INDUSTRY OVERVIEW
A leading paint company in India, has a well established and efficient supply chain that supports the company's operations and helps it meet customer demands efficiently. As a crucial aspect of its supply chain management, demand forecasting plays a vital role in ensuring the smooth delivery of raw materials and finished products. The existing system uses historical data from SAP analytics system on which business logic is applied to determine the forecast.
It intends to leverage the power of machine learning (ML) and automate its forecasting process to consistently improve the accuracy of demand forecasting, reducing under forecasting, and the subsequent loss of sales percentage and inventory levels.
CHALLENGE
To develop an automated forecasting system at the starting of any month and a forecast correction process every 5 days in a month to detect any spike of sales and enables the company to adjust their forecasts, accordingly.
Gaps in the current system:
- The current method of forecasting is defined by business logic and varies from SKU to SKU. The process in manual and is prone to errors / misses.
- SKUs have separate rules for separate categories for the adjustment of the forecast every 5 days based on ending promotions and discounts.
- The existing system is very dynamic in nature with lot of manual intervention to alter the monthly forecasts looking at the sales trend for the month and other external factors.
Why were we brought in?
Ganit hasmade a significant dent in various industries using data science and analysis. Ganit partners with clients to translate their data into a tangible, insightful plan of action that delivers on a measurable impact to the clients’ topline & bottom-line growth.
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