The present paper proposes an integrated production–distribution planning approach for a textile and apparel supply chain. Tactical and operational decisions are considered in the proposed multi-product and multi-period planning problem. Using a rolling horizon, the approach aims at defining optimal quantities to produce, to store and to deliver. The integration consists in coordinating informational flows between producer and retailer. Information sharing will allow the producer to estimate more accurately the future replenishment orders that may happen at the operational level and adjust production capacity requirements accordingly. For this purpose, a two-stage planning approach is devised; the first stage deals with the tactical level while the second stage deals with the operational level. The monthly decisions taken at the tactical planning level are accounted for in the operational planning considering a variable rolling horizon. Moreover, accurate forecasts of future replenishment orders are established based on information sharing and introduced in the operational planning to determine the weekly decisions. Linear programming models are used to build production and distribution plans at the tactical and operational levels. Using real-life data from a textile and apparel Tunisian firm, we show that producer-retailer coordination based on the sharing of current sales information, yields significant cost savings reaching up to 20% of the supply chain cost. These findings can only motivate the partnership between producer and retailer through reliable information sharing in joint tactical-operational and production–distribution planning.
Keywords: Demand forecasting, textile and apparel supply chain, tactical-operational planning, information sharing, rolling horizon
@article{RO_2021__55_3_1171_0,
author = {Safra, Imen and Jebali, Aida and Jemai, Zied and Bouchriha, Hanen and Ghaffari, Asma},
title = {The beneficial effect of information sharing in the integrated production{\textendash}distribution planning of textile and apparel supply chain},
journal = {RAIRO. Operations Research},
pages = {1171--1195},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {3},
doi = {10.1051/ro/2021038},
mrnumber = {4256081},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021038/}
}
TY - JOUR AU - Safra, Imen AU - Jebali, Aida AU - Jemai, Zied AU - Bouchriha, Hanen AU - Ghaffari, Asma TI - The beneficial effect of information sharing in the integrated production–distribution planning of textile and apparel supply chain JO - RAIRO. Operations Research PY - 2021 SP - 1171 EP - 1195 VL - 55 IS - 3 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021038/ DO - 10.1051/ro/2021038 LA - en ID - RO_2021__55_3_1171_0 ER -
%0 Journal Article %A Safra, Imen %A Jebali, Aida %A Jemai, Zied %A Bouchriha, Hanen %A Ghaffari, Asma %T The beneficial effect of information sharing in the integrated production–distribution planning of textile and apparel supply chain %J RAIRO. Operations Research %D 2021 %P 1171-1195 %V 55 %N 3 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021038/ %R 10.1051/ro/2021038 %G en %F RO_2021__55_3_1171_0
Safra, Imen; Jebali, Aida; Jemai, Zied; Bouchriha, Hanen; Ghaffari, Asma. The beneficial effect of information sharing in the integrated production–distribution planning of textile and apparel supply chain. RAIRO. Operations Research, Tome 55 (2021) no. 3, pp. 1171-1195. doi: 10.1051/ro/2021038
[1] , and , Coordination mechanism, risk sharing, and risk aversion in a five-level textile supply chain under demand and supply uncertainty. Eur. J. Oper. Res. 282 (2020) 93–107. | MR | DOI
[2] , , , and , Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk. Math. Prob. Eng. (2014) 1–10. | DOI
[3] , and , Tabu search with path relinking for an integrated production–distribution problem. Comput. Oper. Res. 38 (2011) 1199–1209. | MR | Zbl | DOI
[4] , and , Fashion retail forecasting by evolutionary neural networks. Int. J. Prod. Econ. 114 (2008) 615–630. | DOI
[5] , and , Spécificités et problématiques des produits à durée de vie courte. In: Proceeding of the 8th International conference on Logistics and SCM Research. RIRL 2010, Bordeaux (2010).
[6] and , A memetic algorithm with dynamic population management for an integrated production–distribution problem. Eur. J. Oper. Res. 195 (2009) 703–715. | Zbl | DOI
[7] , Integrated production and outbound distribution scheduling: review and extensions. Oper. Res. 58 (2010) 130–148. | Zbl | DOI
[8] and , Integrated scheduling of production and distribution operations. Manage. Sci. 51 (2005) 614–628. | Zbl | DOI
[9] , , and , Designing a decision-support system for new product sales forecasting. Expert Syst. App. 37 (2010) 1654–1665. | DOI
[10] and , Supply Chain Management: Strategy, Planning and Operation, 6th edition. Pearson Education (2016).
[11] , and , Integrated fabric procurement and multi-site apparel production planning with cross-docking: a hybrid fuzzy-robust stochastic programming approach. Appl. Soft Comput. 92 (2020) 106–267. | DOI
[12] , , and , A review and critique on integrated production–distribution planning models and techniques. J. Manuf. Syst. 32 (2013) 1–19. | DOI
[13] , and , Integrated production and distribution planning for perishable food products. Flexible Serv. Manuf. J. 24 (2012) 28–51. | DOI
[14] , and , Multi-stage stochastic supply chain planning in textile and apparel industry under demand uncertainty with risk considerations. Int. J. Serv. Oper. Manage. 29 (2018) 289–311.
[15] and , A new Bi-level production-routing-inventory model for a medicine supply chain under uncertainty. Int. J. Data Network Sci. 2 (2018) 15–26. | DOI
[16] and , An integrated production–distribution planning of dairy industry – a case study. Int. J. Logistics Syst. Manage. 30 (2018) 225–245. | DOI
[17] and , An integrated aggregate and detailed planning in a multi-site production environment using linear programming. Int. J. Prod. Res. 43 (2005) 4431–4454. | Zbl | DOI
[18] and , Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem. Eur. J. Oper. Res. 182 (2007) 663–6820. | Zbl | DOI
[19] , , , and , An integrated model of supply Network and production planning for multiple fuel products of multi-site refineries. Comput. Chem. Eng. 32 (2008) 2529–2535. | DOI
[20] and , Forecasting and Inventory Management of Short Life-Cycle Products. Oper. Res. 44 (1996) 131–150. Special Issue on New Directions in Operations Management. | Zbl | DOI
[21] , , and , Enhancement of supply chain resilience through inter-echelon information sharing. Flexible Serv. Manuf. J. 29 (2017) 260–285. | DOI
[22] , , , and , Integrated production inventory routing planning for intelligent food logistics systems. IEEE Trans. Intell. Trans. Syst. 20 (2019) 867–878. | DOI
[23] and , Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry. Omega 41 (2013) 369–382. | DOI
[24] , and , Multi-objective evolutionary approach for supply chain network design problem within online customer consideration. RAIRO:OR 51 (2017) 135–155. | MR | Numdam | Zbl | DOI
[25] , and , Integrated production–distribution models: a state of the art. In: 29th Conference of the Belgian operational Research Society (2015).
[26] , How to use diffusion models in new product forecasting. J. Bus. Forecasting Methods Syst. 15 (1996) 6–9.
[27] , and , Forecasting demand for single-period products: a case study in the apparel industry. Eur. J. Oper. Res. 211 (2011) 139–147. | DOI
[28] , , and , Mathematical programming models for supply chain production and transport planning. Eur. J. Oper. Res. 204 (2010) 377–390. | MR | Zbl | DOI
[29] , and , Demand forecasting in the fashion industry: a review. To appear in: Int. J. Eng. Bus. Manage. (2013). DOI: 10.5772/56840.
[30] and , Joint cyclic production and delivery scheduling in a two-stage supply chain. Int. J. Prod. Econ. 119 (2009) 55–74. | DOI
[31] , and , Integrated production distribution problem in a partial backorder and order refusal environment. Int. J. Manage. Concepts Phil. 12 (2019) 296–311. | DOI
[32] , , , , and , Optimizing the Norwegian Natural gas production and transport. Interfaces 39 (2009) 46–56. | DOI
[33] , and , Integrating multi-product production and distribution in newspaper logistics. Comput. Oper. Res. 35 (2008) 1576–1588. | Zbl | DOI
[34] , , , and , Capacity planning in Textile and apparel supply chains. IMA J. Manage. Math. 30 (2019) 209–233. | MR
[35] and , A vendor–buyer integrated inventory system with variable lead time and uncertain market demand. Oper. Res. 20 (2020) 491–515.
[36] , and , A vendor–buyer inventory model with lot-size and production rate dependent lead time under time value of money. RAIRO:OR 54 (2020) 961–979. | MR | Numdam | DOI
[37] , and , Collaborative production–distribution planning in supply chain: a fuzzy goal programming approach. Transp. Res. Part E-Logistics Transp. Rev. 44 (2008) 396–419. | DOI
[38] and , Style goods pricing with demand learning. Eur. J. Oper. Res. 196 (2009) 1058–1075. | MR | Zbl | DOI
[39] , and , Supply chain design and multilevel planning-An industrial case. Comput. Chem. Eng. 32 (2008) 2643–2663. | DOI
[40] , and , Estimation of consumer demands: an application to US apparel expenditures. J. Textile Apparel 1 (2000) 1–9.
[41] , and , An automatic textile sales forecast using fuzzy treatment of explanatory variables. J. Textile Apparel Technol. Manage. 2 (2002) 1–12.
[42] , and , A global forecasting support system adapted to textile distribution. Int. J. Prod. Econ. 96 (2005) 81–95. | DOI
[43] and , Optimal production allocation and distribution supply chain networks. Int. J. Prod. Econ. 111 (2007) 468–483. | DOI
[44] , and , Fashion retail supply chain management: a review of operational models. Int. J. Prod. Econ. 207 (2019) 34–55. | DOI
[45] , , and , Sharing quality information in a dual-supplier network: a game theoretic perspective. Int. J. Prod. Res. 49 (2010) 199–214. | Zbl | DOI
[46] and , Information management strategies and supply chain performance under demand disruptions. Int. J. Prod. Res. 54 (2016) 8–27. | DOI
[47] , Sales forecasting using extreme learning machine with applications in fashion retailing. Decis. Support Syst. 46 (2008) 411–419. | DOI
[48] , and , An intelligent fast sales forecasting model for fashion products. Expert Syst. App. 38 (2011) 7373–7379. | DOI
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