Abstract
Managing a Collaborative Network (such as a supply chain) requires setting and pursuing objectives. These can be represented and evaluated by formal Key Performance Indicators (KPIs). Managing a supply chain aims to stretch its KPIs towards target values. Therefore, any Collaborative Network’s goal is to monitor its trajectory within the framework of its KPIs. Currently potentiality (risk or opportunity) management is based on the capacity of managers to analyze increasingly complex situations. The new approach presented in this paper opens the door to a new methodology for supply chain potentiality management. It offers an innovative data-driven approach that takes data as input and applies physical principles for supporting decision-making processes to monitor supply chain’s performance. With that approach, potentialities are seen as forces that push or pull the network within its multi-dimensional KPI space.
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References
Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks. In: Wang, K., Kovacs, G.L., Wozny, M., Fang, M. (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 26–40. Springer, Boston, MA (2006). https://doi.org/10.1007/0-387-34403-9_4
Christopher, M.: Logistics & Supply Chain Management. Financial Times Prentice Hall, Harlow (2011)
Taleb, N.N.: The Impact of the Highly Improbable. Random House (2007)
Gunasekaran, A., Patel, C., Tirtiroglu, E.: Performance measures and metrics in a supply chain environment. Int. J. Oper. Prod. Manag. 21, 71–87 (2001)
Neely, A., Gregory, M., Platts, K.: Performance measurement system design: a literature review and research agenda. Int. J. Oper. Prod. Manag. 15, 80–116 (1995). https://doi.org/10.1108/01443579510083622
Kaplan, R.S., Norton, D.P.: Putting the balanced scorecard to work. In: The Economic Impact of Knowledge, pp. 315–324. Elsevier (1998). https://doi.org/10.1016/B978-0-7506-7009-8.50023-9
Anderson, S.W., Young, S.M.: The impact of contextual and process factors on the evaluation of activity-based costing systems. Acc. Organ. Soc. 24, 525–559 (1999). https://doi.org/10.1016/S0361-3682(99)00018-5
Neely, P.A.: Perspectives on Performance: The Performance Prism. The Evolution of Business Performance Measurement Systems, 8
Stewart, G.: Supply-chain operations reference model (SCOR): the first cross-industry framework for integrated supply-chain management. Logist. Inf. Manag. 10, 62–67 (1997). https://doi.org/10.1108/09576059710815716
Lima-Junior, F.R., Carpinetti, L.C.R.: Quantitative models for supply chain performance evaluation: a literature review. Comput. Ind. Eng. 113, 333–346 (2017). https://doi.org/10.1016/j.cie.2017.09.022
Gunasekaran, A., Kobu, B.: Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int. J. Prod. Res. 45, 2819–2840 (2007). https://doi.org/10.1080/00207540600806513
Wagner, S.M., Bode, C.: An empirical investigation into supply chain vulnerability. J. Purchasing Supply Manag. 12, 301–312 (2006). https://doi.org/10.1016/j.pursup.2007.01.004
Colicchia, C., Strozzi, F.: Supply chain risk management: a new methodology for a systematic literature review. Supply Chain Manag. 17, 403–418 (2012). https://doi.org/10.1108/13598541211246558
Gorecki, S., Ribault, J., Zacharewicz, G., Ducq, Y., Perry, N.: Risk management and distributed simulation in Papyrus tool for decision making in industrial context. Comput. Ind. Eng. 137, 106039 (2019). https://doi.org/10.1016/j.cie.2019.106039
Tummala, R., Schoenherr, T.: Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP). Supply Chain Manag. 16, 474–483 (2011). https://doi.org/10.1108/13598541111171165
Hillson, D.: Extending the risk process to manage opportunities. Int. J. Project Manag. 20, 235–240 (2002). https://doi.org/10.1016/S0263-7863(01)00074-6
Edwards, P.J., Bowen, P.A.: Risk Management in Project Organisations. Elsevier, Amsterdam (2005)
Benaben, F., Lauras, M., Montreuil, B., Faugère, L., Gou, J., Mu, W.: A physics-based theory to navigate across risks and opportunities in the performance space: application to crisis management. Presented at the Hawaii International Conference on System Sciences (2020)
Olsson, R.: In search of opportunity management: is the risk management process enough? Int. J. Project Manag. 25, 745–752 (2007). https://doi.org/10.1016/j.ijproman.2007.03.005
Eren-Dogu, Z.F., Celikoglu, C.C.: Information security risk assessment: Bayesian prioritization for AHP group decision making. Int. J. Innov. Comput. Inf. Control 8, 8019–8032 (2011)
Arikan, R., Dağdeviren, M., Kurt, M.: A fuzzy multi-attribute decision making model for strategic risk assessment. Int. J. Comput. Intell. Syst. 6, 487–502 (2013). https://doi.org/10.1080/18756891.2013.781334
de Oliveira, U.R., Marins, F.A.S., Rocha, H.M., Salomon, V.A.P.: The ISO 31000 standard in supply chain risk management. J. Clean. Prod. 151, 616–633 (2017). https://doi.org/10.1016/j.jclepro.2017.03.054
Khemiri, R., Elbedoui-Maktouf, K., Grabot, B., Zouari, B.: A fuzzy multi-criteria decision-making approach for managing performance and risk in integrated procurement–production planning. Int. J. Prod. Res. 55, 5305–5329 (2017). https://doi.org/10.1080/00207543.2017.1308575
Mojtahedi, S.M.H., Mousavi, S.M., Makui, A.: Project risk identification and assessment simultaneously using multi-attribute group decision making technique. Saf. Sci. 48, 499–507 (2010). https://doi.org/10.1016/j.ssci.2009.12.016
Taillandier, P., Stinckwich, S.: Using the PROMETHEE multi-criteria decision making method to define new exploration strategies for rescue robots. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, Kyoto, Japan, pp. 321–326. IEEE (2011). https://doi.org/10.1109/SSRR.2011.6106747
Bana e Costa, C.A, De Corte, J.M., Vansnick, J.C.: Macbeth. Int. J. Inf. Technol. Decis. Making 11, 359–387 (2003)
Clément, A., Marmier, F., Kamissoko, D., Gourc, D., Wioland, L.: Robustesse, résilience: une brève synthèse des définitions au travers d’une analyse structurée de la littérature. In: MOSIM 2018 - 12ème Conférence internationale de Modélisation, Optimisation et SIMulation (2018)
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Cerabona, T., Lauras, M., Faugère, L., Gitto, JP., Montreuil, B., Benaben, F. (2020). A Physics-Based Approach for Managing Supply Chain Risks and Opportunities Within Its Performance Framework. In: Camarinha-Matos, L.M., Afsarmanesh, H., Ortiz, A. (eds) Boosting Collaborative Networks 4.0. PRO-VE 2020. IFIP Advances in Information and Communication Technology, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-030-62412-5_34
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