Client is leading telecom player with 4000 field technicians deployed for installation, maintenance and network operations.

To design a planning optimizer for enhancing the productivity and utilization of technicians w.r.t. contractors by skill removals and efficiency changes on the basis of forecast demand and supply.

• A gradient boosting model was used to construct and confirm the input-output relationship between supply, demand, skill-mix, efficiencies and productivity
• A Bayesian optimization layer was used to solve the problem to generate optimized skill-mix based on efficiency changes and skill-removals
• More than 2000 engineered variables
• Contextual and agile modelling

• Optimized selection of skill-mix of technicians and contractors resulted in a 7% increase in productivity of technicians across 8 districts.
• Selective skill removals of contractual work force helped in better work allocation to technicians and enhanced utilization by reducing idle time by 30 mins in a day.