سرمایه گذاری به طور همزمان، عملیات و برنامه ریزی مالی در زنجیره های تامین : رویکرد بهینه سازی مبتنی بر ارزش

Asset utilization is a major mid-term lever to increase shareholder value creation. Since rough-cut planning of capacity (dis-)investments is performed at the long-term level, detailed timing of adjustments remains for the mid-term level. In combination with capacity control measures, capacity adjustment timing can be used to optimize asset utilization. This paper provides a corresponding framework for value-based performance and risk optimization in supply chains covering investment, operations, and financial planning simultaneously. We illustrate the benefits of the approach using a case-oriented example, and highlight the value of using flexible capacity options and postponing of capacity-related decisions in an uncertain environment.

Since creating shareholder value is commonly considered the paramount business goal (Young and O'Byrne, 2001), frameworks for value-based management (VBM) are also discussed within the supply chain context (Walters, 1999 and Lambert and Pohlen, 2001). Top-level performance metrics such as discounted Free Cash Flow (FCF) or Economic Value Added (EVA) and corresponding value driver trees to drill down the performance metric into operational levers are prevalent concepts of VBM (Rappaport, 1998). Risk implications are typically considered indirectly via risk-adjusted cost of capital (Kaplan and Atkinson, 1998). In contrast to the aforementioned explanatory frameworks, Lainez et al. (2009) and Hahn and Kuhn (2011b) provide model-driven approaches to value-based performance and risk management in supply chains. Whilst Lainez et al. (2009) focus on the long-term level of strategic network design for a planning period of 2–10 years, Hahn and Kuhn (2011b) cover the mid-term level of sales and operations planning with a planning period of 6–18 months (Fleischmann et al., 2008). At the mid-term level, asset utilization is one of the major value drivers from a value-based planning perspective besides operating profit margin and operational cash flow (Walters, 1999). Capacity (dis-)investments in technical equipment and capacity control measures modifying supply and/or demand represent the two levers to manage asset utilization ( Olhager et al., 2001 and Buxey, 2003). Hahn and Kuhn (2011b) only focus on capacity control measures, and do not consider capacity (dis-)investments. Capacity adjustments can create additional value, but involve considerable risk potential due to costs of overcapacity or lost sales as well as physical degradation and depreciation (van Mieghem, 2003). Moreover, physical (dis-)investment decisions are inextricably interlinked with the corresponding financial decisions (Shapiro, 2007) and their impact on liquidity as well as overall value creation. An integrated approach to simultaneous investment, operations, and financial planning is therefore required that considers value-based implications. Capacity adjustments and equipment replacement typically involve a planning period of several years depending on the average useful life of the machine, and NPV-based approaches are thus utilized to evaluate the investment decision (Luss, 1982). Corresponding decisions are considered together with decisions on facility locations at the long-term level of strategic network design (Goetschalckx and Fleischmann, 2008). However, decision models for strategic network design only provide support on sizing and rough-cut timing of capacity (dis-)investments due to their long-term perspective and aggregated (semi-)annual time buckets (Fleischmann et al., 2008). Detailed timing of capacity adjustments and equipment replacement remains for the mid-term level. Consequently, an integrated approach to capacity (dis-)investment timing and capacity control as part of sales and operations planning (S&OP) is required to manage asset utilization comprehensively. A corresponding unified framework has not yet been discussed, especially with respect to robust and risk-mitigating strategies in capacity (dis-)investment planning. The aim of this paper is to develop a decision support framework for mid-term investment, operations, and financial planning in supply chains utilizing an integrated approach to value-based performance and risk optimization. We extend the paper of Hahn and Kuhn (2011b) to develop a comprehensive approach to capacity management taking into account related (dis-)investment and financing decisions from a value-based perspective. The remainder of this paper is structured as follows: Section 2 provides a literature review on the domains relevant for this research. In 3 and 4, we outline the conceptual approach and describe a corresponding decision model. Section 5 highlights implications of the approach using a case-oriented example. We conclude the paper in Section 6 with a summary of the findings and an outlook for further research.

A decision framework for simultaneous investment, operations, and financial planning in supply chains is presented using an integrated approach for value-based performance and risk management. The decision model implements the Economic Value Added (EVA) concept as a prevalent metric of value-based performance. The approach supports integrated decision-making for mid-term S&OP and capacity adjustment timing based on long-term instructions from strategic network planning. A unified approach for capacity adjustment and equipment replacement planning is developed. Robust optimization methods are applied to mitigate operational risk impact and implement a postponement approach for capacity planning. The benefits of the approach are highlighted using a case-oriented example. Practical implications from the case-oriented example can be summarized as follows: asset utilization is confirmed as a major value driver at the mid-term level, and a comprehensive approach is thus required to align different levers of capacity supply and demand management. Integrated planning of capacity adjustment timing and capacity control in S&OP reveals considerable improvement potential compared to a conventional sequential approach. Capacity adjustment timing is critical in the event of limited flexible capacity options as well as gradually changing aggregate demand. Postponing (dis-)investment decisions in a rolling horizons approach increases flexibility and creates additional upside potential in terms of EVA since the robust optimization approach hedges against the impact of negative scenarios. Although selective matters are simplified in the case-oriented example to focus the numerical analyses on the aspects of our research, the underlying assumptions and fundamental relationships still remain valid. Taking a more general perspective on the business problem in focus reveals some limitations of the approach presented. At least three parameters of the decision model could be subject to management considerations that would change the basic conditions of the problem: the maximum level of overtime capacity, the lead time for (dis-)investments, and the level of accuracy of the demand information. Increasing flexible overtime capacity could improve responsiveness towards customers resulting in a higher operating profit margin. Covering different manufacturing technologies or collaborating with a leasing company could reduce lead times for adjustments in technical equipment, allowing for a better match of capacity supply and demand. Although the expected value of perfect information (EVPI) is calculated in the approach to quantify the impact of incomplete information, the decision model does not consider the benefits and costs of further information gathering. Even though the decision model and the case-oriented example originate from the consumer goods industry, the approach could be also applied to companies in other industries, such as chemicals, pulp and paper, and metals, with a centralized planning process/function responsible for S&OP. Specialized SCM software vendors provide corresponding standard software for integrated business planning using mixed-integer linear programming to optimize financial performance metrics in sales, operations, and working capital management. However, the software packages do not apply robust optimization methods to provide decision support for risk management and do not cover capacity adjustment and equipment replacement planning. From our point of view, practitioners would strongly benefit from the corresponding functionality being available as standard packaged software. Integrated capacity management improves asset utilization and reduces lost sales, ultimately creating additional economic value. Real decision support for risk management would be a substantial improvement since simple scenario analysis does not provide a robust and implementable solution across all scenarios. The impact of further parameters should be evaluated with respect to future research. The capital loss factor and different depreciation methods can have considerable impact on (dis-)investment decisions. Furthermore, the approach presented in this paper can be extended in several directions. Considerations regarding technology selection and economies of scale could lead to interesting results regarding specificity and size of investments as well as hedging strategies in capacity (dis-)investment planning. Extending the decision model towards a hierarchical planning framework by introducing a short-term planning level below mid-term S&OP would be a more rigorous approach. This would allow investigation of the effects of detailed lot-sizing in production and distribution planning as well as the implications of short-term financial planning in supply chains.