Coen Jeukens is vice president of global customer transformation at ServiceMax. He works with customers and prospects to fully unlock the true value and potential of their service organizations. Prior to joining ServiceMax, Coen was the services contract director at Bosch where he implemented an outcome-based business model, with highly impressive results. Coen is also a regular keynote speaker at prominent field service conferences around the globe.
Industrial manufacturers (OEMs) and their partners are under constant pressure to move fast without losing control. Products and services need to make it to market faster, without quality hiccups. Their costs need to stay in line, regardless of supply chain disruption. The results of these pressures show up most clearly in the installed base — the products creating operational value and costs out in the world. For industrial manufacturers that design, build, sell, and support physical products, this is where performance is proven, costs accumulate, and life-time customer value is either realized or lost.
To make it real, consider an example. A manufacturer has three product lines, each designed by different engineering teams and manufactured in plants across the US, Europe, and Asia. Products are sold directly and through a network of dealers. Service organizations span specialized internal teams and broad independent vendors. Over ten years, more than $5 billion in equipment has been shipped. Then comes an acquisition: a fourth product line, a new sales channel, and another $1 billion in installed assets added overnight.
Suddenly, the organization is responsible for a $6 billion installed base spread across regions, product families, and customers. Just as product design is becoming more complex, digital and connected, visibility into how each product line was designed, built, configured, used, and serviced continues to be scattered across systems and teams.
What happens when scale outpaces visibility
Despite having a full stack of enterprise applications, the company struggles to form a single view of its products over time. Design data lives in product lifecycle management (PLM), core components in supply chain management (SCM), manufacturing and cost data in ERP and manufacturing execution systems (MES), commercial information in customer relationship management CRM, and service history in service management (FSM).
They’re all integrated to power functional execution—development, manufacture, sale and service—yet information about the full lifecycle of a product is still fragmented, inconsistent in quality, and difficult to aggregate across all those systems. As a result, critical questions, are not easily answered.
- Which product variants are costing the most to support?
- Which design choices are driving repeat failures in the field?
- Which installed assets are profitable — and which are not?
Without clarity, decisions about design, service, quality, and needed investments are often made with partial or outdated insight — not because data doesn’t exist, but because it proliferates exponentially and is difficult to unify in a meaningful way.
Attempts to unify product lifecycle data fall short
Many industrial companies invest in a broad set of enterprise systems across the product lifecycle. In response to growing business and data volumes, many companies have also invested in Master Data Management (MDM) and Business Intelligence (BI) tools to capture and organize information for improvements in consistency and reporting.
These investments are necessary — but they are not sufficient.
MDM and BI solutions are typically owned and operated by data management teams and analysts. Their role is to standardize data models, oversee master records, and create reports from multiple sources. However, they’re often removed from the day-to-day business decisions that engineering, service, sales, and quality teams need to make. As a result, operational insight depends on translations and transactions: business questions are handed to analysts, queries are built, data is extracted and reconciled, and results are delivered — often weeks later.
For these solutions, each new question, product line, or acquisition adds more logic, more exceptions, and more manual intervention. As the time required to generate insights expand, poor decisions proliferate and cascade across business functions:
- Design: Lacking insights from previous versions hinders product improvement.
- Sales: Unclear product performance makes it hard to assess portfolio relevance or identify top and underperforming products and customers.
- Quality: Incomplete field data prevents verification of product performance and timely corrective actions.
- Service: Ineffective maintenance results from missing as-designed prescriptions and as-built records and product modifications add new failure risks that impact service performance.
Solving the right lifecycle data problem
The resulting business impact is less about a lack of data, and more about a growing gap between data and decision-making. Insights become slow, costly, and directionless as the business evolves and grows. They grow further out of reach for the teams responsible for improving products, optimizing service, and managing the installed base day-to-day.
The challenge goes beyond collecting data or reporting information, and now includes connecting it with enough context, continuity, and accessibility to support confident decisions at scale.
These problems need a different kind of solution.
Stay tuned for Part 2: When Engineering and Service Share the Same Asset View