The synthesis model as a planning tool for effective supply chains resistant to adverse events

Semenov, Iouri; Jacyna, Marianna

doi:10.17531/ein.2022.1.16

Artykuł - szczegóły

Tytuł artykułu

The synthesis model as a planning tool for effective supply chains resistant to adverse events

Autorzy

Semenov Iouri , Jacyna Marianna

Treść / Zawartość

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DOI

10.17531/ein.2022.1.16

Warianty tytułu

Języki publikacji

Abstrakty

The article presents the problem of planning effective modular supply chains to resist adverse events. The lack of effective models for planning such supply chains based on the synergy of individual links widens the knowledge gap in this area. The analyses confirm the legitimacy of forming effective and reliable supply chains ready for fitting supplies to specific orders, adaptation to flexible and innovative transformations, and minimization of time losses and costs of restoring supply capacity in case of a threat. The authors provide a theoretical analysis of the problem and present a proprietary approach to constructing reliable modular supply chains in the automotive industry. It has been shown that the synergy effect can measure the effectiveness of the design of such chains. A proprietary synthesis model was presented. The model can serve as a tool to support the planning of modular supply chains that are resistant to adverse events.

Słowa kluczowe

synthesis model structural-functional module supply chains effectiveness process reliability

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2022

Tom

Vol. 24, no. 1

Strony

140--152

Opis fizyczny

Bibliogr. 67 poz., rys., tab.

Twórcy

autor

Semenov Iouri

iouri.semenov@wsb.szczecin.pl

WSB University in Poznań, 5 Powstańców Wielkopolskich, 61-895 Poznań, Poland

https://orcid.org/0000-0002-2250-9591

autor

Jacyna Marianna

marianna.jacyna@pw.edu.pl

Warsaw University of Technology Faculty of Transport, 75 Koszykowa str. Warsaw, Poland

https://orcid.org/0000-0002-7582-4536

Bibliografia

1. Autry C W, Bobbitt LM. Supply chain security orientation: conceptual development and a proposed framework. The International Journal of Logistics Management 2008; 19(1): 42-64, https://doi.org/10.1108/09574090810872596.
2. Aylward D, Clements M. Crafting a local-global nexus in the Australian wine industry. Journal of Enterprising communities: People and Places in the Global Economy 2008; 2(1): 73-87, https://doi.org/10.1108/17506200810861267.
3. Barchański A, Żochowska R. Estimation of critical gaps and follow-up times at median uncontrolled T-intersection. Archives of Transport 2021; 60(4): 105-124, https://doi.org/10.5604/01.3001.0015.6030.
4. Bayardo R Jr, Efficiently Mining Long Patterns from Databases. Proceeding of the ACM-SIGMOD International Conference on Management of Data, 1998; 85-93, https://doi.org/10.1145/276305.276313.
5. Berger S, Bierwirth C. Solutions to the request reassignment problem in collaborative carrier networks Transportation Research Part E 2010; 46: 627-638, https://doi.org/10.1016/j.tre.2009.12.006.
6. Christopher M, Holweg M. Supply Chain 2.0: managing supply chains in the era of turbulence. International Journal of Physical Distribution and Logistics Management 2011; 41(1): 63-82, https://doi.org/10.1108/09600031111101439.
7. Clark KB, Baldwin CY. Design Rules: The Power of Modularity. Cambridge MIT Press 1999, https://doi.org/10.7551/mitpress/2366.001.0001.
8. De Blok C, Meijboom B, Luijkx K, Schols J, Schroeder R. Interfaces in Service Modularity: A Typology Developed in Modular Healthcare Provision. Journal of Operations Management 2014; 32 (4): 175-189, https://doi.org/10.1016/j.jom.2014.03.001.
9. Deloitte Consulting. The Deloitte Automotive Value Chain Industry Model 2017; https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consumer-business/us-cb-future-of-the-automotive-supplier-industry-outlook.pdf.12/2017.
10. Doran D. Rethinking the supply chain: an automotive perspective. Supply Chain Management: An International Journal 2004; 9(1): 102-109,https://doi.org/10.1108/13598540410517610.
11. Eckbo E. Horizontal Mergers, Collusion, and Stockholder Wealth, Journal of Financial Economics 1983; 11: 241-273, https://doi.org/10.1016/0304-405X(83)90013-2.
12. Eppinger S, Browning, T. Design Structure Matrix Methods and Applications (Engineering Systems), MIT Press 2016.
13. Faisal MN, Banwet DK, Shankar R. Supply Chain Risk mitigation: modelling the enablers. Business Process Management Journal 2006; 12(4): 535-552, https://doi.org/10.1108/14637150610678113.
14. Frazzon EM. et al.: Spare parts supply chains' operational planning using technical condition information from intelligent maintenance systems Annual Reviews in Control 2014, https://doi.org/10.1016/j.arcontrol.2014.03.014.
15. Ganguly K. Integration of analytic hierarchy process and Dempster-Shafer theory for supplier performance measurement considering risk. International Journal of Productivity and Performance Management 2014; 63(1): 85-102, https://doi.org/10.1108/IJPPM-10-2012-0117.
16. Ghadge A, Dahi S, and Kalawsky R. Supply Chain Risk Management: present and future scope. International Journal of Logistics Management 2012; 23(3): 313-339, https://doi.org/10.1108/09574091211289200.
17. Ghadge A, Dan S, Chester M, Kalawsky R. A systems approach for modelling supply chain risks. Supply chain management: An International Journal 2013; 18(5): 523-538, https://doi.org/10.1108/SCM-11-2012-0366.
18. Göhlich D, Hildebrand S, Schellert D. Augmented DSM sequencing to support product development planning. International Design Conference - DESIGN 2018; 1139-1148, https://doi.org/10.21278/idc.2018.0434.
19. Gupta S. Mergers and acquisitions for enhancing supply chain competitiveness. Journal of Marketing and Operations Management Research; Hauppauge Tom 2, 2012; 3: 129-147.
20. He P, Li J. Vehicle routing problem with partly simultaneous pickup and delivery for the cluster of small and medium enterprises. Archives of Transport 2018; 45(1): 35-42, https://doi.org/10.5604/01.3001.0012.0940.
21. He W. An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network. Discrete Dynamics in Supply Chain Management 2013; 5: 1-7, https://doi.org/10.1155/2013/537675.
22. Heckmann I. Towards Supply Chain Risk Analytics: Fundamentals, Simulation Optimization. Springer, 2016, https://doi.org/10.1007/978-3-658-14870-6.
23. Ho C. et al.: Measuring system performance of an ERP-based supply chain. International Journal of Production Research 2006; 45 (6): 1255-1277, https://doi.org/10.1080/00207540600635235.
24. Izdebski M, Jacyna-Gołda I, Markowska K, Murawski J. Heuristic algorithms applied to the problems of servicing actors in supply chains. Archives of Transport 2017; 44(4): 25-34, https://doi.org/10.5604/01.3001.0010.6159.
25. Jacyna M, Izdebski M, Szczepański E, Gołda P. The task assignment of vehicles for a production company, Symmetry-Basel 2018; 11(10): 1-19, https://doi.org/10.3390/sym10110551.
26. Jacyna M, Jachimowski R, Szczepański E, Izdebski M. Road vehicle sequencing problem in a railroad intermodal terminal - simulation research. Bulletin of the Polish Academy of Sciences, Technical Sciences 2020; 68(5): 1135-1148.
27. Jacyna M, Semenov I. Models of vehicle service system supply under information uncertainty. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(4): 694-704, https://doi.org/10.17531/ein.2020.4.13.
28. Jacyna M, Wasiak M, Lewczuk K, Chamier-Gliszczyński N, Dąbrowski T. Decision problems in developing proecological transport system. Annual Set The Environment Protection 2018; 20: 1007-1025.
29. Jacyna-Gołda I, Lewczuk K. The method of estimating dependability of supply chain elements on the base of technical and organizational redundancy of process. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017;19(3): 382-392, https://doi.org/10.17531/ein.2017.3.9.
30. Jacyna-Golda I, Wasiak M, Izdebski M, Lewczuk K, Jachimowski R, Pyza D. The Evaluation of the Efficiency of Supply Chain Configuration. Proceedings of the 20th international scientific conference Transport Means 2016, Kaunas Univ Technol, 953-957.
31. Jacyna-Gołda I. Evaluation of operational reliability of the supply chain in terms of the control and management of logistics processes, [in:] Safety and Reliability: Methodology and Applications/ Nowakowski T. [i in.] (ed.), CRC Press Taylor & Francis Group, 2015: 549-558.
32. Jia F, Rutherford C. Mitigation of supply chain relational risk caused by cultural differences between China and the West. The International Journal of Logistics Management 2010; 21(2): 251-270, https://doi.org/10.1108/09574091011071942.
33. Jing Zhu et al. Effects of upstream and downstream mergers on supply chain profitability. European Journal of Operational Research 2016; 249(1): 131-143. https://doi.org/10.1016/j.ejor.2015.08.030
34. Jixin Zhao J, Meng Ji Bo Feng. Smarter supply chain: a literature review and practices. Journal of Data, Information and Management 2020; 2: 95-110. https://doi.org/10.1007/s42488-020-00025-z
35. Johnson N, Elliott D, Drake P. Exploring the role of social capital in facilitating supply chain resilience. Supply Chain Management: An International Journal 2013; 18(3): 324-336, https://doi.org/10.1108/SCM-06-2012-0203.
36. Jufang Li, Yuan T, Xiao X, Yu G. Applied Research of an improved Apriori algorithm in the logistics industry. 5th International Conference on Measurement, Instrumentation and Automation ICMIA 2016; 356-360.
37. Khan O, Christopher M, Burnes B. The impact of product design on supply chain risk: a case study. International Journal of Physical Distribution & Logistics Management 2008; 38(5): 412-432, https://doi.org/10.1108/09600030810882834.
38. Khan O, Christopher M, Creazza A. Aligning product design with the supply chain: a case study. Supply Chain Management: An International Journal 2012; 17(3): 323-336, https://doi.org/10.1108/13598541211227144.
39. Kim CH, Weston RH, Hodgson A, Lee KH. The complementary use of IDEF and UML Modelling approaches. Computers in Industry 2003; 50(1): 35-56, https://doi.org/10.1016/S0166-3615(02)00145-8.
40. Kunlin Z, Gang R. Study of supply chain monitoring system based on IDEF method. International Conference on Logistics Systems and Intelligent Management (ICLSIM) 2010; 278 - 281.
41. Li G, Fan H, Lee PK, Cheng TCE. Joint supply chain risk management: An agency and collaboration perspective. International Journal of Production Economics 2015; 164: 83-94, https://doi.org/10.1016/j.ijpe.2015.02.021.
42. Lin Y, Zhou L. The impacts of product design changes on supply chain risk: a case study. International Journal of Physical Distribution & Logistics Management 2011; 41(2):162-186, https://doi.org/10.1108/09600031111118549.
43. Luksch, S. After sales supply chain risk management. University of Louisville 2014; (5): 137.
44. Manuj I, Esper TL, Stank TP. Supply chain risk management approaches under different conditions of risk. Journal of Business Logistics 2014; 35(3): 241-258, https://doi.org/10.1111/jbl.12051.
45. Michlowicz E, Wojciechowski J. A method for evaluating and upgrading systems with parallel structures with forced redundancy. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (4): 770-776, http://doi.org/10.17531/ein.2021.4.19.
46. Mitchell M. et al. The impact of industry shocks on takeover and restructuring activity. Journal of Financial Economics June 1996; 41(2): 193-229, https://doi.org/10.1016/0304-405X(95)00860-H.
47. Moufad I, Jawab F. Mixed applied survey methodology for planning/enforcement of urban logistics delivery bays- An application to the Moroccan context. Archives of Transport 2020; 55(3): 95-110, https://doi.org/10.5604/01.3001.0014.4237.
48. Nooraie SV, Parast MM. A multi-objective approach to supply chain risk management: Integrating visibility with supply and demand risk. International Journal of Production Economics 2015; 161: 192-200, https://doi.org/10.1016/j.ijpe.2014.12.024.
49. Olson DL. A review of supply chain data mining publications. Archives, January 2020 - June 2020; 1(1-2).
50. Peters VJT. et. Al. Interfaces in service modularity: a scoping review. International Journal of Production Research 2018; 56(20): 6591-6606, https://doi.org/10.1080/00207543.2018.1461270.
51. Qazi A, Quigley J, Dickson A, Ekici ŞÖ. Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies. European Journal of Operational Research 2017; 259(1): 189-204, https://doi.org/10.1016/j.ejor.2016.10.023.
52. Reisig W. The Synthesis Problem. In: Jensen K., van der Aalst W.M.P., Balbo G., Koutny M., Wolf K. (ed.) Transactions on Petri Nets and Other Models of Concurrency VII. Lecture Notes in Computer Science, Springer 2013; 7480, https://doi.org/10.1007/978-3-642-38143-0_8.
53. Riley JM, Klein R, Miller J, Sridharan V. How internal integration, information sharing, and training affect supply chain risk management capabilities. International Journal of Physical Distribution & Logistics Management 2016; 46(10): 953-980, https://doi.org/10.1108/IJPDLM-10-2015-0246.
54. Rudyk T, Szczepański E, Jacyna M. Safety factor in the sustainable fleet management model. Archives of Transport 2019; 49(1): 103-114, https://doi.org/10.5604/01.3001.0013.2780.
55. Sawik T. Stochastic versus Deterministic Approach to Coordinated Supply Chain Scheduling. Mathematical Problems in Engineering 2017, https://doi.org/10.1155/2017/3460721.
56. Spring M, Santos J. Interfaces in Service and Process Modularity. Paper presented at the 5th International Seminar on ervice Architecture and Modularity, Copenhagen, January 16-17, 2014.
57. Szczepański E, Jachimowski R, Izdebski M, Jacyna-Gołda I. Warehouse location problem in supply chain designing: a simulation analysis. Archives of Transport 2019; 50(2): 101-110. https://doi.org/10.5604/01.3001.0013.5752.
58. Tse Y K, Chung SH, Lim MK. Quality Risk in Global Supply Network. Journal of Manufacturing Technology Management 2011; 22(8):1002-1013, https://doi.org/10.1108/17410381111177458.
59. Valverde R. Data Mining: A Tool to Increase Productivity in Supply Chain Management. Electronic Journal 2015, https://doi.org/10.2139/ssrn.2642145.
60. Van der Laan MR. The Feasibility of Modularity in Professional Service Design: Towards Low Cost Person-centred Care. PhD diss., University of Groningen 2015, https://doi.org/10.1108/IJOPM-06-2015-0370.
61 Wang Y, Wiegerinck V, Krikke H, Zhang H. Understanding the purchase intention towards remanufactured product in closed-loop supply chains: An empirical study in China. International Journal of Physical Distribution & Logistics Management 2013; 43(10): 866-888, https://doi.org/10.1108/IJPDLM-01-2013-0011.
62. Wieland A, Marcus Wallenburg C. The influence of relational competencies on supply chain resilience: a relational view. International Journal of Physical Distribution & Logistics Management 2013; 43(4): 300-320, https://doi.org/10.1108/IJPDLM-08-2012-0243.
63. Yeh YP. Identification of factors affecting continuity of cooperative electronic supply chain relationships: empirical case of the Taiwanese motor industry. Supply Chain Management: An International Journal 2005; 10(4): 327-335, https://doi.org/10.1108/13598540510612802.
64. Zepeda ED, Nyaga GN, Young G J. Supply chain risk management and hospital inventory: Effects of system affiliation. Journal of Operations Management 2016; 44: 30-47, https://doi.org/10.1016/j.jom.2016.04.002.
65. Zhenhong Lang. The Improved Apriori Algorithm based on Matrix Pruning and Weight Analysis. AIP Conference Proceedings 2018; 040113, https://doi.org/10.1063/1.5033777.
66. Żochowska R, Soczówka P. Method for identifying hazardous road locations at the intersection of tramlines and road traffic. Scientific Journal of Silesian University of Technology. Series Transport. 2017; 97: 201-213, https://doi.org/10.20858/sjsutst.2017.97.18.
67. Zsidisin GA, Ellram LM, Carter JR, Cavinato JL. An analysis of supply risk assessment techniques. International Journal of Physical Distribution & Logistics Management 2014; 34(5): 397-413, https://doi.org/10.1108/09600030410545445.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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