Optimization of Stock Forecasting in Bali Retail Businesses to Support the Digital Economy Using Weighted Moving Average (WMA) Approach

Authors

  • Welda Welda Institut Bisnis dan Teknologi Indonesia (INSTIKI)
  • I Gede Eka Dharsika Institut Bisnis dan Teknologi Indonesia (INSTIKI)
  • Ida Bagus Gede Sarasvananda Institut Bisnis dan Teknologi Indonesia

DOI:

10.33395/sinkron.v8i4.14149

Keywords:

Forecasting, Sales, WMA, Retail, Bali

Abstract

The development of the digital economy provides new challenges for the retail sector, especially in stock management. Accurate stock management is a key factor in improving operational efficiency and minimizing the risk of overstock and understock. This research aims to optimize stock forecasting in retail businesses in Bali using the Weighted Moving Average (WMA) method. WMA gives greater weight to the most recent data in order to forecast future demand for goods. Sales data from 2017 to 2021 was collected and used as the basis for forecasting. The forecasting process was conducted for several products, including Dolphin and Dua Kelinci. The results show that WMA is able to provide accurate predictions, especially for products with stable demand patterns. For Dolphin products, the WMA forecast for January 2024 predicted a demand of 14.8 units, with a Mean Absolute Deviation (MAD) of 3.64. Dua Kelinci products, however, experienced more fluctuations in demand, with a forecasted January 2024 demand of 7.6 units and a MAD of 4.3. Despite some variations, WMA proved to be more accurate compared to simpler methods like Simple Moving Average (SMA). By using WMA, retailers can more efficiently manage stock, improve customer satisfaction, and reduce the risk of overstocking or understocking. This research confirms the importance of integrating advanced forecasting methods in supporting the competitiveness of the retail sector in the digital economy era.

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How to Cite

Welda, W., Dharsika, I. G. E., & Sarasvananda, I. B. G. (2024). Optimization of Stock Forecasting in Bali Retail Businesses to Support the Digital Economy Using Weighted Moving Average (WMA) Approach. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2519-2530. https://doi.org/10.33395/sinkron.v8i4.14149

Issue

Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024

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Copyright (c) 2024 Welda Welda, I Gede Eka Dharsika, Ida Bagus Gede Sarasvananda

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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