Client Story

When bad data is costing billions of dollars

One of the world’s leading high-tech manufacturers was in trouble. Without clear, agreed-upon data to support decision-making, management was essentially trying to steer the company through dense fog. Revenue and cost figures were inconsistent from one department to the next, and nobody truly “owned” the data. A vivid snapshot of the problem: an employee in one department thought the company made 55,000 products, but an employee in a different department thought it made 6,000. To paraphrase one executive: “We have no clue what’s going on. Everyone’s story is a good one—and none of them is right.”

Lacking a single version of the truth, executives had no way of knowing the real financial impact of their decisions, let alone the potential trade-offs they could make. Supply and demand were wildly out of sync, and inaccurate forecasts meant erratic and often wasteful capital expenditures. The manufacturer was unable to see costs or financial contributions broken out by customer, product, or any other actionable level. As a result, the company was losing billions of dollars annually. Alarmed by the worsening situation, the executives and board of directors reached out to AlixPartners.

Advanced software tools provide a single version of the truth

AlixPartners’ digital experts quickly saw that the manufacturer was adrift because its data infrastructure was inadequate. Reporting to the CFO—who was very keen to be able to manage by the numbers—our remit was to help the company transition from its revenue-centric model to one more focused on profit so it could self-fund growth. Our first step was to clean up the data so the company could have accurate insights into performance, learn which products and operations areas were profitable, and, identify the biggest obstacles to running an effective operation. With a newly clear view of profitability by customer and by product, we could quickly start building software tools that would enable the company to take a consistent approach to business planning and profitability.

Throughout this project, we illustrated the value of using clean, consistent data sets to create optimal operating models. As an example, we developed a cost model for a “virtual manufacturing operation” that allowed executives to estimate the cost of advanced technologies, identify underutilized assets, and evaluate the cost of excess capacity.

When it really matters

With a unified approach to data collection and use, the manufacturer identified a host of opportunities that collectively have the potential to cut its annual losses by about a third. Also, the company found it could divest a host of process equipment with little or no impact on productivity—a move that could release considerable cash. At the same time, implementing our software tools quickly yielded recurring profit impact of more than $100 million a year. Management’s new visibility into the company’s operations enabled it to increase capacity by 10%. The company was now able to make sales forecasting consistent, improving demand accuracy by more than 10%.

The new operational transparency also enabled the company to avoid $100 million-plus in capital purchases by aligning the marketing forecast with capital spend schedules. Last but not least, the unified data approach underpinned new pricing structures with key customers that yielded an additional $20 million in profit margin compared with traditional pricing.

Perhaps most valuable of all in the long term: with a platform of clean, consistent data to work with, the executive team began to envision ways to automate decision-making, further improving its effectiveness. Executives soon saw how to use supply chain analytics to identify which customers to serve, with which products, in which markets, and by which operating model. With a centralized sales and operational planning system to rely on, the executives could align not only on operational decisions but also on wider use of management tools that would help them even further in areas such as R&D portfolio management and product profitability analysis.