Abstract
Emergencies will bring the great threat to the stability and coordination of supply chain, such as temporary interruption of raw materials supply, strong fluctuation of demand and distorted information transmission, which will lead to the breakdown of whole supply chain and threaten the survival of enterprises in supply chain. Based on the influence factors of emergency diffusion and supply chain structure in uncertain environment, this paper studies the diffusion effect of emergency and establishes an improved Bass diffusion model. On this basis, information diffusion simulation is carried out. Finally, management suggestions are proposed on supply chain emergency diffusion in uncertain environment.
Similar content being viewed by others
References
Anupindi R, Akella R (1993) Diversification under supply uncertainty. Manag Sci 39(8):944–963
Axsater S, Zhang W (1999) A joint replenishment policy for multi echelon inventory control. Int J Prod Res 59(1–3):243–250
Baghalian A, Rezapour S, Farahani RZ (2013) Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur J Oper Res 227(1):199–215
Bartezzaghi E, Verganti R (1995) Managing demand uncertainty through order over planning. Int J Prod Econ 40(2–3):107–120
Berger PD, Gerstenfeld A, Zeng AZ (2004) How many suppliers are best? A decision-analysis approach. Omega 32(1):9–15
Chen S, Lee H, Moninzadeh K (2016) Supply chain coordination with multiple shipments: the optimal inventory subsidizing contracts. Oper Res 64(6):1320–1337
Claypool E, Norman BA, Needy KLS (2014) Modeling risk in a design for a supply chain problem. Comput Ind Eng 78:44–54
Davis T (1993) Effective supply chain management. Manag Rev 8:35–46
Dobrila P, Rajat R, Radivoj P (1998) Modeling and simulation of a supply chain in an uncertain environment. Eur J Oper Res 109:299–309
Du JM, Li X, Yu LA, Ralescu D (2017) Fuzzy bilevel programming for multi-depot vehicle routing problem with hazardous materials transportation. Inf Sci 399:201–218
Fink S, Amacom (2002) Crisis management: planning for the inevitable. Am Manag Assoc 4(3):875–876
Fynes B, de Buraca S, Marshall D (2004) Environmental uncertainty, supply chain relationship quality and performance. J Purch Supply Manag 10(4–5):179–190
Gullu R, Onol E, Erkip N (1999) Analysis of an inventory system under supply uncertainty. Int J Prod Econ 59:377–385
Hishamuddin H, Sarker RA, Essam D (2013) A recovery model for a two-echelon serial supply chain with consideration of transportation disruption. Comput Ind Eng 64(2):552–561
Iii JJB, Clark RJ, Williamson DW et al (2013) Building a self-organizing urban bus route. In: IEEE sixth international conference on self-adaptive and self-organizing systems workshops, pp 66–70
Johasotn FR, Boylan JE (1996) Forecasting for item with intermittent demand. J Oper Res Soc 49:113–121
Kenmeyer AM, Zinn W, Erogly C (2009) Proactive planning for catastrophic events in supply chains. J Oper Manag 27(2):141–153
Khouja M (1999) The single period (news-vendor) inventory problem: a literature review and suggestions for future research. Omens 27:537–553
Kleindorfer PR, Saad GH (2010) Managing disruption risks in supply chains. Prod Oper Manag 14(1):53–68
Kouvelis P, Zhao W (2016) Supply chain contract design under financial constraints and bankruptcy costs. Manag Sci 62(8):2341–2357
Kwon O, Im GP, Lee KC (2007) MACE-SCM: a multi-agent and case based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties. Expert Syst Appl 33(3):690–705
Li J, Yao X, Sun X, Wu D (2018) Determining the fuzzy measures in multiple criteria decision aiding from the tolerance perspective. Eur J Oper Res 264(2):428–439
Natarajan R, Goyal SK (1994) Safety stocks in JIT environments. Int J Oper Prod Manag 14(10):64–71
Pal B, Sana SS, Chaudhuri K (2012) A three layer multi-item production-inventory model for multiple suppliers and retailers. Econ Model 29(6):2704–2710
Ruiz-Torres AJ, Mahmoodi F, Zeng AZ (2013) Supplier selection model with contingency planning for supplier failures. Comput Ind Eng 66(2):374–382
Sawik T (2014) Optimization of cost and service level in the presence of supply chain disruption risks: single vs. multiple sourcing. Comput Oper Res 51(3):11–20
Silver EA, Pyke DF, Peterson R (1998) Inventory management and production planning and scheduling. Wiley, New York, pp 280–295
Sodhi MMS, Chopra S (2004) Managing risk to a void supply-chain breakdown. MIT Sloan Manag Rev 46(1):53–61
Sweet AL (1980) An ad hoc method for forecasting series with zero values. IIE Trans 21(1):97
Wang J, Shu YF (2005) Fuzzy decision modeling for supply chain management. Fuzzy Sets Syst 150(1):107–127
Xu X, Hao J, Yu L, Deng Y (2019) Fuzzy optimal allocation model for task-resource assignment problem in collaborative logistics network. IEEE Trans Fuzzy Syst 27(5):1112–1125
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 71871222), the Humanities and Social Science Research Project of Shandong Universities (J17RB103), Shandong Social Science Planning Research Project (19CGLJ31), Qingdao Social Science Planning Project (QDSKL1801035). It also was supported by the Fundamental Research Funds for the Central Universities (18CX04004B).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
Additional information
Communicated by X. Li.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Deng, Y., Jiang, M. & Ling, C. An improved diffusion model for supply chain emergency in uncertain environment. Soft Comput 24, 6385–6394 (2020). https://doi.org/10.1007/s00500-019-04134-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-04134-9