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Designing Robust SCNs Under Risk

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Designing Value-Creating Supply Chain Networks

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Abstract

In this chapter, many of the models formulated previously are revisited and extended to be able to cope with risk. Initially, the structure of planning horizons under risk is examined. Then, the concepts of responsiveness, resilience, and robustness in an SCN design context are discussed. Subsequently, basic stochastic programming notions are introduced. The sample average approximation (SAA) method and coherent risk measures are explained and their application to SCN design is illustrated. In addition, we show how resilience strategies can be taken into account in the formulation of stochastic design models. Finally, a generic approach for the generation and evaluation of robust SCN designs is presented and illustrated with an example.

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Notes

  1. 1.

    In Chap. 10, the term uncertainty was defined as value neutral and the term risk was used to refer to the possibility that undesirable outcomes could occur, which is congruent with the terminology used in the risk analysis literature and with the English meaning of the word. This chapter draws heavily on the stochastic programming literature (Birge and Louveaux 2011). In this context, and in finance, the term risk refers to the volatility of possible outcomes associated with a decision (see Sect. 2.4) and, more specifically, to the chance that a decision’s actual return will be different than expected. The expression downside risk is used to refer to possible undesirable (below average) results. We adopt this meaning in this chapter.

  2. 2.

    We use \({\mathbf{y}}\) in what follows instead of \({\mathbf{y}}_{1}\) to simplify the notation but it should be clear that when the cycle index h is omitted, we are always concerned with the SCN design for the first reengineering cycle (h = 1).

  3. 3.

    Note that other modeling approaches such as chance-constrained programming and robust optimization can be used to tackle these issues but they are not studied in this book.

  4. 4.

    Usually, a much larger number of scenarios would be generated and their evolutionary paths would be selected randomly using the probabilities \({\text{p}}_{\kappa } ,\kappa \in {\rm K}\), as indicated in the Monte Carlo procedure found in Fig. 10.21. Here we define a single scenario for each evolutionary path to keep the size of the example to a minimum.

  5. 5.

    The Premium Solver upgrade sold by Frontline Systems (www.solver.com) was used to solve the MIP obtained because it is too large for the default Excel solver.

  6. 6.

    A much larger number of scenarios should be generated to obtain a robust design. We kept N and M deliberately small in this example to be able to solve the SAA models with Excel and to illustrate the approach succinctly.

  7. 7.

    We assume here to simplify that the backup DC always has sufficient capacity (including local recourse capacity) to cover transferred orders. The policy, however, can be extended to involve a third DC or an external supplier if required.

  8. 8.

    This can be done by aggregating or sampling the daily period. Klibi et al. (2015) show that period sampling gives better results.

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Authors and Affiliations

  1. Université Laval, Quebec City, Canada

    Alain Martel

  2. KEDGE-Business School, Bordeaux, France

    Walid Klibi

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  1. Alain Martel
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  2. Walid Klibi
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Correspondence to Alain Martel .

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Martel, A., Klibi, W. (2016). Designing Robust SCNs Under Risk. In: Designing Value-Creating Supply Chain Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-28146-9_11

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  • DOI: https://doi.org/10.1007/978-3-319-28146-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28144-5

  • Online ISBN: 978-3-319-28146-9

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