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Über dieses Buch

In two volumes, Planning Production and Inventories in the Extended Enterprise: A State of the Art Handbook examines production planning across the extended enterprise against a backdrop of important gaps between theory and practice. The early chapters describe the multifaceted nature of production planning problems and reveal many of the core complexities. The middle chapters describe recent research on theoretical techniques to manage these complexities. Accounts of production planning system currently in use in various industries are included in the later chapters. Throughout the two volumes there are suggestions on promising directions for future work focused on closing the gaps. Included in Volume 1 are papers on the Historical Foundations of Manufacturing Planning and Control; Advanced Planning and Scheduling Systems; Sustainable Product Development and Manufacturing; Uncertainty and Production Planning; Demand Forecasting; Production Capacity; Data in Production and Supply Chain Planning; Financial Uncertainty in SC Models; Field Based Research in Production Control; Collaborative SCM; Sequencing and Coordination in Outsourcing and Subcontracting Operations; Inventory Management; Pricing, Variety and Inventory Decisions for Substitutable Items; Perishable and Aging Inventories; Optimization Models of Production Planning Problems; Aggregate Modeling of Manufacturing Systems; Robust Stability Analysis of Decentralized Supply Chains; Simulation in Production Planning; and Simulation-Optimization in Support of Tactical and Strategic Enterprise Decisions. Included in Volume 2 are papers on Workload and Lead-Time Considerations under Uncertainty; Production Planning and Scheduling; Production Planning Effects on Dynamic Behavior of A Simple Supply Chain; Supply and Demand in Assemble-to-Order Supply Chains; Quantitative Risk Assessment in Supply Chains; A Practical Multi-Echelon Inventory Model with Semiconductor Application; Supplier Managed Inventory for Custom Items with Long Lead Times; Decentralized Supply Chain Formation; A Cooperative Game Approach to Procurement Network Formation; Flexible SC Contracts with Options; Build-to-Order Meets Global Sourcing for the Auto Industry; Practical Modeling in Automotive Production; Discrete Event Simulation Models; Diagnosing and Tuning a Statistical Forecasting System; Enterprise-Wide SC Planning in Semiconductor and Package Operations; Production Planning in Plastics; SC Execution Using Predictive Control; Production Scheduling in The Pharmaceutical Industry; Computerized Scheduling for Continuous Casting in Steelmaking; and Multi-Model Production Planning and Scheduling in an Industrial Environment.



Chapter 1. Production Planning Under Uncertainty with Workload-Dependent Lead Times: Lagrangean Bounds and Heuristics

Advances in modeling production system behavior over the past three decades have made it apparent that work-in-process (WIP) inventories, created by the dynamics of manufacturing systems, are an important characteristic of these systems. Furthermore, the recognition of the importance of lead times and the variability of lead times for system performance has lent additional emphasis to this behavior. Consequently, consideration of WIP and lead times has been a key aspect addressed in the analysis and development of various types of scheduling and job release systems, of which Kanban and workload-based methods are examples.
Gregory Dobson, Uday S. Karmarkar

Chapter 2. Production Planning and Scheduling: Interaction and Coordination

In many organizations, production planning is part of a hierarchical planning, capacity/resource allocation, scheduling and control framework. The production plan considers resource capacities, time periods, supply and demand over a reasonably long planning horizon at a high level. Its decision then forms the input to the more detailed, shorter-term functions such as scheduling and control at the lower level, which usually have more accurate estimates of supply, demand, and capacity levels. Hence, interaction between production planning and production scheduling/control is inevitable, not only because the scheduling/control decisions are constrained by the planning decisions, but also because disruptions occurring in the execution/control stage (usually after schedule generation) may affect the optimality and/or feasibility of both the plan and the schedule. If the overall performance of the production system is to be improved, disruptions must be managed effectively, with careful consideration of both planning and scheduling decisions. This chapter focuses on the interaction between production planning and scheduling, emphasizing the coordination of decisions, with special emphasis on making robust decisions at both levels in the face of unexpected disruptions. We provide examples and realistic scenarios from semiconductor manufacturing.
Yiwei Cai, Erhan Kutanoglu, John Hasenbein

Chapter 3. The Effects of Production Planning on the Dynamic Behavior of a Simple Supply Chain: An Experimental Study

Sophisticated supply chain planning systems, also known as Advanced Planning and Scheduling (APS) systems, have become commonplace in industry, and constitute a multibillion dollar software industry (Musselman and Uzsoy 2001; de Kok and Fransoo 2003; Stadtler and Kilger 2004). Many of these models rely to some degree on mathematical programming formulations of multistage productioninventory systems, which have been discussed extensively by (Saad 1982; Voss and Woodruff 2003; Johnson andMontgomery 1974; Hax and Candea 1984) and in this volume by Missbauer and Uzsoy. However, there has been little study in the literature of the effects of these production planning models on the dynamic behavior of supply chains. The dynamic behavior of supply chains over time has been studied in the system dynamics literature for several decades (Sterman 2000; Forrester 1962), leading to a growing understanding of the effects of information and material delays on the behavior of these systems, such as the bullwhip effect (Chen et al. 1998; Chen et al. 2000; Dejonckheere et al. 2003; Dejonckheere et al. 2004). However, the production planning procedures used in these models are generally feedback control procedures, with little ability to predict future states of the system and behave in a reactive manner. It is also quite difficult to interface optimization-based production planning models to standard system dynamics software. Hence, there is very little work of which we are aware that examines the effect of optimization-based planning procedures on the dynamic behavior of the supply chain in a systematic manner.
Seza Orcun, Reha Uzsoy

Chapter 4. Supply and Demand Synchronization in Assemble-to-Order Supply Chains

In this chapter, we describe a methodology for effectively synchronizing supply and demand through the integrated use of supply and demand flexibilities. While most prior literature focuses on the concept of Available-To-Promise (ATP) to determine product availability, we propose a new methodology called Available-To-Sell (ATS) that incorporates firm-driven product substitutions into capitalize on up-sell and alternative-sell opportunities in the production planning phase. ATS aims at finding marketable product alternatives that replace demand on supply-constrained products while minimizing expected stock-out costs for unfilled product demand and holding costs for leftover inventory. It enables a firm to maintain a financially viable and profitable product portfolio, taking effective actions to avoid excess component inventory, and articulating marketable product alternatives. We formulate a mathematical programming model to analyze the performance of ATS, and show how to exploit the structural properties of the model to develop an efficient solution procedure utilizing column generation techniques. The model can easily be embedded into a firm’s supply chain operations to improve day-to-day flexibility.
Markus Ettl, Karthik Sourirajan, Pu Huang, Thomas R. Ervolina, Grace Y. Lin

Chapter 5. Quantitative Risk Assessment in Supply Chains: A Case Study Based on Engineering Risk Analysis Concepts

In recent years, numerous events have shown the extent to which companies, and subsequently their supply chains, are vulnerable to uncertain events. We have witnessed many supply chain malfunctions (with substantial consequences) due to supply and demand disruptions: affected companies reported, on average, a 14% increase in inventories, an 11% increase in cost, and a 7% decrease in sales in the year following the disruption (Hendricks and Singhal 2005). Component shortages, labor strikes, natural and manmade disasters, human errors, changes in customer taste, technological failures, malicious activities, and financially distressed and, in extreme cases, bankrupt partners, among many others, can cause disruptions in supply chains:
Léa A. Deleris, Feryal Erhun

Chapter 6. A Practical Multi-Echelon Inventory Model with Semiconductor Manufacturing Application

Semiconductor manufacturing is an operationally complex, financially capital intensive business. While companies try to keep up with technology, they try to manage their operations effectively by increasing their capacity utilization, improving manufacturing yields, and reducing cycle times and inventory levels.
Kaan Katircioglu, Guillermo Gallego

Chapter 7. A Mechanism Design Approach for Decentralized Supply Chain Formation

In this chapter, we describe a category of supply chain formation problems where the supply chain planner or the Central Design Authority (CDA) is faced with the decision of choosing a partner or service provider for each supply chain stage so as to meet delivery targets and schedules at minimum cost. We first look into the case, where the CDA has access to all the relevant information required to solve this problem. Such a supply chain formation problem with complete information becomes a plain optimization problem in a centralized framework. Since it is quite impractical for the CDA to have access to all the information, we next consider the incomplete information case. In this setting, the individual managers of the supply chain stages are not loyal to the CDA but are rational, intelligent, and autonomous entities always pursuing maximization of their individual payoffs and not necessarily revealing their true private values. The supply chain formation problem now becomes a mechanism design problem followed by an optimization problem. Our specific contribution is to show that Vickrey–Clarke–Groves (VCG) mechanisms provide a natural and compelling model for such problems. We propose a decentralized framework to solve the underlying mechanism design problem. We illustrate our approach with the help of an example of forming a three stage distribution process for a typical automotive supply chain.
Dinesh Garg, Y. Narahari, Earnest Foster, Devadatta Kulkarni, Jeffrey D. Tew

Chapter 8. Procurement Network Formation: A Cooperative Game Approach

In this chapter, we are interested in a procurement network formation problem. We present a case for modelling the procurement network formation problem as a shortest path cooperative game. We investigate recent results in shortest path cooperative games and their implications to the procurement network formation problem. We then enhance the model for procurement network formation by incorporating asymmetry in the information that agents have. Specifically, we model the procurement network formation problem as a shortest path cooperative game with incomplete information. We point out the incentive compatible core as an appropriate solution concept for this category of games. We then review the current state of the art on the topic of incentive compatible core, pose a conjecture and end with some directions for future work.
T. S. Chandrashekar, Y. Narahari

Chapter 9. Designing Flexible Supply Chain Contracts with Options

In many industries, original equipment manufacturers (OEMs) are focusing on a limited range of core competencies and choosing to procure nonessential goods or services from suppliers and third-party service providers. The result is a highly decentralized supply chain, where each supply chain partner attempts to maximize its own profit objective, based on its own private information. Given the uncertainties present in both procurement decisions, often manifested in substantial forecast errors in materials or service requirements, the execution of supply agreements can have a significant impact on the firm’s operational and financial performance. Supply chain contracts are contractual agreements governing the pricing and exchange of goods or services between independent partners in a supply chain. Properly designed supply contracts are an effective means to share the demand and supply risk and better coordinate such decentralized supply chains. It is widely recognized that suppliers and buyers can benefit from coordination and thereby improve the overall performance of the supply chain as a whole, as well as, frequently the performance of each individual party.
Feng Cheng, Markus Ettl, Grace Y. Lin, Maike Tonner, David D. Yao

Chapter 10. Build-to-Order Meets Global Sourcing: Planning Challenge for the Auto Industry

Auto manufacturers today face many challenges: The industry is plagued with excess capacity that drives down prices, international competitors are seizing share at both ends of the market and consumers are well informed about options and prices. All these factors combine to heighten competitive pressures, squeeze margins, and leave manufacturers struggling to increase revenues and market share.
Melda Ormeci Matoglu, John Vande Vate

Chapter 11. Practical Modeling in Automotive Production

We all want to make a difference. We all want our work to enrich the world. As production planners, we have a great opportunity. Since the industrial revolution, production planning has enabled industry to extract the most from the era’s manufacturing technology. Henry Ford’s assembly line, with its associated production planning, dramatically improved production efficiency. While production planning continues to advance productivity thereby enhancing society’s prosperity and quality of life, the benefits from production systems modeling are often not realized in practice. As noted by the editors, there is a widening gap between research and the needs of industry. The cause is not that the models are not sophisticated enough to capture the complexities of the real world. Neither is it that there is a lack of technology transfer. From our experience in industry, the gap arises from underdeveloped modeling. Underdeveloped modeling is diverting us from making a bigger difference. To impact production, we need models that can be put into practice. Not necessarily simple, but actionable. If a firm cannot act on a model, the model (and its associated solution methodologies) will not enhance the firm’s performance. The authors admit to straying from this advice themselves. But we have learned that models implemented gratify the most. Therefore, we propose that the most fruitful future research direction is practical modeling.
Jonathan H. Owen, Robert R. Inman, Dennis E. Blumenfeld

Chapter 12. Why Is It So Hard to Build and Validate Discrete Event Simulation Models of Manufacturing Facilities?

Discrete event simulation modeling methods are widely used to evaluate the performance of manufacturing systems. These models provide factory management the ability to test different manufacturing methods and operational policies before a factory is built, or before significant changes are made to a facility. These policies could include testing of proposed manufacturing methods, layout methodologies, production equipment assignment strategies, equipment maintenance and repair policies, operator staffing scenarios, and factory automation configurations. Using these models, it is possible to evaluate the performance or predict future performance based on a detailed sensitivity analysis of these different operating variables. This ability has proven to be invaluable, especially if the analysis is performed before major investment decisions are made or prior to finalizing factory designs. Consequently, discrete event simulation modeling continues to be an enabling capability for performance evaluation of manufacturing systems.
Seth A. Fischbein, Edward Yellig

Chapter 13. A Practical Approach to Diagnosing and Tuning a Statistical Forecasting System

Most, if not all, commercial enterprises require some form of demand forecasting for financial and operations planning. For financial planning, a high level, aggregate forecast (e.g., in dollar value) of major product groups or geographies is sufficient. For operations planning, a more detailed forecast, such as forecast by product or even by product-location, is necessary. A manufacturing enterprise employing a make-to-stock strategy needs a demand forecast to plan what products and how much of each to build. A make-to-order manufacturer uses a demand forecast to plan the purchase of parts and materials and its production capacity. A retailer needs a demand forecast to determine how much of each product to stock at the different retail locations. Other service enterprises utilize a demand forecast to plan and locate their capacity (for both labor and equipment). We focus on the latter situation in this chapter, namely detailed, product level forecasts that drive the planning of a supply chain.
Ying Tat Leung, Kumar Bhaskaran

Chapter 14. The Ongoing Challenge: Creating an Enterprise-Wide Detailed Supply Chain Plan for Semiconductor and Package Operations

In the mid-1980s, Karl Kempf of Intel and Gary Sullivan of IBM independently proposed that planning, scheduling, and dispatch decisions across an enterprise’s demand-supply network were best viewed as a series of information flows and decision points organized in a hierarchy or set of decision tiers (Sullivan 1990). This remains the most powerful method to view supply chains in enterprises with complex activities. Recently, Kempf (2004) eloquently rephrased this approach in today’s supply chain terminology, and Sullivan (2005) added a second dimension based on supply chain activities to create a grid (Fig. 14.1) to classify decision support in demand-supply networks. The row dimension is decision tier and the column dimension is responsible unit. The area called global or enterprise-wide central planning falls within this grid.
Kenneth Fordyce, Chi-Tai Wang, Chih-Hui Chang, Alfred Degbotse, Brian Denton, Peter Lyon, R. John Milne, Robert Orzell, Robert Rice, Jim Waite

Chapter 15. Production Planning in the Plastics Industry

In a highly competitive plastics industry, manufacturing good quality products is not sufficient to maintain a competitive global position. A plastic company must have the ability to deliver customized products to its customers’ doorsteps anywhere in the world – in the right quantity, at the right time, and at reasonable cost. In effect, the supply chain has become part of the product offering. With increasing supply chain complexity, this requirement puts an unprecedented burden on decision systems that allocate global capacities. Major manufacturers supply a variety of plastics to many different industries, including automotive, appliance, computer, and medical equipments. To meet the customer demand, the companies maintain manufacturing plants all over the globe. Until recently, most producers practiced a regional manufacturing philosophy wherein each product was made in the region (e.g., America, Europe, or Pacific) where it was ordered. In recent years, however, as consumer products manufacturing has shifted to the Pacific, the demand for plastics used in those products has also shifted to that region. Even as plastics companies rush to add new manufacturing capacity in the Pacific, most of them still face a geographic imbalance between capacity and demand with excess capacity in the America and insufficient capacity in the Pacific. Consequently, many have been forced to abandon the regional approach in favor of a global approach to their manufacturing operations. Although a better balance between capacity and demand remains the primary motivation, a global supply chain offers additional opportunities for reducing manufacturing costs, including: (1) economies of scale from centralizing production, (2) lower raw material costs, as these can now be sourced globally, and (3) ability to take advantage of tax breaks offered by certain countries to set up and operate plants even if that region has sufficient capacity.
Rajesh Tyagi, Srinivas Bollapragada

Chapter 16. Model Predictive Control in Semiconductor Supply Chain Operations

Maintaining agility in a multi-echelon multi-product multi-geography supply chain with long and variable manufacturing lead times, stochastic product yields, and uncertain demand is a difficult goal to achieve. The approach advocated here is based on a practical application of control theory that includes a model of the system being controlled, feedback from previous results, feed-forward based on demand forecasts, and optimization of both the financial results and the control actions applied to achieve them. This Model Predictive Control (MPC) approach has been employed in the continuous-flow process industry for many years, and has been independently suggested for supply chains by a number of academic research teams. This chapter describes a large-scale application of the approach in the semiconductor industry.
Karl Kempf, Kirk Smith, Jay Schwartz, Martin Braun

Chapter 17. Models and Methods for Production Scheduling in the Pharmaceutical Industry

The pharmaceutical marketplace is dominated by large multinational companies, competing worldwide, with a global presence in branded products. Retaining marketshare requires standards of product quality and reliability close to 100%, attained at sustainable cost. Wholesalers and final customers expect reliability and quality from pharmaceutical companies, which face an increasing challenge to achieve such standards. Reliable production plans are critical to this aim. In fact, to achieve 100% availability of final products, it is not sufficient to attain excellence in each phase of the planning process from strategic planning to real-time scheduling, but it is also important to effectively manage the coordination between these different phases.
Dario Pacciarelli, Carlo Meloni, Marco Pranzo

Chapter 18. Developing a Computerized Scheduling System for the Steelmaking–Continuous Casting Process

Scheduling problems in manufacturing have been studied extensively in the literature. Nevertheless, the development and implementation of a computerized scheduling system in industry can raise a number of scientific questions that are still unsolved, concerning both the scheduling algorithms for special environments and the design decisions of the scheduling system. This paper presents a case study where a computerized scheduling system for the steelmaking continuous casting process of a steel plant has been developed and implemented successfully. We describe the scheduling system and its implementation for this practical case. But, equally important, we describe the decision problems that typically as in our case occur in the course of the development and implementation process, and we outline some of the scientific decision support currently available. From this we derive open questions in scheduling research that hopefully will stimulate future research.
Hubert Missbauer, Wolfgang Hauber, Werner Stadler

Chapter 19. A Multi-Model Approach for Production Planning and Scheduling in an Industrial Environment

This Chapter Reports On A Real-Life Implementation Of A Planning-Cum-Scheduling System In A Discrete-Continuous Industry That Produces Rolled Steel Wire And Cable Of Different Qualities And Specifications. The Chapter Describes In Some Detail The Industrial Environment Of The Application, The Experimentation That Was Conducted Prior To Implementation In Order To Tune The Various Parameters Of The Decision-Support System, And The Operational Problems That Arise Which Demand Careful Planning As Well As Dynamic Corrective Action. We Present A Multi-Model Approach That Combines Optimization Modules With Heuristic, Meta-Heuristic, Simulation And Multi-Criteria Modules In A Coherent Planning And Scheduling System Together With A Novel Architecture Of The Information-Decision Process. A Description Of The Different Models Collaborating At Different Decision Levels Is Also Given. The Implementation Of The Information/Control System Together With The Results Obtained To Date From The System Is Discussed. We Highlight The Advantages And Limitations Of The Current Version Of The System Design And Conclude With The Current Avenues Of Investigation, Which We Are Pursuing In Cooperation With The Enterprise, And The Anticipated Future Developments.
Abdelhakim Artiba, Valerie Dhaevers, David Duvivier, Salah E. Elmaghraby

Chapter 20. Fuzzy Logic-Based Production Scheduling and Rescheduling in the Presence of Uncertainty

Production scheduling represents a major administrative and management issue in modern production planning and control. Ever since the first results of modern scheduling theory appeared some 50 years ago, scheduling research has attracted a lot of attention from both academia and industry. The diversity of scheduling problems, the large-scale dimension and dynamic nature of many modern problem-solving environments make this a very complex and difficult research area.
Sanja Petrovic, Dobrila Petrovic, Edmund Burke

Chapter 21. The Summing-Up

At the end of this volume, several years in the making, it is worth reflecting upon the objectives the editorial team had in mind when we started this project. We were motivated by the disturbing observation that although academia seemed to view the problems of planning production and inventories as a largely solved problem, with basic formulations established and agreed upon, there was broad consensus among our industrial colleagues that the available models and solution techniques were a long way from representing the full richness and complexity of the task as they encountered it in their daily operations. Our objective with these volumes was to address this situation by bringing together a leading group of researchers and practitioners to delineate the broader boundaries of the problem and present the state of the art in various related areas. A representative sample of leading-edge industrial contributions illustrates the state of the art in industrial practice, while a selection of research contributions with a more academic bent explores specific aspects of new areas.
Karl Kempf, Pınar Keskinocak, Reha Uzsoy


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