Living in a world where Industries are fueled by data, having ability to harness its complete power becomes a necessity which when accomplished results in a leap in business planning to make smart data driven decisions. To achieve such accuracy, we make use of AWS Forecast to utilize every byte of the data to provide deeper understanding and gain insights from the data. Ganit helps the client to unlock their latent potential by providing them with efficient solutions to their specific business problems.
Ganit can help you minimise flaws in decisions while accelerating growth through our extensive experience in providing cutting-edge solutions to our clients.
We have helped various clients to improve their production and distribution planning by forecasting their demand for a certain time range, without which company would have had to bear high inventory costs or may face stock outage. We have also helped clients by providing prediction even in cases with near to no historical data, all by using AWS hosted services like AWS Deep AR+ model (with RTS), AWS AutoMl model (with RTS) to keep cost and manual input minimal. While maintaining the accuracy and quick delivery of service all as per client’s requirements.
At Ganit while we do strive to achieve operational and performance efficiency for our clients, delivering tangible results to them through consistent effort remains our priority. We have helped a major retail chain company by estimating the impact of the cross interactions between different variants of the same products, thereby improving their overall efficiency. We considered different variables such as SKU mix, pricing, promotion, buying cycle, festivals for forecast generation, which resulted in a boost in accuracy from 36% to new and improved 76% accuracy which meant the company will now have 15% lesser stockouts and they are now well equipped to battle future demands. The solution developed does not only provides them with new possible opportunities but provides them the flexibility to take risks by eliminating some of the uncertainty associated with the decision-making process.