Alp Ustundag

Evaluating RFID investment on a supply chain using tagging cost sharing factor

Radio frequency identification (RFID) is a promising technology for optimising supply chain processes, as it improves manufacturing and retail operations from forecasting demand to planning, managing inventory, and distribution. In this study, a simulation model is used to calculate the net present value (NPV) of an RFID investment on a three-echelon supply chain and to examine the effects of sharing the tagging cost between supply chain members on the NPV at the echelon level. In the proposed model, NPVs for the retailer, distributor, and manufacturer are calculated for two cases. In the first case, the tagging cost is shared according to the expected benefit of each supply chain member using the tagging cost sharing factor. In the second case, the tagging cost is shared equally between supply chain members. Furthermore, we investigate the difference in the NPVs for a supply chain member change as the end-customer demand value increases.

Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis

Purpose The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary (RFM) considerations as well as demographic factors. In this study, the impacts of RFM and demographic attributes have been challenged in order to enrich factors that lend comprehension to customer segmentation. Different types of scenario were designed, performed and evaluated meticulously under uniform test conditions. The data for this study were extracted from the database of a global pizza restaurant chain in Turkey. This paper summarizes the findings of the study and also provides evidence of its empirical implications to improve the performance of customer segmentation as well as achieving extracted rule perfection via effective model factors and variations. Accordingly, marketing and service processes will work more effectively and efficiently for customers and society. The implication of this study is that it explains a clear concept for interaction between producers and consumers. Design/methodology/approach Customer relationship management, which aims to manage record and evaluate customer interactions, is generally regarded as a vital tool for companies that wish to be successful in the rapidly changing global market. The prediction of customer behaviors is a strategically important and difficult issue because of the high variance and wide range of customer orders and preferences. So to have an effective tool for extracting rules based on customer purchasing behavior, considering tangible and intangible criteria is highly important. To overcome the challenges imposed by the multifaceted nature of this problem, the authors utilized artificial intelligence methods, including k-means clustering, Apriori association rule mining (ARM) and neural networks. The main idea was that customer clusters are better enhanced when segmentation processes are based on RFM analysis accompanied by demographic data. Weighted RFM (WRFM) and unweighted RFM values/scores were applied with and without demographic factors and utilized to compose different types and numbers of clusters. The Apriori algorithm was used to extract rules of association. The performance analyses of scenarios have been conducted based on these extracted rules. The number of rules, elapsed time and prediction accuracy were used to evaluate the different scenarios. The results of evaluations were compared with the outputs of another available technique. Findings The results showed that having an appropriate segmentation approach is vital if there are to be strong association rules. Also, it has been determined from the results that the weights of RFM attributes affect rule association performance positively. Moreover, to capture more accurate customer segments, a combination of RFM and demographic attributes is recommended for clustering. The results’ analyses indicate the undeniable importance of demographic data merged with WRFM. Above all, this challenge introduced the best possible sequence of factors for an analysis of clustering and ARM based on RFM and demographic data. Originality/value The work compared k-means and Kohonen clustering methods in its segmentation phase to prove the superiority of adopted segmentation techniques. In addition, this study indicated that customer segments containing WRFM scores and demographic data in the same clusters brought about stronger and more accurate association rules for the understanding of customer behavior. These so-called achievements were compared with the results of classical approaches in order to support the credibility of the proposed methodology. Based on previous works, classical methods for customer segmentation have overlooked any combination of demographic data with WRFM during clustering before proceeding to their rule extraction stages.

Fuzzy decision making model for selection of real time location systems

A fuzzy decision making model is developed for selection of the appropriate Real Time Location System (RTLS) technology for companies operating in different sectors.Three main criteria are determined namely economic, technical and implementation factors for the model.The Fuzzy Analytic Hierarchy Process (FAHP) method is proposed to select the appropriate RTLS technology.The model is applied to a hospital in Istanbul considering three types of RTLS systems which are given as IR-RF hybrid, UHF RFID and Active RFID.Since the scores of the hybrid system for economic, implementation and technical factors are higher comparing the other technologies, it is selected as the best alternative. Real Time Location Systems (RTLS) have gained importance in contemporary world since they allow real time positioning of assets, people and workflows. They can be used in different sectors to increase work efficiency and quality in areas of application. The selection of the appropriate RTLS technology becomes a major decision problem since it has a multi-criteria structure which includes both qualitative and quantitative factors. In this study, a decision making model is developed for selection of the appropriate RTLS technology for companies operating in different sectors. Three main criteria are determined by existing literature and with the help of the experts, namely economic, technical and implementation factors. The Fuzzy Analytic Hierarchy Process (FAHP) method is proposed to select the appropriate RTLS technology. Also sensitivity analysis is performed according to the incremental rates of main criteria. The model is applied to a hospital in Turkey considering three types of RTLS systems which are given as IR-RF hybrid, UHF RFID and Active RFID. Since the scores of the hybrid system for economic, implementation and technical factors are higher comparing the other technologies, it is selected as the best alternative.

The Cost–Benefit Models for RFID Investments

RFID technology does not require line of sight and provides fast and accurate reading from a great distance without human intervention. Albeit RFID is superior to other identification systems, such as barcode, complicated cost and benefit structure inhibits worldwide adoption. As a response, there is a wide range of cost–benefit models used by both industry and academy. The aim of this chapter is to review RFID cost and benefit factors and provide a comprehensive overview of RFID cost–benefit models. Besides hardware and middleware cost, service cost factors, including compliance cost, training cost, and transition cost makes up for the total RFID system cost structure. Benefit factors are more complex than cost factors as the limited pilot studies or studies from other application areas are unable to reveal the full set of benefits. Also, intangible benefit factors such as increased customer satisfaction or increased supply chain collaboration are not easily quantifiable. RFID cost–benefit models are classified as conventional models, uncertainty-based models, and decision-making models. Conventional models include return on investment models, break-even models, internal rate of return and net present value analysis. Although practical enough to help during the initial stages of investment analysis, these models are static, rely on oversimplifying assumptions, and ignore the variability of the system. Uncertainty-based models, such as simulation or real options models generate more accurate results, but require extensive modeling and data gathering effort. Decision-making models, on the other hand, facilitate selection among alternatives or help to visualize the underlying structure of decision-making process of RFID investment. Considering the limitations of each model, analysts should take caution when introducing a single type of model and rather utilize a group of different models together.

A Hybrid Model for Forecasting Sales in Turkish Paint Industry

Sales forecasting is important for facilitating effective and efficient allocation of scarce resources. However, how to best model and forecast sales has been a long-standing issue. There is no best forecasting method that is applicable in all circumstances. Therefore, confidence in the accuracy of sales forecasts is achieved by corroborating the results using two or more methods. This paper proposes a hybrid forecasting model that uses an artificial intelligence method (AI) with multiple linear regression (MLR) to predict product sales for the largest Turkish paint producer. In the hybrid model, three different AI methods, fuzzy rule-based system (FRBS), artificial neural network (ANN) and adaptive neuro fuzzy network (ANFIS), are used and compared to each other. The results indicate that FRBS yields better forecasting accuracy in terms of root mean squared error (RMSE) and mean absolute percentage error (MAPE).

A Conceptual Framework for Economic Analysis of REID Reverse Logistics via Simulation

Radio frequency identification (RFID) technology introduces the opportunity for increased material and product visibility by facilitating easy tracking and identifying of containers and their contents. Throughout the manufacturing or distribution processes, this technology has been utilized by various organizations. In addition to wide use of disposable RFID tags, reusable RFID tags premises desired economic and efficient supply chain management Considering the reusable RFID tags, the price of the tag,related quality level and logistics policies are crucial. In this study, a conceptual framework for economic analysis of RFID reverse logistics via simulation is presented. Proposed methodology is demonstrated through a hypothetical case study.

Value of RFID Enhanced Maintenance in Aerospace Industry

RFID technology can find a wide range of application areas in many sectors, such as automotive, logistics, retail, etc. Nowadays, RFID becomes more important in aerospace industry. Especially, in aircraft maintenance activities, RFID improves productivity and makes processes faster which reduces cycle times and inventories. In addition, it helps avoid manual errors and thus improve quality. In this study, the recent RFID applications for maintenance activities are explained to reveal the potential of improvement. Next, a case study for RFID enhanced maintenance in aerospace industry is explained, and then a cost-benefit analysis of RFID implementation at this case is carried out. In this analysis, the NPV method is used with Monte Carlo simulation to consider different uncertainty levels of cost savings provided by RFID implementation.

Linear Programming for Distribution Network Design Under Fuzzy Environment

A key issue in contemporary supply chain management, Distribution Network Design (DND) addresses location and capacity of warehouses and retail outlets, together with the manufacturing plant and supply sources. An effective DND should not only provide the minimization of transportation and inventory costs, but also increase the level of service available to the customers. When designing a distribution network, parameters can not be frequently determined as crisp values. Fuzzy logic is utilized in many engineering applications so as to handle imprecise data. This chapter provides a mathematical programming basis for distribution network design problem under imprecise data environment. After providing background information about DND problem as well as fuzzy linear programming, fuzzy linear programming model of DND problem is solved for different α-cut values.

Maturity and Readiness Model for Industry 4.0 Strategy

Companies that transform their businesses and operations regarding to Industry 4.0 principles face complex processes and high budgets due to dependent technologies that effect process inputs and outputs. In addition, since Industry 4.0 transformation creates a change in a business manner and value proposition , it becomes highly important concept that requires support of top management for the projects and investments. Therefore, it requires a broad perspective on the company’s strategy, organization, operations and products. So, the maturity model is suitable for companies planning to transform their businesses and operations for Industry 4.0. It is a very important technique for Industry 4.0 in terms of companies seeking for assessing their processes, products and organizations and understanding their maturity level. In this chapter, existing maturity models for Industry 4.0 transformation are reviewed and a new Industry 4.0 maturity model is proposed.

A web-based DSS for fuzzy distribution network optimization

Purpose – Distribution network design (DND) has become an important strategic decision for supply chain managers with increasing competitive nature of the industry nowadays. The purpose of this paper is to propose a web-based decision support system (DSS) for fuzzy distribution network optimization. For this purpose, a web-based DSS using fuzzy linear programming model is proposed to solve DND problem under uncertainty and a framework is created to optimize a distribution network. Design/methodology/approach – In this study, the fuzziness in distribution network optimization is addressed. Fuzzy linear programming is used in a DSS to consider the uncertain and imprecise data. A web-based DSS architecture is presented. Furthermore, as an application, distribution network optimization is conducted for a company in the ceramics industry. Findings – By using this DSS, the optimal transshipment amounts in the distribution network and the required facility and distribution centers can be determined for different...