CONWIP implementation in a system with cross-trained teams

Although a significant amount of research exists on the implementation of the CONstant Work In Process (CONWIP) production control strategy in different types of manufacturing environments, the challenge posed by the complex flow of multiple products through the case manufacturing system of this work necessitates defining a unique type of item release rule to operate with the CONWIP control. The case system is a chair cover production line, which has sets of cross-trained teams that can process its different product models with different levels of efficiency. This work focusses on the CONWIP control strategy, but with the introduction of a new item release rule for prioritising items for release into the system. Results from simulation experiments show that operating CONWIP with an item release rule that is synchronised with the work rate of each of the system's cross-trained teams improves its performance. In comparison with a First in First out (FIFO) rule, this item release rule shortens the system throughput time. It also increases the chances of items being processed by the team most suitable for them, thereby minimising the quality and efficiency issues that might occur in systems that consist of teams with varying levels of proficiency for processing the different product models. Irrespective of the item release rule that is applied, CONWIP achieves a better distribution of workload amongst the teams than a Push control achieves.     

Semi-heterarchical production planning structures in the support of team-based manufacturing

Manufacturing enterprises are facing fierce competition within the modern global market, resulting in a requirement for continuous improvement of their production management practices. In this context, a number of manufacturing paradigms such as team-based manufacturing have been adopted to provide the flexibility, agility and responsiveness required to cope with the volatility of production demands. The introduction of team-based working practices necessitates a clear definition and delegation of responsibilities to various teams and often requires significant changes in production planning and control procedures. The successful implementation of such a distributed production approach is then dependent on the way that the production teams manage their internal activities and cooperate with other teams to achieve overall manufacturing goals. A novel team-based distributed production planning and control approach based on a semi-heterarchical structure is presented that aims to support the autonomous and cooperative attributes of various production teams within manufacturing enterprises.     

Performance measurement for new product development: a model based on total costs

The modern market is characterized by consumer demand for quality products (at lower prices), reduced time to market, and cost-savings pressures on manufacturers. To survive in this competitive environment, enterprises must be able to manage and measure their performance with respect to new product development (NPD). This paper presents a cost-effective performance-measurement system for NPD. The model is based on the concept of total-costs analysis. This provides a transparent statistical model that assists management and employees to understand the importance of doing things right the first time. The model incorporates the interactions among NPD teams, manufacturing teams, and customers. It also integrates the results of instances of poor quality at various stages of the NPD process, including: (1) development; (2) manufacturing (the internal customer's perspective); and (3) external customers. A total-costs analysis is thus developed and utilized in planning and establishing a measurement system for NPD performance. The model can be used to satisfy both internal and external customers, and to ensure optimal value for investment in NPD teams.     

Scheduling manufacturing systems for delayed product differentiation in agile manufacturing

Production of customized products to respond to changing markets in a short time and at a low cost for agile manufacturing can be implemented with delayed product differentiation in a manufacturing system. The successful implementation of delayed product differentiation lies in efficient scheduling of the manufacturing system. Scheduling problems in implementing delayed product differentiation in a general flexible manufacturing system are defined, formulated and solved here. The manufacturing system consists of two stages: machining and assembly. At the machining stage, a single machine is used to produce standard component parts for assembly products. These parts are then assembled at the assembly stage by multiple identical assembly stations to form customized products. The products to be produced in the system are characterized by their assembly sequences represented by digraphs. The scheduling problem is to determine the sequence of products to be produced in the system so that the maximum completion time (makespan) is minimized for any given number of assembly stations at the assembly stage. Based on the representation of assembly sequence of the products, three production modes are defined: production of a single product with a simple assembly sequence ; production of a single product with a complex assembly sequence ; and production of N products . According to the three defined production modes, the associated scheduling problems are defined as G s scheduling problems, G c scheduling problems and N-product scheduling problems, respectively. Optimal and heuristic methods for solving the scheduling problems are developed. The computational experiment shows that the heuristics provide good solutions to the scheduling problems.     

Production planning system for a combination of make-to-stock and make-to-order products

This paper deals with the problem of designing a production planning system for a combination of make-to-stock (MTS) and make-to-order (MTO) products where the production facility produces both types of products. Important management points in such production systems are to design an efficient production planning system which can shorten the manufacturing time of MTO products as well controlling the unfilled rate of MTS products to the market demand at a low level. A hierarchical production planning model is introduced in order to design the production planning system. The buffer capacity is set as a design variable for determining production capacity at a higher planning level, and the rule for allocating the production capacity to types of products is adopted as a design variable at a lower level. First, we analyse how these two design variables affect the unfilled rate of MTS products and the average manufacturing time of MTO products. Second, we clarify the relationship of the buffer capacity with the manufacturing time of MTO products and with the required buffer inventory level of MTS products which can maintain the unfilled rate of MTS products to the market demand within an acceptable limit. Third, we show an example of the range of design variables which can control the unfilled rate of MTS products and the manufacturing time of MTO products within their acceptable limits, if the individual limits of the performance measure is given to respond to customer orders quickly.     

Agile manufacturing: Enablers and an implementation framework

Tougher competitive situations have led to increasing attention being paid to customer satisfaction, of which timely and customized services are the key concepts. As the product life cycle becomes shortened, high product quality becomes necessary for survival. Markets become highly diversified and global, and continuous and unexpected change become the key factors for success. The need for a method of rapidly and cost-effectively developing products, production facilities and supporting software, including design, process planning and shop floor control system has led to the concept of agile manufacturing. Agile manufacturing can be defined as the capability to survive and prosper in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. This article details the key concepts and enablers of agile manufacturing. The key enablers of agile manufacturing include: (i) virtual enterprise formation tools/metrics; (ii) physically distributed manufacturing architecture and teams; (iii) rapid partnership formation tools/metrics; (iv) concurrent engineering; (v) integrated product/production/business information system; (vi) rapid prototyping tools; and (vii) electronic commerce. A conceptual framework for the development of an agile manufacturing system and future research directions are presented in this paper. This framework takes into account the customization and system integration with the help of business process redesign, legal issues, concurrent engineering, computer-integrated manufacturing, cost management, total quality management and information technology.     

Manufacturing process analysis with support of workflow modelling and simulation

Process analysis is recognized as a major stage in business process reengineering that has developed over the last two decades. Manufacturing process analysis (MPA) is defined as performance analysis of the production process. A manufacturing process analysis framework is outlined with emphasis on linking a company's strategy to operational process. Two issues, namely process modelling and simulation based analysis, are investigated. A compound workflow model (CWM) is proposed to provide graphic presentation of the production process that can be easily understood. Also it can be used directly by simulation to study the impacts of scheduling policy and analyse the process performance. A two-stage simulation analysis method is provided to quantitatively and efficiently define cause-and-effect relations to identify drivers for improvement. The manufacturing environment, PSC (production planning, scheduling and control) factors and the process structure are three main concerns considered in the simulation. An example is discussed in the final part of the paper.     

Building High-Performing Autonomous Teams in Complex Manufacturing Settings: A Naturalistic Research Approach

This study contributes to the engineering management body of knowledge by providing a unique and rich view into the development of self-managed teams in manufacturing settings using a naturalistic research approach. We uncover the complexity of multi-level factors at the individual, team, and organizational level that influence the successful development of high-performing self-managed teams. This research identifies and describes the major drivers of effective autonomous teamwork in low volume, high complexity (LVHC) manufacturing settings. A key theoretical contribution of this study is a depiction of production work teams that describes the dynamic process of generating and further advancing autonomous behaviors in LVHC manufacturing settings. Engineering managers can use the findings from this article to help them understand the precursors and conditions for building effective self-managed teams in LVHC manufacturing settings. The results can also contribute to the design of training programs aimed at building highly effective, self-managed work teams.     

Two-stage hybrid flow shop batching and lot streaming with variable sublots and sequence-dependent setups

A paint manufacturing firm's customers typically place orders for two or more products simultaneously. Each product belongs to a family that denotes batching compatibility during manufacturing. Further, products can be split into several sublots to allow overlapping production in a two-stage hybrid flow shop wherein various identical, capacitated machines operate in parallel at each stage. We present a mixed-integer linear program (MILP) for this integrated batching and lot streaming problem with variable sublots, incompatible job families, and sequence-dependent setup times. The model determines the number and size of sublots for each product and the production sequencing for each sublot such that the total weighted completion time is minimised. To promote practical implementation, we develop and evaluate heuristics to efficiently solve this problem.     

Development of a decision-support system for the formulation of manufacturing strategy

Corporate strategy can be divided into various substrategies, such as marketing, manufacturing, research and development, etc. In the current enterprise environment, products are highly varied and product life-cycles are shortening owing to customers' changing needs and market competition. In such an environment, the formulation of a good manufacturing strategy is critical for the success of manufacturing enterprises. In this research, for the establishment of good manufacturing strategy, we propose a framework enabling a decision-support system for the modelling and analysis of the manufacturing system. For this task, we have constructed a single integrated object model that contains information about products, processes and resources in the manufacturing system. The model is flexible, allowing the objects in the model to be defined according to the levels of detail required by analysis. The process model can then be transformed to a generalized stochastic Petri net (GSPN). Furthermore, a Petri net separation procedure is presented to relieve the complexity of the analytic method. By using the analytic analysis of GSPN, the system throughput is easily obtainable. An example is included to demonstrate the applicability of this framework. The proposed decision-support system for manufacturing strategy formulation enables (1) the analysis of the relationship between strategic input variables and performance measures, (2) the scenario evaluation for coordination between the manufacturing department and other departments, and (3) the formulation of manufacturing strategy using what-if analysis against dynamic manufacturing environments.     

Simulating alternative production policies with sequence-dependent costs

This article studies various sequencing and inventory rules in a manufacturing environment with nonlinear technological coefficients and stochastic demand. Multiple products require setup on a single machine and setup time and setup cost decrease with repeated setups. Furthermore, setup operations for different products have common components and an item can benefit from the setup operation of another item. The single-level, multi-item lot size model is used to model the production environment. The learning curve is used to represent this decrease in setup time with repeated setups. The learning transmission between items affects the scheduling of the products and the resulting model considers simultaneous decisions about lot sizing and sequencing in a nonlinear formulation. The problem is formulated and various production policies are simulated. Two sequencing rules and four inventory rules are examined. A simulation experiment of 6400 runs is used to compare the schedules produced by simple policies and those produced by more involved ones. A statistical analysis of the simulation results indicates that the simple rules perform equally well and in some cases better than the computationally harder rules.     

INCAP – applying short-term flexibility to control inventories

Inventory based capacity control (INCAP) is a very simple method that allows inventory levels to be effectively controlled by using short-term capacity flexibility in make-to-stock settings. Moreover, INCAP can be used for finished goods inventories as well as for semi-finished goods inventories. The basic idea is to define upper and lower inventory limits and to adjust capacities if the inventory level reaches either limit. Should the inventory fall below the lower limit, capacity is increased to prevent stock-outs. Similarly, in order to avoid excess inventories the capacity is decreased should the inventory rise above the upper limit. INCAP is thus able to control inventory levels within a defined inventory range. In order to do so, it applies short term measures like over-time or extra shifts to increase capacities and cancels work hours or shifts to decrease capacities. Simulation experiments based on data from the automotive industry show that INCAP is able to improve the performance of a manufacturing system in that it significantly reduces the inventory levels necessary for guaranteeing a satisfactory service level. Overall, INCAP is found to be a straightforward but powerful method, able to cope both with uncertainties in production output as well as with varying demand. However, some restrictions do apply: INCAP depends not only on a minimum level of short-term capacity flexibility, but also in the standard set-up presented here it is limited to make-to-stock environments with similar products.     

Modelling and simulation of just-in-time flexible systems

The increasing worldwide competition requires economical manufacture, high quality and short delivery time. The Just-In-time (JIT) philosophy of manufacturing is increasingly being considered by manufacturing organizations, as a response to the increased pressure to supply high quality products with short delivery times and at low cost. A very simple shop floor control system that was developed by Toyota in the 1970s specifically for their Just-In-Time assembly plant, has received considerable attention in the Western World, and is known as the ‘kanban’ system (literally translated as ‘card’ system). Japan’s success has prompted many scholars and practitioners, to turn their attention to Japanese management practices. This article is to address the modelling, simulation and implementation issues of Just-In-Time in flexible manufacturing environments. Priority nets are used for modelling and analysis of the kanban system. A large number of simulation runs are conducted/presented to probe the behaviour of the system, with respect to different parameter changes.     

Aggregate production process design in global manufacturing using a real options approach

A main source of competitive advantage is derived from the cost efficiency offered by firms’ manufacturing and logistics operations. Consequently, firms typically globalise their operations whereby they may exploit the comparative advantages—defined as production functions—of the nations in which they are present. Production process design thus arises as a significant issue. The research presented in this paper targets two fundamental questions attached to production process design that multinational companies face, namely: (i) should plants that are located in different countries but producing similar products use similar production processes?; and (ii) given that the firm's policy is to use similar production processes, how should the production processes be designed? Among others, the paper shows, by way of a numerical illustration of a binational manufacturing network, that the option of choosing freely upon production process design for the respective facilities in certain cases adds little to firm value. In fact, the value of this option tends to zero as the volatility rate increases when the exchange rate is modelled as a geometric Brownian motion without drift rate, implying that firms should employ similar production processes throughout their manufacturing networks. That is, a market value approach stands up for the so-called copy-exactly approach to production process design in these settings. We furthermore show the effects of economies of scale on the optimal production process design.     

A distributed virtual factory in agile manufacturing environment

There is a growing recognition that current manufacturing enterprises must be agile, that is, capable of operating profitably in a competitive environment of continuously changing customer demands. The use of a virtual enterprise (VE) is becoming increasingly prevalent, and that has been made possible, in part, due to the significant advances in communication and information technology in recent years. A manufacturing system is one of the competitive factors that forms an effective VE. Therefore, for manufacturers wishing to obtain a contract in VE, it is crucial to present attractive and competitive offers to other coalition members. There are several criteria to these offers, such as cycle time, fulfilment of due date or quick shipping date, cost, and quality assurance for the ordered products. Manufacturing system simulation could endorse the basic estimates of the criteria for the strategic offer. The distributed simulation model concept provides practical solutions to facilitate such a large-scaled precise simulation model in the VE environment, because it is constructed as the integration of several manufacturing simulation models of each production module in the factory. As a solution to realize this integration, we propose a Distributed Virtual Factory (DVF) concept that consists of distributed precise simulation models connected by several synchronization mechanisms named Time Bucket algorithms. A DVF enables precise evaluations of the whole manufacturing system, especially in terms of the material flows. In this study, we introduce an Activity Based Costing (ABC) method into the DVF architecture to estimate the detailed cost analysis of the products. The methodology facilitates strategic enterprise management to prepare the request for the bids in the VE environment. The effectiveness of the proposed concept in agile manufacturing is discussed with simulation experiments.     

Loading and control policies for a flexible manufacturing system

An experimental investigation of operating strategies for a computer-controlled flexible manufacturing system is reported. The system is a real one, consisting of nine machines, an inspection station and a centralized queueing area—all interconnected by an automatic material-handling mechanism. The operating strategies considered involve policies for loading (allocating operations and tooling to machines) and real-time flow control. A detailed simulation was employed to test alternatives. The results are different from those of classical job shop scheduling studies, showing the dependence of system performance on the loading and control strategies chosen to operate this flexible manufacturing system. Loading and control methods are defined that significantly improve the system's production rate when compared to methods which were previously applied to the system. Finally, some conclusions are presented concerning the control of these automated systems.     

Estimating the distribution and variance of time to produce a fixed lot size given deterministic processing times and random downtimes

Managing production throughput variability is necessary to reduce system costs and improve throughput. Critical in this management is predicting how system changes, like reduced repair times, will change throughput variability. Many manufacturing plants produce products in fixed lot sizes to meet production targets which causes system variability to be manifested as variability in the time to produce the lot. We derive analytical approximations of the density function and variance of the time to produce a fixed lotsize on a single workstation with deterministic processing times and random downtimes. These expressions can be applied to larger production systems, by modelling the system as a single workstation with parameters that approximate the system's output. This result quantifies how workstation failures, repair times, and speed impact the density and variance of time to produce a fixed lotsize. The density function also generates a cycle time distribution when the lost size is one. This is useful in discrete event simulation.     

Combining MPC and integer operators for capacity adjustment in job-shop systems with RMTs

With today's worldwide competition, manufacturing companies are faced with challenges to respond to volatile market demands quickly and flexibly while maintaining a cost-effective level of production. Capacity adjustment is one of the major approaches to cope with such uncertain fluctuations, balance capacity and load and improve the effectiveness of manufacturing control. Instead of flexible staffs, working time and outsourcing, in this paper, we consider a machinery-based capacity adjustment via Reconfigurable Machine Tools (RMTs) to compensate for unpredictable events. To include these tools effectively on the operational and tactical layer, we propose a complementing feedback approach using model predictive control (MPC) to identify the potential of RMTs for a better compliance with logistics objectives and a sustainable demand oriented capacity allocation. To this end, we formulate a reconfiguration rule for the determination of the triggered RMTs and propose three strategies for resolving the integer assignment of RMTs: floor operator, genetic algorithm as well as branch and bound. Utilising simulation, we demonstrate the effectiveness of the proposed method for a four-workstation job-shop system.     

Optimal batching in a wafer fabrication facility using a multiproduct G/G/c model with batch processing

Wafer fabrication, the first portion of semiconductor manufacturing, typically involves numerous batch-processing operations. These operations play an important role in determining how the system performs in terms of throughput, WIP and cycle time. In this paper, batch sizes that minimize the expected cycle time of batch-processing operations for a real-world semiconductor manufacturer are determined by a new approximated analytical model. This model, denoted by G / G ( bp ) , represents multiple products, multiple servers, batch-processing, incompatible products and unequal batch service size queues. Incompatible products mean that different products are not allowed to be in the same batch. Unequal batch service size means that batch service sizes depend upon products. Steady-state approximation formulas for cycle time and WIP of this queuing system are derived. These approximate performance measures are compared with those of discrete-event simulation. The results are reasonable and the approximation formulas much more computationally efficient than conducting the corresponding simulation studies. Finally, a batch-processing system with the goal of minimizing the total expected cycle times of items by determining the 'optimal' batch sizes is presented. Solutions are obtained using genetic algorithms.     

Modelling and analysis for sequentially optimising production,maintenance and delivery activities taking into account product returns

This paper develops and analyses a stochastic optimisation problem with a service level constraint for generating a sequentially optimal plan of production, maintenance and delivery activities in a deteriorating manufacturing system. Stochastic demand along with product returns are both assumed the latter of which allows for restocking products returned by the customer which are still new and thus in saleable condition. A constrained production/maintenance/delivery problem with service level, stochastic demand, delivery time, failure rate and product returned is formulated based on quadratic model. This quadratic formulation is adapted to provide an inventory, delivery, production and maintenance policies. The objective of this paper is to study the delivery time influence on the planning of the production, maintenance and delivery activities. Finally, we present simulation results to illustrate the exploitation of the proposed approach.