Challenges of strategic supply chain planning and modeling
Introduction
Until recently, strategic planning exercises in many companies were based on qualitative, managerial judgments about future directions of the firm and the markets in which they compete. Supply chain options were often ignored. In the past few years, however, important supply chain decisions, such as those relating to acquisitions or new product introductions, have been incorporated in these exercises. Managerial interest has been stimulated in part by a growing commitment at all levels of planning to fact-based supply chain management, which has served to emphasize its importance to the competitiveness of the firm.
Fact-based management requires the development and application of descriptive and prescriptive models for extracting knowledge from the firm’s enterprise resource planning and other transactional databases. Descriptive models include those used to forecast customer demands, compute manufacturing and distribution costs using activity-based costing methods, or project the future costs of key raw materials. Prescriptive models, which are constructed from descriptive models, are optimization models that assist supply chain managers in making better decisions. While a wide variety of descriptive models have been applied in understanding the form and functioning of the firm’s supply chain, the most prevalent and effective prescriptive models are those based on linear and mixed integer programming, which may be optimized using rigorous methods possibly combined with heuristics.
Companies in the oil and chemical industries have been leaders for almost 50 years in the development and use of linear and mixed integer programming models to support decision-making at all levels of planning. Strategic planning problems faced by these companies have gown increasingly complex in recent years due to complications in crude oil markets, rapid changes in multinational demand markets, an over-abundance of data, and several other factors (Lasschuit & Thijssen, 2003, Neiro & Pinto, 2003). Other process manufacturing industries have more recently turned to the use of optimization models in seeking efficient long-term use of their capital equipment. Shah (2003) discusses modeling applications in the pharmaceutical industry where uncertainties associated with clinical trials and competitive behavior make strategic planning difficult and important.
In short, recent attempts at fact-based strategic planning in process manufacturing and other companies have created intriguing new challenges for modeling practitioners and the managers who are their clients. The purpose of this paper is to review these challenges and to suggest new areas of research aimed at harmonizing managerial judgment with quantitative analysis of strategic planning problems. Our discussion will be divided into four overlapping topics:
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Enlarging the scope of strategic supply chain planning studies and models: Strategic planning studies are too often focused on narrowly defined supply chain and other business problems faced by a company. Optimization models applied to these problems could often be expanded in scope to more holistically analyze the company’s strategy if management were willing to entertain and authorize such expansions.
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Reflecting theories of strategy in data-driven optimization models: Theories of strategy, which are not inhibited by the demands of empirical validation and specific managerial decision-making, offer qualitative and quantitative concepts that modeling practitioners may employ in building richer, more comprehensive optimization models.
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Formalizing scenario planning, applying stochastic programming and modeling risk: Uncertainty plays a major role in strategic planning. Human decision-makers can better prepare for and respond to these uncertainties by comprehensive scenario planning and by the development and application of stochastic programming models that allow risks to be hedged and constrained.
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Expanding business processes to exploit fact-based analysis of strategic plans: Consistent, continual and effective fact-based strategic planning requires business expansion to establish new processes for collecting data, applying descriptive and prescriptive models, and implementing decisions.
Because each of these topics is vast and our space is limited, we will discuss them at a high level and provide references for interested readers. The paper concludes with a brief summary.
Section snippets
Enlarging the scope of strategic supply chain planning studies and models
An increasing number and range of manufacturing and distribution companies are performing strategic supply chain studies based on insights from supply chain network optimization models. The term “network” connotes the importance of holistic and integrated analysis of a firm’s geographically dispersed suppliers, plants, distribution centers, and markets. Despite managerial interest in expanding the scope of strategic analysis, current supply chain network optimization studies are still too timid
Reflecting theories of strategy in data-driven optimization models
Theories of strategy are derived from human experience and intuition about how firms achieve and sustain competitive advantage in seeking profits. These theories provide concepts that are important to the construction of data-driven models for strategic supply chain studies, which, as we have argued in Section 2, should attempt to incorporate relevant demand management and corporate financial decisions. Examples of strategic theory include: five forces affecting competition (Porter, 1980); the
Formalizing scenario planning, applying stochastic programming and modeling risk
Strategic planning exercises should identify major uncertainties about the firm’s future to assist senior management in developing effective contingency plans and hedging strategies for coping with them. In this section, we discuss three related methodologies to be considered when designing and carrying out exercises to address uncertainty: scenario planning, stochastic programming, and risk management.
Scenario planning is a methodology intended to assist senior managers in defining scenarios
Expanding business processes to exploit fact-based analysis of strategic plans
Our discussion thus far has focused on concepts underlying the design, implementation and application of fact-based optimization models to support strategic planning of the firm’s supply chain. We argued that such planning can and should incorporate demand management and corporate financial decision-making. The many successful efforts to date of optimization models applied to such planning problems confirm the assertion that technical requirements are not barriers to new and deeper
Conclusions
While recognizing that the number of successful modeling applications for strategic supply chain planning is increasing rapidly, we announced our intention at the start to examine approaches for extending their scope. Our motivation was that state-of-the-art models ignore decisions involving revenues, marketing campaigns, hedging against uncertainties, investment planning and other corporate financial decisions, and many other aspects of enterprise planning that interact with supply chain
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