DOI: 10.14457/tu.the.2019.43
title: Fuzzy approaches in supply chain and new product development applications

description: Uncertainty is an important issue in real-world decision-making problems. In many situations, it is difficult for decision makers (DMs) to provide an exact quantitative information. They are more comfortable to provide an information as linguistic terms or as a range. Also, it is difficult to interpret the qualitative information, which is ambiguous and uncertain in nature. In this study, approaches to handle an uncertain information are introduced and applied to supply chain and new product development applications. The applications are summarized as follows.Supply chain application: Supplier Selection and Order Allocation (SSOA) problem is one of the critical problems in a supply chain management. The main SSOA decisions are related to what product will be ordered, in what quantities, and from which supplier(s). Practically, decision makers are often required to select between several alternatives, using many criteria. In general, the process of SSOA is complex since it involves both qualitative and quantitative criteria. The process becomes more complicated if it involves with uncertain information, e.g., inventory cost, purchasing cost, defective items, and on-time delivery. This study focuses on how to handle multiple aspects of SSOA problems in uncertain environment by proposing a new fuzzy multiple objective linear programming model. A numerical example is used to show the applicability of the proposed model.New product development (NPD) application: Due to a rising of online marketing, there are abundant of Original Design Manufacturer (ODM) clients in the market. To capture the market share, it is necessary for them to launch a customer-oriented product, which leads to the customer satisfaction and a success of the product. However, identifying customer need is difficult in practice since there are ambiguities and uncertainties in human decisions. Thus, decision-support tool is needed to help ODM manufacturers keep customer's focuses in all stages of the new product development (NPD) process. However, ODM clients' tasks receive little attention in the literature and there is no decision support for ODM clients in NPD.Motivated from these limitations, a customer-oriented linguistic approach for decision support on NPD for ODM clients is proposed in this study. The study focuses on three ODM clients' tasks for developing a new beverage product. These tasks are as follows.1.Identifying customer-oriented product concept2.Providing product specification to ODM manufacturers3.Screening an evaluation on go/no-go product.To support these three ODM clients' tasks, three models are developed.The first model is prioritizing customer-oriented product concepts. As a result, a set of suitable product concepts is identified. In this model, a linguistic computation approach based on membership functions is applied to prioritize customer-oriented product concepts.The second model is translating customer requirements to manufacturing requirements. As a result, ODM manufacturers are able to identify ODM clients' needs in manufacturing terms. In this model, a linguistic computation based on term index is used to analyze customers' preferences on product characteristics. Also, a new computational linguistic algorithm is proposed.Lastly, the third model is evaluating customer-oriented product performance. As a result, ODM clients can make a decision whether a new customer-oriented product should be launched or not. Here, the product performance is determined from the difference between the interval target linguistic terms and the interval perceived linguistic terms. In this model, a linguistic computation based on term index is used to analyze the interval perceived linguistic terms from customers.The main critical challenge in developing these three models is the loss of information from the approximation process in retranslating computed linguistic information to its initiated domain. Generally, the results of computing linguistic information do not match their initial linguistic terms. Thus, the approximation process is needed to retranslate the computational linguistic results into their initial domain. However, the approximation process usually leads to the loss of information. This loss of information implies a lack of precision in the results. Hence, it is important to develop models for supporting ODM clients' tasks that can avoid the loss of information during the evaluation processes. In this study, such an issue is the main concern in developing three models.To demonstrate the effectiveness and applicability of the proposed models, a case study of developing a new soy milk beverage product is used. Consequently, all models show their abilities over the existing models. In summary, the effort in this study is to analyze linguistic information available in ODM clients' tasks to provide a recommendation on NPD for ODM clients.