Computational complexity of algorithms for MRP and JIT production planning problems in enterprise resource planning systems

An enterprise resource planning system (ERP) is the information backbone of many manufacturing companies. At the core of ERP is a conventional material requirements planning (MRP) production planning system or a variation of MRP when just-in-time (JIT) principles are used in manufacturing. MRP and JIT both organize production planning into a hierarchy of long-, medium- and short-range problems. In all there are eight different problems. Some are common to MRP and JIT, others are specialized for a particular system. This paper analyses the computational requirements of these problems. This is important for ERP because it plans for large numbers of products (e.g. 50 000 products at 3M Company and 44 000 products at States Industries in Oregon) at large numbers of locations (e.g. 82 locations in 21 countries for Visteon Automotive Systems of Michigan and 19 locations at Boeing). We show that adequate algorithms exist for some problems, but better algorithms are needed for other problems if ERP is to provide useful production plans.     

The impact of JIT on capacity management: A case study and analysis

This article describes the production planning and control techniques used at Verbatim Computer Disk Company in Charlotte, NC. The factory operates by management policy on a 24 hours-per-day, 7 days-per-week, 363 days-peryear, basis as if it were a process industry. Production of discrete units, computer disks, follows repetitive manufacturing methods including the implementation of just-in-time JIT methods into an existing material requirements planning MRP system. Production is based on the actual customer orders received from Marketing for disks from both US customers and international markets. Marketing uses price adjustments in coordination with Production Planning to ensure the factories operate at 100 capacity and within predefined inventory levels. This article discusses the relationship between Marketing and Manufacturing which allows for the attainment of a process industry-like economies of scale for a line of discrete products. Key characteristics of the production planning and control system are identified as are the current operating problems.     

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.     

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.     

An information system for Just-In-Time

In recent years, U.S.'s worldwide industrial and economic leadership has been seriously challenged by many foreign countries. In order to counter the problem, many U.S. manufacturing companies have sought effective materials management systems such as MRP and the ‘Just-In-Time’ (JIT) system as used by many Japanese companies. Even through many practitioners and academicians have been studying the JIT concept and have recommended ways of adapting the concept to the U.S. manufacturing environment, not very many organizations are enjoying benefits of JIT system in full scale because of misunderstanding of information needs in JIT system. The objectives of this paper are (1) to identify information requirements of the materials management systems under JIT concept, (2) to compare them against the requirements under MRP system, and (3) to propose an integrated management information system to support materials management system in JIT environment. The goal of this paper is to facilitate more effective implementation of the JIT system, and stimulate further research on the subject matter.     

Kanban implementation between a heavy manufacturing department and foundry suppliers

Implementation of JIT methods often begins in an assembly department with the introduction of kanban. Only when the implementation program begins in the fabrication areas supplying the assembly department do the most serious problems with JIT arise. The farther back in the supply chain JIT is implemented, the harder problems become, especially in the management of outside suppliers such as foundries where economics still requires the batching of jobs. Faced with supplying a tractor assembly line, department 500, a producer of heavy cast iron transmission cases using transfer lines and MRP, implemented kanban not only to supply the assembly area, but also back to their own supplying foundries. The results were positive and suggest a need for creativity in JIT implementation rather than a strict adherence to a cookbook approach.     

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.     

Resource-constrained materials requirements planning- MRP rc

Computerized MRP systems used in industrial practice have been criticized by researchers as well as industrial users with respect to their limited ability to generate feasible production schedules. In this paper the lot size planning stage of a typical MRP system is considered. Several modelling alternatives for multi-level lot sizing are discussed with the help of a small example. A resourceconstrained approach to lot sizing- called MRP rc- is presented that cooperates with standard production planning and control systems. In this approach the item-by-item lot size planning is substituted by the heuristic solution of a multi-level multi-item dynamic capacitated lot sizing problem with setup times for general product structures.     

A structured methodology to implement judiciously the right JIT tactics

In order to enhance the competitiveness and efficiency of manufacturing operations, many companies arc looking at implementing key strategic technologies. Two of the most predominant programs are ‘just-in-time’ and ‘synchronous organizations’. However, their impacts on improving quality and reducing time-to-market have been mixed. It is not very clear why, in some cases, results are poor, when in a similar situation, programmes have proved to work well. There are conflicting reasons reported for such a discrepancy. Some have argued that during J IT implementation either a right mix of tactics was not selected at the outset or the process was not carefully monitored to see whether a mid-course correction or change in tactics was necessary. In order to (a) protect the manufacturing and strategic teams from making the same/similar mistakes and (b) sustain a series of successful activity throughout during the strategic implementation cycle, this paper outlines a structured methodology. The method utilizes a matrix-based procedure to dynamically (over time) measure the effectiveness of a line of JIT tactics against the organization's principles and objectives. The operating procedure suggests first (a) using a method for monitoring the changing conditions of market and business and then (b) using the metrics to guide the management with a new line of tactics that might have better impact on the newly aligned company goals. In an effort to help managers and engineers decide on a proper line of tactics to implement JIT, a line of JIT quality matrices (JQM) is developed. JQM provides a framework to guide group managers to ‘plan, pick and choose’ a set of effective JIT techniques. An approach similar to quality function deployment (QFD) is used to generate the JIT house and their corresponding JQM matrices. With the JQM-based structured methodology, managers can design the best line of JIT strategy blended with JIT theory and adapted to the manufacturing environments in which it is expected to operate.     

Design and implementation of an integrated production planning system for a pharmaceutical manufacturing concern in India

SmithKline Beecham (SBPI) is a major player in the Indian pharmaceutical industry. Though relatively small in terms of turnover, it has performed Pharmaceuticals (India) exceptionally well in terms ofprofitability and growth rate. The firm has substantial cash reserves. However, the firm operates in 'mass production' mode at present, with the established finished goods inventory norms being in the range of 1 to 1.5 months. Competitive pressure on the firm is now forcing it to move towards world class manufacturing and the application of concepts from MRP II, JIT and OPT is under consideration. This paper describes the considerations that went into the justification, design and implementation of an integrated production planning and control system in this situaion.     

Experimental push/pull production planning and control system

We introduce an experimental push/ pull production planning and control software system which is designed as an alternative to a MRP-II system for mass manufacturing enterprises in China. It has the following distinguishing features: (1) putting the philosophy of JIT into the master production scheduling of MRP-II via the earliness/ tardiness production planning method; (2) controlling material input by push and processing/ assembly by pull; and (3) adjusting the parameters of the production line by the‘ suggestion for improvement of production line’ module. Simulation results have shown that the proposed system can achieve better planning and control performance than existing systems.     

Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

Education: MRP/GT: A FRAMEWORK FOR PRODUCTION PLANNING AND CONTROL OF CELLULAR MANUFACTURING

American industry typically uses material requirements planning (MRP) for manufacturing control. This paper presents production planning and control procedures based on group technology (GT), which is used in British manufacturing. A translation between these ideas and the American approach is made, and it is suggested that a combination of MRP and GT is viable. The problems that arise, especially in the areas of lot-sizing and sequencing/scheduling, and their possible solutions are discussed. Since increased adoption of GT is expected within the next decade, an intensified research effort is warranted in these and other areas so that existing MRP systems can be modified or extended to handle cellular manufacturing control problems. Subject Areas: Material Requirements Planning and Production/Operations Management.     

An Experimental Analysis of Capacity-Sensitive Setup Parameters for MRP Lot Sizing

Most material requirements planning (MRP) systems apply standard costing (absorption costing) approaches to define setup costs that are used as fixed (time invariant) setup parameters in single-level lot-sizing methods. This paper presents a computationally simple approach for estimating more appropriate setup parameters based on estimates of work-center shadow prices. These setup parameters then are used in traditional single-level MRP lot-sizing procedures. The shadow price of capacity at each work center is calculated as the increase in the overall inventory carrying cost for each additional hour of capacity lost to setups. The opportunity cost of a setup for an order subsequently is determined based on the routing information for each order and is used by traditional MRP lot-sizing procedures to calculate lot sizes. A simulation experiment compares the performance period order quantity lot sizing with capacity-sensitive setup parameters with the fixed accounting-based setup parameters. The simulation replicates the planning and control functions of a typical MRP system. The results of the experiment show that capacity-sensitive setup parameters can make significant reductions in both carrying cost and lateness and can achieve many of the benefits of optimized production technology in the context of an MRP system.     

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.     

Manufacturing in the Nineties By H.J. STRUDEL and P.DESRUELLE(Van Nostrand Reinhold,New York, 1992). £31.50.

Abstract

Looking for a world model for the manufacturing information flow diagram has brought us to the conclusion that it is impossible to build an all-encompassing, all-inclusive system. The redundancy and overlap would make the system excessively complex and ineffective. The best way to generalize a model of this type is to simplify. The result is that we eliminate nearly the entire production control mechanism that is found in the labour intensive MRP system (Fig. 1). We come up with a system that is much closer to the material intensive JIT system (Fig. 2). The conclusion is that effective production control requires that you choose which resource you wish to optimize, and then select a system that controls the plant around the selected resource base. This is the function of books like International Management and Production. A second conclusion would be that complexity (MRP—Fig. 1) gains very little over simplicity (JIT—Fig. 2 or World Model—Fig. 4) in productivity or efficiency. As Shozo Hibino says in his book Breakthrough Thinking, we need to run our plants smarter, not harder.     

分布式供应链中基于准时制的原油采购计划方法

本文研究了分布式供应链多供应商/多炼油厂的原油采购计划问题,建立了在有限资源约束下的准时制多目标采购优化数学模型,以实现总采购费用和供应链循环时间最小。然后,本文将非线性规划模型转化为线性规划模型,并利用层次分析法(Analytic Hierarchy Process,AHP)与多目标规划相结合的方法求解模型得到采购计划方案。数值仿真结果表明本文所提出的原油采购方法的有效性和实用性。     

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.     

MRP in a job shop environment using a resource constrained project scheduling model

One of the most difficult tasks in a job shop manufacturing environment is to balance schedule and capacity in an ongoing basis. MRP systems are commonly used for scheduling, although their inability to deal with capacity constraints adequately is a severe drawback. In this study, we show that material requirements planning can be done more effectively in a job shop environment using a resource constrained project scheduling model. The proposed model augments MRP models by incorporating capacity constraints and using variable lead time lengths. The efficacy of this approach is tested on MRP systems by comparing the inventory carrying costs and resource allocation of the solutions obtained by the proposed model to those obtained by using a traditional MRP model. In general, it is concluded that the proposed model provides improved schedules with considerable reductions in inventory carrying costs.     

A possibilistic aggregate production planning model for brass casting industry

This article discusses a possibilistic aggregate production planning (APP) model for blending problem in a brass factory; the problem computing optimal amounts of raw materials for the total production of several types of brass in a planning period. The model basically has a multi-blend model formulation in which demand quantities, percentages of the ingredient in some raw materials, cost coefficients, minimum and maximum procurement amounts are all imprecise and have triangular possibility distributions. A mathematical model and a solution algorithm are proposed for solving this model. In the proposed model, the Lai and Hwang's fuzzy ranking concept is relaxed by using ‘Either-or’ constraints. An application of the brass casting APP model to a brass factory demonstrates that the proposed model successfully solves the multi-blend problem for brass casting and determines the optimal raw material purchasing policies.