Inventory management model to increase the rotation of textile products through the S&amp;OP process and material requirements planning (MRP) in textile companies in Lima

Echevarria, Alejandro Ballon -; Tejada, Javier Castillo; Ugarte, Cristina Hernandez -

doi:10.1109/coniiti57704.2022.9953682

Cited by 1 publication

(1 citation statement)

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“…However, there were no integrations with other parties such as suppliers. Various studies that had been conducted emphasize the need of inventory models in real world problems such as pharmaceutical inventory [22], textile industries [23], grocery retail [24], and many more. In particular, case studies that contain uncertain parameters can be found in numerous reports from various fields such as supply chain management [25], mechanical devices [26,27], and transportation [28], further demonstrating that uncertain parameters naturally occur in real world problems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

Sutrisno,

Widowati,

Sunarsih

et al. 2023

JESA

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This study addresses the integration of supplier selection and inventory management issues that involve discounts, with a unified decision-making support formulated through a mathematical programming approach. The challenges addressed encompass uncertain parameters such as defective goods rates, late delivery rates, and demand, some of which are treated as probabilistic/random variables under data availability assumptions, while others are managed as fuzzy variables where data is not explicitly required. The joint problems were synthesized into a piecewise fuzzy-probabilistic optimization model with the aim of minimizing total operational costs, and the optimal decision was deduced by solving this model. Further, the model was constructed incorporating multi-period observations, indicating its ability to generate optimal solutions for multiple procurement activity periods. Computational simulations were executed to demonstrate the calculation of the optimal decision and to appraise the proposed model. All calculations were performed in LINGO 19.0 optimization software, leveraging its uncertain programming package. The computational process employed the generalized reduced gradient -a popular method for solving optimization problems due to its requirement of only a differentiable objective function -in conjunction with the branch and bound algorithmrecognized for its simplicity in branching and bounding feasible solutions. The results affirmed that the proposed model successfully delivered the optimal solution for the problem at hand. Therefore, the proposed model is deemed appropriate for implementation by practitioners in manufacturing/retail industries as a decision-making tool to curtail operational costs associated with their procurement and warehousing operations.

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