The risk adjustment of required rate of return for supply chain infrastructure investments.

Abstract

The purpose of this article is to apply a risk-adjusted required rate of return to evaluate supply chain capitol investments. As part of the design methodology, a computer simulation provides expected cash flows resulting from alternative supply chain investments. These cash flows are discounted at a risk-adjusted required rate of return. The analysis represents a process to measure the risk inherent in supply chain investments. The logistics and supply chain literature has not addressed this problem of the risk inherent in a specific supply chain project. A corporate-wide hurdle rate applied to usually conservative supply chain investments may result in less than adequate investments in supply chain infrastructure.

Prior studies have determined risk-adjusted required rate of return for the entire firm, an enterprise's entire supply chain network, but not an individual project within the supply chain. This study calculates a required rate of return for a specific supply chain investment project using a discrete simulation model rather than the more common mathematical model. Individual supply chain investment projects may have less risk or possibly more risk than reflected by a corporate hurdle rate or a supply chain hurdle rate. Using a standard required rate of return could result in too little or too much investment in supply chain facilities.

In this study, when the risk-adjusted rate was employed to discount expected cash flows, only two of eleven alternatives evaluated were acceptable investments. The best investment with the highest return was a 12 percent increase in ship loading rate combined with a 33 percent increase in rail unloading capacity. It provided a benefit-cost ratio of 1.25.

The limited availability of publicly traded firms that invest in supply chain projects represents a constraint, limiting the accuracy of estimating the market risk factor. Nevertheless, the practical implication is that, when making supply chain infrastructure investment decisions, it is advisable to adjust risk factors in evaluating such capital investments. This approach is preferable to using a corporate-wide hurdle rate, typically too high for such conservative investments. A firm-wide hurdle rate might result in under investment in supply chain facilities.

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Strategic logistics decisions normally consider infrastructure investment, including supply chain capacity levels and design configuration (Novack et al. 1992). Logistics and supply chain managers should identify and estimate the costs, revenues, and risks associated with related investments. These elements remain difficult to quantify with any degree of certainty. The competition for an organization's funds demands that the "value added" by investment projects to the enterprise be carefully measured (Speh and Novack 1995).

Some organizations evaluate all projects against an entity-wide target rate of return. However, many capital projects in the supply chain are relatively less risky, as they involve costs savings that are measurable and rather certain, but may generate less than spectacular rates of return. An adjustment to the target return based on the riskiness of the project might prove indispensable. Investments in logistics assets with the same risk command the same required rate of return (Pringle and Harris 1987). Consequently, lower target rates of return should be reasonable for less risky supply chain projects.

This article describes a simulation model of an intermodal transfer and bulk commodity blending facility and the financial analysis of potential changes to the actual plant capacity and design configuration. The model estimated cash flows (measured as annual cost savings) due to an increase in capacity. The cash flows are adjusted for the same volume and scheduling performance. The risk-adjusted required rate of return from several possible capital investment projects in logistics infrastructure was computed based on the business risk of proxy firms that normally undertake these same types of investments in supply chain assets. Benefit-cost ratios were then calculated to determine which projects exceeded the required rate of return for the risk levels of these investments.

RELATED LITERATURE ON SUPPLY CHAIN MODELING AND RISK

The current research literature increasingly recognizes the importance of supply chain risks (Cavinato 2004; Spekman and Davis 2004; Ritchie and Brindley 2007; Kish 2007; Krishnan and Shulman 2007). The importance of global competition and technological change, with firms searching for sources of competitive advantage, has increased the importance of risk analysis in supply chains (Christopher and Lee 2004; Zsidisin et al. 2004; Cavinato 2005; Tang 2006; Tomlin 2006; Bogataj and Bogataj 2007; Craighead et al. 2007; Golda and Philippi 2007). Mintzberg and Waters (1985), Paulsson (2004), and Kleindorfer and Wassenhove (2003) viewed the analysis of the risk associated with supply chain decisions as strategic.

Quantitative Models and Supply Chain Design

Much of the published research in the area of supply chain network design relates to mathematical programming models. Examples include Arntzen et al. (1995) and Camm et al. (1997), who employed integer programming models to address supply network design issues. More recently, Cochran and Marquez-Uribe (2005) employed an integer programming model to evaluate alternative investments in supply chain capacity.

In alternative quantitative approaches to this issue, Levy (1995) captured risk issues related to different supply network designs in a simulation model. Swaminathan et al. (1998) also applied simulation modeling risk-benefit analysis to re-engineered supply chains. Agrell et al. (2004) applied game theory to evaluate a multi-stage supply chain.

Quantitative Models and Risk Analysis

There is some literature about the investment decision making in supply chain networks under conditions of risk and uncertainty. Huchzermeier and Cohen (1996) applied a multi-period stochastic programming to evaluating design risks in a global supply chain network. Lee and Tang (1998) developed a stochastic inventory model to examine network tradeoffs under conditions of uncertainty. Applequist et al. (2000) presented a metric for evaluating supply chain design projects where significant elements of risk and uncertainty are present.

Alonso-Ayuso et al. (2003) and Goh et al. (2007) applied stochastic models to analyze supply chain uncertainties in multi-stage networks. Ojala and Hallikas (2006) investigated how firms with supply chains make investment decisions and the risks associated with those investments. There have also been multi-criteria decision-making models applied to supply chain risks analysis, such as studies by Nagurney and Matsypura (2005).

Research Contribution

This study is concerned with the strategic planning function of network configuration that deals with manufacturing facilities and distribution centers. It does not address other supply management issues such as product or customer assignment. Spearman (2007) suggested that risk in manufacturing supply chains results from the failure to operate them under conditions different from those for which they were originally designed. The research reported herein focuses on this concern by evaluating an existing logistics facility that is an integral part of a larger supply chain.

Our research offers two contributions to the evaluation of supply chain risk. It incorporates a risk premium in the discount rate, rather than trying to adjust the cash flows themselves. This latter approach is more common. Secondly, the risk analysis is applied to a specific node that is part of a much larger supply chain network.

Prior studies have determined risk-adjusted required rate of return for the entire firm, an enterprise's entire supply chain network (Applequist 2000), but not an individual project within the supply chain. This study calculates a required rate of return for a specific supply chain investment project using a discrete simulation model rather than the more common mathematical model. Individual supply chain investment projects may have less risk or possibly more risk than reflected by a corporate hurdle rate or a supply chain hurdle rate. Using a standard required rate of return could result in too little or too much investment in supply chain facilities.

Incorporating risk into the decision of whether to add capacity or not to an actual facility that is part of a much larger supply chain, the model estimates cash flows resulting from proposed capacity changes. The study then determines whether the resulting returns provide enough payback to justify the project under the estimated risk.

Methodological Issues

Queuing theory provides numerous models for describing a waiting-line situation, such as an intermodal facility that is integrated into a supply chain. In terms of a queuing model, ship arrivals at the facility are selected for service according to a priority procedure, usually first-come, first-served. If the service center is occupied, the arriving ship joins a queue, possibly with others already waiting. If the service center is empty, the arriving ship receives service immediately. After the service is performed, the ship leaves the port system.

If an analytical model can be developed to determine the optimal ship-loading rate, rail unloading, and quantity of storage, the mathematics of optimization could be used to obtain a solution. If not, simulation analysis can be employed to obtain a solution (Gross and Harris 1998). The research literature suggests that simulation techniques are preferable to mathematical approaches for the analysis of supply chain facilities. These experiments, or simulations, permit inferences to be drawn about proposed systems without building them; they can also study actual operating systems that are costly to conduct real-world experiments on--such as supply chain facilities--without disturbing them.

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