Team design

Refining how disparate engineering teams work together to achieve maximum productivity

Natural Resources, Oil & Gas

Refining how disparate engineering teams work together to achieve maximum productivity

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A global engineering company’s oil and gas division faced two related issues. One: operating costs as a percentage of spending had been rising, but the division lacked a sufficient supply of engineering talent to address operational issues. Two: this shortage of engineers had constrained growth and investment in other business priorities. Management was seeking ways to enhance the productivity by 10 percent over the ensuing 12 months in order to get more out of its existing stable of engineers.

Insight and Action

QuantumBlack was brought in to perform analysis on the division’s operations to identify opportunities to increase productivity and performance. Our efforts began by aggregating data from six product lines across more than 100 geographical locations. Initial findings indicated that the division could boost its productivity by 13 to 27 percent by transforming its team design and reducing process fragmentation. We determined that the division could address the majority of this gap (around 70 percent) through more effective resource allocation and improved work processes.

One of the key findings was that the scheduling and availability of engineers was critical to avoiding disruptions. The optimal design of teams needed to account for the scheduling and planned absences of engineers that often left projects languishing for weeks at a time. These insights enabled the division to unlock the maximum productivity of its engineers.


  • 22Percentincrease in overall productivity through better team design and improved internal processes
  • 14Percentboost in productivity in the first 12 months (equal to $35 million)

To support regular monitoring and continual improvement, QuantumBlack built an early-warning capability that provided management with a dashboard that was continuously updated based on analytics models. This visibility enabled management to make interventions before projects became derailed.