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.     

The Integration of Cost and Capacity Considerations in Material Requirements Planning Systems

Within the sequential framework of material requirements planning (MRP), a master production schedule (MPS) of end-item production is prepared and a bill-of-material processor is used to convert the MPS into a plan for needed subassemblies, parts, and materials. This study examines the impact of different procedures for considering inventory-related costs and capacity limitations in the two phases of planning: master production scheduling and bill-of-material (BOM) processing. A total of nine procedures are considered for integrating the two phases of planning. The results indicate that the integrated procedures have a significant effect on the trade-offs among inventory-related costs, work load variations, over/under time costs, and excess work loads. Further, the results suggest that the method used to develop the MPS has the primary influence on these trade-offs, but the method used by the BOM processor can sometimes have a moderating effect.     

AN ANALYSIS OF CAPACITY PLANNING PROCEDURES FOR A MATERIAL REQUIREMENTS PLANNING SYSTEM*

This paper is concerned with planning work-center capacity levels in manufacturing firms that employ a material requirements planning (MRP) system. It presents four procedures for developing work-center capacity plans designed to insure the production of components and assemblies as specified by the MRP plan and the master production schedule (MPS). These procedures (capacity planning using overall factors, capacity bills, resource profiles, and capacity requirements planning) are compared using simulation analysis. The results indicate that the performance of a procedure when measured against the MPS depends on the operating conditions of the manufacturing system. The results also indicate that the choice of a particular procedure often represents a compromise among the benefits of improved MPS performance, the costs of preparing and processing data, and the premium expenses required for more frequent adjustments in work-center capacity levels.     

AN EVALUATION OF FORECAST ERROR IN MASTER PRODUCTION SCHEDULING FOR MATERIAL REQUIREMENTS PLANNING SYSTEMS.

Typical forecast-error measures such as mean squared error, mean absolute deviation and bias generally are accepted indicators of forecasting performance. However, the eventual cost impact of forecast errors on system performance and the degree to which cost consequences are explained by typical error measures have not been studied thoroughly. The present paper demonstrates that these typical error measures often are not good predictors of cost consequences in material requirements planning (MRP) settings. MRP systems rely directly on the master production schedule (MPS) to specify gross requirements. These MRP environments receive forecast errors indirectly when the errors create inaccuracies in the MPS. Our study results suggest that within MRP environments the predictive capabilities of forecast-error measures are contingent on the lot-sizing rule and the product components structure When forecast errors and MRP system costs are coanalyzed, bias emerges as having reasonable predictive ability. In further investigations of bias, loss functions are evaluated to explain the MRP cost consequences of forecast errors. Estimating the loss functions of forecast errors through regression analysis demonstrates the superiority of loss functions as measures over typical forecast error measures in the MPS.     

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.     

Planning and Control in Multi-Cell Manufacturing

This research compares Material Requirements Planning (MRP), Kanban, and Period Batch Control (PBC) as alternative approaches to the planning and control of multi-cell manufacturing involving flow cells and assembly. Since previous research on performance of these systems in cellular manufacturing has been primarily conceptual, the experiments reported here provide new insights into their comparative performance. The results show that the production environment is a major factor in system choice. Three operating factors—Master Production Schedule (MPS) volume variation, MPS mix variation, and setup time/lot size—clearly affect system choice. All systems performed well under Justin-Time (JIT) conditions; there was no advantage to Kanban. Under the mixed conditions of high MPS variation, but small setup time/lot sizes, PBC produced superior performance compared to Kanban and MRP. Under non-JIT conditions, MRP was seen as clearly more effective. Finally, the results indicate that when conditions permit very small lot sizes relative to requirements, Kanban may perform best, even when MPS variation is high.     

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.     

Effect of forecast errors on rolling horizon master production schedule cost performance for various replanning intervals

Master production schedules are usually updated by the use of a rolling schedule. Previous studies on rolling schedules seem to form the consensus that frequent replanning of a master production schedule (MPS) can increase costs and schedule instability. Building on previous research on rolling schedules, this study addresses the impact of overestimation or underestimation of demand on the rolling horizon MPS cost performance for various replanning frequencies. The MPS model developed in this paper is based on actual data collected from a paint company. Results indicate that under both the forecast errors conditions investigated in this study, a two-replanning interval provided the best MPS cost performance for this company environment. However, results from the sensitivity analysis performed on the MPS model indicate that when the setup and inventory carrying costs are high, a 1-month replanning frequency (frequent replanning) seems more appropriate for both of the above forecast error scenarios.     

Evaluation of safety stock methods in multilevel material requirements planning (MRP) systems

This paper evaluates alternative methods of establishing the safety stock level taking into consideration of historical measures of forecasting accuracy and the needs for master production scheduling and material requirement planning under a rolling time horizon. A computer model is used to simulate the forecasting, master production scheduling and material planning activities in a company that produces to stock and the production activities are managed by multilevel MRP systems. The simulation output is analysed to evaluate the impact of safety stock methods on MRP system performance. The result of the study shows that using safety stock can help to reduce total cost, schedule instability and improve service level in the MRP systems. Guidelines are developed to help managers select methods to determine safety stock in MRP system operations.     

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.     

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.     

A Model for Master Production Scheduling in Uncertain Environments

In uncertain environments, the master production schedule (MPS) is usually developed using a rolling schedule. When utilizing a rolling schedule, the MPS is replanned periodically and a portion of the MPS is frozen in each planning cycle. The cost performance of a rolling schedule depends on three decisions: the choice of the replanning interval (R), which determines how often the MPS should be replanned; the choice of the frozen interval (F), which determines how many periods the MPS should be frozen in each planning cycle; and the choice of the forecast window (T), which is the time interval over which the MPS is determined using newly updated forecast data. This paper uses an analytical approach to study the master production scheduling process in uncertain environments without capacity constraints, where the MPS is developed using a rolling schedule. It focuses on the choices of F, R, and T for the MPS. A conceptual framework that includes all important MPS time intervals is described. The effects of F, R, and T on system costs, which include the forecast error, MPS change, setup, and inventory holding costs, are also explored. Finally, a mathematical model for the MPS is presented. This model approximates the average system cost as a function of F, R, T, and several environmental factors. It can be used to estimate the associated system costs for any combination of F, R, and T.     

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 statistical analysis of yield loss in material requirements planning systems

In most of the research done on material requirements planning (MRP) systems it has been assumed that production and assembly operations incur no yield loss. This assumption has been dropped and performance of popular lot-size adjustment rules borrowed from the single-stage reject allowance problem has been tested in an MRP environment. Both the effect of the rules on the component inventory, and their ability to meet the master production schedule requirements have been analysed. An appropriate statistical design has been presented.     

FREQUENCY OF REPLANNING IN A ROLLING HORIZON MASTER PRODUCTION SCHEDULE FOR A PROCESS INDUSTRY ENVIRONMENT: A CASE STUDY

This study addresses the problem of replanning frequency for a rolling horizon master production schedule (MPS) in a process industry environment under demand certainty. The major contribution of this paper is the demonstration of how the appropriate replanning frequency for a MPS can be determined under the conditions of minimum batch-size production restrictions in a rolling planning horizon setting. In addition, the problem environment for this study is an actual MPS operation that includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements, and multiple goals. Actual data from a paint company are used to determine the appropriate replanning frequency for a rolling horizon MPS. Results indicate that a 2-month replanning frequency was the best at this firm because of the significant cost savings it provided when compared to actual company performance and the other replanning intervals.     

Master Scheduling in Assemble-To-Order Environments: A Capacitated Multiobjective Lot-Sizing Model

Basic characteristics of an assemble-to-order environment make effective master scheduling extremely difficult. Limited resource capacities and dynamic customer end-item demand contribute to the complexity of the master production scheduling problem. To gain flexibility and responsiveness within this system, the master production schedule (MPS) focuses at the component level. This research proposes a master scheduling technique for manufactured components which combines a multiobjective capacitated multi-item/multi-stage lot-sizing model with an interactive multiple objective optimization solution procedure. To evaluate the model's performance as a realistic and practical master scheduling tool, this study focuses on the National Cash Register (NCR) electronics manufacturing facility in Columbia, South Carolina.     

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.     

A diagnostic analysis of the impact of forecast errors on production planning via MRP system nervousness

Abstract. The purpose of this paper is to examine the impact of forecast errors on the performance of a multi-product, multilevel production planning system via MRP system nervousness. The accuracy of forecasting methods was at one time a major concern of production scheduling and inventory control. However, with the advent of material requirements planning (MRP) systems, the significance of selecting an accurate forecasting method has diminished. Inaccurate forecast results are taken as a fact of life in production planning. Instead of attempting to develop an accurate forecasting method, efforts have been devoted towards providing an appropriate buffering method ai the master production schedule level or on the shop floor level to counteract fluctuations in demand. MRP is capable of rescheduling planned orders as well as open orders to restore the priority integrity after the disruptive changes of forecast errors occur. Nevertheless, excessive rescheduling may lead to a problem, generally referred to as system nervousness. This study investigates this problem by means of a computer simulation model. The results show that the presence of forecasi     

A simulation study of performance of MRP based systems under interaction effects

This paper pertains to a study carried out on a typical Material Requirements Planning (MRP) based system. An integrated MRP based system was developed. This integrated package facilitates studying the effects of variations in Master Production Schedules (MPS), Bill of Materials (BOM) structure, inventory supports, lot sizing, capacity planning, scheduling rules and shop floor conditions. The system behaviour is captured using simulation. The direct and interacting roles of uncertainties, capacity variations, scheduling rules and lot sizing rules on the system performance have been studied in detail. The details of the system, the modules and their capabil-iiies and the analyses of results are presented in this paper.     

Rolling horizon master production schedule performance: A policy analysis

In this paper, a simulation experiment has been developed to examine the combined influence of the design, inventory and environmental factors on the cost performance of a rolling horizon master production schedule. Specifically, a 2 5 factorial design was used to examine the effects associated with three rolling schedule design policies, one inventory policy and one environmental condition of forecast error on MPS cost performance. The study was based on actual data from a paint company. Results suggest that the choice of appropriate lot-size and inventory policies have a significant influence on MPS costs and that there are indeed important interactions between these policies and other design factors of a rolling schedule.