Abstract:
Purpose – Inventory 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/approach – The 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.
Findings – When 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/value – The 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
