Supply chain resilience is widely receiving attention during the past decade. Collaboration and visibility enhancement in supply chains is a key to achieve resilience and robustness in supply chains. Collaborative Planning, Forecasting and Replenishment (CPFR) is always been one of the difficult, yet powerful tool for collaboration in supply chains. Companies, in general attempt to address the technological side of changes, but avoid addressing the non-technological side of it, while implementing CPFR. This paper aims to explore the technological and non-technological enablers of CPFR, separately considering the Indian manufacturing industries and study their causal relations, using the Interpretive Structural Modeling (ISM). The results are beneficial, as managers can concentrate on causal enablers, while implementing CPFR. The success factors for implementation can slightly vary across different industries, but the applicability of the result is wider due to several common issues that arise during its implementation. Thus, the paper aims to provide directions for considering the most influencing enablers that can act as critical factors in the successful implementation of the CPFR. These influential enablers can be given much focus to reduce the vulnerabilities and to enhance the resilience capabilities of firms and their supply chains.
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DOI : 10.1051/ro/2022075
Keywords: Supply chain resilience, Collaborative Planning, Forecasting and Replenishment (CPFR), information sharing, interpretive structural modeling (ISM)
@article{RO_2022__56_4_2139_0,
author = {Hemant, Joshi and Rajesh, R and Daultani, Yash},
title = {Causal modelling of the enablers of {CPFR} for building resilience in manufacturing supply chains},
journal = {RAIRO. Operations Research},
pages = {2139--2158},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {4},
doi = {10.1051/ro/2022075},
zbl = {1493.62052},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2022075/}
}
TY - JOUR AU - Hemant, Joshi AU - Rajesh, R AU - Daultani, Yash TI - Causal modelling of the enablers of CPFR for building resilience in manufacturing supply chains JO - RAIRO. Operations Research PY - 2022 SP - 2139 EP - 2158 VL - 56 IS - 4 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2022075/ DO - 10.1051/ro/2022075 LA - en ID - RO_2022__56_4_2139_0 ER -
%0 Journal Article %A Hemant, Joshi %A Rajesh, R %A Daultani, Yash %T Causal modelling of the enablers of CPFR for building resilience in manufacturing supply chains %J RAIRO. Operations Research %D 2022 %P 2139-2158 %V 56 %N 4 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2022075/ %R 10.1051/ro/2022075 %G en %F RO_2022__56_4_2139_0
Hemant, Joshi; Rajesh, R; Daultani, Yash. Causal modelling of the enablers of CPFR for building resilience in manufacturing supply chains. RAIRO. Operations Research, Tome 56 (2022) no. 4, pp. 2139-2158. doi: 10.1051/ro/2022075
[1] , , and , Retailing and supply chains in the information age. Technol. Soc. 22 (2000) 5–31. | DOI
[2] , Supply chain resilience: a multi-level framework. Int. J. Logistics Res. App. 22 (2019) 533–556. | DOI
[3] , , and , Forecasting models in the manufacturing processes and operations management: systematic literature review. J. Forecasting 39 (2020) 1043–1056. | MR | DOI
[4] and , Collaborative supply chain management. Bus. Process Manage. J. 13 (2007) 390–404. | DOI
[5] , On the benefits of collaborative forecasting partnerships between retailers and manufacturers. Manage. Sci. 53 (2007) 777–794. | Zbl | DOI
[6] and , Exploring the experiences of collaborative planning initiatives. Int. J. Phys. Distrib. Logistics Manage. 31 (2001) 266–289. | DOI
[7] , , and , Dynamic digital factories for agile supply chains: an architectural approach. J. Ind. Inf. Integr. 15 (2019) 111–121.
[8] and , Strategic tactical and operational production-distribution models: a review. Int. J. Technol. Manage. 28 (2004) 151–171. | DOI
[9] and , Analyzing of CPFR success factors using fuzzy cognitive maps in retail industry. Expert Syst. App. 39 (2012) 10438–10455. | DOI
[10] , , and , Managing logistics outsourcing relationships: an empirical investigation in China. J. Bus. Logistics 31 (2010) 279–299. | DOI
[11] and , Developing strategic partnerships in the supply chain: a practitioner perspective. Eur. J. Purchasing Supply Manage. 6 (2000) 117–127. | DOI
[12] and , Demand collaboration: what’s holding us back? Supply Chain Manage. Rev. 8 (2004) 54–61.
[13] , How contextual factors shape CPFR collaborations: a theoretical framework. Supply Chain Forum Int. J. 7 (2006) 16–26. | DOI
[14] , , , , , and , Is collaboration paying off for firms? Bus. Horizons 49 (2006) 61–70. | DOI
[15] , , and , A holistic and structured CPFR roadmap with an application between automotive supplier and its aftermarket customer. Int. J. Adv. Manuf. Technol. 91 (2017) 1567–1586. | DOI
[16] and , A supply chain model of vendor managed inventory. Transp. Res. Part E: Logistics Transp. Rev. 38 (2002) 75–95. | DOI
[17] , and , Collaborative forecasting in the food supply chain: a conceptual framework. Int. J. Prod. Econ. 158 (2014) 120–135. | DOI
[18] and , A multi-product model for evaluating and selecting two layers of suppliers considering environmental factors. RAIRO: Oper. Res. 51 (2017) 875–902. | MR | Zbl | Numdam | DOI
[19] and , Transporte colaborativo: conceituação, benefcios e práticas. Rev. Tecnol. 13 (2007).
[20] , CPFR: an emerging supply chain tool. Ind. Manage. Data Syst. 103 (2003) 14–21. | DOI
[21] , Comparing the factors that influence the adoption of CPFR by retailers and suppliers. Int. J. Logistics Manage. 27 (2016) 931–946. | DOI
[22] , , and , A study on factors for retailers implementing CPFR – a fuzzy AHP analysis. J. Syst. Sci. Syst. Eng. 19 (2010) 192–209. | DOI
[23] , , , and , Barriers to the transition towards off-site construction in China: an interpretive structural modeling approach. J. Cleaner Prod. 197 (2018) 8–18. | DOI
[24] and , CPFR. Supply Chain Manage. Rev. 1 (2000) 80–88.
[25] and , Trust and IT innovation in asymmetric environments of the supply chain management process. J. Comput. Inf. Syst. 54 (2014) 10–24.
[26] and , Developing a resilient supply chain through supplier flexibility and reliability assessment. Int. J. Prod. Res. 54 (2016) 302–321. | DOI
[27] , and , Inter-organizational information systems visibility in buyer–supplier relationships: the case of telecommunication equipment component manufacturing industry. Omega 39 (2011) 667–676. | DOI
[28] and , Information sharing in a supply chain. Int. J. Manuf. Technol. Manage. 1 (2000) 79–93. | DOI
[29] , and , A win–win collaboration approach for a two-echelon supply chain: a case study in the pulp and paper industry. Eur. J. Ind. Eng. 4 (2010) 493–514. | DOI
[30] , Effects of enterprise technology on supply chain collaboration: analysis of China-linked supply chain. Enterp. Inf. Syst. 6 (2012) 55–77. | DOI
[31] and , The study of CPFR implementation model in medical SCM of Taiwan. Prod. Planning Control 25 (2014) 260–271. | DOI
[32] and , Analyzing the barriers of green textile supply chain management in Southeast Asia using interpretive structural modeling. Sustainable Prod. Consumption 17 (2019) 176–187. | DOI
[33] , , and , The evolution of sales forecasting management: a 20-year longitudinal study of forecasting practices. J. Forecasting 25 (2006) 303–324. | MR | DOI
[34] , , , and , Developing lean and responsive supply chains: a robust model for alternative risk mitigation strategies in supply chain designs. Int. J. Prod. Econ. 183 (2017) 632–653. | DOI
[35] , and , Portfolio selection with robust estimators considering behavioral biases in a causal network. RAIRO: Oper. Res. 53 (2019) 577–591. | MR | Zbl | Numdam | DOI
[36] , and , Collaboration and integration through information technologies in supply chains. Int. J. Technol. Manage. 28 (2004) 259–273. | DOI
[37] , , and , A framework for collaborative planning, forecasting and replenishment (CPFR). J. Enterp. Inf. Manage. 28 (2015) 838–871. | DOI
[38] , and , A hybrid approach to the study of CPFR implementation enablers. Prod. Planning Control 26 (2015) 1090–1109. | DOI
[39] , , , , and , An application of AHP in the development process of a supply chain reference model focusing on demand variability. Oper. Res. 15 (2015) 337–357.
[40] , Technological capabilities and supply chain resilience of firms: a relational analysis using Total Interpretive Structural Modeling (TISM). Technol. Forecasting Soc. Change 118 (2017) 161–169. | DOI
[41] , Optimal trade-offs in decision-making for sustainability and resilience in manufacturing supply chains. J. Cleaner Prod. 313 (2021) 127596. | DOI
[42] , Sustainability performance predictions in supply chains: grey and rough set theoretical approaches. Ann. Oper. Res. 310 (2022) 171–200. | Zbl | DOI
[43] , A novel advanced grey incidence analysis for investigating the level of resilience in supply chains. Ann. Oper. Res. 308 (2022) 414–190. | MR | DOI
[44] , and , Predicting resilience in retailing using grey theory and moving probability based Markov models. J. Retail. Consumer Ser. 62 (2021) 102599. | DOI
[45] , From societal fragility to sustainable robustness: some tentative technology trajectories. Technol. Soc. 32 (2010) 342–351. | DOI
[46] , and , Autonomic computing in manufacturing process coordination in industry 4.0 context. J. Ind. Inf. Integr. 19 (2020) 100159.
[47] , On the benefits of CPFR and VMI: A comparative simulation study. Int. J. Prod. Econ. 113 (2008) 575–586. | DOI
[48] , and , Supply chain collaboration. Int. J. Phys. Distrib. Logistics Manage. 33 (2003) 531–549. | DOI
[49] , and , Improving collaboration between large and small-medium enterprises in automobile production. Enterp. Inf. Syst. 12 (2018) 19–35. | DOI
[50] , Perspectives in supply chain risk management. Int. J. Prod. Econ. 103 (2006) 451–488. | DOI
[51] , and , Vendor-managed inventory in the retail supply chain. J. Bus. Logistics 20 (1999) 183–204.
[52] , , and , Critical factors for CPFR success in the Chinese retail industry. J. Int. Commerce 4 (2005) 23–39. | DOI
[53] and , Building supply chain collaboration: a typology of collaborative approaches. Int. J. Logistics Manage. 18 (2007) 174–196. | DOI
[54] , and , Information support for alliances: performance implications. J. Bus. Logistics 23 (2002) 67–82. | DOI
[55] , and , Information sharing and collaborative behaviors in enabling supply chain performance: a social exchange perspective. Int. J. Prod. Econ. 148 (2014) 122–132. | DOI
[56] , , and , Learning curves in collaborative planning, forecasting, and replenishment (CPFR) information systems: an empirical analysis from a mobile phone manufacturer. J. Oper. Manage. 31 (2013) 285–297. | DOI
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