Inventory Management with Demand Forecast for Eyeglass Lenses Using The Time Series Method at An Optical Store

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Thobias Adriel Silaen
Yelita Anggiane Iskandar


In the healthcare sector, supply chain management is one of the most important elements since in the logistics of medical devices and pharmaceutical products, patient satisfaction is the main focus in efforts to improve service quality. One of the problems that often occurs in the optical supply chain is inventory control. The optical store is one of the microenterprises engaged in optometric health services. The enclosed supply chain is a three-echelon model, where the store is at the second level. The process of ordering lenses at the store from suppliers is not carried out based on predicted demand. The determination of the safety stock amount and the reorder point also still has a fairly low accuracy there. This is indicated by overstock and stock-out situations that still occur frequently in this company. Overstock causes the product to be damaged because it has been stored for too long and stock out causes lost sales. To solve the problem in this research, the prediction of future demand is overcome by using several time series methods, such as cyclical models, cyclical trend models, and ARIMA models. Forecasting result validation is implemented by calculating the calculation of errors using MAPE, MAD, and MSE then it was found that the forecasting model chosen to predict the demand for the lenses is a cyclical trend model. The result of the demand forecasting and safety stock size calculation with 3 service levels are used as input to determine the reorder point. After observing the condition of the company and the targets set by the company, the calculation results with a service level of 90% is the most possible to be implemented.

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How to Cite
Adriel Silaen, T., & Iskandar, Y. A. (2023). Inventory Management with Demand Forecast for Eyeglass Lenses Using The Time Series Method at An Optical Store. Journal of Emerging Supply Chain, Clean Energy, and Process Engineering, 2(2), 85–97.
Author Biography

Yelita Anggiane Iskandar, Department of Logistics Engineering, Faculty of Industrial Technology, Universitas Pertamina, Indonesia