Success Story
Enhancing field service efficiency by improving demand predictability for a Global Hi-Tech OEM
About the client
A leading ATM manufacturer & self-service solution provider operating across the US
Challenges
- The customer was operating in multiple US geographies and serving over 100K order requests per month nationally. However, the customer lacked a data-driven workforce planning and allocation process which led to idle times, impacted service order fulfillment, which in turn, caused revenue leakage and diminished brand image
Solutions
1.Innover developed an advanced Machine Learning driven service order demand forecasting model by region in just 6 weeks by leveraging past 24 months historical order data
2.Analyzed demand seasonality and order completion time for each task category, peak hours; and identified statistically significant time-slots for future planning
3.Created a demand planning and capacity allocation simulator dashboard
4.Delivered field force capacity (hours) allocation recommendations, in terms of task category and time slot
Impact Delivered
$650
Annual cost savings
30%
Overtime reduction
16%
Idle time reduction
Partners
