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.     

Managing demand variability and operational effectiveness: case of lean improvement programmes and MRP planning integration

Although a lean focus is widely popular in general manufacturing environments, there are limited studies that empirically investigate how lean improvement programmes can be integrated with a material requirement planning (MRP) system. The aim of this research is to examine if the integration of lean improvement programmes with a MRP system has a positive effect on operational effectiveness when manufacturers are experiencing high levels of customer demand variability. Based on the contingency theory perspective and using data collected from the international manufacturing strategy survey (IMSS-V), a sample of 382 international manufacturing companies are first divided into two groups based on the degree of their customer demand fluctuation. Moderated hierarchical regression is then applied to each group to empirically test the research hypotheses. Statistical results suggest that operational effectiveness significantly improves when lean improvement programmes support MRP systems as manufacturers are experiencing high levels of fluctuations in demand.     

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.     

The Production of Several Items in a Single Facility with Linearly Changing Demand Rates

In this paper we extend the ELSP model to allow for linearly changing demand rates over a fixed planning horizon. This extension of the ELSP research provides a model that can be used in coordinating the production and marketing planning activities in a firm. The model allows the user to evaluate the impact of changes in product demand on production costs and customer service. We solve the model using a standard nonlinear programming package (MINOS) and show through examples based on actual production data how the model can be used to support coordinated production and marketing planning.     

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.     

AN IMPROVED HEURISTIC FOR STAFFING TELEPHONE CALL CENTERS WITH LIMITED OPERATING HOURS

Many telephone call centers that experience cyclic and random customer demand adjust their staffing over the day in an attempt to provide a consistent target level of customer service. The standard and widely used staffing method, which we call the stationary independent period by period (SIPP) approach, divides the workday into planning periods and uses a series of stationary independent Erlang‐c queuing models—one for each planning period—to estimate minimum staffing needs. Our research evaluates and improves upon this commonly used heuristic for those telephone call centers with limited hours of operation during the workday. We show that the SIPP approach often suggests staffing that is substantially too low to achieve the targeted customer service levels (probability of customer delay) during critical periods. The major reasons for SIPP‘ s shortfall are as follows: (1) SIPP's failure to account for the time lag between the peak in customer demand and when system congestion actually peaks; and (2) SIPP’ s use of the planning period average arrival rate, thereby assuming that the arrival rate is constant during the period. We identify specific domains for which SIPP tends to suggest inadequate staffing. Based on an analysis of the factors that influence the magnitude of the lag in infinite server systems that start empty and idle, we propose and test two simple “lagged” SIPP modifications that, in most situations, consistently achieve the service target with only modest increases in staffing.     

Adjusting MRP for dynamic differentiation of identical items for process customisation

In the make-to-order (MTO) environment, different customer orders often require similar products that share identical basic features but require different customisation processes. To achieve efficiency, these products are often manufactured in a group at the initial stages. A significant issue then emerges as to how to differentiate identical items at the intermediate stages so that they can be dispatched to different kinds of work centres for process customisation. This article adjusts the current material requirements planning (MRP) approach for the MTO mode. A yield-allocating approach is then proposed for adjusting the material demand plans according to real-time process quality information. An approach for dynamically differentiating identical customised items at intermediate stages is then presented. How to implement the proposed methodology in a computer-based environment is also briefly introduced. A case study demonstrates that the proposed methodology can fulfil the dynamical differentiation task and can result in more reasonable differentiation decisions than the traditional MRP approach.     

基于服务能力均衡的网购供应链资源整合决策

为客户提供满意的个性化服务是网购企业提升客户体验价值和竞争力的关键,满意的个性化服务需要对网购企业后台运行的供应链资源进行有效整合。在网购供应链资源整合特征分析基础上,从网购供需服务能力动态协调与均衡的角度出发,基于网购个性化服务模式分析并挖掘出资源整合的主导因素,建立了整合决策的优化数学模型,搭建了改进的蚁群寻优算法来实现整合决策优化的求解。最后通过算例验证了方法的有效性与可行性。     

Material planning with uncertain product quality†

Traditional material requirement planning (MRP) is a system procedure to determine input quantities and ordering times to meet a specified demand in a deterministic framework. A variety of modifications to MRP have been proposed to incorporate different forms of uncertainty in production systems. In this paper, wc consider the case where the uncertainty is due to quality variations in the production process, and we develop an approach which links material planning with quality variations and control.     

MATERIAL REQUIREMENTS PLANNING UNDER UNCERTAINTY

This paper presents a framework for characterizing and studying the uncertainty which can affect inventory investment and service level performance in a material requirements planning (MRP) system. It also presents the results of a simulation experiment which compared two techniques (safety stock and safety lead time) for building inventory to protect against uncertainty. The simulation results disclose consistent differences between the techniques in protecting a representative part against timing and quantity uncertainty in both demand and supply for the part. The paper not only provides some initial insights into the behavior of MRP systems under uncertainty, but also establishes some guidelines for choosing between safety stock and safety lead time.     

MRP system performance under lumpy demand environments

Material requirements planning (MRP) systems are deemed to deal with master schedules with lumpy demand patterns better than any other production scheduling system. Past studies have advocated important advantages of using MRP systems. The objective of this paper is to look into the impact of patterns of demand lumpiness on the performance of MRP systems by a simulation study. Results show that there is an important threshold point in terms of degree of lumpiness at which MRP system performance starts to deteriorate in the operating conditions considered. If master production schedules (MPS) can be controlled by manufacturers, MRP users should exercise caution to introduce demand lumpiness in MPS to improve system performance. If not, MRP users should then examine the given lumpiness and choose an appropriate lot-sizing rule that has been shown to take advantage of the effect of demand lumpiness.     

Optimizing Customer Forecasts for Forecast‐Commitment Contracts

We study a “Forecast‐Commitment” contract motivated by a manufacturer's desire to provide good service in the form of delivery commitments in exchange for reasonable forecasts and a purchase commitment from the customer. The customer provides a forecast for a future order and a guarantee to purchase a portion of it. In return, the supplier commits to satisfy some or all of the forecast. The supplier pays penalties for shortfalls of the commitment quantity from the forecast, and for shortfalls of the delivered quantity from the customer's final order (not exceeding the commitment quantity). These penalties allow differential service among customers. In Durango‐Cohen and Yano (2006), we analyzed the supplier's problem for a given customer forecast. In this paper, we analyze the customer's problem under symmetric information, both when the customer is honest and when he strategically orders more than his demand when doing so is advantageous. We show that the customer gains little from lying, so the supplier can use his control over the contract parameters to encourage honesty. When the customer is honest, the contract achieves (near‐)coordination of the supply chain in a great majority of instances, and thus provides both excellent performance and flexibility in structuring contracts.     

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.     

Freezing the Master Production Schedule Under Demand Uncertainty

Managing the trade-off between achieving a stable master production schedule (MPS) and being responsive to changes in customer requirements is a difficult problem in many firms where providing a high level of customer service is viewed as an important competitive factor. One alternative for managing this trade-off is to freeze an agreed portion of the MPS. This paper investigates the impact of adjustments in the design parameters of MPS freezing methods on two performance measures (MPS lot-sizing cost and stability) under stochastic demand conditions in a rolling planning horizon environment given a service level target. Simulation experiments are reported which indicate that many of the conclusions regarding the design of MPS freezing methods obtained under deterministic demand conditions hold under stochastic demand.     

Coping with Time‐Varying Demand When Setting Staffing Requirements for a Service System

We review queueing‐theory methods for setting staffing requirements in service systems where customer demand varies in a predictable pattern over the day. Analyzing these systems is not straightforward, because standard queueing theory focuses on the long‐run steady‐state behavior of stationary models. We show how to adapt stationary queueing models for use in nonstationary environments so that time‐dependent performance is captured and staffing requirements can be set. Relatively little modification of straightforward stationary analysis applies in systems where service times are short and the targeted quality of service is high. When service times are moderate and the targeted quality of service is still high, time‐lag refinements can improve traditional stationary independent period‐by‐period and peak‐hour approximations. Time‐varying infinite‐server models help develop refinements, because closed‐form expressions exist for their time‐dependent behavior. More difficult cases with very long service times and other complicated features, such as end‐of‐day effects, can often be treated by a modified‐offered‐load approximation, which is based on an associated infinite‐server model. Numerical algorithms and deterministic fluid models are useful when the system is overloaded for an extensive period of time. Our discussion focuses on telephone call centers, but applications to police patrol, banking, and hospital emergency rooms are also mentioned.     

A Service Provider's Elicitation of Its Customers’ Demand Distributions by a Price Mechanism

In a service environment a service provider needs to determine the amount and kinds of capacity to meet customers’ needs over many periods. To make good decisions, she needs to know the probability distribution of her customers’ demand in each period. We study a situation in which customers’ demand for a given service is random in each period, but inelastic, or modeled well by this assumption, and cannot be delayed to the next period. This article presents a mechanism that allows a service provider to learn the distribution of a customer's demand by offering him a set of contracts through which he can partially prepay for future service for a reduced cost for units of service based on anticipated needs. We describe the form of a set of contracts that will cause the customer to reveal his demand distribution as he minimizes his expected costs. To justify the effort of organizing and offering contracts, we present an application that demonstrates the cost savings to the service provider with better capacity planning using the truthfully elicited distribution.     

Resource Flexibility Issues in Multistage Manufacturing

We test the situational impact of two types of resource flexibility, machine flexibility and labor flexibility, in a material requirements planning (MRP)-driven production system. Machine flexibility has not been treated in prior multistage research, only labor flexibility. Machine flexibility is closely related to a plant's positioning strategy. A process-focused plant opts for considerable machine flexibility by choosing general-purpose equipment. Resource flexibility, if effective, can be an attractive alternative to two other types of buffers, inflated inventories and costly capacity cushions. Our simulation results, using factor settings established earlier by a panel of managers, show that resource flexibility is indeed an effective buffer against uncertainties such as end-item demand variability, capacity bottlenecks, equipment failures, and yield losses. Machine flexibility is especially helpful in environments characterized by high uncertainties, tight capacities, and large lot sizes. Worker flexibility has a similar, but less dramatic, impact. Benefits are most striking with customer service, rather than with inventory or labor productivity. Finally, we show that simultaneous introduction of both machine and labor flexibility yields only marginal improvements over either kind of flexibility alone.     

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.     

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。