Abstract
Business management involves collecting information, goods, and funds as they move from supplier to manufacturer to wholesaler to retailer to consumer. Such business comprises interconnected parts that can be fundamentally complex and dynamic. A disturbance in one subnet of the system may thus have an opposed impact on another subnets, thus disturbing the business. Disruptions can have expensive and extensive results. This research aims to improve an increased Bayesian network method to consider business disruptions. The goal is to develop strategies that can diminish the opposed impacts of the disruptions and improve overall system reliability. Two influence agents are specified: the Bayesian and junction lack influence agents. An industrial model is used to demonstrate the proposed application, making the business more reliable. Moreover, two network learning methodologies are reviewed to update the probabilities in a model. The neural network seems to be a more favorable updating tool.
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References
Klir GJ (1985) Architecture of systems problem solving. Plenum, New York
Weaver W (1948) Science and complexity. Am Sci 36:536–544
Yates FE (1978) Complexity and the limits to knowledge. Am J Physiol Regul Integr Comp Physiol 235(5):201–204
Beamon BM (1998) Supply chain design and analysis: models and methods. Int J Prod Econ 55(3):281–294
Lambert DM, Cooper MC, Pagh JD (1998) Supply chain management: implementation issues and research opportunities. Int J Logist Manag 9(2):1–19
Mentzer JT et al (2001) Defining supply chain management. J Bus Logist 22(2):1–25
van der Vorst JGAJ, Beulens AJM (2002) Identifying sources of uncertainty to generate supply chain redesign strategies. Int J Phys Distrib Logist Manage 32(6):409–430
Vachon S, Klassen R (2002) An exploratory investigation of the effects of supply chain complexity on delivery performance. IEEE Trans Eng Manage 49:218–230
Perona M, Miragliotta G (2004) Complexity management and supply chain performance assessment: a field study and a conceptual framework. Int J Prod Econ 90(1):103–115
Kearney AT (2004) The complexity challenge: a survey on complexity management across the supply chain. https://www.atkearney.de/content/misc/wrapper.php/id/49230/name/pdf_complexity_management_s_1096541460ee67.pdf
PricewaterhouseCoopers (2009) 9th Annual Global CEO Survey: globalization and complexity. PricewaterhouseCoopers
Koudal P, Engel DA (2007) Globalization and emerging markets: the challenge of continuous global network optimization. In: Lee HL, Lee C-Y (eds) Building supply chain excellence in emerging economies. Springer Science+Business Media, New York
Choi TY, Dooley KJ, Rungtusanatham M (2001) Supply networks and complex adaptive systems: control versus emergence. J Oper Manag 19(3):351–366
Pathak SD, Dilts DM (2002) Simulation of supply chain networks using complex adaptive system theory. IEMC ‘02 I.E. International Engineering Management, Cambridge, UK, pp. 655–660
H. Wildemann, Komplexitätsmanagement: Vertrieb, Produkte, Beschaffung, F&E, Produktion, Administration, in TCW Report. 2000, TCW Transfer-Centrum: GmbH, Munich
Sivadasan S, et al. (2002) Policies for managing operational complexity in the supply chain. Proceedings of Manufacturing Complexity Network Conference, Cambridge
Sivadasan S (2004) Supply Chain Complexity”. In: New S, Westbrook R (eds) Understanding supply chains: concepts, critiques and futures. Oxford University Press, Oxford
Kaluza B, Bliem H, Winkler H (2006) Strategies and metrics for complexity management in supply chains. In: Blecker T, Kersten W, Schmidt E (eds) Complexity management in supply chains: concepts, tools and methods. Erich Schmidt Verlag, Berlin
Wagner S, Bode C (2007) An empirical investigation into supply chain vulnerability. J Purchasing Supply Chain Manage 12:301–312
Craighead C et al (2007) The severity of supply chain disruptions: design characteristics and mitigation capabilities. Decis Sci 38(1):131–156
Sheffi Y (2005) The resilient enterprise: overcoming vulnerability for competitive advantage. MIT Press, Cambridge
Kleindorfer P, Saad G (2005) Managing disruption risks in supply chains. Prod Oper Manag 14(1):53–68
Blackhurst J et al (2005) An empirical derived agenda of critical research issues for managing supply-chain disruptions. Int J Prod Res 43(19):4067–4081
Blackhurst J, Wu T, O’Grady P (2007) Methodology for supply chain disruption analysis. Int J Prod Res 45(7):1665–1682
Min H, Zhou G (2002) Supply chain modeling: past, present and future. Comput Ind Eng 43(1):231–249
Petrovic D, Roy R, Petrovic R (1999) Supply chain modeling using fuzzy sets. Int J Prod Econ 59:443–453
Huang G, Lau J, Mak K (2003) The impact of sharing production informationv on supply chain dynamics: a review of the literature. Int J Prod Res 41(7):1483–1517
Zurawski R, Zhou M (1994) Petri nets and industrial applications: a tutorial. IEEE Trans Ind Electron 41(6):567–583
Murata T (1989) Perti nets: properties, analysis and applications. Proc IEEE 77(4):541–580
David R, Alla H (1994) Petri nets for modeling of dynamic systems-a survey. Automatica 30(2):175–202
Li G et al (2006) Enhancing agility by timely sharing of supply information. Supply Chain Manage 11(5):425–443
Croson R, Donohue K (2006) Behavioral causes of the bullwhip effect and the observed value of inventory information. Manag Sci 52(3):323–336
Maatman A et al (2002) Modeling farmer’s response to uncertain rainfall in Burkina Faso: a stochastic programming approach. Oper Res 50(3):399–414
Cocks K (1968) Discrete stochastic programming. Manag Sci 15(1):72–79
Bellman R, Zadeh L (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):141–164
Tanaka H, Okuda T, Asai K (1974) On fuzzy mathematical programming. J Cybernet 3:37–46
Zimmermann H (1976) Description and optimization of fuzzy systems. Int J Gen Syst 2:209–215
Sahinidis N (2004) Optimization under uncertainty: state-of-the art and opportunities. Comput Chem Biomol Eng 28:971–983
Negoita C (1981) The current interest in fuzzy optimization. Fuzzy Sets Syst 6(3):261–269
Niedermayer D (1998) An introduction to bayesian networks and their contemporary applications. University of Saskatchewan. Technical Report 184-3-5440.
Thomas M (2002) Supply chain reliability for contingency operations. Annual Reliability and Maintainability Symposium
Towill D (1996) Time compression and supply chain management a guided tour. Supply Chain Manage 1(1):15–27
Childerhouse P et al (2003) Information flow in automotive supply chains—present industrial practice. Ind Manag Data Syst 103(3):137–149
Jensen F (2001) Bayesian networks and decision graphs. Springer, New York
Anthony M, Bartlett PL (1999) Neural network learning: theoretical foundations. Cambridge University Press, Cambridge
Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. 23rd International Conference on Machine Learning, Pittsburgh
Kaelbling LP, Littman M, Moore A (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285
Russell SJ, Norvig P (eds) (2003) Articial intelligence: a modern approach, 2nd edn. Prentice Hall, New Jersey
Cheng J, Bell D, Lui W (1997) Learning Bayesian networks from data: an efficient approach based on information theory. 6th International Conference on Information and Knowledge Management
Heckerman D, Geiger D, Chickering D (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243
Cooper G, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309–347
Herskovits E (1991) Computer-based probabilistic network construction. Medical Information Sciences, Stanford University, Stanford
Wallace C, Korb KB, Dai H (1996) Causal discovery via MML. 13th International Conference on Machine Learning (ICML ‘96). Morgan Kaufmann, San Francisco
Chickering D, Geiger D, Heckerman D (1994) Learning Bayesian networks is NP-hard. Microsoft Research, Microsoft Corporation
Cooper G (1995) A Bayesian method for learning belief networks that contain hidden variables. J Intell Inf Syst 4(1):71–88
Binder J et al (1997) Adaptive probabilistic networks with hidden variables. Mach Learn 29(2–3):213–244
Thiesson B (1995) Accelerated quantication of Bayesian networks with incomplete data. First International Conference on Knowledge Discovery and Data Mining (KDD-95). AAAI
Lauritzen SL (1995) The EM algorithm for graphical association models with missing data. Comput Stat Data Anal 19(2):191–201
Dempster A, Laird N, Rubin D (1977) Maximum likehood from incomplete data via the EM algorithm. J R Stat Soc 39:1–38
Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo
Lee TS, Feller SJ, Jr EEA (1992) Applying contemporary forecasting and computer technology for competitive advantage in service operations. Int J Oper Prod Manage 12:28–42
Zhao X, Xie J, Leung J (2002) The impact of forecasting model selection on the value of information sharing in a supply chain. Eur J Oper Res 142:321–344
Gardner ES (1985) Exponential smoothing: the state of the art. J Forecast 4(1):1–28
Haykin, S. (1998). Neural Networks: a comprehensive foundation. McMillan Publ. Co., New York
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Arasteh, A., Aliahmadi, A. & Omran, M.M. Considering the business system’s complexity with a network approach. Int J Adv Manuf Technol 70, 869–885 (2014). https://doi.org/10.1007/s00170-013-5321-2
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DOI: https://doi.org/10.1007/s00170-013-5321-2