Data Analytics

Managing the Inventory Forecast Through our Key Data Analytic Solutions

Overview of Our Client

Our client was a well-known grocery store in the United Kingdom. hey have roughly 100 product categories and approximately 15000 SKUs (Stock Keeping Unit). Business principles built over time were widely used in the current inventory planning process for promo and non-promo time periods. There was a pressing need to go over them again and construct a prediction model. As a result, a solid demand forecasting process will emerge, one that appropriately accounts for promotional impact.

Challenges Faced by Our Client

Predicting the sales outcome was one of our client's biggest concerns. The concept of sales forecasting is important in every firm since it makes decision-making easier. Our customer was having trouble estimating cost and income, which they needed to forecast their short and long-term performance. Forecasting was critical, particularly for new and recently introduced products. Because of the accumulating product listings, the client was having trouble managing inventories and forecasting sales.

How Did Allianze InfoSoft Assist Our Client to Improve the Sales Forecasting?

We at Allianze InfoSoft think that data is an organization's soul. As a result, data analytic is a crucial notion in the creation of strategic business decisions. The item*store*day process is important to us since it represents the quantity of products collected in a category on a given day. The following are the steps that demonstrate our involvement. Our data analytic team will collect sales and promotional data efficiently. We combine the data in order to achieve a consistent item*store*day resolution. During the pre-processing stage, we remove holiday impacts as well as trend effects from the data.

Through the use of a combination of time series and frequency-based approaches, we developed predictive models for the calculation of baseline and promotional uplift. In addition, we created a category and sub-category (in some cases) for SKUs that did not fit the range or time period criteria in the first place. Our expertise used the store-level daily sales distribution statistics to break down the weekly sales into daily sales. This was done for the sake of easy analysis. We were successful in identifying products that were similar to the new products. This was accomplished by grouping together more than eight characteristics and building a model based on them.

Finally, by automating the process and developing an end-to-end solution, it will be possible to optimize the system for daily and weekly updates.

What Was the End Result?

Our client was highly satisfied with our renderings as it helped them to overcome the challenges with ease. There was an average improvement of around 18%. The sale forecasting accuracy was strengthened due to our perfectly organized data analytic process.

Conclusion

As soon as you grasp the fundamentals of how things work, you can start looking for methods to make it operate better. This is especially true in the case of sales forecasting. As a result of continuously projecting your sales, you will be able to discover various sections in the sales process. Once you've identified the issues, you can delve into the present process surrounding them and experiment with ways to make it better, confident that a positive outcome would lessen the bottlenecks in your sales projections.