The impact of the net present value on the assessment of the dynamic performance of e-commerce enabled supply chains

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Abstract

This paper shows the impact of using the net present value (NPV) on parameter selection in the ordering policy of a production planning and control system. Using a well understood and documented systems dynamics model of a supply chain, the NPV is used as an objective function to determine the discounted future variance costs resulting from the model's dynamics. The NPV of the Variance (NPVv) is defined and applied to the model under three scenarios; traditional, electronic-point-of-sales enabled and vendor managed inventory. It is shown that the resulting management implications are sensitive to the selection of the NPVv parameters.

Introduction

E-commerce has led to potential for information transparency in the supply chain. New novel forms of supply chain structures have been enabled such as collaborative planning, forecasting and replenishment (CPFR) and vendor managed inventory (VMI). Such structures may have a positive impact on the dynamic behaviour of supply chains leading to reduced total logistics costs.

Although the benefits of supply chain management (SCM) strategies are often promoted actual bottom-line cost benefits are not always cited. This may be due to the complexities associated with determining actual costs and revenues throughout an actual supply chain (Tan et al., 2002). Researchers propose alternative approaches to measuring supply chain performance, such as balanced score cards (Bullinger et al., 2002), bullwhip (Towill and McCullen, 1999), uncertainty (Childerhouse and Towill, 2002) and multivariate techniques (Tan et al., 2002; Kim and Narasimhan, 2002). But as all SCM decisions ultimately have monetary consequences research on novel and robust forms of cash flow analysis have been proposed such as Net Present Value (NPV) (Grubbström, 1999), cash-to-cash (Farris and Hutchison, 2002) and target costing (Lockamy and Smith, 2000).

In this paper the NPV is investigated as a financial measure of the dynamic behaviour of supply chains. The NPV ascertains the time value of money invested in a business. Grubbström (1967) has shown that where the economic consequences of production planning decisions need to be known then the NPV may be applied.

The raison d’être for this paper are:

  • to utilise a financial measure to assess the dynamic behaviour of supply chains,

  • determine the impact of NPV on parameter selection,

  • extend “hard engineering” know-how into the financial arena,

  • apply NPV via system dynamics simulation,

  • develop a single unified measure of performance that amalgamates a number of key dynamic characteristics.

While Wikner (1994) has undertaken some analysis of applying the NPV to a systems dynamics model of a production and inventory control system, Naim et al. (2004) have shown that the standard NPV is not a sufficient criterion for analysing the dynamic behaviour of such a closed-loop form of system. There is a need to extend the NPV criterion to encapsulate costs associated with the variances that occur in the system variables. This paper describes the development of the new criterion and how it may then be applied within a supply chain context.

This paper uses the automatic pipeline, inventory and order-based production control system (APIOBPCS) as a benchmark to determine the impact of using the NPV in assessing “on costs”. The APIOBPCS archetype has been shown to model decision making heuristics as given in the MIT Beer Game (Sterman, 1989) and has led to insights on the impact of information transparency (Mason-Jones and Towill, 1997).

The paper next reviews literature on supply chain dynamics highlighting some recent developments on simulating the impact of information transparency as well as other SCM strategies. The paper then describes the simulation method adopted and the model used. This is followed by a presentation of the NPV formulae and its application. Next the need to use an alternative form of the NPV is justified, which is then applied to e-commerce supply chains as replicated by the MIT Beer Game. Finally, the results are discussed and managerial implications considered.

Section snippets

The construct of supply chain dynamics

Much of the pioneering work into aspects of supply chain dynamics was undertaken by Forrester in the late 1950s (Forrester, 1958), using a simple but representative simulation model of a production distribution system, more commonly referred to now as a supply chain. Originally developed as a detailed case study to highlight the principles of industrial dynamics, that is, that structure causes dynamic behaviour, Forrester's work has been widely quoted in business and academic literature on SCM.

Simulation model formulation

A simulation-based approach to understanding the impact of the NPV criterion on supply chains dynamics is utilised. First computer simulation is utilised to understand the NPV criterion on a single echelon in the supply chain and to develop a new more appropriate criterion. The new criterion is then applied for the first time on Beer Game results using the raw data generated by Disney et al. (2004).

APIOBPCS is an enhancement of the well-known inventory and order-based production control system

Dynamic analysis of APIOBPCS using the standard NPV

To calculate NPV for an APIOBPCS we look at three flows:

  • revenue brought about by sales (p=market price of each product item),

  • costs associated with production (c=production costs per item),

  • inventory holding costs (h=inventory cost per item and unit time period).

Cash flow becomesCashflow,CF(t)=pCONS(t)-cCOMRATE(t)-hAINV(t).

Strictly speaking, CF(t) should also incorporate a fixed cost, C but for the purposes of analysis will be ignored because this remains constant during a simulation run and it

Dynamic analysis of APIOBPCS using the variance NPV

Stalk and Hout (1990, p. 65) propose an “on-cost” metric, estimated to be proportional to the cubic function of the area between the oscillating output and the neutral axis.

Hence,Oncost=f(0|ΔORATE(t)|dt)3,where ΔORATE(t) is the difference between the actual order rate and a defined “neutral axis”, or target. More simply,Oncost=f(0|ΔORATE(t)|dt),where ΔORATE(t)=TARGET(t)−ORATE(t).

Now, consider the variation in inventory from a set target, or EINV. This represents the increased costs of

Applying the NPVv to e-commerce supply chains

Disney et al. (2004) have examined the impact of e-commerce on supply chain dynamics using the MIT Beer Game. They used the game to implement a number of scenarios including a traditional structure, electronic point of sales transparency and VMI. It is appropriate to use the Beer Game because as John et al. (1994) pointed out, the heuristics used by players of the game (Sterman, 1989) may be replicated by the APIOBPCS model.

The three supply chain scenarios researched are summarised in Fig. 7. A

Discussion and conclusions

The paper has shown the impact of the net present value criterion on parameter settings for a generic production planning and control system. The analysis indicates that there is a need to develop an NPV criterion for the variance in both production and inventory costs. This new Variance NPV, or NPVv, has been applied and tested in a supply chain context using the MIT Beer Game. The literature review has highlighted the characteristics of supply chain dynamics and the need for modelling and

Acknowledgements

Prof. Naim would like to thank the Royal Academy of Engineering for providing a Global Research Award that enabled him to visit Linköping Institute of Technology for 4 months and undertake the research documented. Thanks are also due to all those people he worked with in the Department of Production Economics in the Linköping Institute of Technology, who made his time there both enjoyable and extremely productive.

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