Corporate Commute become 2X Faster by applying AI & ML
Posted on 19 June 2019
Founded in 2009, MoveInSync is one such startup that is thriving to become a crucial player in the ecosystem. Today, MoveInSync is providing solutions to more than 100organisations like Google, Microsoft, Adobe, Oracle, Wipro and other Fortune 500 companies across 200 office locations in 22 cities in India and Sri Lanka.
Utilising AI and ML The Right Way
It analyzes historical data patterns to predict demand, identify supply gaps, predict travel times, suggest employee clubbing and cab deployments. Essentially, AI/ML is used in 3 areas: Routing, Demand Forecasting, and Sentimental Analysis.
Routing
MoveInSync routing algorithm is a self-correcting engine that periodically leverages AI engines to identify errors in its output and review execution data from the trip. It collects a large volume of data by geotaging the entire trip, capturing employee boarding points, the route followed, and travel times. The AI engines periodically monitor this data to deliver higher employee satisfaction and cost optimization.
Demand Prediction
MoveInSync is using ML models to predict a demand pattern from an area at specific hours during the day for customer organizations. using “Time series forecasting”. MoveInSync predicts the area wise demand, enabling transport managers to utilize cabs to their maximum and ensure operational efficiency.
Analytics
Talking about the analytics aspect, they run on the Jasper engine. For most of the customers, MoveInSync has achieved 10 to 30% cost savings through analytics backing informed decision-making.
Customer Centricity
The company makes sure that it analyzes how it interacts with the product, and then measure these metrics to conclude the area of the problem — tracking, scheduling or any other gaps that might be present.