OPTIMIZING INVENTORY COSTS FOR FOOD PRODUCTS IN METRO KAZAKHSTAN THROUGH EFFECTIVE FORECASTING AND TARGET STOCK LEVEL EVALUATION
Loading...
Date
2023
Authors
Suranshy, Akzhan
Nabidollayeva, Fatima
Maulenkul, Saule
Omarov, Temirlan
Journal Title
Journal ISSN
Volume Title
Publisher
School of Engineering and Digital Sciences
Abstract
Metro Cash and Carry Kazakhstan LLP, one of the biggest wholesale companies in
Central Asia, needs robust and reliable demand forecasting techniques to maintain its position
as a B2B leader in the market. Accurate forecasting algorithms are essential to plan inventory,
manage supply chain operations, optimize sales, and ultimately improve customer
satisfaction. By applying advanced demand forecasting techniques (machine learning and
advanced statistical methods) the company can acquire valuable insights into market trends,
empowering the business to take well-informed decisions and stay competitive in the market.
The report demonstrates the results of a study that is aimed to improve demand
forecasting for Metro Cash and Carry Kazakhstan. The study contrasted the company's current
demand forecasting algorithm with statistical and machine learning approaches for different
products and stores. In terms of RMSE for both products and stores, the statistical approach
WMA, and the machine learning method LightGBM yielded the best results. Also, the study
assessed the performance of WMA and LightGBM by forecasting demand on a daily and
weekly basis for five products and three stores additionally. The findings showed that WMA
was more accurate at forecasting weekly demand values than LightGBM but could not
outperform the machine learning method on a daily forecasting basis in terms of RMSE. For
the validation purpose of the forecasting results, the actual sales data was compared with the
forecasted results by RMSE at every step of the project. Additionally, the forecasted demand
data was used in the Simulation to calculate inventory costs in comparison with the company's
forecasting method. Both WMA and LightGBM significantly saved inventory costs (ordering,
holding, and stock-out costs). Moreover, the company's Target Stock Level (TSL) is compared
with suggested TSLs while analyzing target stock levels for two product categories. As a
result, the analysis showed that the proposed TSL was smaller than the company's TSL, which
decreased the total inventory cost. Moreover, the proposed TSL was validated with actual
sales of categories to ensure that the proposed TSL meets all customer demands. The project
concludes that the suggested strategies can improve the accuracy of demand forecasts, hence
increasing product availability levels, and decreasing total inventory costs, potentially
enhancing competitiveness and economic returns. For future work, it was suggested to
develop a general guideline for using proposed forecasting techniques for various food
product categories. Similarly, it was recommended to expand the calculation of the proposed
TSL to other local food categories.
Description
Keywords
Type of access: Restricted, stock availability, forecasting, WMA, LightGBM, Target Stock Level, Simulation, Total Inventory cost
Citation
Suranshy, A. Nabidollayeva, F. Maulenkul, S. Omarov, T. (2023). Optimizing inventory costs for food products In metro Kazakhstan through effective Forecasting and target stock level evaluation. School of Engineering and Digital Sciences