مشکل برنامه ریزی ظرفیت در شرکت های "ساخت برای سفارش"
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|11916||2009||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Mathematical and Computer Modelling, Volume 50, Issues 9–10, November 2009, Pages 1461–1473
This paper addresses the short-term capacity planning problem in a make-to-order (MTO) operation environment. A mathematical model is presented to aid an operations manager in an MTO environment to select a set of potential customer orders to maximize the operational profit such that all the selected orders are fulfilled by their deadline. With a given capacity limit on each source for each resource type, solving this model leads to an optimal capacity plan as required for the selected orders over a given (finite) planning horizon. The proposed model considers regular time, overtime, and outsourcing as the sources for each resource type. By applying this model to a small MTO operation, this paper demonstrates a contrast between maximal capacity utilization and optimal operational profit.
Manufacturing firms apply various policies for fulfilling customer orders. Some firms choose to fill orders through finished goods inventory. Such a policy is referred to in the literature as make-to-stock (MTS). Other firms choose to start working on an order only after it has been placed. Such a policy is referred to as make-to-order (MTO). There are a variety of MTO operations, depending on the timing the manufacturer gets involved in the product’s life cycle . The major difference between MTO and MTS is that MTS makes standard products using a standardized process, which do not exist for MTO at the time of capacity planning. Unlike in MTS, which hold finished goods in inventory as a buffer against variations in customer demand, MTO operations hold capacity in reserve to meet customer demand . The most important aspect in MTO is the effective and efficient use of available capacity to meet customer demands. Since unused capacity represents a loss in revenue, an MTO operation manager needs to be conservative for holding their capacity. Capacity planning determines the resources requirement of an organization to sustain a given demand over a planning horizon. There are three tiers of capacity planning in terms of their planning horizon. The long-term capacity planning focuses on yearly resources requirement of plants and divisions for new and existing product lines and processing technologies, subject to demand forecast and availability of capital investment funds. It determines (1) facility locations and plant capacities, (2) major supplier’s plans and their vertical integration, (3) production technology such as new processing techniques or new automation systems, and (4) principle operation modes and production methods. The fundamentals of long-term capacity planning are mostly the same for both MTO and MTS operations. The medium-term capacity planning focuses on setting monthly or quarterly resources requirement for each plant for typically a one-year planning horizon. It decides on workforce level, raw materials and inventory policy by product group and department. Based on sales’ forecasts, it generates production capacity plans for (1) labor-employment level (layoffs, hiring, recalls, vacations, overtime, and part-timer), (2) inventory policy, (3) utility requirements, (4) facility modifications, (5) outsourcing, and (6) major material-supply contracts. Capacity requirements may vary from period to period in their regular time labor, overtime labor, inventory, and subcontracting. Two conventional medium-term (aggregate) planning approaches for MTS are: (1) matching demand and (2) level capacity. With the matching demand approach, production capacity in each time period varies to exactly match the aggregate demand as forecasted for that time period, by hiring and laying-off workers. With the level capacity approach, production capacity is held constant over the planning horizon; and the difference between the constant production rate and the varying demand rate is made up by inventory, backlog, overtime labor, part time labor, temporary labor, and/or subcontracting. An MTO operation usually adopts a hybrid approach of both. On one hand, it needs to maintain a certain level of production capacity for its core competency. On the other, it cannot leverage on inventory, as every order is a backorder and it requires customization. The common practice thus is to maintain a minimum level of production capacity, and liberally relies on overtime and subcontracting to adjust its capacity and to accommodate demand fluctuation. The short-term capacity planning sets a daily or weekly capacity plan for a planning horizon, long enough to accommodate each order’s lead time. The objective of short-term capacity planning is to ensure an appropriate match between the resources availability and the capacity requirement for a production plan at the work center level . For an MTO operation, it has to specify resources requirement of each labor and machine type for each customer order at its component level. Each customer order first is translated into internal orders and detailed work orders, which are then summarized into a load schedule (time-phased capacity requirements) by labor and/or equipment, in coordination with materials arrival. A typical MTO operation routinely considers the use of alternative sources such as overtime and outsourcing, in order to meet work order’s deadline. To assure a smooth production, an MTS operation usually imposes a freeze period, in which no change to the production plan can be made. In an MTO operation, however there is no freeze period imposed. An MTO manager has to constantly adjust to administrative and engineering changes to an existing order, while deciding if potential orders should be turned down or accepted into the system.
نتیجه گیری انگلیسی
Make-To-Stock (MTS) and Make-To-Order (MTO) are the two commonly used operation modes. An MTS operation relies on demand forecast and inventory and assumes products and their processes are predefined. MTO enterprise accepts only backorders and keeps no inventory for finished goods. Product and process designs are made to customer specifications, after the order is placed. When an MTO operation engages in bidding for potential order, four questions need to be answered. They are: (1) Does it have the technical capability to handle the order? (2) Does it have the production capacity to accommodate the order? (3) Can it complete the order in time for delivery? (4) How much is the profit from the order? All four questions are closely inter-twined, while the last three are directly related to the short-term capacity planning problem. This paper addresses the short-term capacity planning for MTO and aims at an optimal answer to the above three questions. Every MTO operation constantly faces the challenges of pricing for a bid and meeting the due date when a bid is accepted. Both challenges are closely tied to capacity requirement. This paper addresses the short-term capacity planning problem in an MTO operation environment and presents a mathematical formulation for this problem. This problem considers maximizing the operational profit by choosing the best order mix, while meeting their due dates with available resources over a planning horizon. It assumes that the operation does not have to accept all orders and no late delivery is acceptable. It considers technical precedence between jobs of an order. The model was solved using the mixed-integer program solver CPLEX for the primary purpose of model validation. Several examples were used to demonstrate the working of the model and how it can benefit a manager in an MTO environment. This model considers overtime and outsourcing as additional resources for a variable cost. Both are vital to a typical MTO operation. The computational experience shows that the commercial system can only solve the proposed capacity planning model for small problems. More efficient algorithms are needed for solving problems of industrial scale.