Postponed product differentiation with demand information update

This paper studies the problem of how to coordinate postponed product differentiation and forecast update to improve manufacturing efficiency. We consider a two-stage model of multiple products with a common component. In stage 1, the manager obtains a prior demand distribution of each product and decides the production quantity of the common component. In stage 2, the demand forecast is updated and the common component is differentiated into various final products. Then the final demand of each product is realized and inventory leftover (shortage) is assessed. We use stochastic programming to model this problem, and propose an optimal bundle-type algorithm to solve it. Furthermore, we develop some simple and effective approximation algorithms for several special cases. Extensive numerical experiments are conducted to show the effectiveness of the approximation algorithms, to compare the performance between the traditional production model and the postponement production model, and to examine the impact of parameters on the performances of the two systems.     

Linking of Manufacturing Strategy,Market Requirements and Manufacturing Attributes In Technology Choice: An Expert System Approach

This paper suggests a framework for acquisition of new manufacturing technology that links the manufacturing strategy, market requirements and manufacturing attributes using an expert system approach. This paper takes a closer took at traditional notion of manufacturing-marketing coordination and attempts to find its links with manufacturing capabilities provided by recent evolving new manufacturing technology. For this purpose we deal with different steps involved in the strategic evaluation process beginning with the choice of a suitable competitive strategy by a firm. Then the chosen competitive strategy is linked with the market requirements, system attributes and appropriate manufacturing technology choices. The proposed model is integrated by an expert system approach that includes strategic factors of both a tangible and an intangible nature and is implemented by using the VP-Expert shell.     

Non-linear relationship between industrial service offering and sales growth: The moderating role of network capabilities

In an effort to approach the mixed findings regarding the relationship between a firm's industrial service offering and its performance, this study analyzes the impact of the industrial service offering on sales growth and the moderating role of network capabilities. The proposed research model is tested based on data from 91 Finnish manufacturing firms. Our results demonstrate a non-linear effect of the service offering on sales growth. We also find that network capabilities enhance the effect of the service offering on sales growth. For managers of manufacturing firms, the results imply that the active development of a comprehensive service offering should be implemented in conjunction with the development of organizational capabilities, such as network capabilities, to create value and promote improved performance.     

"Managing" Learning by Doing: An Empirical Study in Semiconductor Manufacturing

This article examines the contributions of human resource and organizational practices to the development and supply chain management interface. It addresses this issue in the context of the semiconductor industry by highlighting the importance of these practices for learning‐based improvement in manufacturing. One of the most important factors for competitiveness in the semiconductor industry is the ability to manufacture new process technologies with high yields and low cycle times. The more effective management of new process technologies within the manufacturing facility aids firms in managing production costs, volumes, and inventories. Efficient management of new process development and introduction translates into enhanced internal supply chain management performance by improving the design of internal workflows, manufacturing performance, and the acquisition and installation of new manufacturing processes. Because much of the knowledge that underpins semiconductor manufacturing is idiosyncratic, however, firm‐level differences in human resource and organizational practices are likely to have consequences for performance. The article derives learning curve models of the rate of improvement in manufacturing yield (i.e., the rate of learning) and cycle time (i.e., the speed of production) following the introduction of a new process technology in a manufacturing facility. It then tests the influence of the use by semiconductor manufacturers of teams for problem solving and intrafirm knowledge transfer, the level of internal adoption of information technology (IT), and more extensive and effective workflow and production scheduling systems on manufacturing performance. It finds that the manners in which semiconductor manufacturers allocate engineering resources to problem‐solving activities, utilize information technology in the manufacturing facility, schedule production, and control the “shop floor” influence the levels and rates of improvement in both manufacturing performance measures. The article makes several contributions to the literature on product and process development and, accordingly, to research on the product development/supply chain interface. In particular, the model of organizational‐based learning provides a better understanding of the determinants of learning‐based performance improvement. In particular, better manufacturing performance results not strictly from greater cumulative volume but also from the actions of managers that affect the organization of establishment‐level problem‐solving activities and information exchange. The article also demonstrates that human resource and organizational practices in both the development and the adoption of new process technologies improve manufacturing performance by accelerating new product introduction, improving workflow, and enhancing the efficiency of manufacturing processes.     

A model for supply management of agile manufacturing supply chains

This paper addresses the configuration problem of Manufacturing Supply Chains (MSC) with reference to the supply planning issue. Assuming that the manufacturing system is composed of different stages, we present a technique for the strategic management of the chain addressing supply planning and allowing the improvement of the MSC agility in terms of ability in reconfiguration to meet performance. More in detail, we enhance a previous design method by some of the authors that employs digraph modeling and integer linear programming to optimally design the MSC. The original approach avoids supply chain disruption and stock out and, at the same time, can manage spare parts distribution. In order to take into account the level of demands and maximum production capacities with single/multiple sourcing, in this new formulation we introduce supplier capacity constraints. A case study is presented describing the optimal MSC configuration of an Italian manufacturing firm. The obtained results show that the design method provides managers with key answers to issues related to the supply chain strategic configuration and agility, e.g., choosing the right location for distributors and retailers for enhanced MSC flexibility and performance.     

Lockout/tagout and operational risks in the production control of manufacturing systems with passive redundancy

This paper addresses problems associated with production control and occupational safety in a manufacturing system prone to failure involving two machines working in passive redundancy. Machines turning out one part experience two modes of failure and repair: firstly, where failure occurs when a machine remains in fair condition; and, secondly, where such failure results in outright breakdown. Accordingly, we examine both modes of failure for their impact on a flexible manufacturing system (FMS) with respect to production control in terms of costs associated with lockout/tagout procedures and corrective maintenance. This study seeks to identify optimal costs related to backlogs, inventories and maintenance over an infinite planning horizon, along with levels of occupational risk where production control includes efficient planning of lockouts/tagouts. Our study offers numerical methods which may be employed to achieve optimal conditions in setting control policies. A numerical example and sensitivity analysis support this approach.     

Performance evaluation of production systems monitored by statistical process control and off-line inspections

Quality and productivity performance measures are very often considered in separate phases of the production system design process. However, the production system architecture affects the efficiency of the quality control system as well as the quality control configuration has an impact on the performance of the production system.The paper proposes a new analytical method for evaluating the performance of production systems in which statistical process control (SPC) techniques are implemented. Machines behaviour is monitored by measuring quality characteristics of the produced parts through off-line inspection devices and sampling inspections. The numerical results show the good accuracy of the proposed method, provide new insight in the relations among the two areas and pave the way to the joint design of production logistics and quality control systems.     

Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems

This paper presents a method to find the optimal production, repair/replacement and preventive maintenance policies for a degraded manufacturing system. The system is subject to random machine failures and repairs. The status of the system is deemed to degrade with repair activities. When a failure occurs, the machine is either repaired or replaced, and a replacement action renews the machine, while a repair action brings it to a degraded operational state, with the next repair time increasing as the number of repairs increases as well. A preventive maintenance action is considered in order to improve the reliability of the machine, thereby reducing the amount of disruptions caused by machine failures. The decision variables are the production rate, the preventive maintenance rate and the repair/replacement switching policy upon machine failure. The objective of the study is to find the decision variables that minimize the overall cost, including repair, replacement, preventive maintenance, inventory holding and backlog costs over an infinite planning horizon. The proposed model is based on a semi-Markov decision process, and the stochastic dynamic programming method is used to obtain the optimality conditions. A numerical example is given to illustrate the proposed model, and a sensitivity analysis is considered in order to confirm the structure of the control policy and to illustrate the usefulness of the proposed approach.     

Single item inventory control under periodic review and a minimum order quantity

In this paper we study a periodic review single item single stage inventory system with stochastic demand. In each time period the system must order none or at least as much as a minimum order quantity Q_min. Since the optimal structure of an ordering policy with a minimum order quantity is complicated, we propose an easy-to-use policy, which we call (R, S, Q_min) policy. Assuming linear holding and backorder costs we determine the optimal numerical value of the level S using a Markov Chain approach. In addition, we derive simple news-vendor-type inequalities for near-optimal policy parameters, which can easily be implemented within spreadsheet applications. In a numerical study we compare our policy with others and test the performance of the approximation for three different demand distributions: Poisson, negative binomial, and a discretized version of the gamma distribution. Given the simplicity of the policy and its cost performance as well as the excellent performance of the approximation we advocate the application of the (R, S, Q_min) policy in practice.     

Defining inventory control points in multiproduct stochastic pull systems

Multistage pull production systems have been widely implemented in recent years and constitute a significant aspect of lean manufacturing. One of the important considerations in such systems is identifying the control points, i.e. where in the multistage sequence to locate the output buffers. Allowable container/batch sizes, optimal inventory levels, and ability of systems to automatically adjust to stochastic demand depend on the location of these control points yet the issue of optimal location has not been widely addressed. This paper considers a multiproduct pull setting where part types compete with each other for common production resources. In this environment it is important to consider factors such as lead time variability and to include the corresponding queuing aspects into the model. Each workstation is modeled as a GI/G/1 queue. Waiting times spent by parts at workstations are approximated using a decomposition/recomposition algorithm. Necessary and sufficient conditions are provided for the optimality of a single control point. Conditions under which multiple control points are optimal are investigated along with the impact of product mix and utilization parameters on the number of control points. Analytical model results are validated by simulation.     

Flexibility of manufacturing systems,strategic change and performance

The past decade has witnessed an increase of interest in flexibility, which bestows on a firm the ability to respond promptly to market opportunities and changing technologies. The development of capabilities to be flexible rests on the mandate of top management, helps firms manage environmental uncertainty, and tends to enhance firm performance. This research paper aims to establish an analytical approximation that considers how the determinants of manufacturing flexibility at the system level affect the desired strategic change in organizations, as well as subsequent performance. We propose the hypotheses that both environmental factors and internal resources affect flexibility, this latter aspect influencing the organization's performance. The problems that stem from measuring manufacturing flexibility in terms of fit were also analyzed. In order to do this, we have made a wide-ranging trans-national study, within the framework of the European Union, using data from 403 European firms. The Structural Equation Model (SEM) technique has been used to contrast the hypotheses. The results show that manufacturing flexibility at system level can be a critical factor in the process of strategic change, which means that it can have an impact on the desirability of strategic change or on the more specific strategic fit.     

Costing RAM design and test analysis model for production facility

The objective of this paper is to provide a model for effective implementation of costing RAM (reliability, availability and maintainability) management in the design and procurement of production facility. This research proposed a two-stem model of costing RAM design and test for production facility. In Step 1, a static model is proposed to find an initial system configuration to meet the required performance based on system RAM and life cycle cost (LCC) and analyze the trade-off relationships between various RAM factors (reliability and maintainability) and LCC. In Step 2, we developed time and failure truncated models for system reliability test and analysis. For computational purposes, we developed computer programs and have shown the sample results. By the sample test run, the proposed model has shown possibilities to provide a good method to analyze system performance evaluation for both design and operational phase. This model can be applied to a wide variety of systems, not only for costing RAM of the production facilities but also for the other kind of equipment.     

Simulation optimization-based decision support tool for steel manufacturing

To buffer against increasing global competition and variability in the price of raw materials, steel manufacturers continuously strive to improve operations and lower costs. In this research, we employ a simulation optimization approach to develop a decision support tool to aid in strategic and operational decision-making. Specifically, we investigate work-in-process inventory levels and potential manufacturing process modifications to reduce utilization costs. A simulation model captures the complex nature of the system while an optimizer searches the solution space and sends trial solutions to the simulation for evaluation. Experimentation suggests that significant daily cost savings are possible by modifying current inventory practices and production process capabilities. Overall, the work demonstrates the ability of the solution approach to analyze complex industrial systems and identify potential improvements in a short time frame.     

Coordinating dispersed product development processes: A contingency perspective of project design and modelling

Managing worldwide supply pipeline operations concerns coordination and control of every step of the chain process starting from raw material sourcing, production, finally to distribution of market-specific items in retail places, all the way from product value inception and engineering, through manufacturing design, to the worldwide logistic planning. From systems perspectives, their relationships and interactions determine the overall performance. Coordinating such systems is very human-inclusive, characterised by abstract, ill-structured information interchange among well-partitioned expert groups. In this paper we documented the experience and implications of managing and modelling product development activities from a contingent perspective of interdependence. In our investigations amongst six international fashion corporations, crucial activity tasks in different countries were analyzed and evaluated within the context of launching schedule-driven fashion products. At the outset, we present the problem context, the issues arising from coordinating product development systems, and the approach we use to deal with the issues, i.e. modelling and manipulating the process interaction. We put forth a dependency-based process performance simulation, the related approach of data capture and the attribute constructs to represent the interactivity relationship. Finally, we discuss the computation process and evaluation strategy, which is indeed inspired by today's simulation-based optimization concepts. An effective GA heuristics is duly implemented.     

Robust optimal policies of production and inventory with uncertain returns and demand

We consider an inventory and production planning problem with uncertain demand and returns, in which the product return process is integrated into the manufacturing process over a finite planning horizon. We first propose an inventory control model for the return and remanufacturing processes with consideration of the uncertainty of the demand and returns. Then a robust optimization approach is applied to deal with the uncertainty of the problem through formulating a robust linear programming model. Moreover, properties on the robust optimization model are studied, and an equivalent robust optimization model based on duality theory is obtained which allows the solutions to be derived more efficiently. Finally, we provide a set of numerical examples to verify the effectiveness of the approach and analyze the effects of the key parameters on the solutions.     

The bullwhip effect in capacitated supply chains with consideration for product life-cycle aspects

This paper presents an analysis of the bullwhip effect and net-stock amplification in a three-echelon supply chain considering step-changes in the production rates during a product's life-cycle demand. The analysis is focused around highly complex and engineered products (e.g., automobiles), that have relatively long production life-cycles and require significant capital investment in manufacturing. Using a simulation approach, we analyze three stages of the product life-cycle including low volumes during product introduction, peak demand, and eventual decline toward the end of the life-cycle. Parts of the simulation model have been adopted by a major North-American automotive OEM as part of a scenario analysis tool for strategic supply network design and analysis. The simulation results show that performance of a system as a whole deteriorates when there is a step-change in the life-cycle demand. While restriction in production capacity does not significantly impact the bullwhip effect, it increases the net stock amplification significantly for the supply chain setting under consideration. Furthermore, a number of important managerial insights are presented based on sensitivity analysis of interaction effect of capacity constraints with other supply chain parameters.     

Workload control under continuous order release

Workload control is a production planning and control concept specifically designed for complex manufacturing environments. Past research on Workload control has been essentially focused on discrete order release. This means that release of orders to the shop floor takes places on a periodic basis. Continuous order release has been somehow neglected, in spite of its apparent potential for improving system performance, including the reduction of order flow times. This paper presents a simulation study of this order release approach. The study contributes for improving the basis for setting workload norms, selecting the workload control strategy and deciding upon routing alternatives under continuous order release.     

Incremental Automation with Sampling Applied to an Advanced Manufacturing System

Capital budgeting models for project selection have been extensively explored, but relatively little attention has been given to the aspects of post-audit and project control for the projects implemented. This paper examines the area of project control and proposes a method that incorporates post-audit information as an active element in the decision to maintain or terminate an initiated project. Project selection is primarily based on anticipated performance, as determined from the most accurate information available. However, in a sequential decision environment, information that is accurate at one point in time may become inaccurate at another. This paper will show how the Dirichlet distribution can be utilized to formulate appropriately weighted prior probability beliefs, and how these initial beliefs can be updated as we receive post-audit information. To do this, we incorporate categorized cash flow data in a unique Bayesian-based framework. To illustrate the use of the Dirichlet distribution, we present a case study of an actual automation decision for a flexible manufacturing system. This case study also demonstrates how decision strategies can be improved by using post-audit information, when compared with conventional methods.     

Short-term decision support system for maintenance task prioritization

Maintenance operations have a direct influence on production performance in manufacturing systems. Short-term production analysis is imperative to enable manufacturing operations to optimally respond to dynamic changes in the system behavior. However, most of the conventional decision support systems for production and maintenance focus on long-term statistic analysis, which is usually not applicable to a short-term period. Maintenance task prioritization is crucial and important for short-term analysis to reduce unnecessary or improper maintenance activities, especially when availability of maintenance resources is limited. The existing methods for maintenance priority assignment are often through heuristic methods or experience, which could cause unscheduled downtime and production losses. In this paper, a short-term decision support system for maintenance task prioritization based on the system operating conditions is introduced. The impact factor for priority assignment is obtained theoretically. A case study based on the simulation of an automotive assembly line illustrates that the proposed short-term system improves the system performance with a lower cost than the long-term method.     

REAL OPTION MODELS FOR MANAGING MANUFACTURING SYSTEM CHANGES IN THE NEW ECONOMY

The manufacturing environment is becoming increasingly dynamic with upsurges in electronic-commerce, supply chain management, forecasting, and procurement and resource planning. It also includes trends toward more process data acquisition and analysis, shorter production runs, and more stringent quality requirements. These drivers lead to an opportunity for companies to collect and use information to identify changes that will affect their manufacturing systems. In conjunction with an industry partner who produces home fashion products, we developed a case-study that highlights four major manufacturing transitions: new product introduction; moving a product from research and development (R&D) to commercialization: new plant location; and starting or restarting production of existing products. These types of changes cross many levels of the operation - including the product level, plant level, and organizational level - and typically present significant operational challenges. We use this case-study to motivate the theoretical and applied research needed to support a real option framework for system changes in manufacturing. The key elements of our framework are to quantify manufacturing changes, develop a real option model for these activities, value the options to identify the best scenarios, and integrate these elements so that we can monitor and manage the overall process. The advantage of this approach is that it allows us to directly incorporate a market driven perspective, tying the manufacturing operations with the organizational economic goals.