Original equipment manufacturers (OEMs) in a broad range of industries, from aviation and power generation equipment to oil and gas equipment suppliers, can enhance profits, carve a highly defensible niche in their markets and retain customers longer through world-class industrial asset management (IAM). The existing operating models, however, face two structural challenges: limited machine-to-machine coverage and suboptimal data analysis.
Limited remote equipment connectivity results in inaccurate installed base (IB) data that hinders service opportunity assessment and can compromise up to 20 percent of services revenues. Margins are then reduced because of the increased service delivery cost (as much as 25 percent) in the absence of remote machinery diagnostics. Unscheduled asset downtime impacts the end-customer's profitability and consequently their satisfaction, resulting in damage to the OEM's client relationship and ultimately its product range's reputation.
In aftermarket service contracts, inaccurate failure forecasting and the associated costs lead to inadequate pricing. In service operations, the first-time fix rate by service technicians also suffers because of lack of proper triage, spare parts availability and poor service scheduling.
Quantifying and proactively managing contract risk is essential but often not done properly. Poor reliability due to not implementing the necessary engineering design changes results in higher maintenance and quality costs, which impacts service margins.
Figure 1 shows how a poor operating model can potentially lead to the loss of 10 to 15 percent of revenues and 15 to 20 percent of profits for new and existing service contracts.
Figure 1. A typical $1-billion service business leaves $80 to $100 million in missed revenues on the table along with $10 to $15 million in lost profits.
It is clear that aftermarket services linked to remote diagnostic devices will see significant growth in the next five to 10 years. OEMs looking for a new profit center can grow with that demand. Based on projected demand for machine-to-machine technology, Harbor Research estimates that total value-added service revenue has the potential to grow from more than $50 billion in 2010 to $204 billion by 2014, a compound annual growth rate of nearly 32 percent. The research points to asset management, supply logistics and energy management as major drivers of this market expansion.
Two prominent examples confirm these trends. GE's sales of product-related services were about 30 percent of its $43.4 billion in revenue in 2012. That was up 4 percent over the previous year. However, GE's operating profit of $12.5 billion was a 6-percent increase over the year before. Rolls Royce's innovative business and operating model, called TotalCare service, is a lease and maintenance contract for each engine sold. The model's success can be seen in sales that have doubled in the last five years, with more than half of those revenues and about 70 percent of its profits coming from the TotalCare service business model.
Companies such as GE and Rolls Royce deliver exceptional profits and revenues thanks to an operating model fulfilling key business imperatives of "improvement in asset uptime" and "extending asset longevity."
Because of the financial appeal, transforming service operations often becomes a part of the CEO's agenda, but many OEMs don't possess the specialized knowledge and expertise to tackle the related operational and organizational issues. These issues mainly involve strategies for driving service revenue and methodologies for optimizing service costs. A failure to grasp and resolve these issues is an important reason why service-oriented companies are struggling with developing their best operating models. Experience shows three main components of existing operating model deficiencies:
Existing operating models are frequently inadequate because of imperfect or non-existent links along the service operations chain. From setup and planning through contract management, the full range of service execution support, including transaction processing and reporting, is highly fragmented.
Setup and planning suffer from limited visibility in the existing database. Information on the customer, repair history and equipment is difficult to track due to large numbers, geographic dispersion, reselling and unconsolidated customer lists. An outdated database naturally affects the service opportunity assessment and results in non-profitable service contract pricing.
The equipment diagnosis process is ineffective if real-time equipment condition monitoring is done in a selective manner. Real-time condition monitoring can enable better diagnosis at the initial call level, improved availability of spare parts and tools for field visits, more intelligent field-service operations, and improved training for technicians.
In essence, absence of real-time condition monitoring does not fulfill the "first-time fix" demands of customers, leading to low customer satisfaction. Aberdeen Field Service 2013 research shows abysmally low customer retention rates for OEMs that have a first-time fix rate of less than 80 percent. The net result is that the company is unable to develop a profitable function with service excellence that creates customer confidence and positive brand recognition.
Along with machine-to-machine monitoring and other technologies, legacy field-service platforms are generally among the more disjointed service systems and frequently are incapable of providing an integrated operational view.
Figure 2. Explosive growth in the number of connected devices will pose a challenge if machine-to-machine monitoring is inadequate.
In a study of 156 companies providing field service to customers, the Aberdeen Research Group looked closely at the role of automation. The best-in-class performers were those investing in up-to-date automation technology—automating or overhauling their processes for areas such as enterprise resource planning, improved billing and other financial record-keeping, customer relationship management, parts management, and workforce management.
Aberdeen determined that to achieve best-in-class field-service performance, companies must integrate parts management into scheduling criteria, schedule service tasks more frequently and in a centralized manner, empower field agents with mobile tools and devices with real-time access to information, develop dynamic service resource plans, and use performance management tools to tweak resource plans, schedule parameters and workforce management processes.
Resources for remote monitoring design and connectivity monitoring can be hard to find. Critical resources include senior field-service executives who help select the right machines and the operational parameters, software experts to design and support the deployment, shared services experts to set up the remote operations center, and skills ranging from basic parameter monitoring to high-end functional knowledge and equipment expertise.
Bringing these experts together for various types of equipment across different geographic regions is a complex job. A well-designed IAM operating model helps to ensure consistent, timely and effective service delivery at an optimal cost.
To balance customization and speed, the IAM operating model should be configured according to an individual business model over a standard IAM framework that leverages best practices and industry standards. This IAM framework straddles the three key elements: smart processes, focused technology and the right people. These can be delivered through the industrial asset management portfolio shown in Figure 3.
Figure 3. An industrial asset management portfolio can deliver on business imperatives.
Industrial asset management offers a unique approach that combines specialized consulting, technology and data analysis. It includes machine-to-machine design and deployment support, effective remote monitoring for select machines, robust data management, prognostics for effective asset health management, and in-depth data analysis across the service life cycle.
OEMs can benefit by leveraging industrial asset management capabilities to:
Figure 4. This example shows a steady-state operating model for best-in-class industrial asset management.
A mature IAM framework clearly delineates the roles and activities of the main stakeholders: the OEM, the technology partner and the IAM service provider. The right operating model also helps cover the non-core tasks efficiently through scalable resourcing and supports the focus of OEM teams on effectiveness decisions through data analysis.