دانلود مقاله ISI انگلیسی شماره 21344
عنوان فارسی مقاله

ایجاد مکانیزم های غربالگری سفارش مشتری تحت محیط سفارشی سازی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21344 2014 15 صفحه PDF سفارش دهید 9060 کلمه
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عنوان انگلیسی
Establishing customer order screening mechanisms under customization environment
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 41, Issue 4, Part 1, March 2014, Pages 1071–1085

کلمات کلیدی
سفارشی - ویژگی ها - فرآیند تحلیل سلسله مراتبی () - طراحی وزن سطح دو - استدلال موردی() ( ( - ( - ( -
پیش نمایش مقاله
پیش نمایش مقاله ایجاد مکانیزم های غربالگری سفارش مشتری تحت محیط سفارشی سازی

چکیده انگلیسی

Under the customization production environment, it is necessary for enterprises to offer more varieties of products to cope with the demands of different customers. However, the variation in the customized product specification often leads to problems in the enterprise such as an improper design or an uncoordinated manufacturing procedure that causes a much higher or loss. Therefore, enterprises will be able to take profits under control more precisely if they can tell whether the customer’s order is acceptable and estimate the new product cost. To deal with this issue, the authors attempt to develop an evaluation and decision-making system for the customized orders. In this study, customer’s orders are evaluated by a two-stage model. First, the Analytic Hierarchy Process (AHP) is used for the initial screening of orders. While some orders can be directly accepted or rejected, others may need a double check by the business sectors and customers. In the acceptable orders, the Case-Based Reasoning (CBR) is used to pick up the case most similar to the new product case for the evaluation of product cost and price setting. At last, the PC product is used as an example to illustrate the proposed methodology. The building of an objective order screening system will help enterprises to make up proper strategies regarding the order selection.

مقدمه انگلیسی

In the current few diverse product manufacturing environment, many enterprises have adopted the customization production strategy. The term customization indicates that enterprises offer different products or services for customers to select. In these enterprises, R&D departments are required to provide a more variety of products which not only cope with the individual customers’ demands but also meet the cost and production requirements in the company. It is a characteristic of the few diverse manufacturing concepts (Pine, 1993). In such a customization production environment, product configuration management is a key issue. A product configuration is composed of various materials and components whose constitutions and required quantities are described in the bill of material (BOM). In a product configuration study of computers, 30–85% are wrong and it often results in the abnormality in the following manufacturing process and a longer lead time (Fohn, Liau, Greef, Young, & O’Grady, 1995). In the past, varying ways of solutions have been proposed to solution this problem. Regarding the product configuration database, Hegge and Wortmann (1991) proposed the Generic BOM (GBOM) to solve the problem in product configuration management. They attempted to generate a Generic Product Structure to describe a product and when the manufacturing or production information gets beyond the range of GBOM, an Assembly Structure of product family is adopted. Olsen and Sætre (1998) claimed that the inflexibility of tables is the main reason why BOM cannot be transformed into the table structure. Because tables are unable to reach the requirement of BOM, they proposed syntax to cope with the variance and flexibility in product design. Furthermore, they consider it important to work out a BOM that can handle a great amount of information and variance in product configuration can be brought under control, a customer-oriented BOM mode. Jiao and Tseng (1999) considered that a single product information processing is not sufficient for the requirement in product differentiation. Instead, an integrated platform in the name of Product Family Structure (PFA) is needed. Such a PFA contains a basic products or modules, and new product differentiation can be generation by timely adjustment of PFA according to the market situation so as to meet the customers’ demands. Simpson (2001) proposed another way to build differentiation in the design of product family in an enterprise by Goal Programming and statistical methods. To clarify the objectives and limitations under the customization environment, Akgunduz, Zetu, Banerjee, and Liang (2002) adopted a model similar to linear programming to build up the strategy. Moreover, Tseng, Chang, and Chang (2005) applied the theory of CBR for the construction of product configuration, in which the feature tree starts from the product function and each product configuration is stored in a single specific feature tree and product information is recorded in the nodes and branches. With the theory of CBR, product configurations in the library will be used for comparison. Jiao, Simpson, and Siddique (2007) and Fogliatto, da Silveira, and Borenstein (2012) made an overview regarding the studies on customization. During the customized BOM procedure, enterprises need to efficiently identify customer’s demands, adjust their product design, and deploy the production activity. However, the proliferation phenomenon of the configuration information that comes with the product exploitation procedure often causes a heavy burden to the enterprise. Accordingly, Croxton (2003) proposed an order fulfillment process (OFP) that emphasizes the whole aspects, a framework that contains strategies and operational layers. This is a problem enterprises have to face in the customization production environment. Alternatively, Pil and Holweg (2004) studied the relationship between product variety and order-fulfillment strategies, in which two strategies were make-to-forcast and build-to-order. Then, Tseng and Chen (2006) proposed the binary tree concept to build product configuration. The binary tree is a basic tree diagram data structure mainly for the storage of ranking data. It is an integrated structure particularly helpful in data storage, listing, retrieval, and processing. Overall, most of these studies focus on the management or tools for product configuration. On the other hand, Zhang, Lee, and Xu (2010) proposed an integrated order fulfillment process composed of four parts, including order processing, product configuration and process plan generation, production execution, and product delivery. During the OFP procedure, many communication and coordination jobs among different sectors in a company are involved. In many cases, human errors or carelessness may cause problems in the trial production stage. For example, if the regulation in the product design is too complicated to match the mass production requirement, it will result in an uneven manufacturing procedure, inferior quality, low productivity or high cost. Therefore, if we can solve the product configuration problem in the earlier stage of OFP, the burden the enterprise has to bear will be reduced. For this reason, the present study is focused upon the OFP problem in the customization environment. Though some scholars had dealt with OFP problems in the past, for example, Gharehgozli, Rabbani, Zaerpour, and Razmi (2008), their studies were only related to production model such as Make to Order (MTO) or Make to Stock (MTS) but not suitable for the customization order because customization order frequently changes due to the difficulties in engineering or product specification modification. For the time being, rare literature has been seen regarding how to correctly evaluate orders and effectively make strategy for BOM. As far as OFP is concerned in the customization environment, how the business sector deals with orders is a key issue. Particularly, in most cases where the number of orders is the major criteria for the measurement of personnel performance, the blind acceptance of all orders is often not on the side of other divisions such as product development and production units. What’s worse is that the insufficient communication between units in an enterprise often leads to an unworkable BOM or brings the design team into a difficult situation. It is, therefore, an urgent matter to build up a good management tool for such kind of problems. The profit control in the customization, as a matter of fact, should start from the contact of customer, and after the confirmation of the customer, an initial product configuration can be rapidly generated. After the information transmission to R&D department, sometimes a redesign and modification should be done for the accurate BOM, which can help guarantee the smooth manufacturing procedure. It is clear that the error rate of BOM will reduce if we can take advantage of the previous successful cases where modifications are made to cope with customized demands. More importantly, this will uplift the commonality of parts and reduce to total cost in an enterprise. In this study, the authors attempt to develop a customer order evaluation methodology under the customization environment to help enterprises make correct strategies regarding customization order. The customization order screen mechanisms are primarily dealt in two stages: 1. At Stage 1, the strategy makers identifies the category of order at the contact of customers according to their experience and characteristics in the industry. Some orders can be directly accepted or rejected; others should be reconfirmed by the business sectors and customers in terms of the specification and price. At Stage 1, the Analytic Hierarchy Process (AHP) is adopted for the initial order screening. 2. At Stage 2, for the accepted order, a previous case most similar will be picked up through Case-Based Reasoning (CBR). With related BOM information, the evaluation of order will be conducted by the cost assessment to make sure that the accepted order is profitable, thus reducing the risk in the total OFP procedure. In Section 2, the concepts of product feature and AHP are discussed for the initial screening of order at Stage 1. To implement the order screening at Stage 2, a two-level weight procedure is introduced in Section 3. Then, Section 4 describes the integrated algorithm. In Section 5, a practical computer parts example is illustrated. Finally, conclusions and suggestions are reached in Section 6.

نتیجه گیری انگلیسی

In the customized production environment, the building of an objective order screening mechanism will help enterprises make profit. Moreover, accepting the wrong order will cause enterprises to fall into a hard situation in R&D, production and quality control. To solve such kind of problem, a more objective evaluation at the signing of order contracts is necessary. In addition to appealing for the importance of the issue, a two stage methodology for the solution is proposed in the study. In this study, AHP is first used to select orders, which are divided into acceptable, rejected, and revised ones. In this way, the strategy makers can efficiently select the order whose values are more evident. At second stage, among the acceptable orders, two-level weight calculation and techniques related to Case-Based Reasoning are adopted for the comparison of cases in the library on the basis of product feature tree structure. The two-level weighting design reflects the outcome of AHP process and the characteristics of the product feature tree concepts so as to find out a previous case most suitable for the estimation of new product cost. With these evaluation methods incorporated, strategy makers will have more sufficient information for the screening of customer’s orders. In the future, the AND/OR graph can be added to the product feature tree and consider the dependency and mutual exclusions between product features or between features and modular parts of products. In addition, some other theories such as fuzzy theory can be considered for the weighting of product features for the semantic features or product features of high uncertainty.

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