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.     

Manufacturing feature-based cost management system: a case study in Brazil

Feature costing is a topic of recent discussion related to cost management systems (CMSs) aimed at understanding how product features influence production process costs. It raises as a key issue in mass customisation environments where a single product model can present hundreds of menu options. In this study, we explore the concept of feature costing beyond the use of activity-based costing (ABC). We propose a CMS based on features and common elements as costing objects instead of products. In our model, we use the unit of production effort method to organise the direct manufacturing costs, ABC method to address the indirect cost and the standard methods to compute raw materials costs. We illustrate the method in the production of bus seats in a Brazilian manufacturing company. This study gives different perspectives to the international academic community on the use of a different CMS used by many Brazilian companies.     

Optimal Production and Admission Policies in Make‐to‐Stock/Make‐to‐Order Manufacturing Systems

In this article, we study optimal production and admission control policies in manufacturing systems that produce two types of products: one type consists of identical items that are produced to stock, while the other has varying features and is produced to order. The model is motivated by applications from various industries, in particular, the automobile industry, where a part supplier receives orders from both an original equipment manufacturer and the aftermarket. The product for the original equipment manufacturer is produced to stock, it has higher priority, and its demands are fully accepted. The aftermarket product is produced to order, and its demands can be either accepted or rejected. We characterize the optimal production and admission policies with a partial‐linear structure, and using computational analysis, we provide insights into the benefits of the new policies. We also investigate the impact of production capacity, cost structure, and demand structure on system performance.     

Push-Kanban – a kanban-based production control concept for job shops

Job-shop production in an individual and small-batch manufacturing environment demands producing simultaneously different products in many customised versions and in small numbers. The associated variability of the time-phased work content leads to a high degree of complexity and turbulence in the manufacturing process of such a production type. Therefore, production control becomes significantly more complicated, and many theories on how to cope fail in the real world. Despite the many technological and conceptual advances in the field of job-shop control over the past few decades, there is still a remarkable lack of practical control approaches for job-shop production. For this reason the Push-Kanban production control concept will be introduced in this article. It combines robust push scheduling along with a kanban-driven, decentralised inventory control mechanism to create a holistic control approach, integrating all production control tasks. Thus, it offers a feasible option for controlling job-shop production in industrial practice. This article examines the effectiveness of the concept by means of a simulation study based on real data.     

A reactive approach to material procurement in an SME

The purpose of this paper is to introduce a more reactive approach to material procurement in a small manufacturing enterprise (SME). The approach involves a hybrid top-down and bottom-up approach to the issue of material procurement. The implementation of the approach in the test site is discussed and we show how the company can benefit from its implementation. The approach requires that there is a good interaction and transfer of information between an SME and both its customers and suppliers. The approach also requires a manufacturing firm to have control of the shopfloor and to have access to relevant and accurate information from the shopfloor. This hybrid approach to material procurement coupled with improved production activity control has helped a local manufacturing company to increase its reactivity. A decision support software tool is available which supports this approach and is described in the paper.     

Lowest Cost May Not Lower Total Cost: Using “Spackling” to Smooth Mass‐Customized Production

Consider a manufacturer who mass customizes variants of a product in make‐to‐order fashion, and also produces standard variants as make‐to‐stock. A traditional manufacturing strategy would be to employ two separate manufacturing facilities: a flexible plant for mass‐customized items and an efficient plant for standard items. We contrast this traditional focus strategy with an alternative that better utilizes capacity by combining production of mass‐customized and standard items in one of two alternate spackling strategies: (1) a pure‐spackling strategy, where the manufacturer produces everything in a (single) flexible plant, first manufacturing custom products as demanded each period, and then filling in the production schedule with make‐to‐stock output of standard products; or (2) a layered‐spackling strategy, which uses an efficient plant to make a portion of its standard items and a separate flexible plant where it spackles. We identify the optimal production strategy considering the tradeoff between the cost premium for flexible (versus efficient) production capacity and the opportunity costs of idle capacity. Spackling amortizes fixed costs of capacity more effectively and thus can increase profits from mass customization vis‐à‐vis a focus strategy, even with higher cost production for the standard goods. We illustrate our framework with data from a messenger bag manufacturer.     

Shop floor characteristics influencing the use of advanced planning and scheduling systems

The purpose of this article is to investigate how the manufacturing process, the shop type and the data quality, i.e. the shop floor characteristics, influence the use of advanced planning and scheduling (APS) systems in production activity and control (PAC). The methodology implemented is a multiple case study at three case companies. Each company has different shop floor characteristics, but all use a scheduling module in an APS system, which supports production scheduling. A theoretical framework is developed suggesting how APS system are used in the PAC activities, and which major aspect to consider. The case analysis shows that the scheduling module in APS system, foremost supports sequencing and dispatching. In particular, the shop type is influenced by the decision of how often the APS runs and what freedom is given to the shop floor. The manufacturing process influences how the dispatch list is created. Contrary to the literature presuming that APS systems are most suitable in job shop processes, it is found that the manufacturing process is not a crucial factor when deciding whether APS systems are an appropriate investment. It is found that the level of data quality needed in the APS system depends to a large extent on how the dispatch list is used. For example, is the dispatch list used as a guideline, not a regulation, the need for accurate data in the module is reduced. This article extends the previous literature concerning APS systems by analysing how APS systems influence PAC as a whole and increase the understanding of the challenges of using APS systems in PAC.     

Adaptive agent-based manufacturing control and its application to flow shop routing control

The manufacturing industry is currently facing unprecedented challenges from changes and disturbances. The sources of these changes and disturbances are of different scope and magnitude. They can be of a commercial nature, or linked to fast product development and design, or purely operational (e.g. rush order, machine breakdown, material shortage etc.). In order to meet these requirements it is increasingly important that a production operation be flexible and is able to adapt to new and more suitable ways of operating. This paper focuses on a new strategy for enabling manufacturing control systems to adapt to changing conditions both in terms of product variation and production system upgrades. The approach proposed is based on two key concepts: (1) An autonomous and distributed approach to manufacturing control based on multi-agent methods in which so called operational agents represent the key physical and logical elements in the production environment to be controlled – for example, products and machines and the control strategies that drive them and (2) An adaptation mechanism based around the evolutionary concept of replicator dynamics which updates the behaviour of newly formed operational agents based on historical performance records in order to be better suited to the production environment. An application of this approach for route selection of similar products in manufacturing flow shops is developed and is illustrated in this paper using an example based on the control of an automobile paint shop.     

Managing Supply Risk for Vertically Differentiated Co‐Products

The manufacturing complexity of many high‐tech products results in a substantial variation in the quality of the units produced. After manufacturing, the units are classified into vertically differentiated products. These products are typically obtained in uncontrollable fractions, leading to mismatches between their demand and supply. We focus on product stockouts due to the supply–demand mismatches. Existing literature suggests that when faced with product stockouts, firms should satisfy all unmet demand of a low‐end product by downgrading excess units of a high‐end product (downward substitution). However, this policy may be suboptimal if it is likely that low‐end customers will substitute with a higher quality product and pay the higher price (upward substitution). In this study, we investigate whether and how much downward substitution firms should perform. We also investigate whether and how much low‐end inventory firms should withhold to strategically divert some low‐end demand to the high‐end product. We first establish the existence of regions of co‐production technology and willingness of customers to substitute upward where firms adopt different substitution/withholding strategies. Then, we develop a managerial framework to determine the optimal selling strategy during the life cycle of technology products as profit margins shrink, manufacturing technology improves, and more capacity becomes available. Consistent trends exist for exogenous and endogenous prices.     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

考虑故障停机的生产控制与维修计划联合决策模型

通过生产控制与维修计划协同决策,降低生产成本。首先描述生产过程,分析各项费用。其次,建立了考虑生产过程失控、故障率和故障停时间的生产过程控制、生产计划优化和维修管理联合优化决策的模型。通过模型求解,联合制定出生产过程检查策略、生产计划（经济生产批量、生产批次）以及维修计划（PM间隔期）,实现单位时间内总费用最低的目标。再次,案例研究,分析生产过程失控、故障率和故障停机时间对于经济生产批量、生产过程检查策略和生产系统维修计划的影响。该模型从理论上解决了生产过程控制、生产计划优化和维修管理联合优化决策难题,对于指导企业制定生产计划和生产系统的检修计划,进而提高产品质量、降低生产成本、确保准时交货都具有指导意义和实用价值。     

Design of Stockless Production Systems

In make‐to‐stock production systems finished goods are produced in anticipation of demand. By contrast, in stockless production systems finished goods are not produced until demand is observed. In this study we investigate the problem of designing a multi‐item manufacturing system, where there is both demand‐ and production‐related uncertainty, so that stockless operation will be optimal for all items. For the problem of interest, we focus on gaining an understanding of the effect of two design variables: (i) manufacturing speed—measured by the average manufacturing rate or, equivalently, the average unit manufacturing time, and (ii) manufacturing consistency—measured by the variation in unit manufacturing times. We establish conditions on these two variables that decision makers can use to design stockless production systems. Managerial implications of the conditions are also discussed.     

基于改进策略的混合型制造/再制造系统分析

在制造和再制造并存的混合型系统中,制造商需要同时协调新产品生产过程和旧产品再制造过程,这种双向物流的集成运作使得传统的生产规划和库存管理方式不再适用,如何构建一种有效的控制策略来协调生产过程和再制造过程就显得十分关键。本文在优化库存信息的基础上提出了一种适用于混合系统的改进策略,同时考虑到系统复杂性,主要采用控制理论中的传递函数技术构建了系统模型。仿真结果表明,本文提出的改进策略不仅可以有效协调新产品生产过程和旧产品再制造过程,而且还可以显著改善系统性能。     

Total production control

The new requirements for enterprise flexibility, quality improvement, costs and throughput limes reduction cannot be achieved by using traditional approaches. While US and European industry developed the grand CIM, FMS, CAD/CAM and MRP II projects, Japan introduced just-in-time and lean production, not to demonstrate the possibilities of the new technology but to expose operational inefficiencies and waste in the manufacturing process. The main CIM effort was in the flexibility and productivity improvement, bin its implementation stressed above all the technical aspects of the factory integration and the most flexible production factor, people, remained in the background. The new technology must be implemented into the organizational framework that uses and develops the skills, knowledge and creativity of the human resources. People in the production process need a new kind of decision support in the rapidly changing business environment. A new concept, total production control (TPC), that is a reaction to the present manufacturing problems will be presented. TPC integrates many methods and tools for production system analysis and improvement, production system design and production planning and control. This concept emphasizes a preventive aspect of the problem solution in the production process and supports a production manager at the three time views: past (archive and statistical database); present (shopfloor data capture and production progress monitoring); and future (simulation and modelling tools).     

Co-production of substitutable products

Co-production occurs when a production run produces, simultaneously, more than one type of product. In this paper, we consider co-production planning problems with stochastic yields, multiple processes, limited capacity and non-transitive product demand substitution. We show that these problems can be transformed to a structure where the demand substitutions are transitive. We address the decisions of how to select processes, to determine production quantities for each process and to allocate inventory to product demands. Finally, we provide simple heuristics and report on computational experiments done on randomly generated test cases.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

A heterarchical generic discrete manufacturing simulation model and its application in a world class manufacturing environment

Current factory design and evaluation is very primitive. Factory components are designed in many cases independently. Product and process design are not well integrated. An encompassing framework is needed for iterating through a series of total factory designs, searching for optimal performance. In addition, a vehicle is needed for predicting the performance of a proposed advanced manufacturing system, so that engineers may have a sound means for evaluating such proposals. A heterarchical discrete manufacturing SIMNET II simulation model (SIMCELLS) was developed as a comprehensive methodology for designing and evaluating discrete manufacturing systems. SIMCELLS allows manufacturing systems engineers to experiment with alternative system structures and control strategies while seeking that combination of design features that will produce the desired overall system performance. The model in combination with a modernization programme is enabling a firm to successfully manufacture and sell trucks meeting international standards. The SIMNET II model     

The sensitivity of job mix and load capacity bottlenecks on inventory and due date performance in a manufacturing system

Based on a benchmark job-lot manufacturing system a simulation study was carried out to compare the performance of just-in-time (JIT) shop control system Kanban with conventional job-shop control procedures. The shop control policies were tested under a good manufacturing environment and the effects of job mix and load capacity bottlenecks on various shop control policies were tested. From the simulation results, it is inferred that there are shop control procedures that perform better than the Kanban in a job shop. It has been observed that even with adequate capacity, bottleneck areas surface due to fluctuations in the shop load. Kanban is not appropriate in such a situation because capacity bottlenecks can significantly reduce the ell'ectiveness of a pull system. The disparateness in the processing requirements for jobs can seriously undermine the performance of the shop. This is the type of shop environment where the shop control procedures will be most effective. Although Kanban came out best when the load capacity bottlenecks and the disparateness of the job mix were removed, the selected shop control variable combinations closely approximated the Kanban result. Although many features of JIT can be implemented in any system, companies trying to adopt JIT should remember that Kanban requires a rigid system intolerant of any deviation.     

Literature review of material flow control mechanisms

We review mechanisms which control the flow or material into and within a manufacturing line. These material flow control (MFC) mechanisms address the problem of when to release material into a manufacturing line and when workcentres should be authorized to produce. The MFC mechanisms reviewed include: Kanban, CONWIP, workload regulating, starvation avoidance, BORA, maximum load limit, MRP, the base stock system, workload control, and production authorization cards.