Determining Demand Forecasting and Inventory Policy For Management of Raw Materials With Engineering To Order Characteristics in The Electricity Maintenance Industry

Rosa Shinta Rosyadi; Iwan  Vanany

doi:10.38142/ijesss.v7i2.1463

Authors

Rosa Shinta Rosyadi Sepuluh Nopember Institute of Technology, Surabaya, Indonesia
Iwan Vanany Sepuluh Nopember Institute of Technology, Surabaya, Indonesia

DOI:

https://doi.org/10.38142/ijesss.v7i2.1463

Keywords:

EnEngineering-to-Order (ETO), Inventory Control, Demand Forecasting, Syntetos-Boylan Approximation (SBA)

Abstract

This study examines the optimization of critical raw material inventory control policies in an Engineering-to-Order (ETO) manufacturing unit operating under a “no inventory” policy. The main challenges in the ETO system are demand uncertainty and unique product specifications, which are exacerbated by the prohibition on stocking. Through ABC analysis and CV-ADI classification, three critical materials (Materials 1, 4, and 7) are identified with lumpy and erratic demand patterns. The Syntetos-Boylan Approximation (SBA) forecasting method proves to be the most accurate for these demand patterns. Furthermore, three inventory policy scenarios (existing, continuous review (s, Q), and periodic review (R, S)) are evaluated based on total cost and service level. The results show that the existing policy has a low service level. For Materials 1 and 7, the continuous review (s, Q) system offers the most optimal cost with high service levels (77.14% and 95.30%). Meanwhile, for Material 4, the periodic review (R, S) system proves to be the most efficient with a service level of 95%. This study provides data-driven inventory policy recommendations that can improve operational efficiency, minimize stockout risks, and support production sustainability in an ETO environment with inventory constraints.

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