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Mitigating Unproductive Service Order Truck-Rolls Applying Advanced Machine Learning

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Mitigating Unproductive Service Order Truck-Rolls Applying Advanced Machine Learning

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The significance of service order truck-roll productivity is something that cannot be overstated.Without a well-coordinated service order truck-roll, we wouldn’t have our internet and cable installed.From the customer’s point of view, correct installation is an imperative because they cannot function without a reliable Wi-Fi in homes and offices or anywhere else.

A service provider company rolls out about 100K-200K trucks every month where each trip can cost between $80-$150; out of these trips, around 20%-25% end up being unproductive (failed installation or service repair did not happen) leading to $25M+ yearly cost impact along with revenue risk arising out of losing the customer on account of the failed installation.

Therefore, unproductive truck roll is a problem that needs to be immediate resolved from both customer and service provider side.However, dependencies on multiple business functions such as customer services who book the orders, operations- who plan the dispatches, field-services etc. makes it difficult to mitigate unproductive service order trucks.The need of the hour is to create a reliable data structure that captures every touchpoint in the service order’s journey. Some of these touchpoints would involve interfacing with the customer while others would be internal operations for the service provider.

At Innover, we help our clients by institutionalizing an end-to-end automated unproductive truck-roll mitigation solution (Innover TruckSmart™) which:

Integrates and analyzes over 20 data sources and 100 candidate

Integrates and analyzes over 20 data sources and 100 candidate variables to identify individual causal factors for truck roll failure.

Employs ensemble ML techniques to develop unproductivity risk prediction

Employs ensemble ML techniques  to develop unproductivity risk prediction ‘confluence model’ that captures the combined effect of the causal drivers accounting for interaction effects and collinearity

Recommends ‘action’ for every upcoming truck-roll to mitigate failure risk

Recommends ‘action’  for every upcoming truck-roll to mitigate failure risk

Automates implementation

Automates implementation providing daily extract files for truck dispatch planning & operations team

TruckSmart captures 80% of all unproductive truck-rolls by focusing on just 30% of the overall upcoming truck-roll population – even before a truck is dispatched. This has routinely translated in $3M+ in pure cost basis savings from unproductivity mitigation for our clients, improved customer experience and prevented revenue leakage from order cancelation & customer churn. Moreover, once the model has been deployed, its label propagation feature ensures that it generatively learns from new data points (i.e., new experiences) and as such maintains and improves itself while making a bottom line impact every instance Innover TruckSmart™ platform churns out that daily production file.

What makes TruckSmart™ effective and scalable is the fact that it has been perfected on millions of truck-roll data points across multiple clients. In our experience, some of the key causal drivers and their impact are astonishingly consistent across different companies – e.g., an upcoming truck-roll where a due-date has been missed already is ~3x times more likely to be unproductive, any negative sentiment incoming SMS from the customer increases the unproductivity risk by more than 40% – while the exact impact numbers could slightly vary by company, the differences are not drastic and the overall story hardly changes. This allows a plug & play facility where we can leverage the baseline model and the underlying architecture to customize client-specific ala carte models in just 6-8 weeks, including automated POC implementation. As a client, wouldn’t you call that a smart roll?


  • Innover Team  |  June 17, 2020   |  
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