A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality

Tadayonrad, Yasin; Ndiaye, Alassane Ballé

doi:10.1016/j.sca.2023.100026

Cited by 13 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 In order to get the most out of their inventory, businesses use a variety of methods and techniques to predict demand and get rid of unnecessary stock. 2 Among effective inventory management methods that have emerged as prominent approaches for addressing inventory challenges, Seasonal Decomposition Method (SDM) and Multi-Echelon Inventory Optimization (MEIO) are notable approaches. 3 This study aims to evaluate the efficacy of these two methodologies in the context of inventory minimization and materials forecasting.…”

Section: Introductionmentioning

confidence: 99%

Materials requirement prediction challenges addressed through SDM and MEIO

Ashok,

Sathish,

Ibrahim

et al. 2024

AIP Advances

View full text Add to dashboard Cite

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

show abstract

Section: Introductionmentioning

confidence: 99%