BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Sarkar Bidyut Biman (Techno India University, Calcutta, India), Cortesi Agostino (Ca' Foscari University of Venice), Chaki Nabendu (University of Calcutta, India)
Tytuł
Modeling Demand Forecast Variance in a Distributed Supply Chain Network Using Generalized Stochastic Petri Nets
Modelowanie prognoz zmienności popytu w rozproszonej sieci logistycznej z użyciem uogólnionych stochastycznych sieci Petriego
Źródło
Informatyka Ekonomiczna / Uniwersytet Ekonomiczny we Wrocławiu, 2013, nr 3 (29), s. 128-151, rys., tab., bibliogr. 41 poz.
Business Informatics / Uniwersytet Ekonomiczny we Wrocławiu
Słowa kluczowe
Zarządzanie łańcuchem dostaw, Sieć Petriego
Supply Chain Management (SCM), Petri net
Uwagi
streszcz., summ.
Abstrakt
Jednym z podstawowych problemów w systemach zarządzania łańcuchem dostaw jest rozwiązanie kompromisu między efektywnością a zmiennością potrzeb. W przypadku braku zmienności potrzeb klientów, cykliczności zamówień, portfela zamówień i czasu dystrybucji zarządzanie łańcuchem dostaw jest rutynowym procesem biznesowym. Niestety, w praktyce zarządzania taka sytuacja rzadko występuje, rozwiązanie problemu zmienności zapotrzebowania jest zatem jednym z głównych aktualnych wyzwań, mających na celu zmniejszenie ryzyka bezpiecznego zapasu bez wpływu na realizację potrzeb klientów. W artykule zbadano zmienność łańcucha dostaw przy założeniu wielu dostawców, producentów, dystrybutorów, hurtowników, sprzedawców i klientów, wykorzystując uogólnioną stochastyczną sieć (GSPN). Model pozwala na zachowanie jednolitego stanu zapasów magazynowych w łańcuchu dostaw; rozpatrzono efekt bullwhip (BWE) i niepewności w podejmowaniu decyzji.(abstrakt oryginalny)

There is a trade-off in Supply Chain Management Systems between efficiency and demand variability. When no variation occurs in consumer need, order cycle, product portfolios, and in distribution lead time, then the supply chain would be just a routine business process. Unfortunately, in practice this is not often the case. Thus, ranking demand variability is one of the prime challenges to reduce safety stock without affecting customer demand. This paper studies supply chain demand variability with multiple suppliers, manufacturers, distributors, wholesalers, retailers, and customers as tiers, and each stage as an echelon that faces stochastic demand volatility. A Generalized Stochastic Petri-Net (GSPN) model is proposed in a distributed scenario to synchronize the response capabilities among the players in the chain, and to lower down the supplier demand variance with scheduled ordering policies. Maintaining a uniform inventory stock throughout the chain has two main effects: the bullwhip effect (BWE) will be negligible, and uncertainty in decision making at each echelon will be reduced substantially.(original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Agrawal S., Sengupta R.N., Shanker K., Impact of information sharing and lead time on bullwhip effect and on-hand inventory, "European Journal of Operational Research" 2009, vol. 192(2), pp. 576-593.
  2. Ajmone-Marsan M., Conte G., Balbo G., A Class of generalized Stochastic Petri Nets for the Performance Evaluation of Multiprocessor Systems, "ACM Transactions on Computer Systems"1984, vol. 2(2), pp. 93-122.
  3. Anderson Jr. E.G., Morrice D.J., A simulation game for service-oriented supply chain management: Does information sharing help managers with service capacity decisions. Production and Operations Management, "International Journal of Logistics Management" 2000, vol. 9(1), pp, 40-55.
  4. Bonet P., A Petri Net Tool for Performance Modelling, PIPE (CLEI), 2007, vol. 2(5).
  5. Bottani E., Montanari R., Supply chain design and cost Analysis through simulation, "International Journal of Production Research" 2010, vol. 48(10), pp. 2859-2886.
  6. Boute R.N., Lambrecht M.R., Van Houdt B., Performance evaluation of a production/inventory system with periodic review and endogenous lead times, "Willey Journal of Naval Research Logistics" 2007, 54(4), pp. 462-473, DOI: 10.1002/nav.20222.
  7. Buchmeister B., Investigation of the Bullwhip effect using spread sheet simulation, "International Journal of Simulation Model" 2008, 7(1), pp. 29-41.
  8. Cachon G.P., Randall T., Schmidt G.M., In search of the bullwhip effect manufacturing & service, "Journal of Operations Management" 2007, vol. 9(4), pp. 457-479.
  9. Centeno M.A., Pérez J.E., Quantifying the Bullwhip Effect in the Supply Chain of small-sized companies, Sixth LACCEI International Latin American and Caribbean Conference for Engineering and Technology (LACCEI'2008), 2008.
  10. Chaki, Nabendu, Bhattacharya S., Performance analysis of multistage interconnection networks with a new high-level net model, "Journal of Systems Architecture" 2006, vol. 52(1), pp. 56-70.
  11. Chaki, Nabendu, Sarkar B.B., Virtual data warehouse modeling using petri nets for distributed decision making, "Journal of Convergence Information Technology (JCIT)" 2010, vol. 5(5), pp. 8-21.
  12. Chen F., Drezner Z., Ryan J.K., Simchi-Levi D., Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information, "Management Science" 2000, vol. 4(3), pp. 436-443.
  13. Chen L., Lee H.L., Bullwhip effect measurement and its implications, Duke University Working Papers Series (Currently under revision for Management Science), 2009.
  14. Cunningham, Colleen et al., Design and Research Implications of Customer relationship Management on Data Warehousing and CRM Decisions, [in:] Proceedings of the 2003 Information Resources Management Association International Conference (IRMA 2003), 2003, pp. 82-85.
  15. Dejonckheere J., Disney S.M., Lambrecht M.R, Towill D.R., The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective, "European Journal of Operational Research" 2004, vol. 153(3), pp. 727-750.
  16. Disney S.M.,Towill D.R., Vendor-managed inventory and bullwhip reduction in a two-level supply chain, "International Journal of Operation and Production Management" 2003, vol. 23(6), pp. 625--651.
  17. Forrester Jay Wright, Industrial Dynamics, MIT Press, 1961, ISBN-10: 1883823366.
  18. Fransoo J.C., Wouters-Marc J.F., Measuring the bullwhip effect in the supply chain, "Supply Chain Management: An International Journal" 2000, vol. 5(2), pp.78-89.
  19. Geary S., Disney S.M., Towill D.R., Bullwhip in Supply Chains: Past, Present and Future, 17th International Conference on Production Research, Virginia, 3-7 August 2003.
  20. Han J., Kamber M., Data Mining Concepts and Techniques, Morgan Kaufman Publishers, 2004, ISBN: 81-8147=049-4, pp. 39-106.
  21. Hau L.L., Padmanabhan V., Whang, Seungjin, The bullwhip effect in supply chains,"Sloan Management Review" 1997, vol. 38(3), pp. 93-102.
  22. Hohmann S., Zelewski S., Effects of vendor-managed inventory on the bullwhip effect, "International Journal of Information Systems and Supply Chain Management (IJISSCM)" 2011, vol. 4(3), pp. 1-17.
  23. Huan S., Sheoran S., Wang G., A review and analysis of supply chain operations reference (SCOR) model, "Supply Chain Management: An International Journal" 2004, vol. 9(1), pp. 23-29.
  24. Hussain, Matloub P., Drake R., Dong M.L., Quantifying the Impact of a Supply Chain's Design Parameters on the Bullwhip Effect, 7th Global IEE Conference on Business & Economics, Italy, 2007, pp. 210-213.
  25. Jensen K., Colored Petri Nets - Basic Concepts, Analysis Methods and Practical Use: Basic Concepts, vol. 1, Springer-Verlag, London 1996, p. 234.
  26. Krogstie J., Using a semiotic framework to evaluate UML for the development of models of high quality, Idea Group Publishing, chapter 5, 2001, pp.89-106.
  27. Lee H., Billington C., Managing supply chain inventory: Pitfalls and opportunities, "Sloan Management Review" 1992, 33, pp. 65-73.
  28. Padmanabhan P., Whang S., Information distortion in a supply chain: The bullwhip effect, "Management Sci." 1997, 43, pp. 546-558.
  29. Lewlyn L., Rodrigues R., Hebbar, Sunith, Herle, Ramdev, Bullwhip effect mitigation in trading system: A system dynamics approach, [in:] Proceedings of the World Congress on Engineering, UK, 2011, vol. I, ISSN: 2078-0958.
  30. Obeidat R., Zaatreh Z., Motivation for Integrating Supply Chains Using Service Oriented Architecture Approach, Proceedings of NGMAST,2010, pp.102-105.
  31. Puigjaner L., Lainez J.M., Capturing dynamics in integrated supply chain management, "Computers and Chemical Engineering" 2008, vol. 32 (11), pp. 2582-2605.
  32. Ramchandani C., Analysis of Asynchronous Concurrent Systems by Petri Nets, Project MAC, TR-120, M.I.T., Cambridge 1974.
  33. Sherer S.A., Enterprise applications for supply chain management, "International Journal of Information Systems and Supply Chain Management (IJISSCM)" 2010, vol. 3(3), pp.18-28.
  34. Simchi-Levi D., Kaminsky P., Simchi-Levi E., Designing and Managing the Supply Chain, 3rd edition, McGraw-Hill, Irwin, New York 2008, ISBN: 9780071198967.
  35. Sterman J.D., Modeling managerial behaviour: Misperceptions of feedback in dynamic decision making experiment, "Journal of Management Science" 1989, vol. 35(3), pp. 321-339.
  36. Tado Murata, Petri Nets: Properties, Analysis and Applications, Proceedings of the IEEE, vol. 77, no. 4, April 1989, pp. 541-580, ISBN: 0-387-13723.
  37. Torres O.C., Maltz A.B., Understanding the financial consequences of the bullwhip effect in a multi-echolon supply chain, "Journal of Business Logistic" 2010, vol. 31(1).
  38. Wang E., A Virtual Integration Theory of Improved Supply Chain Performance, "Journal of Management Information Systems" 2006, vol. 23(2), pp. 41-66.
  39. Wang J., Timed Petri Nets: Theory and application, chapter 1 & 2, vol. 3(32), Kluwer Academic Publishers, 1998, pp. 1-134.
  40. Wang W., Analysis of Bullwhip effects in Perishable Product Supply Chain Based on System Dynamics Model, IEEE, International Conference on Intelligent Computation Technology and Automation, 2011, vol. 1, pp. 1018-1021.
  41. Zhuoqun LI., A Multi-stages and tree-shape supply chain model based simulation system for bullwhip effect, "Journal of Computational Information Systems" 2011, vol. 7(1), pp. 281-289.
Cytowane przez
Pokaż
ISSN
1507-3858
Język
eng
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu