Replanning the Master Production Schedule for a Capacity-Constrained Job Shop

This research examines the use of both frozen and replanning intervals for planning the master production schedule (MPS) for a capacity-constrained job shop. The results show that forecast error, demand lumpiness, setup time, planned lead time, and order size have a greater impact on the mean total backlog, total inventory, and number of setups than the frozen and replanning intervals. The study also shows that a repetitive lot dispatching rule reduces the importance of lot sizing, and a combination of repetitive lot dispatching rule and single-period order size consistently produces the lowest mean total backlog and total inventory. The results also indicate that rescheduling the open orders every period produces a lower mean total backlog and total inventory when the forecast errors are large relative to the order sizes. This result suggests that the due date of an open order should be updated only when a significant portion of the order is actually needed on the new due date.     

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 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.     

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.     

及时条件下订货策略的优化模型及求解算法

在供应链管理中,兼顾供需双方的利益,采用合适的订购单价,订购批量及运送次数,供需双方都可以提高自己的利润。本文用双层规划模型描述了及时条件下的订货策略模型。然后阐述了用遗传算法求解该模型的基本思想,最后进行了算法设计并给出一个简单算例验证了本模型及其算法的可行性。     

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.     

Vendor Performance and Alternative Manufacturing Environments*

Today, many American firms are demanding a high level of performance from their major suppliers while at the same time reducing the number of them. Vendor performance is an important aspect of maintaining low production costs and high product quality. In this study, we examine the effects of poor vendor quality and vendor lead time uncertainty in a variety of manufacturing environments using a comprehensive simulation model. The results indicate that the effect of poor vendor performance on various manufacturing firms depends on the number of stocking points and the degree of component commonality. Moreover, disruption of the manufacturing system caused by poor vendor performance can be manifested in higher levels of inventory and order backlogs. We introduce the concept of supplyside uncertainty, as it relates to component-part commonality, to demonstrate that in certain environments commonality reduces order backlogs but increases total inventories and creates an environment that is very sensitive to vendor quality problems. Finally, several conjectures are posited for future research.     

A Decision Support System for Production/ Distribution Planning in Continuous Manufacturing

This paper presents a decision support system (DSS) for managing production/distribution planning in a continuous manufacturing environment. The vendor has multiple plants and distribution centers (DCs). The trading partners have widely varying independent demand patterns. The DSS is designed for use in a multiproduct environment with overlapping raw materials and processing requirements. The production and distribution lead times at plants may span multiple planning periods. The impact of any manual override of a suggested solution can also be evaluated. The DSS is based on a linear programming model with a rolling horizon and was originally designed for a large process industry. Results of a pilot implementation using actual data are also presented, which show potential for significant savings for the company.     

Analysis of Local Decision Rules in a Dual-Kanban Flow Shop

A recent article reported the results of a study on the effects of two kanban policy variables—the length of withdrawal cycle and the type of priority rule—on average customer wait time and total inventory. This study extends that work by adding two kanban policy variables and two performance criteria. It reports the results of simulation experiments that were conducted to determine how four policy variables—withdrawal cycle, priority rule, status of waiting withdrawal kanbans, and number of kanbans influence four performance criteria—average customer wait-time, total inventory, and average number of full containers in the input and output stock points of stations. It was found that the information about waiting withdrawal kanbans in sequencing decisions results in the simultaneous improvement in two conflicting objectives—customer wait time and total inventory. Also, the effects of including the information regarding the status of waiting withdrawal kanbans on system performance are larger than the effects associated with the type of priority rule. The results provide insights into determining the level of each policy variable while fully considering the possible interactions among the variables and the levels of other policy variables to improve system performance. These insights allow for setting the levels of policy variables to make the improvement process smooth.     

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.     

Open Order Rescheduling in Job Shops with Demand Uncertainty: A Simulation Study

This paper addresses the problem of open order rescheduling in a job shop. Results are reported of experiments conducted in a simulation model of an 8-machine job shop. Four different order updating policies are examined. The due-date process is modeled to include several realistic features about the pattern of due-date variability. These features are parameterized and tested at several levels. Tardiness results indicate that open order rescheduling is beneficial only when allowances are loosely set. The results indicate that inventory performance is improved by order rescheduling, particularly in cases when due dates are revised to earlier times than originally forecast.     

Planned Lead Times in Multistage Systems

This study investigates the impact of planned lead times on performance in multistage manufacturing where material requirements planning is used in a make-to-stock environment. We simulate a variety of different operating environments and find: (1) planned lead times are important to customer service levels under all operating environments examined, but have a smaller impact on inventory investment; (2) tight due dates introduced by short planned lead times hurt customer service without saving much inventory; (3) small increases to tight planned lead times improve customer service substantially with small inventory increases; (4) co-component inventories change with planned lead times, and disparity between such inventories is a sign of poor timing coordination; (5) the fixed order quantity rule performs better than the periodic order quantity rule; and (6) tall product structure and large lot sizes require particular attention to planned lead times. The findings also extend the current understanding of planned lead times by including uncertainties such as forecast error, yield loss, and equipment reliability. The study concludes with a way to diagnose and improve poorly set planned lead times.     

A Note on ‘An Approximate Solution to Deterministic Kanban Systems’: A Commentary and Further Insights

In a recent paper, Vemuganti, Batta, and Zhu (1996) stated that the linear programming model suggested by Moeeni and Chang (1990) could result in infeasibility. They also suggested an alternative mathematical formulation for determining the number of kanbans. The intention of this article is twofold. First, we maintain that the finding of Vemuganti et al., while valid, is based on a misplaced emphasis on the purpose of the Moeeni and Chang article. Second, we demonstrate that the Vemuganti et al. formulation is a special case of a more general model proposed by Bitran and Chang (1987).     

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 Splitting in Unbalanced Production Systems*

This research investigates the impact of lot splitting in unbalanced production systems, under a variety of experimental conditions. Scheduling policies specifically designed for use in the presence of a long-term bottleneck, a condition frequently encountered in practice, are developed and tested. Results indicate that when steps are taken at nonbottleneck work centers to capitalize on capacity imbalances through increasing the number of setups and, hence, the variety of products produced, shop effectiveness is improved. The results also indicate that scheduling policies that tend to increase the size of the average process batch retard the overlapping of operations, which is critical to the success of the lot-splitting methodology in reducing flow time. Finally, it is shown that increasing capacity at nonbottleneck work centers along with implementation of effectiveness-oriented scheduling polices leads to improved shop performance.     

Trade-off between quoted lead time and price

In a make-to-order environment, lead time and price can play a crucial role in determining the financial success of a firm. Their importance increases when demand is sensitive to the quoted lead time and price. A model is presented which uses the quoted lead time and price as a mechanism to determine the optimal demand level. The relationships between the model parameters and their impacts on the firm's profit is also analysed. In addition, the effect of the number of job requests, and the mean processing time are examined. Based on the results presented in this paper, there is clear indication that the firm's profit is sensitive to the inventory holding rate, and that the inventory holding cost component is redundant in the presence of a tardiness cost component.     

The relative importance of planning and control systems in achieving good delivery performance

Amongst factors such as quick changeovers and workforce flexibility, managers in 533 UK manufacturing plants ranked a responsive planning and control system as the most important facilitator of good delivery performance on products made-to-order or assembled-to-order. The rankings indicate greater importance to companies in the Household and Engineering sectors than in Process and Electronics, where other factors are dominant. These results are combined with data such as customer lead times and item variety, to characterize and explain differences between the plants in these four sectors. Collectively the results indicate considerable differences in the production planning and control tasks. This implies that general statements on the importance of planning and control systems are inadequate. Practitioners need contextual information in order to ascertain when research is applicable to their circumstances.     

钢铁联合企业的计划与调度系统

钢铁企业的计划与调度是企业高效组织,有序生产,保证产能、经济效益最大化的最核心的生产管理功能.计划部分需要考虑板型设计、物料计划、生产计划等,在详细的排产阶段需要设计热金属交付、连铸、轧制等时刻表甘特图.     

Analysis and Approximation of a JIT Production Line: A Comment

A recent Decision Sciences article by Jordan [9] presented a Markov-chain model of a just-in-time (JIT) production line. This model was used to estimate average inventories and production rates to find the optimal number of kanbans. Results for expected production rate were found to be consistently lower than those obtained by Huang, Rees, and Taylor [8] in a previous Decision Sciences article. Jordan attributed this unexpected outcome to some procedural problems in Huang et al.'s simulation methodology. In this paper, Markov-numerical analysis is used to compare the performance of Jordan's and Huang et al.'s methods of production control. Simulation analysis is then used to determine the effects of finite withdrawal cycle times. Results show that, for equal numbers of kanbans, Huang et al.'s two-card method of production control provides substantially greater expected production rates than Jordan's method. These results suggest that the Jordan model should not be applied to the problem of setting kanban numbers on manual JIT lines. Finally, we comment on the efficiency of Jordan's iterative method to obtain performance measures of tandem queues.