Freezing the Master Production Schedule: Implications for Fill Rate*

Previous research examining alternative ways of dealing with schedule instability has shown that freezing a portion of the master production schedule (MPS) is a cost effective way to reduce instability. While it is often argued that MPS freezing limits the firm's ability to react to changing customer needs, the impact of freezing on customer service is not well understood. We examined the impact of freezing a specified portion of the MPS on the average fill rate under a wide variety of conditions using controlled simulation experiments. The results show that freezing can be implemented without causing a severe reduction in customer service.     

A Comparative Analysis of Master Production Scheduling Techniques for Assemble-To-Order Products

This paper studies the master production scheduling (MPS) activity of manufacturing firms that produce assemble-to-order (ATO) products. It describes four techniques for master scheduling ATO products: end-product bills, modular bills, super bills, and percentage bills. These procedures are compared in terms of the percentage of customer orders delivered late, the mean tardiness of customer order deliveries, and the total cost of inventory using simulation analysis. The results indicate that the performance of an MPS technique is affected by the level of uncertainty of the end products' demands and the degree of component commonality in the product structure. In particular, modular bills produce the highest customer service level and super bills produce the lowest total inventory cost under most operating conditions. The conclusions also suggest that the choice of a particular MPS technique is often a compromise between the benefits of improved MPS performance and the costs of implementing and executing the MPS system.     

LOT‐SIZING RULE AND FREEZING THE MASTER PRODUCTION SCHEDULE UNDER CAPACITY CONSTRAINT AND DETERMINISTIC DEMAND

This paper investigates the performance impact of lot‐sizing rule (LSR) selection and freezing of the master production schedule (MPS) in multi‐item single‐level systems with a single resource constraint under deterministic demand. The results of the study show that the selection of LSRS and the parameters for freezing the MPS have a significant impact on total cost, schedule instability, and the service level of the system. However, the selection of LSRS does not significantly influence the selection of the MPS freezing parameters. The basic conclusions concerning the performance of the freezing parameters under a capacity constraint agreed with previous research findings without consideration of capacity constraints.     

Order Promising and the Master Production Schedule*

Previous research on material requirements planning (MRP) systems has rarely considered the impact of the master production scheduling method used to promise customer orders and to allocate production capacity. Based on a simulation study of an MRP environment, we show that the correct selection of a master production schedule (MPS) method depends on the variance of end-item demand. In addition, we find evidence that the effectiveness of a particular MPS method can be enhanced by holding buffer inventory at the same level in the product structure as in the MPS.     

Assessing Service Level Targets in Production and Inventory Planning*

In production and stock planning, the relationship between customer service, defined as the ability to meet demand for finished goods from in-stock inventory, and expected profits or expected costs can be represented by a simple reliability curve. The shape of this curve depends upon the parameters of the demand process, specifically the expected level of demand, standard deviation and correlation structure, as well as upon the capacities and initial state of the production and inventory system. A model is presented which explicitly determines this trade-off curve for a firm. The model is intended both as an operational model to aid managers in setting revenue and service targets which are compatible with the capacities and resources of the firm, and as a tool for exploring relationships between the parameters of the demand process and the constraints of the physical production and inventory system. The results illustrate that the level of risk depends strongly on the variability of the demand process, the cost structure, the capacities and initial state of the system and, to a lesser extent, the correlation in demand between succeeding periods. Results suggest that establishing service level targets consistent with the firm's strategic orientation must be done in consideration of both the characteristics of the demand process and the capacities of the production and inventory system. The model provides a tool for estimating the premium above unit cost which must be paid to provide a designated service level.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

MEASURING MASTER PRODUCTION SCHEDULE STABILITY UNDER ROLLING PLANNING HORIZONS

Maintaining a stable master production schedule (MPS) is difficult for many firms, especially when material requirements planning is used to manage production operations. This paper is concerned with the problem of measuring MPS stability, and the impact on stability of three important decision variables in managing the MPS within a rolling-horizon framework in a make-to-stock environment: the method used to freeze the MPS, the proportion of the MPS frozen, and the length of the planning horizon for the MPS. Simulation experiments conducted to determine the impact of these decision variables, as well as other important product demand and cost characteristics, on MPS stability are reported. The results indicate MPS stability can be influenced by managerial action directed toward management of the MPS as well as changes in important product cost and requirements characteristics.     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

A Comparison of Reorder Point and Material Requirements Planning Inventory Control Logic*

This paper reports the results of simulation experiments that compared the inventory efficiency (i.e., the customer service level provided by a given level of inventory) for two different inventory policies. One of these policies uses time-phased information on future demands like that found in material requirements planning (MRP) systems, while the other (the reorder point or ROP policy) relies on forecasts implicitly based on average past demands. After establishing that the MRP policies dominate for reasonable conditions, the uncertainty of the forecasts was manipulated until the policy preference was reversed. It requires a very perverse relationship between the forecast and actual demand before ROP beats MRP on inventory efficiency.     

AN APPLICATION OF MATERIAL REQUIREMENTS PLANNING TO HIGHER EDUCATION

Material requirements planning (MRP), a comprehensive planning and control technique based upon the dependent demand principle, enables manufacturing organizations to provide better customer service, reduce inventory investment, and increase resource utilization. This paper extends the dependent demand principle to the service sector and thus provides the basis for applying MRP to higher education. In the short run, administrators can schedule the necessary number of required courses and load each course based on student priorities to provide increased customer (i.e., student) service and faculty utilization. In the long run, course enrollment forecasts can be utilized by administrators to plan and control changes in faculty resources across departments and schools.     

An Advanced Heuristic for Multiple‐Option Spare Parts Procurement after End‐of‐Production

After‐sales service is a major source of profit for many original equipment manufacturers in industries with durable products. Successful engagement in after‐sales service improves customer loyalty and allows for competitive differentiation through superior service like an extended service period during which customers are guaranteed to be provided with service parts. Inventory management during this period is challenging due to the substantial uncertainty concerning demand over a long time horizon. The traditional mechanism of spare parts acquisition is to place a large final order at the end of regular production of the parent product, causing major holding costs and a high level of obsolescence risk. With an increasing length of the service period, more flexibility is needed and can be provided by adding options like extra production and remanufacturing. However, coordinating all three options yields a complicated stochastic dynamic decision problem. For that problem type, we show that a quite simple decision rule with order‐up‐to levels for extra production and remanufacturing is very effective. We propose a heuristic procedure for parameter determination which accounts for the main stochastic and dynamic interactions in decision making, but still consists of relatively simple calculations that can be applied to practical problem sizes. A numerical study reveals that the heuristic performs extremely well under a wide range of conditions, and therefore can be strongly recommended as a decision support tool for the multi‐option spare parts procurement problem. A comparison with decision rules adapted from practice demonstrates that our approach offers an opportunity for major cost reductions.     

A Model for Planning Resource Requirements in Health Care Organizations

In this paper we present a general model and solution methodology for planning resource requirements (i.e., capacity) in health care organizations. To illustrate the general model, we consider two specific applications: a blood bank and a health maintenance organization (HMO). The blood bank capacity planning problem involves determining the number of donor beds required and determining the size of the nursing and support staff necessary. Capacity must be sufficient to handle the expected number of blood donors without causing excessive donor waiting times. Similar staff, equipment, and service level decisions arise in the HMO capacity planning problem. To determine resource requirements, we develop an optimization/queueing network model that minimizes capacity costs while controlling customer service by enforcing a set of performance constraints, such as setting an upper limit on the expected time a patient spends in the system. The queueing network model allows us to capture the stochastic behavior of health care systems and to measure customer service levels within the optimization framework.     

Freezing the master production schedule in multilevel material requirements planning systems under deterministic demand

This paper extends the studies by Sridharan, Berry, and Udayabhanu from single-level MPS systems to multilevel material requirements planning (MRP) systems, and examines the impact of product structure, lot-sizing rules and cost parameters upon the selection of MPS freezing parameters under deterministic demand. A model is built to simulate the master production scheduling and material requirements planning operations in a make-to-order environment. The results show that all the MPS freezing parameters studied have a significant impact upon total inventory costs and schedule instability in multilevel MRP systems. First, the order-based freezing method is preferable to the period-based method. Secondly, the study finds that increasing the freezing proportion reduces both total inventory costs and schedule instability. This finding contradicts the finding by Sridharan et al. in single-level systems. Thirdly, the study finds that a higher replanning periodicity results in both lower total inventory cost and lower schedule instability. The study also indicates that the product structure and lot-sizing rules do not significantly influence the selection of MPS freezing parameters in a practical sense under most situations. However, the cost parameter seems to significantly influence the selection of replanning periodicity.     

Location of Inventories in an MRP Environment

Previous research on MRP systems has rarely considered at what level in a modular sub-assembly product structure to hold inventories. Based on a simulation study of an MRP environment, we show that the correct decision concerning where to hold inventory depends on the variance in end-item demand, the amount of inventory investment, and concomitantly, the desired level of customer service. In particular, for small investment in inventories and moderate end-item demand variance, it is equally effective to hold inventories at the subassembly level or the end-item level. But when end-item demand variance is high, subassembly level inventories are better. As inventory investment grows, however, it is best to use a diversified approach of holding both subassembly and end-item inventories, irrespective of end-item demand variance. The robustness of these conclusions is validated by simulating a hypothetical firm that also uses safety time to hedge against uncertainties.     

COORDINATION ALTERNATIVES IN A MANUFACTURER/DEALER INVENTORY SYSTEM UNDER STOCHASTIC DEMAND

This paper introduces a stochastic model of a distribution system where the stocking location is owned by a dealer (or retailer) and the product is supplied by a manufacturer. Inventory is managed by the dealer, and the manufacturer is responsible for delivery of the product through both regular replenishment and expedite shipment modes. The dealer and the manufacturer share the goal of providing a high level of customer service. Demand, moreover, is a function of the service level offered to the market by the dealer. We develop optimal stock control policies for the cases where each decision maker in turn is dominant and acts unilaterally while being constrained by the supply/demand linkages of the system. We also develop an optimum policy for the case where both levels are managed under centralized control (i.e., both levels cooperate). Results indicate that the expected profit for a dominant dealer (or dominant manufacturer) is higher under decentralized control than the optimal solution for either under centralized control. However, the centralized solution is a global-optimal solution and therefore will guarantee longterm stability. Differences between the various solutions are analyzed explicitly to estimate the cost of coordination.     

考虑渠道权利结构的产品服务能力竞争机制研究——制造商服务视角

本文针对两个制造商和一个零售商构成的产品服务供应链,以制造商提供服务为视角,研究了产品服务能力竞争问题。运用Stackelberg动态博弈,分别从制造商Stackelberg、零售商Stackelberg和垂直纳什三种不同权利结构情形,求解了供应链成员企业的均衡策略。通过分析服务成本与不同渠道权利结构下均衡结果之间的变化关系,提出能够满足不同客户需求特征的产品服务能力竞争策略。结果表明,当价格与服务能力水平都比较低时,RS价格领先型策略能够满足实惠型客户需求;当服务能力水平比较高时,VN服务领先型策略能够满足经济型客户需求;当价格比较高时,MS产品领先型策略能够满足专业型客户需求;当价格与服务能力水平都比较高时,产品服务融合策略能够满足品质型客户需求。最后,结合批发价格、服务能力水平、零售价格与服务成本之间的数值变化关系,分析了产品服务能力竞争策略的有效性。     

Single‐Period Two‐Product Assemble‐to‐Order Systems with a Common Component and Uncertain Demand Patterns

We consider a single‐period assemble‐to‐order system that produces two types of end products to satisfy two independent and stochastic customer orders. Each type of product is used to fulfill a particular customer order and these two products share a common component. Furthermore, one customer may confirm her order before the other one, and the manufacturer needs to make a commitment immediately upon the receipt of each customer order on how many products to be delivered. We propose a model for optimizing the inventory and production decisions under the above ATO environment. We also extend our model to the situation where the manufacturer can fulfill the unsatisfied low‐priority demand using the left‐over inventories after fulfilling the high‐priority demand, in case the low‐priority customer arrives first. Numerical experiments are conducted, which provide some interesting insights on the impact of uncertain demand pattern.     

Planning Service Capacity When Demand Is Sensitive to Delay

This paper presents a model designed to help determine the number of clerks or servers in a retail queuing situation. The model demonstrates a realistic treatment of the effect of waiting time on customer demand and revenues by treating the average waiting time as a form of price. Thus, the demand rate and service level are interrelated. The number of servers is obtained by balancing the cost of additional servers against the revenues and profits arising from greater demand. An example is shown, using data obtained from a supermarket, to demonstrate the insight gained from applying the model. Some extensions and research possibilities are considered, including a treatment of ancillary activities. An appendix examines a continuous version of the model which provides additional insight into the character of the model.     

Retailer's Response to Alternate Manufacturer's Incentives Under a Single‐Period,Price‐Dependent,Stochastic‐Demand Framework*

This article considers the joint development of the optimal pricing and ordering policies of a profit‐maximizing retailer, faced with (i) a manufacturer trade incentive in the form of a price discount for itself or a rebate directly to the end customer; (ii) a stochastic consumer demand dependent upon the magnitude of the selling price and of the trade incentive, that is contrasted with a riskless demand, which is the expected value of the stochastic demand; and (iii) a single‐period newsvendor‐type framework. Additional analysis includes the development of equal profit policies in either form of trade incentive, an assessment of the conditions under which a one‐dollar discount is more profitable than a one‐dollar rebate, and an evaluation of the impact upon the retailer‐expected profits of changes in either incentive or in the degree of demand uncertainty. A numerical example highlights the main features of the model. The analytical and numerical results clearly show that, as compared to the results for the riskless demand, dealing with uncertainty through a stochastic demand leads to (i) (lower) higher retail prices if additive (multiplicative) error, (ii) lower (higher) pass throughs if additive (multiplicative) error, (iii) higher claw backs in both error structures wherever applicable, and (iv) higher rebates to achieve equivalent profits in both error structures.     

Editorial Another milestone

A Master Production Scheduling Decision Support System within a multi-product medical supplies market has the dual task of providing good customer service levels while maintaining minimum reasonable levels of finished goods stock in the face of considerable internal manufacturing lead time and customer demand uncertainty. This paper examines the critical design parameters within an adaptive model highlighting how the total system orders in the internal pipeline are utilized in the decision-making process for assessing how much to load the internal manufacturing pipeline. Two different methods for tracking manufacturing lead times within the adaptive loop are also considered. Classical control concepts are applied within the Decision Support System (DSS) to avoid any long-term drift in finished stocks. Finally scenario analysis is performed via simulation for a set of design parameters and a range of stimuli typical of company operating situations. An effective decision support system design in terms of architecture and parameter settings is recommended based upon the ability of the model to maintain high customer service levels. The DSS readily interfaces between marketing and production functions to enhance company competitive advantage across a wide range of products.