Proactive Decision Support & Optimized Planning
The Retail / E-commerce industry is one of the most dynamic and exciting industry verticals today.
The industry has a variety of players from pure play brick and mortar companies to pure online "e-tailers" to internet market places. Customer demands and product choices makes this area very competitive specifically with the increase in mobile commerce through smart phones.Â
Understanding market demand trends, reacting immediately and optimising orders and inventory and crucial to success of this kind of business.Â
ORMAE provides cutting edge Data Science and Machine Learning algorithms to proactively predict and prescribe the effect of demand variations into actions in areas of logistics, warehousing and inventory planning .
Forecasting
All retail and e-commerce players need accurate and timely forecasting systems. However, the sheer number of organizations still using rudimentary forecasting techniques on Excel sheets, in the day and age of big data is truly surprising!
Also, due to the large number of SKUs and the seasonality, forecasting in the retail problem is a difficult exercise. With the advent of social media, it has become desirable to include social media insights into the forecasting process along with regular seasonal trends. Also, businesses are seeing the need to incorporate unconventional data sources into their forecasting with advanced Machine Learning algorithms.
Optimized decision making through Data Science
While the brick and mortar players are developing an internet presence and venturing into delivery, they need to look at maximizing revenue from their internet offerings at the same time minimize cost on the supply chain through efficient delivery routings.Â
Efficient Data Science algorithms help to model market conditions and customer choice behaviors which becomes a key input to the Supply Chain optimization system.
ORMAE's key solution areas:
 Demand ForecastingÂ
 Product Lifecycle and market trends
 Recommended products in e-commerce portals
 Competitor pricing and market share analysis