Analysis of Collaborative Planning, Forecasting, and Replenishment Approach in Supply Chain Collaboration with Mathematical Model

Yıl 2025, Cilt: 9 Sayı: 2, 175 - 184

Ömer Karakoç

Öz

Market globalization, increased competition, and evolving consumer preferences have made businesses need to adopt more integrated and collaborative supply chain management strategies. The importance of data sharing and collaboration among businesses is growing due to the opportunities presented by technological advancements. This development explores collaborative planning techniques in supply chain management and highlights the potential benefits of integrating Industry 4.0 technology with the Collaborative Planning, Forecasting, and Replenishment (CPFR) model. In this context, the research outlines the theoretical foundations of the CPFR methodology and develops a mathematical optimization model to demonstrate its potential applications. The model enables optimizing decisions related to inventory, backorders, discounts, and orders within a framework where a buyer collaborates with two suppliers. It considers delivery timeframes, capacity limitations, and discount thresholds, providing an analytical framework that supports data-driven decision-making.

Anahtar Kelimeler

: Supply Chain Collaboration , Industry 4.0 , Digital transformation , Flexibility

Tedarik Zinciri İşbirliğinde İşbirlikçi Planlama, Tahmin ve Yenileme Yaklaşımının Matematiksel Modelle Analizi

Öz

Pazar küreselleşmesi, artan rekabet ve değişen tüketici tercihleri, işletmelerin daha entegre ve işbirlikçi tedarik zinciri yönetimi stratejileri benimsemesini gerekli kılmıştır. Teknolojik gelişmelerin sunduğu fırsatlar nedeniyle işletmeler arasında veri paylaşımının ve iş birliğinin önemi artmaktadır. Bu gelişme, tedarik zinciri yönetiminde iş birlikçi planlama tekniklerini incelemekte ve Endüstri 4.0 teknolojisinin İşbirlikçi Planlama, Tahmin ve Yenileme (CPFR) modeliyle entegre edilmesinin potansiyel faydalarını vurgulamaktadır. Bu bağlamda, araştırma CPFR metodolojisinin teorik temellerini özetlemekte ve potansiyel uygulamalarını göstermek için bir matematiksel optimizasyon modeli geliştirmektedir. Model, bir alıcının iki tedarikçiyle iş birliği yaptığı bir çerçeve içinde envanter, geri siparişler, indirimler ve siparişlerle ilgili kararların optimize edilmesini sağlar. Teslimat sürelerini, kapasite sınırlamalarını ve indirim eşiklerini dikkate alarak veri odaklı karar almayı destekleyen analitik bir çerçeve sunmaktadır.

Anahtar Kelimeler

Matematiksel Modelle SCC , Tedarik Zinciri İşbirliği , Endüstri 4.0 , Dijital dönüşüm , Esneklik

Kaynakça

• Ageron, B., Gunasekaran, A. and Spalanzan, A. (2012). Sustainable supply management: An empirical study. International Journal of Production Economics, 140 (1): 168–182.
• Atzeni, E., Iuliano, L., Minetola, P., & Salmi, A., Redesign and cost estimation of rapid manufactured plastic parts. Rapid Prototyping Journal 16(5), 308–317 (2010).
• Audy, J.-F. , Lehoux, N. , D’Amours, S. and Rönnqvist, M. (2012), “A framework for an efficient implementation of logistics collaborations”, International Transactions in Operational Research , Vol. 19 No. 5, pp. 633-657.
• Boddy, D. , Macbeth, D. and Wagner, B. (2000), “Implementing collacoration between organizations: an empirical study of supply chain partnering”, Journal of Management Studies , Vol. 37 No. 7, pp. 1003-1018.
• Boyacigiller, N., The role of expatriates in the management of interdependence, complexity, and risk in multinational corporations. Journal of International Business Studies 21(3), 357–381 (1990).
• Burnette, R. (2010), “CPFR: fact, fiction, or fantasy?”, Journal of Business Forecasting , Vol. 29 No. 4, pp. 32-35.
• Caridi, M. , Cigolini, R. and De Marco, D. (2005), “Improving supply-chain collaboration by linking intelligent agents to CPFR”, International Journal of Production Research , Vol. 43 No. 20, pp. 4191-4218.
• Caridi, M. , Cigolini, R. and Marco, D. (2006), “Linking autonomous agents to CPFR to improve SCM”, Journal of Enterprise Information Management , Vol. 19 No. 5, pp. 465-482.
• Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of operations management, 29(3), 163-180.
• Chang, K.K. and Wang, F.K. (2008), “Applying six sigma methodology to collaborative forecasting”, The International Journal of Advanced Manufacturing Technology , Vol. 39 Nos 9-10, pp. 1033-1044.
• Chang, T.-H. , Fu, H.-P. , Lee, W.-I. , Lin, Y. and Hsueh, H.-C. (2007), “A study of an augmented CPFR model for the 3C retail industry”, Supply Chain Management: An International Journal , Vol. 12 No. 3, pp. 200-209.
• Christopher, M. G., Logistics and Supply Chain Management. London: Pitman (1992).
• Clark, K. B., & Fujimoto, T., Product development performance: Strategy, organization, and management in the World auto industry. Boston, MA: Harvard Business School Press (1991).
• Clemons, E., & Row, M., Information technology and industrial cooperation: The changing economics of coordination and ownership. Journal of Management Information Systems 9(2), 9–28 (1992).
• Dai, W., Cantor, D. E., & Montabon, F. L., A taxonomy of green supply chain management capability among electronics manufacturers in China. Journal of Supply Chain Management 48(2), 73–95 (2012).,
• Danese, P. (2011), “Towards a contingency theory of collaborative planning initiatives in supply networks”, International Journal of Production Research , Vol. 49 No. 4, pp. 1081-1103.
• Du, X.F. , Leung, S.C.H. , Zhang, J.L. and Lai, K.K. (2009), “Procurement of agricultural products using the CPFR approach”, Supply Chain Management: An International Journal , Vol. 14 No. 4, pp. 253-258.
• Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110-128.
• Ellinger, A.E., Improving Marketing / Logistics Cross-Functional Collaboration in the Supply Chain. Industrial Marketing Management 29(1), 85–96 (2000).
• Ferdows, K., Lewis, M. A., & Machuca, J. A. D.: Rapid-fire fulfillment. Harvard Business Review 82(11), 104–110 (2004).
• Gogate, A., & Pande, S. S., Intelligent layout planning for rapid prototyping. International Journal of Production Research 46(20), 5607–5631 (2008).
• Gray, J., & Harvey, T., Quality value banking: Effective management systems that increase earnings, lower costs, and provide competitive customer service. New York: Wiley (1992).
• Grewal, D., Hulland, J., Kopalle, P.K. et al. The future of technology and marketing: a multidisciplinary perspective. J. of the Acad. Mark. Sci. 48, 1–8 (2020).
• Gualandris, J., & Kalchschmidt, M.: Developing environmental and social performance: The role of suppliers’ sustainability and buyer–supplier trust. International Journal of Production Research 54(8), 2470–2486 (2016).
• Hill, C. A., Zhang, G. P., & Miller, K. E. (2018). Collaborative planning, forecasting, and replenishment & firm performance: An empirical evaluation. International journal of production economics, 196, 12-23.
• Hollmann, R. L., Scavarda, L. F., & Thomé, A. M. T. (2015). Collaborative planning, forecasting and replenishment: a literature review. International Journal of Productivity and Performance Management, 64(7), 971-993.
• Ivanov, D., Dolgui, A., Das, A., Sokolov, B. Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds) Handbook of Ripple Effects in the Supply Chain. International Series in Operations Research & Management Science, vol 276. Springer, Cham. (2019)
• Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83- 111.
• Kache, F., & Seuring, S.: Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management 37(1), 10–36 (2017).
• Kagermann, H.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final Report of the Industrie 4.0 Working Group (2014).
• Kamalahmadi, M., & Parast, M. M.: The impact of flexibility and collaboration on supply chain resilience. Supply Chain Management Review 26(3), 312–329 (2021).
• Kazancoglu, I., Sagnak, M., Kumar Mangla, S., & Kazancoglu, Y. (2021). Circular economy and the policy: A framework for improving the corporate environmental management in supply chains. Business Strategy and the Environment, 30(1), 590-608.
• La Bella, A., Morales-Alonso, G., Hidalgo, A., & Levialdi, N. G. (2022). Amazon Vendor Flex model: a business strategic alliance for sustainable development. In IFKAD23 (International Forum on Knowledge Assets Dynamics) Proceedings.
• Lamming, R. C.: Beyond Partnership: Strategies for Innovation and Lean Supply. Hemel Hempstead: Prentice Hall (1993).
• Lapide, L. (2010), “A history of CPFR”, Journal of Business Forecasting , Vol. 29 No. 4, pp. 29-31.
• Lee, H.L., & Whang, S.: E-Business and Supply Chain Integration. Stanford Global Supply Chain Management Forum, SGSCMF-W2-2001 (2001).
• Lejeune, N., & Yakova, N.: On characterizing the 4 C’s in supply chain management. Journal of Operations Management 23(1), 81–100 (2005).
• Li, S.: An integrated model for supply chain management practice, performance, and competitive advantage. Ph.D. Dissertation, University of Toledo (2002).
• Mentzer, J.T. , Soonhing, M. and Zacharia, Z.G. (2000), “The nature of interfir partnering in supply chian management”, Journal of Retailing , Vol. 76 No. 4, pp. 549-568.
• Özçelik, F., & Öztürk, B. A. (2014). A research on barriers to sustainable supply chain management and sustainable supplier selection criteria. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16(2), 259-279..
• Panahifar, F., Heavey, C., Byrne, P. J., & Fazlollahtabar, H. (2015). A framework for collaborative planning, forecasting and replenishment (CPFR): state of the art. Journal of Enterprise Information Management, 28(6), 838-871.
• Poler, R. , Hernandez, J.E. , Mula, J. and Lario, F.C. (2008), “Collaborative forecasting in networked manufacturing enterprises”, Journal of Manufacturing Technology Management , Vol. 19 No. 4, pp. 514-528.
• Petrovic, V. et al.: Additive layered manufacturing: Sectors of industrial application shown through case studies. International Journal of Production Research 49(4), 1061–1079 (2011).
• Ramanathan, U., & Gunasekaran, A.: Supply chain collaboration: Impact of success in long-term partnerships. International Journal of Production Economics 147(1), 252–259 (2012).
• Raghunathan, S. (1999), “Interorganizational collaborative forecasting and replenishment systems and supply chain implications”, Decision Sciences , Vol. 30 No. 4, pp. 1053-1071.
• Sallam, K., Mohamed, M., & Mohamed, A. W. (2023). Internet of Things (IoT) in supply chain management: challenges, opportunities, and best practices. Sustainable Machine Intelligence Journal, 2, (3): 1-32.
• Sari, K. (2008a), “Inventory inaccuracy and performance of collaborative supply chain practices”, Industrial Management & Data Systems , Vol. 108 No. 4, pp. 495-509.
• Sari, K. (2008b), “On the benefits of CPFR and VMI: a comparative simulation study”, International Journal of Production Economics , Vol. 113 No. 2, pp. 575-586.
• Scott-Morton, M. S. (Ed.): The Corporation of the 1990s: Information Technology and Organizational Transformation. New York: Oxford University Press (1991).
• Simatupang, T. M., & Sridharan, R The Collaborative Supply Chain. The International Journal of Logistics Management, 13(1), 15–30. DOI: 10.1108/09574090210806333 (2002). Singh, P. (2023). Digital transformation in supply chain management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation. International Journal of Supply Chain Management, 12(6), 57-63.
• Shu, T. , Chen, S. , Xie, C. , Wang, S. and Lai, K.K. (2010), “AVE-CPFR working chains on the basis of selection model of collaborative credit-granting guarantee approaches”, International Journal of Information Technology & Decision Making , Vol. 9 No. 2, pp. 301-325.
• Teo, T. et al.: Leveraging Collaborative Technologies to Build a Knowledge Sharing Culture at HP Analytics. MIS Quarterly Executive 10(1), 1–18 (2011).
• Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation research part e: logistics and transportation review, 79, 22-48.
• Van Hoek, R.I.: Measuring the Unmeasurable: Measuring and Improving Performance in the Supply Chain. Supply Chain Management 3(4), 187–192 (1998).
• VICS (2004), “CPFR an overview”, available at: www.gs1us.org (accessed January 2025).
• VICS (2010), “Linking CPFR and S & OP: a roadmap to integrated business planning”, available at: www.gs1us.org (accessed January 2025).
• Yuan, X. , Shen, L. and Ashayeri, J. (2010), “Dynamic simulation assessment of collaboration strategies to manage demand gap in high-tech product diffusion”, Robotics and Computer-Integrated Manufacturing , Vol. 26 No. 6, pp. 647-657.
• Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D.: Towards Industry 4.0 – Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-PapersOnLine 48(3), 579–584 (2015).
• Wong, C. Y., Boon-Itt, S., & Wong, C. W. (2011). The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance. Journal of Operations management, 29(6), 604-615.
• Wulfraat, M.: How Amazon Robotics has changed the landscape of fulfillment. MWPVL International (2020).
• Zacharia, Z. G., Nix, N. W., & Lusch, R. F.: Toward a theory of supply chain collaboration: Empirical evidence and future research directions. Journal of Business Logistics 41(2), 117–135 (2020). (3). https://doi.org/10.3390/e23030361