Our Information Analytics team examined the company’s business requirements and split the project into two layers. In the first layer, we prepared the data from incoming jobs, stored it in an intermediate table, and repeated the same for subsequent jobs. The second layer uses this data to conduct predictive analysis and provides forecasted product availability requirements. Product information is continuously replenished through process automation.
Our team used big data analytics tools to process retail store data for the last three years. We employed advanced predictive analytics techniques to determine availability. Our solution integrated Azure Cloud Services to conduct fast-paced data analyses using data on sales, inventory, and the shelf life of the products. The solution provides accurate predictions of product availability at any given store location, any time of the day.
Here are some key aspects of our solution:
- Highly scalable: Our team overhauled inventory management operations to support 20,000 stores and scalable for many more.
- Eliminated data duplication and reduced processing time: By storing single data points in intermediate tables, our team eliminated data duplication and reduced the time taken to complete the process. Our solution produces results 97% faster than the company’s old system.
- Recovered potentially lost sales: By applying advanced analytics insights, the company was able to convert potentially lost sales into revenue.
- Data recovery: The company now has the option to restore data deleted from the system in error within 30 days.