Strategic Investment Planning with Technology Choice in Manufacturing Systems

Shan Ling Li

• Publisher: Taylor & Francis
• Year: 2018
• Print ISBN: 9781138324381
• eBook ISBN: 9780429835094
• Edition: 1
• Topic: Economics
• Subtopic: Business General
• Index: Economics

Originally published in 1994 this book examines problems related to investment planning, capacity additions, and choice of technology in dynamic manufacturing systems characterized by multiple products, dynamic demand growth, uncertainty in demand and availability of alternative technologies. A model-based methodology is developed that focuses on trade-offs between flexible and conventional technology. The research conducted for this book is directed to the development of tools to support investment decisions in production capacity over medium and long-term planning horizons.

I.  INTRODUCTION

1.1  INTRODUCTION

The annual increase in productivity of the United States in the past ten years is the lowest among all industrial nations, including Japan, West Germany, France, Italy, and England (Krajewski and Ritzman 1987). The acquisition of flexible manufacturing technology has been suggested as one solution to reverse this trend. Kantrow (1980) and Gerwin (1989) argue that flexible technology is an essential element to increase a firm’s long-term competitive position. Today many firms have placed huge bets on new flexible technologies.

Many advantages can be enumerated to support the acquisition of flexible manufacturing technology: quick response to the change in market demand, lead time reduction, increased demand due to increased product flexibility, and improved quality of goods and services produced. For example, recent developments in modern technologies such as CAM, CAD, CIM and FMS provide operational flexibility and permit production of a variety of products with little or no change over costs. However, the flexibility that modern technologies can provide is at the expense of the increased cost of acquiring flexible manufacturing capacity, as compared to dedicated technologies with specialized equipment that is designed to produce a limited range of products more efficiently.

However, many firms are finding that their available tools for evaluating investments in flexible technology often contradict the intuition of their managers. Many of them perceive significant benefits from acquiring flexible manufacturing systems (FMS and CIM). Kaplan (1986) acknowledges that even a very careful application of discounted cash flow techniques commonly used to evaluate a potential investment in flexible technology will not capture the strategic benefits of flexibility. Kaplan suggests that managerial judgement be applied to decide whether the strategic benefits of an investment in flexible technology outweigh the difference, or gap, between the investment cost and the quantifiable benefits.

1.2  CONTRIBUTION

In this study, my objective is to model some of the key characteristics of modern technologies in order to describe the economic tradeoffs between acquisition of flexible capacity and the firm’s ability to respond to dynamic, uncertain demands. Since incorporation of features such as scale and scope economies results in nonlinear programs that are hard to solve optimally, I focus on the development of approximation and heuristic approaches.

One of the main contributions of this study is to provide an integration of issues in capacity planning and technology choice. In fact, decisions on appropriate mixes of dedicated and flexible capacity involve many complex considerations such as economies of scale, likely demand patterns, mix flexibility, service level, etc. The models developed in this study are able to capture these important characteristics and to result in managerially meaningful solutions.

Second, I also develop approximations, heuristics, and lower bounds to solve the models for which it is difficult to obtain optimal solutions. The solution procedures are based on easily solvable sequences of subproblems derived from the planning problems. The heuristics require modest computational effort and my results demonstrate that these procedures can be used to obtain a good understanding of the tradeoffs involved in making technology and capacity additions.

Finally, I illustrate the scope of my models and computational procedures by deriving investment strategies and the optimal mix of technologies for some typical scenarios. These results suggest that investments in flexible technology should be made early in the planning horizon, and that flexible technology can play the role of capacity cushion in meeting dynamic (deterministic) demands. I also examine the conditions necessary for a firm to make technology and investment choices to achieve pre-specified service levels.

Finally, I explore the sensitivity of a firm’s optimal capacity investment decision to key components--namely, to demand patterns, to the cost of flexible and dedicated technology, to economies of scale in investment of technologies, to the underlying distribution of product demand, and to the the pre-specified service levels of products.

1.3  ORGANIZATION OF THE STUDY

This study is organized as follows: Chapter 2 reviews literature that addresses issues related to capacity additions over long-term horizons and dynamic growth in demand in the area of investment planning. Chapter 3 examines a two-product dynamic investment model for making technology choices and expansion decisions over a finite planning horizon. Chapter 4 extends the model in Chapter 3 to a multiple-product case. Due to the complexity of the extended model, solution procedures and lower bounds are developed. Chapter 5 incorporates issues related to uncertainty and examines the role of flexible technology and its benefits in a stochastic environment. Chapter 6 contains conclusions and explores opportunities for further research.

II. AN OVERVIEW: TECHNOLOGY SELECTION AND CAPACITY PLANNING FOR MANUFACTURING SYSTEMS

Capacity expansion decisions in most industries usually involve substantial capital investments and have received considerable attention from both academicians and practitioners. These decisions typically require an understanding of the tradeoffs between several related factors and cannot be made in isolation. For example, process and manufacturing technologies in chemical, electric power, fertilizer, engineering and communication industries are highly capital intensive and exhibit substantial scale economies. The availability of alternative technologies suggests that these choices between alternative technologies should be made together with the expansion decisions. For firms facing demands distributed over geographical regions, plant location and expansion decisions are linked and there is also a tradeoff between investment and transportation costs. The reader may note that any combination of these factors together with the dynamics of product demand make technology choice and expansion decisions extremely complex.

In most process industries, automation and integration have been continuing trends for decades and the methodologies and models for supporting capacity and technology decisions must accommodate their more important characteristics. However, in discrete goods manufacturing, recent developments in modern technologies such as flexible manufacturing systems (FMS), computer integrated manufacturing (CIM), computer aided design (CAD), and flexible automation (FA) permit production of a wide variety of products with small changeover costs. Increased competition in the marketplace, particularly from overseas manufacturers, has resulted in short product cycles and has put a premium on flexibility in changing product mix. Together, these factors encourage investments in facilities capable of producing several product families. The presence of economies of scale introduces additional complexity, thus making expansion decisions even more intricate.

My objective in this chapter is to provide the reader with an overview of the methodologies that are available for supporting technology and capacity decisions. This chapter is directed toward practitioners with modeling interests, and researchers with an application focus. The goal is to provide the reader with a flavor of the research in the area and to describe its role in making technology choices and expansion decisions. Thus I concentrate on modeling issues and applications rather than on theoretical results. (For a comprehensive review of the literature focusing on the economic aspects of evaluation of flexible technologies, I refer the reader to a recent survey by Fine and Freund (1990).) Since the resulting decision problems are complex and difficult to solve for exact optima, I elaborate on heuristic procedures and approximation methods that have been developed in this context.

This chapter is organized as follows: in the following section, Section 2.1, I describe the major factors that play a key role in technology and capacity choices. Section 2.2 is devoted to models and methodologies that have been developed to support these strategic and tactical decisions. Several applications of these methods are discussed in Section 2.3. Finally, I conclude in Section 2.4 with a summary of outstanding issues.

2.1 CHARACTERISTICS OF THE TECHNOLOGY SELECTION AND CAPACITY EXPANSION PROBLEM

As indicated in the introduction, a number of factors influence strategic choices related to the technology selection and capacity additions. These include, among others, product mix characteristics, technology alternatives, cost parameters, length of the planning horizon, etc. In this section, I briefly describe the key factors of the capacity planning problem.

Product Mix

The range of products manufactured by a plant is a function of the production technology used and, to a large degree, determines the complexity of the planning problem. The simplest case is represented by a firm producing a single homogeneous product. Electricity generation in the power industry is a classic example that fits this case. The single product model can also be used for firms producing several variants of a basic product line. A stable product mix in such cases will permit aggregation of all variants into a single product. Several examples in the chemical and process industry fit such a model fairly well. In contrast to the fairly homogeneous products in the foregoing examples, the product mix in discrete part manufacturing is quite diverse and dynamic. Between these extremes, a wide range of product mix patterns can be found. For example, in the oil refining industry the product mix is determined primarily by the choice of inputs and by the processing technology. Similarly, in the fertilizer industry, the product mix may consist of substitutable products (for example, coal- and oil-based fertilizers) that are produced from entirely different set of inputs and production processes. In such cases, the planning problem may be modeled as a single product case with alternate technologies.

Product mix plays an important role in technology selection because many factors related to product mix may complicate these decisions. In the case of a single product, the technology choice decision focuses merely on the cost structure of different technologies since all of them have a specific function associated with producing a single product (see Cohen and Halperin (1986)). When a firm produces multiple products, many factors are involved in the decisions, such as operational flexibility (whether it is dedicated, semi-flexible or flexible), a...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• List of Tables
• List of Figures
• Preface
• Acknowledgments
• fm
• Chapter 1 Introduction
• Chapter 2 An Overview: Technology Selection and Capacity Planning for Manufacturing Systems
• Chapter 3 Technology Choice and Capacity Expansion with Two Product Families: Tradeoffs Between Scale and Scope
• Chapter 4 Dynamic Capacity Expansion Problem with Multiple Products: Technology Selection and Timing of Capacity Additions
• Chapter 5 Technology Choice with Stochastic Demands and Dynamic Capacity Allocation: A Two-Product Analysis
• Chapter 5 Conclusion
• Tables
• Figures
• Appendixes
• Appendix B
• Appendix C
• Appendix D
• Appendix E
• Appendix F
• Appendix G
• Appendix H
• Bibliography
• Index