دانلود مقاله ISI انگلیسی شماره 21332
عنوان فارسی مقاله

استفاده از صورت حساب های کاذب سلسله مراتبی مواد برای پذیرش سفارش مشتری و تکمیل دوباره مواد بهینه در مونتاژ طبق سفارش در تولید محصولات غیر مدولار

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21332 2000 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Using hierarchical pseudo bills of material for customer order acceptance and optimal material replenishment in assemble to order manufacturing of non-modular products
منبع

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

Journal : International Journal of Production Economics, Volume 66, Issue 2, 30 June 2000, Pages 171–184

کلمات کلیدی
تولید مونتاژ طبق سفارش - صورتحساب کاذب سلسله مراتبی مواد
پیش نمایش مقاله
پیش نمایش مقاله استفاده از صورت حساب های کاذب سلسله مراتبی مواد برای پذیرش سفارش مشتری و تکمیل دوباره مواد بهینه در مونتاژ طبق سفارش در تولید محصولات غیر مدولار

چکیده انگلیسی

In assemble-to-order manufacturing, often product families with many options and features are offered to the market. Demand for an individual option or feature can be quite low and uncertain. The efficient checking of materials availability during customer order acceptance, and the materials requirement planning are crucial for effective control in this type of industry. Traditionally, modular bills of materials are used for this purpose. If interdependencies exist between features and options in the product family, modular bills are inadequate; instead generic bills of material can be used to model the production options available in a product family. In this paper we develop a variant of the modular bill of material, called the hierarchical pseudoitem Bill of Material, which is the mirror image of the choice tree in the generic bill of materials, and which can be used for checking materials availability, for allocating materials to customer orders, and for materials replenishment. Furthermore, we propose a model to optimize the master production schedule levels for options and features that drive the material replenishments process. The optimization aims at balancing the stock keeping costs of specific parts for options, with the revenues that result from selling products variants with those options.

مقدمه انگلیسی

During the past decades the discrete assembly industry has been faced with an increased product variety, shorter product life cycles and a fragmentation of markets. Tighter competition and the emergence of buyer markets have forced companies to offer a continuously growing and changing product variety to prevent losing market share [1]. As a result more than a few million product variants are already offered in the automobile, aviation and medical equipment industries [2]. What's more, in non-assembly types of industries like insurance, banking, beverage and personal care, this proliferation of product variety can nowadays also be discerned [3]. Unfortunately, these volatile market conditions in the discrete assembly industry have induced a number of problems. Wemmerlöv [4] indicates four problem areas with regard to material coordination in the assembly industry: (a) material structuring. A proper data storage of product structures is important to control production and supply of materials. In this context a large product variety will increase the number of end product variant structures that have to be stored and maintained and consequently the complexity of the data storing process. (b) forecasting. Forecasting demand for each end product variant, becomes an almost impossible task when many variants are involved because the average demand per variant will be low. (c) buffer allocation. A high product variety thus leads to a high demand uncertainty. This uncertainty has to be buffered against with safety stocks. The chances that these stocks are unbalanced and thus will lose their effectiveness, will increase in case of high product variety. (d) order acceptance. Each final product variant is often a combination of subsystem variants. Upon arrival of an order all the required subassembly variants have to be checked on their stock availability. Fast and integral stocks checks are essential to enable short feedback cycles concerning planned delivery times to customers. Table options Wemmerlöv further states that assemble-to-order (ATO) manufacturing is relatively the best manufacturing strategy to deal with a high product variety because it “defers the commitment of material and capacity as long as possible”. An ATO strategy implies the final assembly of end product variants upon arrival of a customer order from subsystem variants. Important logistic functions that should be addressed while adopting this manufacturing strategy are therefore product structuring, buffering against uncertainty, material supply planning and customer order processing. The response to these problems have been threefold. Firstly, the production and final assembly processes are designed such that the end product variety can be realized in the final assembly phase (design for assembly, modular product design, postponement of product specificity). Secondly, the final assembly processes, and sometimes also the subassembly processes, are transformed from batch processes to flow processes. This allows for the successive assembly of all end product variants with batch size one (from a multi-model assembly line to a mixed model assembly line). The material supply to these mixed model assembly lines requires a very tight coordination of the different components and subassemblies needed for each end product, to the different positions in the assembly line. Thirdly, special bills of material are developed to support the customer order acceptance process and the materials supply process. Generally, a customer does not express his requirements in the technical characteristics of the product, such as motor type, brake type, transmission type, etc., but in terms of functional features such as horse power, color, accuracy, speed, etc. A manufacturer may offer a wide range of products, some of which may fit with the functional specifications of the customer. Searching through the product family to look for the best fitting product can be a time consuming process, unless the search process is efficiently organized. The normal solution to this problem is to make use of features and options to identify a specific variant within a product family. A feature can be viewed as a product family characteristic for finished products. Each feature, e.g. the color of a product, is associated with a set of mutually exclusive options. An option is, therefore, a value assigned to a characteristic that represents a property of a product that is relevant for a customer. In this case, the master production schedule items are typically found at the level of major components or important subassemblies [5] that represent mutually exclusive (combinations of) options and features. Forecasting the demand for finished products is often based upon the forecasted demand for the various options. Translating the option forecast to MPS item forecasts is traditionally accomplished by using so called modular bill of material. The MPS items are grouped in planning modules in a modular bill of material. A planning module, therefore, becomes either a set of parts needed to manufacture a finished product with a certain option or combination of options within a product family or, otherwise, a set of parts that is used in every finished product in the family. A forecast for a planning module is based upon the forecast for the whole product family, multiplied by the planning percentage associated with the associated option. The use of modular bills of material is based on a number of assumptions [6]. These are: • There is a one-to-one relationship between an option and a planning module. The bill-of-material associated with a planning module contains all of the components needed to manufacture the product with that option. • There are no dependencies between the options associated with different features. This implies that any option can be chosen for a given feature without affecting the choices of options for other features. In other words, orthogonality exists between the features. • No common items are found in the lower levels of the bills-of-material for the options belonging to a given feature. Van Veen [6] has shown that these assumptions often do not hold true when there is a large number of variants within a product family. As the number of variants increases, the number of options will normally increase and the likelihood of interdependencies will increase. To solve that problem Hegge and Wortmann [7], Van Veen [6] and Hegge [8] have developed a product configurator based on the so-called generic bills of material (see also [2]). For a detailed description of the generic bill of material concept we refer to [7]. Here we give a short summary of its use for selecting a product variant. A generic bill of material captures a generalized product structure which is the technical twin of the commercial choice sheet, i.e., each commercial variant specification mirrors a technical variant specification in the generic bill of material system. The commercial choice sheet shows the commercial features of the product family, for each feature parameter. The value ranges of the parameters for each feature define the combinations of options that are available. For complex products, not all combinations of options may be available. For instance, a motor with a certain horsepower may only be available in combination with a subset of the range of transmission systems available. The search process through the combination of functional product features can be modeled as a choice three. At each decision point the choice made results in selected parameter values. The choice menu at a certain point in the choice tree can be determined by the parameter values selected at earlier decision points in the choice tree. In this way the interdependencies between options are modelled. After all choices have been made, the parameters selected uniquely determine a valid product variant. As an example we consider a manufacturer of desk chairs which offers a range of products to its customers. The generic bill of material of the desk chair product family consists of an underframe, a seat and back. The underframe consists of a stand and wheels. The seat consists of a seat frame and upholstery. The back consists of a back frame and upholstery. The bold boxes and bold lines in Fig. 1 show the generic bill of material of this product family (adapted from [8]. Full-size image (29 K) Fig. 1. The generic bill of material of the desk chair product family. Figure options Each generic item in the product structure has a code which is a four digit number, followed by a single “G” if the item is a generic item, that is, if it represents a group of product variants. The choices that can be made within the desk chair product family are characterized by the following possibilities: • the upholstery of a chair can have any of three colours, white, red or blue, with parameter values (WH), (RD) and (BL). • the underframe can have a swiveling or a non-swiveling stand, with parameter values (SW) and (NO), • the underframe can have wheels or no wheels with parameter values (W) and (N). However, there are restrictions imposed on the combinations that can be chosen. These are: (a) all of the product variants with wheels have a non-swiveling stand, (b) all of the blue variants have a swiveling stand. Table options These choice possibilities and restrictions are represented in Fig. 1 in the decision tables that are associated with the generic items in the BOM. The selections made at each level in the BOM result in parameter values which can be input to the decision tables at lower levels in the BOM. In the decision tables, dashed lines indicate the options to choose from and solid lines indicate a forced choice. If no choice needs to be made, the parameter values determined at higher levels are just carried through to lower levels. The generic BOM contains a choice tree that is used to select a product variant. However, the selected product variant can only be assembled and delivered if all components and subassemblies needed for the product variant are available. As has been pointed out by Vollmann et al. [5] checking availability of materials and allocating materials to customer orders should be done concurrently with the selection of the options that require these materials. A fine example of how this can be done for product families that can be adequately modeled by modular bills of material, can be found in [9]. However, in this paper we deal with product families which cannot be modeled with modular bills of materials, in particular because complex interdependencies exist between options. For this situation the relationship between features and options available in the product family can be modeled by generic bills of materials [7]. Now the question arises how to model the material requirements for these features and options, for checking materials availability, for materials allocation and for materials replenishment. In this paper we develop a variant of the modular bill of material which is the mirror image of the choice tree in the generic bill of material and which can be used for these tasks. Like in the modular bill of material, the bill relates pseudoitems to options in the choice process. Unlike the modular bill of material, the relationship between two pseudoitems is hierarchical. Therefore, we refer to this bill as a hierarchical pseudoitem bill of material. The price of a product variant in the market may depend on the options in the variant. Different options will require different materials. Thus, different product variants may have different contributions to the profit of the firm. Therefore, an important issue in materials control for assemble-to-order manufacturing is the control of the availability of parts such that the costs of materials availability is balanced with possible revenues of selling products variants that require these parts. In this paper we also propose a model that can be used to optimize the availability of critical materials taking into account the commercial value of the options, the stock keeping costs of the critical parts and uncertainty in demand per option. The rest of this paper is organized as follows. In Section 2 we analyze the assemble-to-order process for non-modular products under a large end-product variety, with respect to material supply and customer order acceptance. In Section 3 we derive requirements for the bill of material structure, and we propose the hierarchical pseudobill of material as a technique which satisfies these requirements. In Section 4 we show how the hierarchical pseudobill of material can be used for customer order acceptance against the current inventory position. In Section 5 we propose an optimization model which can be used to determine planning levels for the pseudoitems related to the options or features in the product family. In the optimization the possible revenues of option and features sold are traded-off against the costs incurred by keeping materials in stock, assuming probabilistic knowledge of the levels of demand for features and options. Conclusions are given in Section 6.

نتیجه گیری انگلیسی

In this paper we have studied customer order acceptance and materials requirements control in assemble-to-order manufacturing. We have analyzed the materials control aspects of customer order acceptance and materials supply planning for non-modular product families, showing many interdependencies between options and features. Building on the generic bill of material concept as a tool for customer order specification for this type of product families, we have developed a variant of the modular bill of material, called the hierarchical pseudoitem BOM. Using a small-scale example we have shown how the hierarchical pseudo item bill of materials can be used for checking materials availability and materials allocation concurrently with the customer order specification. Furthermore, we have shown how the hierarchical pseudoitem BOM can be used for setting optimal master production schedule levels for pseudoitems, assuming probabilistic knowledge of the future demand for options and features, assuming knowledge of the contributions to profit of the options, and assuming knowledge of the lead times and stock keeping costs of the parts.

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