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.     

The Ordered Cutting Stock Problem

The one‐dimensional cutting stock problem (CSP) is a classic combinatorial optimization problem in which a number of parts of various lengths must be cut from an inventory of standard‐size material. The classic CSP ensures that the total demand for a given part size is met but ignores the fact that parts produced by a given cutting pattern may be destined for different jobs. As a result, applying the classic CSP in a dynamic production environment may result in many jobs being open (or partially complete) at any point in time—requiring significant material handling or sorting operations. This paper identifies and discusses a new type of one‐dimensional CSP, called the ordered CSP, which explicitly restricts to one the number of jobs in a production process that can be open, or in process, at any given point in time. Given the growing emphasis on mass customization in the manufacturing industry, this restriction can help lead to a reduction in both in‐process inventory levels and material handling activities. A formal mathematical formulation is provided for the new CSP model, and its applicability is discussed with respect to a production problem in the custom door and window manufacturing industry. A genetic algorithm (GA) solution approach is then presented, which incorporates a customized heuristic for reducing scrap levels. Several different production scenarios are considered, and computational results are provided that illustrate the ability of the GA‐based approach to significantly decrease the amount of scrap generated in the production process.     

A finite capacity material requirements planning system

In a discrete parts manufacturing environment, material requirement planning (MRP) is carried out without considering the manufacturing resource capacity. As a result, during implementation, adjustments in planned orders may become necessary. This paper presents a finite capacity material requirements planning algorithm (FCMRP) to obtain capacity-based production plans. These plans need no costly adjustments to satisfy the capacity constraints. Performance of the FCMRP system, when studied through a set of test examples, has been found to be superior to the existing MRP system.     

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.     

Demand and Production Management with Uniform Guaranteed Lead Time

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.     

A routine-based framework implementing workload control to address recurring disturbances

Abstract

This research focuses on responsiveness in high variety manufacturing environments. To achieve it, the article proposes to develop Dynamic Response Capabilities (DRCs) of the manufacturing system defined as the abilities to readjust the planned operating parameters of workload, capacity, and lead time, in the wake of disturbances. To inform their development, built on the Workload Control theory, a routine-based framework is proposed. The framework supports an integrated approach for the implementation of adaptive decision-making routines for workload, capacity, and lead time readjustments at different stages in the order fulfilment process. Findings from two empirical cases show the appropriateness of the framework to develop and utilise DRCs in different settings of disturbances. Results of a simulation study, with one of the case companies, also shows the effectiveness of the framework to drive performance improvements in presence of recurring disturbances leading to demand variability.     

An Application of Master Schedule Smoothing and Planned Lead Time Control

Make‐to‐order (MTO) manufacturers must ensure concurrent availability of all parts required for production, as any unavailability may cause a delay in completion time. A major challenge for MTO manufacturers operating under high demand variability is to produce customized parts in time to meet internal production schedules. We present a case study of a producer of MTO offshore oil rigs that highlights the key aspects of the problem. The producer was faced with an increase in both demand and demand variability. Consequently, it had to rely heavily on subcontracting to handle production requirements that were in excess of its capacity. We focused on the manufacture of customized steel panels, which represent the main sub‐assemblies for building an oil rig. We considered two key tactical parameters: the planning window of the master production schedule and the planned lead time of each workstation. Under the constraint of a fixed internal delivery lead time, we determined the optimal planning parameters. This improvement effort reduced the subcontracting cost by implementing several actions: the creation of a master schedule for each sub‐assembly family of the steel panels, the smoothing of the master schedule over its planning window, and the controlling of production at each workstation by its planned lead time. We report our experience in applying the analytical model, the managerial insights gained, and how the application benefits the oil‐rig producer.     

A New Framework for Manufacturing Planning and Control Systems*

This paper presents a new framework for manufacturing planning and control systems which we call iterative manufacturing planning in continuous time (IMPICT) that appears to have several advantages over the well-known material requirements planning (MRP) framework. IMPICT explicitly considers capacity constraints and total system cost (including tardiness) to determine order sizes, order release/due dates, and operation schedules in a deterministic, multi-level, finite horizon, dynamic demand environment. Continuous time scheduling variables allow setups to be carried over from one period to the next. Three new heuristics built on the IMPICT framework are presented and tested in a simulation-based, full-factorial experiment with a wide variety of problem environments. The benchmark for the experiment was materials requirements planning with operations sequencing (MRP/OS) implemented with best-case, fixed planned lead times. The experiment showed that all three heuristics were statistically better than MRP/OS. The total cost for the order merging (OM) heuristic was 25 percent better than the total cost for MRP/OS. Computational times for OM were substantially larger than for MRP/OS; however, the computational times in the experiment suggest that OM is still computationally viable for large-scale batch manufacturing environments found in industry. IMPICT is superior to standard MRP systems because it explicitly considers capacity constraints and total system costs when it creates a materials plan. IMPICT is superior to linear programming-based approaches to finite loading and scheduling found in the literature because it allows setups to be carried over from one period to another and because it is computationally viable for realistic-sized problems.     

Group technology cell design: a case study

Rising production costs, combined with competitive pressures, have resulted in an increased focus on the need for effective manufacturing planning and control. To remain competitive, organizations are exploring new ways for increasing productivity while reducing work-in-progress inventories and product lead time. This article describes a case study of the successful introduction of production cells in a company engaged in small batch production. The company is described and its manufacturing problems presented. Following a detailed analysis of the utilization of machines in the company's machine shop, a new manufacturing technique, giving improved work flow, is proposed.     

Flexible manufacturing systems performance in U.S. automotive manufacturing plants: a case study

Flexible manufacturing systems (FMS) philosophy is a key weapon in achieving global manufacturing competitiveness. It encompasses a wide range of dimensions to improve all aspects of operational performance metrics. The aim of the study is to examine the current state of flexibility adoption in U.S. automotive manufacturing facilities and its impact on operational performance metrics. It utilizes survey questionnaire developed based on previous work in U.S. manufacturing industry. The survey was originally distributed to 420 facility managers in the U.S. domestic automotive industry. It was revealed that 70% of the respondents had implemented all 15 flexibility dimensions listed in the questionnaire. The data analysis conducted shows that implementation of certain flexibility dimensions will lead to significant improvement in specific operational performance metrics. This considerable finding can be used as a guide for manufacturing managers to achieve certain objectives in operational performance improvement in a rapidly changing environment.     

Measuring Dynamic Capabilities: Practices and Performance in Semiconductor Manufacturing

Research on dynamic capabilities emphasizes the importance and role of organizational routines in explaining interfirm differences in performance. While performance differences are well documented, few empirical analyses explore the processes inside organizations that lead to dynamic capabilities or attempt to define and measure their performance effects. This paper examines one type of dynamic capability – the development and introduction of new process technologies in semiconductor manufacturing. This dynamic capability is an important source of competitiveness in the semiconductor industry, given the short product lifecycles, rapid price declines, and rapid technological advances that define the industry. Because much of the knowledge that underpins semiconductor manufacturing is idiosyncratic, firm-level R&D organization and information technology practices that facilitate problem solving and learning-based improvement provide important and enduring advantages. We derive models of the rate of improvement in manufacturing yield (i.e. the quality of production) and cycle time (i.e. the speed of production) following the development and introduction of new process technologies in manufacturing facilities, and test the empirical specifications of these models. The ways in which semiconductor manufacturers accumulate experience and articulate and codify knowledge within the manufacturing environment build new process development and introduction dynamic capabilities that improve performance.     

A Theoretical Model of Manufacturing Lead Times and Their Relationship to a Manufacturing Goal Hierarchy*

In recent years, manufacturing firms have realized that a new, higher level of global competition causes them to compete simultaneously on multiple manufacturing goals, such as quality, delivery, cost, and flexibility. In response to this realization, considerable research now focuses on the relationship of manufacturing improvement programs to manufacturing goals. However, to date, this research has not investigated the specific underlying statistical relationships between manufacturing goals and the shop floor. This study investigates manufacturing lead time linkages with manufacturing programs and manufacturing goals. The basic purpose of this study is to understand and explain how programs affect the elements of manufacturing lead time and how manufacturing lead time affects manufacturing goal capabilities. By understanding these linkages, managers can logically trace the effects of specific programs to their eventual effects on manufacturing goal capabilities. This study's most important finding is that statistical variations in the elements of lead time cause a tendency for certain manufacturing goals to be more difficult to control and achieve than others because of canonical relationships of lead time variances. To control these lead time variances, successful firms concentrate their early program targets first on achieving “fitness for use” quality, followed by delivery reliability, short delivery lead time and cost, current product flexibility, and lastly, new product flexibility. This study mathematically illustrates which improvement programs most affect manufacturing goals through their relationship to manufacturing lead time variance reduction. It suggests that firms improve goal performance by initially targeting improvement through setup time reduction programs, defect reduction programs, and preventive maintenance programs, to facilitate quality improvements. By targeting specific programs and their related lead time variances, firms improve their manufacturing facility competitiveness with minimum obstacles.     

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     

MODELS FOR OPTIMAL LEAD TIME REDUCTION

Most inventory and production planning models in the academic literature treat lead times either as constants or random variables with known distributions outside of management control. However, a number of recent articles in the popular press have argued that reducing lead times is a dominant issue in manufacturing strategy. The benefits of reducing customer lead times that are frequently cited include increased customer demand, improved quality, reduced unit cost, lower carrying cost, shorter forecast horizon, less safety stock inventory, and better market position. Although the costs of reducing lead times in the long term may be relatively insignificant compared with the benefits, in the short term these costs can have a significant impact on the profitability of a firm. This article develops a conceptual framework within which the costs and benefits of lead time reduction can be compared. Mathematical models for optimal lead time reduction are developed within this framework. The solutions to these models provide methods for calculating optimal lead times, which can be applied in practice. Sensitivity analysis of the optimal solutions provides insight into the structure of these solutions.     

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 technique for uncertainty reduction based on order commonality

The use of commonality is widely diffused as a criterion to reduce uncertainty in demand forecasts for the master production schedule (MPS). Nevertheless, studies have mostly focused on exploiting component commonality in make to stock and assemble to order manufacturing. This paper refers to planning environments with two specific characteristics. First, the degree of certainty of the demand is extremely low, due to product complexity, with poor modularization and standardization, and to the presence of few customers of large dimensions. Second, the delivery lead time is less than the total lead time. In this situation, demand for MPS planning units is extremely uncertain and sporadic. It is therefore necessary to formulate in advance forecasts of customer orders with a redundant configuration. In this paper a technique for the reduction of demand uncertainty is introduced, based on the exploitation of order commonalities. In particular, relations between order commonality and uncertainty reduction in a planning environment with such complex features are illustrated. Then, guidelines for the implementation of the technique in order to reduce over-planning in the master production schedule are provided. Finally, the performances of the technique are empirically analysed by means of both a simulation model and experimental application in a telecommunication systems manufacturer     

SCIENCE-BASED PRODUCT DEVELOPMENT: AN EMPIRICAL STUDY OF THE MAINFRAME COMPUTER INDUSTRY

This paper reports on the results of an empirical study of product development in a complex and novel environment. The work is based on field investigations of recent product development projects performed by all leading mainframe computer producers. The projects focused on the development of complex products based on advanced technologies and probed deeply into their science base. The results show striking differences in development lead time and research and development productivity between different projects. The analysis relates these performance differences to the process for the integration of new technology. Organizations that emphasize the accumulation of system-level knowledge of product and production process and its use in technology evaluation and selection are associated with high productivity and short development lead times. This appears to have a greater impact on development performance in this novel environment than more traditional factors, such as processes for effective crossfunctional integration and for overlapping problem solving.     

EF-based strategies for productivity improvements at wet-etch stations

Abstract

In complex manufacturing environments, Cycle Time (CT) reductions obtained at critical production steps could generate considerable productivity improvements and lead to capacity increases at no investment costs. A wet-etch station operating in a real semiconductor manufacturing plant has been modelled to investigate the impact of different assignment strategies on CT performance. Assignment strategies based on the “Earliest Finish” (EF) concept have been developed and their productivity efficiency compared with a workload balance-based strategy. The EF-based strategies differ from each other for both computational complexity and amount of real-time information required. Encouraging results obtained for a high detail level EF strategy have inspired the development of conceptually similar strategies characterised by reduced implementation efforts. Fundamental implementation constraints, suggested by the manufacturing engineers familiar with the wet-etch station, have been taken into account throughout the different stages of this study. CT reductions ranging between 11% and 28% could be achieved at the modelled wet-etch station.     

基于同质设备多台配置的独立生产单元构建决策

单元化生产作为一种较好实现生产柔性与生产效率融合的先进生产方式,在变种变量需求环境下它已被大量生产企业特别是装配式生产企业所采用。生产单元构建问题是单元化生产系统设计的关键问题和首要问题,也是单元化生产研究领域的一个热点。本文研究设备易复制情形下,通过配置多台同质设备来实现生产单元间无物料移动,并保证生产单元间工作量均衡的独立生产单元构建问题。本文在综合考虑换装时间、加工顺序、设备生产能力、产品需求量等多个实际生产要素的基础上,建立了以平均总流程时间和各生产单元总流程时间与平均总流程时间偏差最小为目标的数学模型,并开发了一个启发式算法来求解数学模型,最后通过数值算例验证了模型和算法的有效性。     

Implementation of new technology: towards greater manufacturing flexibility

Changes in market characteristics over the past several years are forcing firms to eliminate/minimize dependency on costly buffers of capacity, lead time, or inventory. One way to accomplish this is via appropriate adaptation and enhancement of existing technological infrastructure. Recent technological advances present firms with a wide array of alternative technologies. In this paper, we discuss the impact of new technology on the manufacturing environment and the problems associated with its implementation. We present an integrated framework for implementing such technology based on a consistent hierarchy of organizational objectives and a general foundation of constraint theory.