| dc.description.abstract | This study aimed to develop an integrated supply chain optimization model at PT.
Sinar Sosro Gunung Slamat by utilizing a machine learning approach. The
developed model encompassed three main components of the supply chain: demand
forecasting, inventory management, and product distribution. In the forecasting
stage, the Random Forest algorithm was used to predict demand for Teh Botol Kaca
based on historical variables such as sales, price, weather, seasonal trends, per
capita income, and population size. The prediction results showed a very high
accuracy rate of 98.76% with an error of only 1.24%, providing a solid foundation
for more precise production planning. Inventory management was performed using
the Support Vector Regression model, which successfully reduced the risk of
overstock and stock-out by 100%, with simulations demonstrating effectiveness in
maintaining inventory balance, despite relatively high MSE and RMSE values due
to the large data scale. Distribution was optimized through clustering using the K
Means method and determining the shortest delivery routes with the Nearest
Neighbor algorithm. As a result, all delivery routes remained below the maximum
threshold of 1000 km and met the company's Service Level Agreement (SLA). The
system was also adaptive to changes in demand and location conditions. The
novelty of this study lay in the selection of algorithms tailored to the characteristics
of the data, the integration of prediction-inventory-distribution models into a single
automated system, and the system's ability to evaluate and make real-time decisions
based on stock thresholds and SLA. Thus, the developed model not only improved
operational efficiency but also supported data-driven strategic decision-making in
managing the supply chain of the ready-to-drink beverage industry. | en_US |
