of resources in satisfying customers, and that the proposed approach can easily be implemented in practice. (C) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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