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.     

Impact of Information Technology Integration and Lean/Just‐In‐Time Practices on Lead‐Time Performance*

Managers seeking to improve lead‐time performance are challenged by how to balance resources and investments between process improvement achieved through lean/just‐in‐time (JIT) practices and information technology (IT) deployment. However, extant literature provides little guidance on this question. Motivated by both practical importance and lack of academic research, this article examines empirically the relationships among interfirm IT integration, intrafirm IT integration, lean/JIT practices, and lead‐time performance using data from IndustryWeek's Census of Manufacturers ( IndustryWeek, 2006 ). The results provide several new insights on the relationship between IT integration and lean/JIT practices. First, the study confirms that implementing lean/JIT practices significantly reduces lead time. Second, lean/JIT practices mediate the influence of IT integration on lead‐time performance. This suggests that process improvements that result from lean/JIT practices are important contributors to the success of IT integration. Even companies that have experienced success in reducing lead time through lean/JIT practices may benefit from IT integration practices such as those embodied in enterprise resource planning systems. The findings provide managers with empirical evidence and a theoretical framework on the balance between lean/JIT and IT for effecting improvement in lead‐time performance, thus offering practical guidance on this important question. Future research is needed to extend the lean/JIT practices in this study to supply chain practices and explore the relationship between supply chain practices and IT integration.     

Production planning in a MRP/JIT repetitive manufacturing environment

Some literature suggests that the best approach to production planning is a complete implementation of just-in-time. It has also been suggested that JIT replaces material requirements planning, and computerized procedures are an unnecessary complex burden on management especially in a highly repetitive environment. This article examines the JIT programme at the John Deere Engine factory. In 1985 the factory began a JIT programme with the belief that it would replace their MRP system. The results of the JIT programme are examined and management's conclusion that the best approach is a blend of JIT and MRP is explored.     

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.     

Supply chain management: A just-in-time perspective

Just-in-time (JIT) has been a widely recognized production philosophy alternative since the early 1980s. JIT principles and techniques have been widely adopted in many manufacturing firms. More recently, supply chain management has evolved as a discipline focusing on the design, planning and control of processes linking the initial raw materials to the ultimate consumption of the finished product. Supply chain efficiency is dependent on the efficiencies of the individual manufacturing organizations and the ability to connect along the supply chain. In this paper supply chain management from a JIT perspective is investigated, focusing on the linking mechanisms between successive companies and the collective efficiency of the supply chain.     

Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

Some structural properties for the just-in-time level schedule problem

Just-in-time (JIT) is a pull concept applied mainly in repetitive manufacturing systems, and it is characterized by a scenario where only the required units are produced in the required quantities at the required times. It particularly aims at eliminating wastes associated with inventories in the system. A level schedule is desirable for a JIT assembly system, as it serves as an approximation for all forms of smoothing. The min-sum formulation of the assembly line level schedule problem is one of those that has been mainly used in the literature. Using this formulation as a base, we develop some useful structural properties for the problem. Among other things, it is shown that a level schedule would tend to be more difficult to achieve for products (models) with comparatively fewer units in the products composition structure.     

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.     

An Analysis of Dual‐Kanban Just‐In‐Time Systems in a Non‐Repetitive Environment

Recent advances in approaches and production technologies for the production of goods and services have made just‐in‐time (JIT) a strong alternative for use in intermittent and small batch production systems, especially when time‐based competition is the norm and a low inventory is a must. However, the conventional JIT system is designed for mass production with a stable master production schedule. This paper suggests supplementing the information provided by production kanbans with information about customer waiting lines to be used by operators to schedule production in each work‐station of intermittent and small batch production systems. This paper uses simulation to analyze the effect of four scheduling policy variables—number of kanbans, length of the withdrawal cycle, information about customer waiting lines, and priority rules on two performance measures—customer wait‐time and inventory. The results show that using information about customer waiting lines reduces customer wait‐time by about 30% while also reducing inventory by about 2%. In addition, the effect of information about customer waiting lines overshadows the effect of priority rules on customer wait‐time and inventory.     

Comparing reactive Kanban and reactive CONWIP

This paper compares a reactive Kanban system to a reactive CONWIP system under conditions of unstable changes in demand using simulation experiments. After an introduction, a model of the JIT ordering systems, the Kanban and the CONWIP systems, is constructed. In order to obtain the fundamental information for developing a control rule of buffer size, the performance of the two types of the JIT ordering systems is analysed under various stable-demand conditions by simulation experiments. Based on the results, the reactive JIT ordering systems are proposed, and the performance of the proposed systems is investigated. The results showed that both of the proposed systems can react to unstable changes in demand and maintain the mean waiting time of demand at less than the required level. In the reactive Kanban system, the total of the mean work-in-process inventories becomes much less than that in the traditional Kanban system without controlling buffer size. However, in the reactive CONWIP system, the total of the mean work-in-process inventories becomes much more than or nearly equal to that in the traditional CONWIP system without controlling buffer size under the strongly correlated or the weakly correlated processing times, respectively. Based on the results, it can be claimed that, in the proposed systems, the reactive Kanban system is more effective to react to unstable changes in demand than the reactive CONWIP system.     

Dynamic Supply Risk Management with Signal‐Based Forecast,Multi‐Sourcing,and Discretionary Selling

We examine the critical role of advance supply signals—such as suppliers’ financial health and production viability—in dynamic supply risk management. The firm operates an inventory system with multiple demand classes and multiple suppliers. The sales are discretionary and the suppliers are susceptible to both systematic and operational risks. We develop a hierarchical Markov model that captures the essential features of advance supply signals, and integrate it with procurement and selling decisions. We characterize the optimal procurement and selling policy, and the strategic relationship between signal‐based forecast, multi‐sourcing, and discretionary selling. We show that higher demand heterogeneity may reduce the value of discretionary selling, and that the mean value‐based forecast may outperform the stationary distribution‐based forecast. This work advances our understanding on when and how to use advance supply signals in dynamic risk management. Future supply risk erodes profitability but enhances the marginal value of current inventory. A signal of future supply shortage raises both base stock and demand rationing levels, thereby boosting the current production and tightening the current sales. Signal‐based dynamic forecast effectively guides the firm's procurement and selling decisions. Its value critically depends on supply volatility and scarcity. Ignoring advance supply signals can result in misleading recommendations and severe losses. Signal‐based dynamic supply forecast should be used when: (a) supply uncertainty is substantial, (b) supply‐demand ratio is moderate, (c) forecast precision is high, and (d) supplier heterogeneity is high.     

A comment on: A note on the EOQ-JIT relationship

In a recently published note in this journal the need for independent control of the production rate (R) and production time (L) was established in order to adapt the economic production quantity (EPQ) model in JIT settings. In this comment we aim to improve the relevance of EPQ in a JIT environment. We focus on two issues, namely, modelling the production rate as a decision variable and also deriving an expession for the optimal number of batches (n* ) for the raw materials and/or subcomponents needed.     

Information Acquisition and Voluntary Disclosure in an Export‐Processing System

We study a strategic information management problem in the export‐processing trade, where the buyer controls the raw material input and sales and the producer is responsible for production. The production is vulnerable to random yield risk. The producer can exert a costly effort to acquire the private yield rate information and discretionarily share it with the buyer. We develop a sequential Bayesian game model that captures three key features of the system—endogenous information endowment, voluntary disclosure, and ex post information sharing—a significant departure from the literature. The optimal disclosure strategy is driven by the trade‐off between the gains from Pareto efficiency improvement and self‐interested overproduction. It is specified by two thresholds on yield rate: only the middle‐yield producers (with yield rate between these two thresholds) share private information to improve supply‐demand match; the low‐ and high‐yield producers withhold information to extract excess input from the buyer. The buyer in response penalizes nondisclosure with reduced input and rewards information sharing with a larger order. This strategic interaction is further exacerbated by the double marginalization effect from decentralization, resulting in severe efficiency loss. We examine the effectiveness of three corrective mechanisms—vertical integration, mandatory disclosure, and production restriction—and reveal the costs of information suppressive effect and overinvestment incentive and the benefit from concessions on the processing fee. Our study endogenizes the asymmetric supply risk and provides the first attempt to rationalize the strategic interactions of informational and operational incentives in the export‐processing system.     

An Evaluation of the NERJIT Priority Rule in a Kanban‐Controlled Flowshop

Significant progress in production and information technologies and innovations in management of operations during the last couple of decades have made the production of small lots and deployment of Just‐In‐Time (JIT) concepts in flowshops possible. As a result, some researchers and practitioners have been seeking to improve the performance of non‐repetitive systems using JIT concepts. In this process, the JIT concepts that were originally designed for mass production have been modified to adapt JIT to non‐repetitive systems. This article uses a priority rule that is based on real‐time demand and production information for sequencing jobs in a kanban‐controlled flowshop. The analysis of the effect of this priority rule; the number of kanbans; the length of the withdrawal cycle; First‐Come, First‐Served (FCFS); and Shortest Processing Time (SPT) on four performance measures—customer wait time, total inventory, input stock‐point inventory, and output stock‐point inventory, shows that the use of this priority rule results in a significant reduction of customer wait time and a slight decrease in inventory.     

考虑顾客缺货反应的多产品两阶段JIT联合订货

为了解决多产品两阶段联合订货决策问题,综合考虑顾客缺货反应及资源限制对订货决策的影响,提出资源限制条件下考虑CRS的多产品两阶段JIT联合订货策略。在此基础上,以零售商收益最大化为目标,建立了多产品两阶段JIT联合订货决策模型,并从初始解生成和殖民地国家革命方式两方面设计了改进的帝国主义竞争算法。从Gruen调查报告中选取三种不同规模的算例验证了模型和算法的适用性和有效性。研究表明：本文提出的考虑顾客缺货反应的多产品两阶段JIT联合订货方法既可以提高零售商的总收益,也能提高顾客的服务水平;当单位库存资源机会收益相近时,同组产品间不会发生替代情况,而当同组产品间的单位库存资源机会收益差距较大,且高于其他产品组的单位库存资源机会收益时,则该产品组便会出现缺货和替代情况,且若该组缺货产品的剩余资源单位机会收益小于其他组产品的单位资源机会收益,则该产品的剩余资源便会转移给其他缺货产品。研究结论可为零售商多产品两阶段JIT联合订货决策提供有益的参考。     

Intelligent scheduling model and algorithm for manufacturing

This paper puts forward an intelligent scheduling model based on Hopfield neural network and a unified algorithm for manufacturing. The energy computation function and its dynamic state equation are derived and discussed in detail about their coefficients (parameters) and steps (Delta t) in iteration towards convergence. The unified model is focused on the structure of the above function and equation, in which the goal and penalty items must be involved and meet different schedule models. The applications to different schedule mode including jobshop static scheduling, scheduling with due-date constraint or priority constraint, dynamic scheduling, and JIT (just in time) scheduling are discussed, and a series of examples with Gantt charts are illustrated.     

Real‐Time Revision of Order Quantities with Capacity Constraint: A Single Period Model

Manufacturing firms would like to maximize customer satisfaction by providing them with what they need when they need it. This, however, would mean continual variations in production quantities, and component orders from suppliers. A flexible manufacturing system can help alleviate costs related to modification of production quantities. The capacity of such a system, however, has to be limited because of high investment cost. Further, unless there is a long‐term relationship, suppliers may levy a high surcharge for last minute changes in order size. We model a hybrid control policy comprising an advance (pre‐production) order size agreed upon with suppliers, and a provision for real‐time order revision at a given rate of surcharge. We show that a rank‐order of products can be used for real‐time revisions, and that a strong buyer‐supplier relationship that keeps these surcharges low can actually help increase profits for both parties. We study issues such as compatibility between JIT and flexibility, and the impact of market conditions on overall profitability.     

Human resource management in the just-in-time environment

Successful just-in-time (JIT) implementation requires a multiskilled group-oriented and self-disciplined workforce with good quantitative skills. Little discussion exists in the JIT literature concerning the development of such a workforce. The human resource management (HRM) literature deals with employee issues but does not identify strategies for achieving a JIT workforce. In order to identify appropriate HRM strategies for a JIT environment, a comprehensive model is presented to show the critical linkage between JIT and HRM. Interactions between legal issues, management and union, and human input are examined. The impact of US labour laws on HRM practices is discussed and contrasted with Japanese practices. The literature review of HRM-related JIT articles suggests that HRM practices in the areas of staffing, training and development, compensation, and employee retention can be used to develop an effective JIT workforce. The article will help production managers to understand the impact of specific HRM strategies that are appropriate for developing a JIT workforce.     

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.     

The permutation flow shop problem with just in time production considerations

This research presents a variation to the permutation flow shop problem where Just In Time (JIT) production requirements are taken into account. The model developed in this research employs dual objectives. In addition to the traditional objective of minimizing the production makespan, minimization of Miltenburg's material usage rate is also incorporated. In this model, multiple units of any product are permitted in the production sequence. However, the minimization of material usage rates attempts to prevent batch scheduling of products and allows unit flow of products as required in demand flow manufacturing. A solution method is proposed for determining an optimal production sequence via an efficient frontier approach and Simulated Annealing (SA). Test problems and specific performance criteria are used to assess the solutions generated by the proposed method. Experimental results presented in this paper show that the use of the efficient frontier and SA provide solutions that approach the optimal solution for the performance measures used in this research.