Mehdi Rajabi Asadabadi

A customer based supplier selection process that combines quality function deployment, the analytic network process and a Markov chain

Abstract The overall objective of this paper is to introduce a customer oriented supplier selection method. Although the supplier selection problem has previously been investigated, an effective solution to consider the dynamics of Customer Needs (CNs) in finding the best supplier has not yet been proposed. Such a method takes into account customer needs as a determinant factor in finding the best supplier and considers possible changes in the priorities of customer needs as time passes. In this study a method integrating the analytic network process (ANP), quality function deployment (QFD), and a Markov chain is used to address the supplier selection problem. This proposed method utilizes a Markov chain to trace the changing-priorities of customer needs and find a pattern for them. The ANP–QFD method then connects this pattern to product requirements (PRs) and PRs to supplier qualifications. This combination develops a customer based supplier selection method. The best supplier is selected based on the changing-priorities of customer needs. Although the customer needs priorities keep changing, one supplier is selected as the best one. This study introduces an innovative customer based approach to select the best supplier that is independent of initial CNs.

A hybrid QFD-based approach in addressing supplier selection problem in product improvement process

Article history: Received January 6 2014 Received in Revised Format June 15 2014 Accepted July 17 2014 Available online July 24 2014 This paper is about creating a hybrid QFD-based approach in which the best supplier is selected considering changing customer needs. In most previous studies employing a QFD approach, the possibility of changing customer needs is ignored. On the other hand, supplier selection is a challenging problem that could have been addressed by such a QFD. This paper attempts to create a hybrid QFD-based approach in which the internal relations between the elements are considered. It connects the new QFD to suppliers’ qualifications to create a hybrid supplier selection process. The best suppliers are selected based on the priorities of customer needs for each level of the product improvement plan. When a product is to be developed, the proposed methodology seems to create an efficient solution for supplier selection problem with respect to quality factors.

A Markovian-QFD approach in addressing the changing priorities of the customer needs

Purpose n n n n nThe purpose of this paper is to introduce a solution to the problem of changing priorities of customer needs (CNs) in quality function deployment (QFD). Customer preferences and priorities are not very stable and they may change before products are ready for the market. Therefore, finding CNs accurately is a key to reach a higher level of customer satisfaction through improving products. n n n n nDesign/methodology/approach n n n n nIn the proposed model, a Markov chain is employed to model the changing priorities of CNs. The Markov chain finds a pattern of future CNs, the main inputs of QFD. The QFD method is applied to translate CNs into product requirements (PRs). The analytic network process (ANP) is attached to QFD to ensure that all the relations among the elements, inner and outer, are taken into consideration during the translation process. Thus, CNs are received and adjusted by a Markov chain. n n n n nFindings n n n n nThe application of Markov chains for an ANP-QFD model develops an adequate method of finding a pattern of changing priorities of CNs. This pattern enables the ANP-QFD method to work independent of the initial CNs, and originates a Markovain ANP-QFD. n n n n nOriginality/value n n n n nThis study originates a stochastic ANP-QFD model. There have been several papers employing various tools and techniques such as the ANP or analytic hierarchy process for QFD to find accurate relations between PRs and CNs. While there are a few papers applying Markov chains to predict the future of the relations of QFD, there is no study which traces the changes in priorities of the CNs during the improvement process. This is addressed by applying a Markovian ANP-QFD. The model is validated through a case study.

ZBWM: The Z-number extension of Best Worst Method and its application for supplier development

Abstract Best Worst Method (BWM) has recently been proposed as a method for Multi Criteria Decision Making (MCDM). Studies show that BWM compared with other methods such as Analytic Hierarchy Process (AHP), leads to lower inconsistency of the results while reducing the number of required pairwise comparisons. MCDM methods such as BWM require accurate information. However, it often happens in practice that a level of uncertainty accompanies the information. The main aim of this paper is to address this problem and provide an integration of BWM and Z-numbers, namely ZBWM. Providing BWM with Z-numbers enables the BWM method to handle the uncertainty of information of a multi-criteria decision. Additionally, the capabilities of the proposed method in the process of utilizing the linguistic information dealing with big data are highlighted. The proposed method is examined to address a supplier development problem. By experimental results, we show that ZBWM results lower inconsistency when compared with BWM. A Z-number contains subjectivity in its fuzzy part, which can be addressed in future applications of ZBWM.

Logistic informatics modelling using concept of stratification (CST)

The concept of stratification (CST) is a reform in problem solving approaches in computer science introduced and developed by Lotfi Zadeh [1]. In this approach, the target set is a set of the initial states. The environment around the target is then systematically identified and strata around the target are gradually built using the concept of ‘incremental target enlargement’. We advocate that this approach is useful in different areas; however, it is essential to highlight the potential applicability of the approach by presenting different examples. In this paper illustrative examples in Information Dominance (ID) and in requirement elicitation in contracting, are structured using CST. These examples show the application of the concept in the contexts of logistic informatics and contracting and assists the modelling process. Of particular relevance are Example 4 and 5. Example 4 exposes situations that are not dealt with in the current form of CST. The study, therefore, extends CST by considering the possibility of occasional state repetition while using the same input. Further, a 3D version of CST is structured and examined through example 5. This proposed version of the concept illustrates how the Fuzzy Inference System (FIS) benefits the user when applying CST.

A REVISION ON COST ELEMENTS OF THE EOQ MODEL

Abstract The overall objective of this paper is to investigate the fundamental cost elements of the traditional EOQ model and develop the model by expiring some of its unrealistic assumptions. Over the last few decades, there have been numerous studies developing the EOQ model, but the basic cost elements of the EOQ model have not been investigated efficiently. On the other hand, the capital cost of buying inventories seems to be important to be investigated separately as well as holding cost and ordering cost in the model. In this paper, the capital cost of the inventory and possible stepwise increases in holding and setup cost are taken into account to make a revised formula to compute the economic order quantity. The proposed model involves explicitly the capital cost of buying the inventories in the EOQ model to ensure the decision makers that their financial concerns are considered in the revised model and the new order quantity results the minimum total cost.

Targets of Unequal Importance Using the Concept of Stratification in a Big Data Environment

AbstractThe concept of stratification (CST) has recently been proposed as an innovative approach in problem solving. CST takes a recursive approach to solve problems. It considers a system which has to transition through states until it arrives to a state which belongs to a desired set of states, namely a target set. The states can be stratified by enlarging the target (absorbing adjacent states). Incremental enlargement is a means to identify possible paths to achieve the target. Such an enlargement can also be used to degrade the target when the original target is not reachable. nAlthough the characteristics of the concept, such as incremental enlargement, enhance its potential application in robotics, artificial intelligence, and planning and monitoring, there is a major shortcoming in the approach, namely its inability to consider targets of unequal importance. This study considers two targets of unequal importance for the system in CST, labelled Bi-Objective CST model (BOCST). In comparison with the original proposed CST model in this research, a version of CST with finite states which is much easier to apply than the original CST is proposed, labelled fuzzy CST. Following that, a combination of Fuzzy CST and BOCST (FBO-CST) is proposed. The model is then employed to address a restaurant selection problem using data from Google. The example illustrates how the model should be applied in a big data environment. By defining finite state CST and considering targets of unequal importance, this study is expected to facilitate future applications of CST.

A developed slope order index (SOI) for bottlenecks in projects and production lines

This paper finds a sequence of m jobs on one processor with the minimum total cost as a solution to the sequencing problem where the raw materials are either expensive to buy or carry. There have been numerous studies considering m jobs on one processor which consider various cost factors such as the total penalty cost. One of the important, but less investigated cost factors, in the previous studies, is the inventory carrying and its relevant capital costs. The inventory costs such as the holding cost and capital cost must be considered in proposing a solution to the sequencing problem. In this paper, by taking those costs into account to address the sequencing problem, a developed slope order index is computed to enable decision makers to a sufficient cost saving sequence of m jobs on one processor. This paper contributes the current knowledge by proposing a new sequencing solution in which some previously less observed costs are considered. The result of this paper can also be employed in scheduling of m jobs where there is a bottleneck and the inventories are expensive or their holding costs are considerable.

Letter: The Concept of Stratification and Future Applications

Abstract The main purpose of this letter is to draw attention to a recent concept, namely Concept of Stratification (CST) developed by Zadeh [1]. CST describes a system that transitions through a number of states in order to arrive at a desired state. CST is a problem-solving approach, which is easy while effective. Therefore, CST seems very likely to emerge in coming years as a major interest area in areas such as soft computing, Artificial Intelligence (AI), robotics, Natural Language Processing (NLP), and big data. In this expository letter, the advantages and the main shortcoming of CST are reviewed. The concept is explained and areas that the concept is likely to be applied are discussed. Considering the generality of the original CST proposed by Zadeh, it is possible to consider different versions for CST to be applied in future studies. Hence, versions of CST including fuzzy CST, a 3DCST, and multiple systems and multiple CSTs are presented. This work is a first step in a vast range of applications of CST. Researchers, especially those applying soft computing tools such as fuzzy sets theory and granulation, are encouraged to examine the capability of CST in addressing significant real-world problems.

A fuzzy game based framework to address ambiguities in performance based contracting

Avoiding ambiguity and fuzziness in the determination of the requirements is a crucial factor in the success of Performance Based Contracting (PBC). To date, there is a research gap because insufficient studies have been undertaken to address this significant issue in the pro-curement process. Previous studies that have been con-ducted on requirement specification and elicitation are limited to software engineering. This study investigates this issue in the procurement process and proposes an integrated framework using Natural Language Pro-cessing (NLP), game theory and fuzzy logic. This re-search contributes to contract theory by opening a new line of research which paves the way for leveraging arti-ficial intelligence techniques in automated or semi-automated contract monitoring.