Predictive Analytics to Improve Inventory Performance: A Case Study of an FMCG Company

Patdono Suwignjo · Lisda Panjaitan · Ahmed Raecky Baihaqy · Ahmad Rusdiansyah

16 citations0/0 files storedOperations and Supply Chain Management An International Journal

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Big Data and Business Intelligence Supply Chain and Inventory Management Forecasting Techniques and Applications

Abstract

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 R 2 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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 16 citations exist upstream.

References

2022Predictive Analytics for Demand Forecasting – A Comparison of SARIMA and LSTM in Retail SCM 2020Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting 2022The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events 2022The Use of Predictive Analytics in Finance 2009Inventory and distribution strategies for retail/e-tail organizations 2023Inventory management of new products in retailers using model-based deep reinforcement learning 2022Intelligent inventory management approaches for perishable pharmaceutical products in a healthcare supply chain 2023Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.