How AutoML is Accelerating Time to Value on Data Science Use Cases Posted on : Jan 29 - 2021

Today, businesses are realizing the critical importance of investing in robust data science workflows. Data-driven decision making has been well documented in numerous industry reports and case studies to deliver a transformative impact on business processes and help create winning strategies even in an uncertain market.

Investing in data science processes can add value in several ways – influencing critical decisions in recruitment, marketing, sales, supply chain, operations and many more. Let us take a closer look at a popular ML use case and how decision making can be improved by applying ML and deriving value from it.

Forecasting Daily Demand By Store

By capturing and analyzing store data, AI and ML models can be used to forecast the actual demand levels at retail stores. This is very useful in predicting overstock and understock situations – thus helping retail organizations to fine-tune their supply chain and working capital.

Retail chains often find it challenging in ensuring that the right product is available at the right time and the right place. This optimization can provide immense cost savings and also help to build effective customer relationships. If the retail organization is holding too much inventory, then the store may find several challenges in disposing of it. It may have to resort to tactics such as deep discounting (lowering the profit margin), or in many cases even having to sell off the inventory at lower than the purchase price. Purchasing more inventory than needed also reflects a poor allocation of working capital. Conversely, if the store holds less inventory than needed, then it will experience loss of sales, customer frustration and consequently a negative impact on the customer relationship metrics. 

For retail organizations, optimizing the inventory and stock level is a priority. Traditionally, they do this through lengthy spreadsheet-based and statistical analysis models such as ARIMA. Such solutions, which can be very tedious and time-consuming, often fail to provide granular and actionable insights in a short time-frame to provide valuable recommendations to optimize stock levels.

Machine Learning, Deep Learning and Artificial Intelligence models can provide solutions to this. By churning through volumes of in-store data, such models can provide nuanced answers to the desired stock levels at each store – helping solve overstock and understock issues, by doing accurate forecasting at massive scale. There are a number of variables which impact the output value – such as seasonality, historical sales, demography, current trends. Machine learning models can be used to take these into account in order to come up with a working and accurate model. View More