This is part 5 of 13 from “Analytics: The Widening Divide,” a report on the findings of the 2011 New Intelligent Enterprise Global Executive Study and Research Project.
Like most organizations, BAE Systems once used analytics primarily for the basics – modeling costs and analyzing other financial information. However, when the global defense contractor moved into long-term “performance-based” contracts for its military and technical services it needed to strengthen its analytical capability. The new performance-based contracts shifted long-term risk of equipment availability from customers to BAE Systems.
To make this business model work, BAE Systems needed analytics. So five years ago, Michael Peters, Head of Business and Solution Modeling for BAE Systems, was appointed to address this issue. The business challenge, he explained, was to answer the fundamental business questions posed by the new strategy. “How do we know we can guarantee the availability of the particular system we’re offering? How do we know we will make revenue on this and can actually perform against the key performance indicators in the contract, and, indeed, what should the KPIs be?” He needed to find an integrated and consistent approach to making those contract decisions so that BAE Systems understood the relationship between cost, performance, revenue and risk.
Peters put together a methodology and a small team to support the new business model. His analytics champions from across the business units showed leaders in the major programs that a common methodology, which worked for the air sector, would also work for its land and sea divisions. Now, with mature capabilities in the air sector and growing capabilities elsewhere, the common methodology is used to embed analytical capability in projects, enabling leaders to make data-driven decisions for formulating contract commitments and optimizing through-life performance.
How does a small core team, just four people and a network of subject matter experts in the business units, change the mindset within a global company to enable a shift in analytical thinking to support their major programs? From the beginning, Peters was fortunate to have two very senior sponsors. These connections bolstered credibility when his corporate team engaged business units on the relevance of business and solution modeling and ensured effective sponsorship through the allocation of resources to support the business’s priority programs.
At the same time, Peters’ team began developing and demonstrating a whole suite of training courses. Best practices were put into the company’s Life Cycle Management processes, with techniques regularly shared at communities of practice events. After five years, the goal of all these activities remains the same: to make sure consistent “best practice” analytical capabilities for modeling solutions and business impact are embedded in BAE Systems’ projects at the point of use.
The central analytics team can advise, train and initiate. But once an analytics project begins, the individual business unit takes control of the ongoing work, and funds the required expertise. Peters helps them with this transition by using his network of contacts to quickly form virtual teams of subject matter experts from across BAE Systems' global talent pool and external consultancies to meet the needs of each team, bringing the best combination of skills to that particular business’s problem.
Working together, the centralized team, business unit experts and virtual teams have radically increased speed of response. “When we first modeled a performance based ‘availability’ project in the air sector, it took a considerable period because we had to learn, develop and adapt new techniques, and because it was such a huge program,” Peters said. “After several iterations with similar projects and the reuse of models developed over the last five years, the air sector can now do its modeling and analysis relatively quickly and support to decision making now takes hours rather than weeks. Generic building blocks are created to re-use analytical know-how across projects.” He pointed out, however, that reuse of models from one project to another has inherent risks unless very carefully done; hence the need for continued training, updating and sharing of expertise.
On average, Peters has found the payback on the analytics investment to be on the order of 20–50 to 1, much of it as direct savings to customers. By using analytics to take on performance risk while passing on the cost savings, BAE Systems moves closer and closer to its customers, and farther and farther away from competitors.