رویکرد های چند سطحی به مدیریت تقاضا در محیط های پیچیده: یک مدل تحلیلی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|8697||2001||13 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 71, Issues 1–3, 6 May 2001, Pages 221–233
Recent studies have shown that as demand becomes irregular and complex (i.e., lumpy), a possible approach for managing such uncertainty is to collect information directly from customers. This implies that the sales units have to move closer to customers, analyse their likely requirements, and collect quantitative and structured data as well as qualitative and subjective insights. However, as integration with individual customers increases and data collection capabilities improve the organisational configuration of most companies becomes ever more complex and the aggregation of forecasts more difficult. This paper discusses two approaches to managing demand uncertainty in complex environments. In the first (termed decentralised order overplanning), sales units are responsible for forecasting the demand of each customer and defining requirements. In the second (termed centralised order overplanning), forecasts provided by sales units are aggregated and further elaborated by manufacturing to define item requirements. By means of an analytical model (which describes the forecasting and planning process as a Bayesian–Markovian process), we show that the centralised method out-performs the decentralised approach by virtue of the ability to exploit the additional information provided by commonalities between customers requests. However, this advantage has to be balanced against organisational costs. Since the centralised method splits responsibilities for forecasting and slack control between sales and manufacturing units, major conflicts are likely to arise, the focus and commitment on forecasting accuracy may be compromised, and information may be lost when individual forecasts are sent to the manufacturing unit
The demand for end items and modules is becoming more and more irregular or lumpy (i.e. the demand pattern shows various periods of zero or low demand followed by peaks). This trend is due to increasing requests for customised products, and to mergers and acquisitions that reduce the number of potential customers. In the past, such irregularity was associated to certain demand and make-to-order (MTO) manufacturing. In MTO environments, requirements are irregular and sporadic because of high product customisation, but customers are also willing to accept a delivery lead time (DLT) that exceeds the manufacturer's total lead time (TLT). In today's turbulent markets, however, customers are asking for ever shorter delivery lead times. Though firms often try to reduce their TLT, firms in many industries can no longer make to order (e.g. telecommunications apparatus, textile machines, tooling machines, machine control systems, etc.). In these industries the demand is both lumpy and uncertain. Firms therefore seek to forecast future requests precisely in order to meet customer requirements. Researchers have proposed various techniques to forecast lumpy demand (see for example  and ) on the basis of past trends. Unfortunately, the inherent extreme variability and unpredictability of the demand process make these methods ineffective . A possible solution is to reduce demand uncertainty by extending the information base for forecasts . In this respect, an interesting approach is to anticipate future requirements by using early information generated by customers during the purchasing process. This method, which involves a Bayesian use of information, has already been implemented in more stable environments ,  and  or when dealing with seasonal demand ,  and . However, application in contexts with uncertain lumpiness shows substantial peculiarities and therefore requires a specific method called order overplanning . Order overplanning is a consistent set of forecasting and planning rules based on over-stated forecasts of future orders from each customer. Consequently, the method differs from classical techniques, since it forecasts future customer orders rather than future levels of demand for an item. Past research  and various case studies have proved that in complex markets, suppliers continue to gather information about future requests from their customers. In other words, since past demand data provide very poor information on future requests, firms tend to gather information about future demand from their (few) customers. In these markets, the product is generally complex and expensive, meaning that a long and informative purchasing process precedes the order release. During this process, the customer evaluates the offers and specific proposals of various suppliers, who, in turn, can evaluate whether and what the customer is going to purchase. The customer purchasing process is therefore a relevant source of information for the supplier's forecasting and planning process. Past research has shown that order overplanning performs reasonably well when facing lumpy demand . Nevertheless, previous models leave some relevant questions unanswered, especially as far as the organisation and management of the forecasting and planning processes are concerned. Indeed, in order to sense market requirements better, firms in these environments have complex organisational structure including a manufacturing unit (for each product family) and various sales units focused on different customer areas (see Fig. 1). It is clear that the information needed for forecasting and planning should come from the sales units. Furthermore, the information will obviously be incorporated in planning decisions by the MPC unit. We will examine who should manage the uncertainty in demand and how this task should be performed. The objective of this paper is to discuss the organisational issues related to the collection and use of information from the market, and to investigate whether the manufacturing or the sales units should manage demand uncertainty . The structure of the paper is as follows. Section 2 proposes two organisational alternatives, while Section 3 briefly illustrates the methodology we use to compare their performance. Section 4 presents the results of the analytical model, and Section 5 discusses the organisational costs and problems associated to the two solutions. Finally, Section 6 draws some conclusions and highlights future developments in this area.
نتیجه گیری انگلیسی
Order overplanning is an uncertainty management method practised by companies facing very sporadic and irregular demand. It applies a basic principle: anticipate future demand by collecting early information (often qualitative and subjective insights) on customer requirements. Its peculiarity rests in the fact that demand lumpiness means this early information is captured by focusing on the purchasing process of each single customer. This focus requires the organisation to strengthen sales units’ data collection capability, but has a potential drawback in that it lacks a comprehensive vision of the overall market requests for a given item, and may therefore miss some commonalities between different customer requests. This paper discusses two different methods to implement the order overplanning approach. In the first method, which we have termed decentralised, the sales units are responsible for collecting information from each single customer and defining the item requirements. In the second method (the centralised approach), the manufacturing unit aggregates the forecasts of different sales units and exploits commonalities between these forecasts to define the item requirements. We have shown that the centralised method out-performs the decentralised approach in terms of service level and inventories. This is due to the additional information provided by the aggregate analysis of the forecasts. The advantages of the centralised method become more marked in those cases where the information derived from commonalities is significantly higher than that from the individual sales units. This occurs when the information provided by the sales units is relatively poor, when the number of potential customers in the market is not exceptionally low, and when ordering frequencies are intermediate. On the other hand, the advantages of the decentralised method lie in organisation. The method is based on clear, specific responsibilities for forecasting and planning. The sales units are stimulated to collect data from customers and control slack. In contrast, the centralised method splits responsibilities between the sales units and manufacturing, giving rise to ambiguity in slack control, conflicts among units, and a loss of information content when transferring forecasts from sales to manufacturing. The choice between the two approaches therefore depends on the level of demand lumpiness a company is facing and on its internal organisational culture and capability. However, this paper shows that there is a continuous spectrum of methods linking complex environments to the simple case. As the complexity in demand (i.e. with increasingly lumpy and uncertain requirements, fewer customers and fewer customer requests) and the information that can be collected by focusing on each individual customer increases, the most suitable demand management approach moves from classical, part-oriented methods (i.e., forecast based on analysis of past data for a given item), to centralised order overplanning, and decentralised order overplanning.