Green inventory controlling analysis of coal using the lot sizing technique: Lesson learned form nickel mining industry

Asmal, Sapta; Darmawan, Armin; Mulyadi, .; Mudiastuti, Retnari Dian; Nilda,; Tappang, A. A.

doi:10.1088/1755-1315/343/1/012005

Cited by 1 publication

(2 citation statements)

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“…backlogging, time windows, production capacity, inventory constraints, etc. Asmal et al (2019) compared and analyzed the Silver-Meal (SM) method and WW algorithm to minimize total inventory cost (TIC). The results revealed that about 35.08% of inventory cost was reduced by implementing the SM method and WW algorithm.…”

Section: Effect Of Lot-sizing Rules On Mrpmentioning

confidence: 99%

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Unleashing an ML-based selection criteria for economic lot sizing in a smart batch-type production system

Kumar

Singh

Khope³

2022

TQM

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PurposeInventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.Design/methodology/approachThe current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.FindingsWhen demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.Originality/valueThe ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

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Section: Effect Of Lot-sizing Rules On Mrpmentioning

confidence: 99%

“…backlogging, time windows, production capacity, inventory constraints, etc. Asmal et al. (2019) compared and analyzed the Silver–Meal (SM) method and WW algorithm to minimize total inventory cost (TIC).…”

Section: Literature Reviewmentioning

confidence: 99%