Capacity planning in textile and apparel supply chains

Safra, Imen; Jebali, Aïda; Jemaı̈, Zied; Bouchriha, Hanen; Ghaffari, Asma

doi:10.1093/imaman/dpy006

Cited by 16 publications

(18 citation statements)

References 44 publications

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“…Our approach is based on two mathematical models that are developed at two different decision levels [21,22]. The first model focuses on a tactical level of a 6-month horizon with a monthly periodicity and decides on pre-season quantities to be placed internally and with overseas subcontractors with long lead times.…”

Section: Approach Descriptionmentioning

confidence: 99%

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Enhancement of Textile Supply Chain Performance through Optimal Capacity Planning

Safra¹,

Ghachem²

2021

Lean Manufacturing

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Manufacturing companies in the textile and apparel field face stiff competition due to the globalization of trade between suppliers, producers and customers. To meet this challenge, they need to be efficient by adopting new lean manufacturing approaches and new analysis and management tools leading to more flexible and agile production and distribution processes. For the textile and apparel industry, where products’ life cycle is short due to fashion changes, a new integrated approach of production and distribution planning is needed. Based on linear programming techniques and integrating subcontracting activities, our approach takes into account the characteristics of demand, including its short life cycle, seasonality and fashion effect. For these reasons, a sequential approach is adopted, combining tactical and operational decision levels for production and distribution activities, in order to satisfy customer needs at lower cost by reacting quickly to changes and delivering on time. The deployed approach is structured according to the DMAIC lean tool. Validated on real instances, this approach proves its efficiency by achieving cost reduction when internal production capacity is adequately and efficiently planned.

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Section: Approach Descriptionmentioning

confidence: 99%

“…Constraints (20) with the objective function set the underutilized internal production capacity. Constraints (21) ensure that all production quantities are delivered to the warehouses.…”

Section: Appendix a The Tactical Planning Modelmentioning

confidence: 99%

Enhancement of Textile Supply Chain Performance through Optimal Capacity Planning

Safra¹,

Ghachem²

2021

Lean Manufacturing

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“…To the best of our knowledge, we notice that it is the first time that the uncertainty of the production capacity in terms of workforce availability is considered in a two-stage stochastic programming approach to model a biobjective tactical integrated production-distribution planning considering sea-air intermodal transportation. Finally, thanks to a real-world case study of the textile-apparel industry, we contemplate a more realistic situation than those examined in [20,21] by considering two sources of uncertainty and multiobjective scheme in our production-distribution model in order to minimise total costs along with maximising the customer satisfaction level in terms of minimising late deliveries.…”

Section: Related Literaturementioning

confidence: 99%

“…is approach is firstly defined in [31]. As shown in equations (19) and (20), the biobjective optimisation problem becomes a mono-objective optimisation problem by converting the second objective function into a constraint using the value ofε 2 .…”

Section: Resolution Approachmentioning

confidence: 99%

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An Integrated Production-Distribution Planning Problem under Demand and Production Capacity Uncertainties: New Formulation and Case Study

Abid

Ayadi

Masmoudi

2020

Mathematical Problems in Engineering

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In this study, we propose to solve a biobjective tactical integrated production-distribution planning problem for a multisite, multiperiod, multiproduct, sea-air intermodal supply chain network under uncertainties. Two random parameters are considered simultaneously: product replenishment orders and production capacity, which are modelled via a finite set of scenarios, using a two-stage stochastic approach. A corresponding mathematical model is developed, coded, and solved using the LINGO 18.0 software optimisation tool. This model aims to simultaneously minimise the total costs of production in both regular and overtime, inventory, distribution, and backordering activities and maximise the customer satisfaction level over the tactical planning horizon. The AUGMECON technique is applied to handle with the multiobjective optimisation. The applicability and the performance of the proposed model are tested through a real-life case study inspired from a medium-sized Tunisian textile and apparel company. Sensitivity analysis on stochastic parameters and managerial insights for the studied supply chain network are argued based on the empirical findings.

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The Adoption of Business Intelligence Systems in Textile and Apparel Industry: Case Studies

Ahmad

Miskon

2019

Advances in Intelligent Systems and Computing

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Capacity planning in textile and apparel supply chains

Cited by 16 publications

References 44 publications

Enhancement of Textile Supply Chain Performance through Optimal Capacity Planning

Enhancement of Textile Supply Chain Performance through Optimal Capacity Planning

An Integrated Production-Distribution Planning Problem under Demand and Production Capacity Uncertainties: New Formulation and Case Study

The Adoption of Business Intelligence Systems in Textile and Apparel Industry: Case Studies

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