TY - JOUR
T1 - Predictive Analytics to Improve Inventory Performance
T2 - A Case Study of an FMCG Company
AU - Suwignjo, Patdono
AU - Panjaitan, Lisda
AU - Baihaqy, Ahmed Raecky
AU - Rusdiansyah, Ahmad
N1 - Publisher Copyright:
© 2023 Operations and Supply Chain Management Forum. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Predictive analytics is a methodology used to predict the outcome of future events with the use of historical data. Predictive analytics comes in very handy in various fields such as finance, manufacturing, healthcare, and even supply chain. Not only in those fields, but predictive analytics is also useful in managing inventory. However, we find that there is a lack of studies focusing on the implementation of predictive analytics to predict inventory status (overstock, understock) by considering inventory level and demand forecast. This study is inspired by a real-world problem at one of the largest FMCG companies in Indonesia. With so many product types to manage, this company often faces problems of understocked and overstocked inventory. This study attempts to solve that problem by employing big data and predictive analytics approaches. The gradient boosting model is used because it is an improvement of the decision tree model. The data that are used as predictors are inventory level, inventory week cover, historical sales, and demand forecast. The target variable for classification is inventory status which is divided into three classes, namely understock, normal, and overstock. Meanwhile, the target variable for the regression model is the amount of understock/overstock. The result of the classification model has an accuracy of 0.84 for category 1 products, 0.76 for category 2 products, and 0.74 for category 3 products. While the result of the regression model is an R2 of 0.89 for category 1 products, 0.76 for category 2 products, and 0.74 for category 3 products. The data that comes from the prediction model are visualized in a dashboard. The visualization dashboard displays the data using heatmaps and line graphs, so the information can be used for further analysis.
AB - Predictive analytics is a methodology used to predict the outcome of future events with the use of historical data. Predictive analytics comes in very handy in various fields such as finance, manufacturing, healthcare, and even supply chain. Not only in those fields, but predictive analytics is also useful in managing inventory. However, we find that there is a lack of studies focusing on the implementation of predictive analytics to predict inventory status (overstock, understock) by considering inventory level and demand forecast. This study is inspired by a real-world problem at one of the largest FMCG companies in Indonesia. With so many product types to manage, this company often faces problems of understocked and overstocked inventory. This study attempts to solve that problem by employing big data and predictive analytics approaches. The gradient boosting model is used because it is an improvement of the decision tree model. The data that are used as predictors are inventory level, inventory week cover, historical sales, and demand forecast. The target variable for classification is inventory status which is divided into three classes, namely understock, normal, and overstock. Meanwhile, the target variable for the regression model is the amount of understock/overstock. The result of the classification model has an accuracy of 0.84 for category 1 products, 0.76 for category 2 products, and 0.74 for category 3 products. While the result of the regression model is an R2 of 0.89 for category 1 products, 0.76 for category 2 products, and 0.74 for category 3 products. The data that comes from the prediction model are visualized in a dashboard. The visualization dashboard displays the data using heatmaps and line graphs, so the information can be used for further analysis.
KW - FMCG
KW - big data
KW - gradient boosting
KW - inventory
KW - predictive analytics
UR - http://www.scopus.com/inward/record.url?scp=85199371883&partnerID=8YFLogxK
U2 - 10.31387/oscm0530390
DO - 10.31387/oscm0530390
M3 - Article
AN - SCOPUS:85199371883
SN - 1979-3561
VL - 16
SP - 293
EP - 310
JO - Operations and Supply Chain Management
JF - Operations and Supply Chain Management
IS - 2
ER -