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Developing a Model for Measuring Severity of Effects Caused by Interconnected Units in Electronic Supply Chains | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 6، دوره 49، شماره 2، اسفند 2017، صفحه 181-186 اصل مقاله (4.32 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2017.11487.4943 | ||
| نویسندگان | ||
| A. Kazemi* ؛ L. Ahmadpour | ||
| Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran | ||
| چکیده | ||
| For many electronic supply chain networks in the world that can comprise hundreds of companies with several tiers of suppliers and intermediate customers, there are numerous presenting risks to consider. In the electronic supply chain, the situation are even worse, for the characteristics of this supply chain: excessive lean management, global sourcing and the rather more uncertain market demand. Electronic companies are forced to manage their supply chains effectively to increase efficiency and reactivity. This paper proposes a mathematical model for estimating the severity of interactions between supply chain’s units and how their affect on the entire supply chain. Based on the model, scholars can model supply chains easily with considering interconnected units. Basic characteristics of supply chains are considered in the model. The units, which are used to simulate the members of supply chains, produce appropriate products by intelligent choices. The relationships on units are connected by their activities ; then, the proposed model is applied to an experimental example. The model yields its numerical parameters and responses by means of Lingo software. | ||
تازه های تحقیق | ||
[1] G. Applequist, J. Pekny, G. Reklaitis, Risk and uncertainty in managing chemical manufacturing supply chains, Computers & Chemical Engineering, 24(9-10) (2000) 2211-2222. [2] T. Assavapokee, W. Wongthatsanekorn, Reverse production system infrastructure design for electronic products in the state of Texas, Computers & Industrial Engineering, 62(1) (2012) 129-140. [3] W.H. Baker, L. Wallace, Is information security under control?: Investigating quality in information security management, IEEE Security & Privacy, 5(1) (2007). [4] S. Bose, J. Pekny, A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain, Computers & Chemical Engineering, 24(2-7) (2000) 329-335. [5] M.A. Cohen, H.L. Lee, Resource deployment analysis of global manufacturing and distribution networks, Journal of manufacturing and operations management, 2(2) (1989) 81-104. [6] C.W. Craighead, J. Blackhurst, M.J. Rungtusanatham, R.B. Handfield, The severity of supply chain disruptions: design characteristics and mitigation capabilities, Decision Sciences, 38(1) (2007) 131-156. [7] F.D. Mele, G. Guillén, A. Espuna, L. Puigjaner, An agent-based approach for supply chain retrofitting under uncertainty, Computers & chemical engineering, 31(5-6) (2007) 722-735. [8] P. Georgiadis, M. Besiou, Sustainability in electrical and electronic equipment closed-loop supply chains: a system dynamics approach, Journal of Cleaner Production, 16(15) (2008) 1665-1678. [9] A. Gupta, C.D. Maranas, A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty, Industrial & Engineering Chemistry Research, 39(10) (2000) 3799-3813. [10] A. Gupta, C.D. Maranas, Managing demand uncertainty in supply chain planning, Computers & chemical engineering, 27(8-9) (2003) 1219-1227. [11] J.K. Deane, C.T. Ragsdale, T.R. Rakes, L.P. Rees, Managing supply chain risk and disruption from IT security incidents, Operations Management Research, 2(1-4) (2009) 4. [12] J. Li, F.T. Chan, An agent-based model of supply chains with dynamic structures, Applied Mathematical Modelling, 37(7) (2013) 5403-5413. [13] Y. Li, Z. Lin, L. Xu, A. Swain, “Do the electronic books reinforce the dynamics of book supply chain market?”–A theoretical analysis, European Journal of Operational Research, 245(2) (2015) 591-601. [14] P.-K. Marhavilas, D. Koulouriotis, V. Gemeni, Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000–2009, Journal of Loss Prevention in the Process Industries, 24(5) (2011) 477-523. [15] A. Mele, Asymmetric stock market volatility and the cyclical behavior of expected returns, Journal of financial economics, 86(2) (2007) 446-478. [16] S. Mohebbi, X. Li, Designing intelligent agents to support long-term partnership in two echelon e-Supply Networks, Expert Systems with Applications, 39(18) (2012) 13501-13508. [17] A. Nagurney, J. Cruz, J. Dong, D. Zhang, Supply chain networks, electronic commerce, and supply side and demand side risk, European journal of operational research, 164(1) (2005) 120-142. [18] E. Perea-Lopez, B.E. Ydstie, I.E. Grossmann, A model predictive control strategy for supply chain optimization, Computers & Chemical Engineering, 27(8-9) (2003) 1201-1218. [19] M. Sakawa, I. Nishizaki, Y. Uemura, Fuzzy programming and profit and cost allocation for a production and transportation problem, European Journal of Operational Research, 131(1) (2001) 1-15. [20] D. Simchi-Levi, E. Simchi-Levi, P. Kaminsky, Designing and managing the supply chain: Concepts, strategies, and cases, McGraw-Hill New York, 1999. [21] G. Stoneburner, A.Y. Goguen, A. Feringa, Sp 800-30. risk management guide for information technology systems, (2002). [22] C.H. Timpe, J. Kallrath, Optimal planning in large multi-site production networks, European Journal of Operational Research, 126(2) (2000) 422-435. [23] W. Wang, Y. Zhang, Y. Li, X. Zhao, M. Cheng, Closed-loop supply chains under reward-penalty mechanism: Retailer collection and asymmetric information, Journal of cleaner production, 142 (2017) 3938-3955. | ||
| کلیدواژهها | ||
| Supply chain؛ Interconnected units؛ Propagated effect | ||
| مراجع | ||
|
[1] G. Applequist, J. Pekny, G. Reklaitis, Risk and uncertainty in managing chemical manufacturing supply chains, Computers & Chemical Engineering, 24(9-10) (2000) 2211-2222.
[2] T. Assavapokee, W. Wongthatsanekorn, Reverse production system infrastructure design for electronic products in the state of Texas, Computers & Industrial Engineering, 62(1) (2012) 129-140.
[3] W.H. Baker, L. Wallace, Is information security under control?: Investigating quality in information security management, IEEE Security & Privacy, 5(1) (2007).
[4] S. Bose, J. Pekny, A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain, Computers & Chemical Engineering, 24(2-7) (2000) 329-335.
[5] M.A. Cohen, H.L. Lee, Resource deployment analysis of global manufacturing and distribution networks, Journal of manufacturing and operations management, 2(2) (1989) 81-104.
[6] C.W. Craighead, J. Blackhurst, M.J. Rungtusanatham, R.B. Handfield, The severity of supply chain disruptions: design characteristics and mitigation capabilities, Decision Sciences, 38(1) (2007) 131-156.
[7] F.D. Mele, G. Guillén, A. Espuna, L. Puigjaner, An agent-based approach for supply chain retrofitting under uncertainty, Computers & chemical engineering, 31(5-6) (2007) 722-735.
[8] P. Georgiadis, M. Besiou, Sustainability in electrical and electronic equipment closed-loop supply chains: a system dynamics approach, Journal of Cleaner Production, 16(15) (2008) 1665-1678.
[9] A. Gupta, C.D. Maranas, A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty, Industrial & Engineering Chemistry Research, 39(10) (2000) 3799-3813.
[10] A. Gupta, C.D. Maranas, Managing demand uncertainty in supply chain planning, Computers & chemical engineering, 27(8-9) (2003) 1219-1227.
[11] J.K. Deane, C.T. Ragsdale, T.R. Rakes, L.P. Rees, Managing supply chain risk and disruption from IT security incidents, Operations Management Research, 2(1-4) (2009) 4.
[12] J. Li, F.T. Chan, An agent-based model of supply chains with dynamic structures, Applied Mathematical Modelling, 37(7) (2013) 5403-5413.
[13] Y. Li, Z. Lin, L. Xu, A. Swain, “Do the electronic books reinforce the dynamics of book supply chain market?”–A theoretical analysis, European Journal of Operational Research, 245(2) (2015) 591-601.
[14] P.-K. Marhavilas, D. Koulouriotis, V. Gemeni, Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000–2009, Journal of Loss Prevention in the Process Industries, 24(5) (2011) 477-523.
[15] A. Mele, Asymmetric stock market volatility and the cyclical behavior of expected returns, Journal of financial economics, 86(2) (2007) 446-478.
[16] S. Mohebbi, X. Li, Designing intelligent agents to support long-term partnership in two echelon e-Supply Networks, Expert Systems with Applications, 39(18) (2012) 13501-13508.
[17] A. Nagurney, J. Cruz, J. Dong, D. Zhang, Supply chain networks, electronic commerce, and supply side and demand side risk, European journal of operational research, 164(1) (2005) 120-142.
[18] E. Perea-Lopez, B.E. Ydstie, I.E. Grossmann, A model predictive control strategy for supply chain optimization, Computers & Chemical Engineering, 27(8-9) (2003) 1201-1218.
[19] M. Sakawa, I. Nishizaki, Y. Uemura, Fuzzy programming and profit and cost allocation for a production and transportation problem, European Journal of Operational Research, 131(1) (2001) 1-15.
[20] D. Simchi-Levi, E. Simchi-Levi, P. Kaminsky, Designing and managing the supply chain: Concepts, strategies, and cases, McGraw-Hill New York, 1999.
[21] G. Stoneburner, A.Y. Goguen, A. Feringa, Sp 800-30. risk management guide for information technology systems, (2002).
[22] C.H. Timpe, J. Kallrath, Optimal planning in large multi-site production networks, European Journal of Operational Research, 126(2) (2000) 422-435.
[23] W. Wang, Y. Zhang, Y. Li, X. Zhao, M. Cheng, Closed-loop supply chains under reward-penalty mechanism: Retailer collection and asymmetric information, Journal of cleaner production, 142 (2017) 3938-3955. | ||
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