Inventory model using Machine Learning for demand forecast with imperfect deteriorating products and partial backlogging under carbon emissions

The nature of complementary products is the dependency of one product on the other for utility. Further, the complementary products with deteriorated nature create a challenging environment for a suitable strategy. How does an industry deal with complementary deteriorated products at the retailer's house? The present study deals with two complementary deteriorating products in a two-echelon supply chain management. The deterioration of complementary products increases with time, and the deterioration rate is taken as the function of time. Complementary deteriorating products are manufactured by two manufacturers and sold to a common retailer. Manufacturers use a flexible production system to produce products and a single-setup-multiple-delivery policy to deliver those products. Under the flexible production system, emissions from setup, holding products, disposal, and transportation of products are considered. A carbon tax policy is used to reduce emissions. This study aims to optimize the total profit of the supply chain by finding a suitable sales strategy. The total profit is maximized by acquiring the optimal values of the retail price, cycle time of the retailer, production rate, and number of shipments. The numerical result shows that the profit becomes maximum when complementary products are less dependent on each other. The result indicates that adapting the flexible production system increases the total profit by 1.86 % and decreases total emissions by 5.36 % than a traditional production system.

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On the use of machine learning in supply chain management: a systematic review

Babai,

Arampatzis,

Hasni

et al. 2023

IMA Journal of Management Mathematics

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Machine learning (ML) has evolved into a crucial tool in supply chain management, effectively addressing the complexities associated with decision-making by leveraging available data. The utilization of ML has markedly surged in recent years, extending its influence across various supply chain operations, ranging from procurement to product distribution. In this paper, based on a systematic search, we provide a comprehensive literature review of the research dealing with the use of ML in supply chain management. We present the major contributions to the literature by classifying them into five classes using the five processes of the supply chain operations reference framework. We demonstrate that the applications of ML in supply chain management have significantly increased in both trend and diversity over recent years, with substantial expansion since 2019. The review also reveals that demand forecasting has attracted most of the applications followed by inventory management and transportation. The paper enables to identify the research gaps in the literature and provides some avenues for further research.

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Inventory model using Machine Learning for demand forecast with imperfect deteriorating products and partial backlogging under carbon emissions

Cited by 4 publications

References 44 publications

Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review

Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review

Complementary deteriorating products within a flexible production system: a sustainable approach

On the use of machine learning in supply chain management: a systematic review

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