Client Overview
The client, a major consumer goods company, is having issues with demand changing, stockpiles, and periods of stockouts during busy seasons. Conventional methods used by companies were not flexible enough to respond to new market trends and did not provide up-to-date information.
Since the company worked in several regions, making the supply match the local demand remained a constant problem. They required a data-powered approach to manage their inventory more efficiently and get details about demand.
About the Company
Challenges
Unexpected shifts in demand led to both stockouts and having too many stocks on hand. The old system was not able to cope with seasonal surges in demand.
Complex Demand
Changes in purchasing patterns mean stores cannot estimate their product requirements easily. Many times, there were mismatches between what companies were making and what their customers expected.
Data Silos
It was hard to see both inventory and sales together because the software was not the same for each area. Somewhat often, the analysis took longer, and the accuracy of forecasts went down.
Slow Adaptation
It was hard for the company’s old process to keep up with the swift changes happening in the market. It resulted in missing chances to sell and having products that could not be sold.
Cost Overruns
Because demand planning was inefficient, the company ended up with too much inventory and related costs. The situation affected profits and also the way resources were distributed.
Solutions
A demand forecasting engine based on machine learning was formed by our team. The key parts of the process were
Data Merging
Combine POS data, saved sales reports, calendars for marketing events, weather forecasts, and signals from social media.
Feature Engineering
Collected data that stands out, such as seasonal trends and holidays, as well as information regarding the stores themselves.
Demand Forecast
Developed linear programming (XGBoost and LSTM) to estimate demand for each product and location.
Visualization & Reporting
Provided users with a dashboard tool that gives inventory forecast confidence scores.
Tech Stack
Languages & Tools : Python, Pandas, Prophet, XGBoost, LSTM
Cloud Platform : Google Cloud Platform (BigQuery, Vertex AI)
Visualisation : Power BI
Data Sources : API integrations with ERP, POS, and third-party sources
Business Impact
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