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How to Build a More Profitable Industrial Asset Management Function

The goal of industrial asset management, or IAM, is to maximize the value of a company's assets. An effective IAM solution can make the business more efficient, reduce downtime and improve service delivery. And yet, for many companies, the current IAM operating model just isn't cutting it. For original equipment manufacturers or OEMs in particular, the IAM function leaves a great deal of unrealized potential on the table – in some instances, up to 20 percent service cost reduction, 15 percent revenue enhancement, and 15 percent asset up-time improvement.

How to Build a More Profitable Industrial Asset Management Function

Why is this? The answer, in short, is that the existing operating models of these companies face a set of real, yet surmountable, structural challenges. Luckily, the flaws in these IAM models also reveal the key on how to fix them.

The problem with existing IAM models

For many OEMs in a broad range of industries from aviation and power generation equipment to oil and gas equipment suppliers, the main problem is limited “machine to machine” or M2M connectivity. With limited coverage comes inaccurate installed based or IB data that can hinder service opportunity assessment and potentially compromise up to 20 percent of services revenues. Moreover, the absence of remote machinery diagnostics can increase service delivery costs by as much as 25 percent and ultimately reduce margins. In addition, unscheduled asset downtime can impact the end-customer’s profitability and consequently, their relationship with the business. Even a single hour of unscheduled downtime can result in millions of dollars lost. The other drawback with current operating models is ineffective data analysis. Flawed data analysis leads to inaccurate forecasting which ultimately leads to inaccurate pricing. In service operations, the first-time fix rate by service technicians also suffers due to a lack of proper triage, unavailability of spare parts and inefficient service scheduling. And for many OEMs, poor data analysis can impact engineering design changes that can lead to higher maintenance and poor reliability.Transforming operations requires a specialized knowledge and expertise that can tackle related operational and organizational issues. Experience shows that these issues are mainly of two kinds. The first includes strategies for driving revenue. And the second, methodologies for optimizing service costs. A failure to grasp and resolve these issues is an important reason why service-oriented companies struggle with developing their best operating models. Deeper scrutiny, however, shows three main components of existing operating model deficiencies. And creating an effective IAM framework requires addressing these three components: processes, technology and people.

Fixing the process

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 functions, systems and departments are highly disaggregated. Fixing these fragmented processes requires first and foremost, increasing visibility into the existing installed base. What’s more, comprehensive real-time equipment condition monitoring is crucial. The equipment diagnosis process is ineffective if real-time monitoring is done selectively. By implementing real-time condition monitoring, companies can get better diagnosis or triage 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.

Updating technology

Companies need to look at more than just updating outdated M2M technology. Legacy field service platforms are generally among the more disjointed service systems and frequently are incapable of providing the business with an integrated operational view.

A study conducted by Aberdeen Research Group looked closely at the role of automation among 156 companies providing field service to customers.  It found that the “best-in-class” performers were those that were 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 in order to achieve best-in-class, field service performance companies must be able to use technology to do things like integrating parts management into scheduling criteria, scheduling service tasks more frequently and in a centralized manner and empower field agents real-time access to information via mobile tools and devices. And for those that truly want to sit on the cutting edge of technology, Big Data analytics, or the use of highly advanced analytic techniques on large and diverse data sets, has opened up a world of possibilities for businesses. According to a 2013 McKinsey Global Institute study, productivity increases from sensor analytics, automated predictive maintenance and enhanced field services is predicted to increase manufacturing GDP by as much as $115bn by 2020.

Finding the right people

Not surprisingly, finding people with Big Data skills is not easy. And finding the right people for remote monitoring design and connectivity monitoring can be just as difficult. These areas require a broad range of experts with very different niches. To start, the organization needs senior field service executives who can select the right machines and operational parameters. It also requires software experts to design and support deployment; shared services experts to set up the Remote Operations Center (ROC); and those with skills ranging from basic parameter monitoring to high-end functional knowledge and equipment expertise. And bringing these experts together for various types of equipment, across various geographic regions, at a global scale is a complex job. Companies must be prepared to invest in finding and supporting these resources in order to create a well-designed IAM operating model that can ensure consistent, timely, and effective service delivery at an optimal cost.

Ingredients for success

OEMs are facing many strategic and operational challenges, including cost pressures, increasing competition and emerging market complexity. In a time when the global economy has never been more competitive, with investments in capital projects dwindling in many areas, progressive OEMs are shifting towards aftermarket service as a driver of profitable growth and a way to stay relevant to the end-customer.  For many with a globally distributed asset base, service delivery can suffer. The best service delivery, however, is evolving toward creating an optimal industrial asset management function. With the right model, OEMs can develop an integrated IAM function that generates profits and increases customer satisfaction – both the ultimate ingredients for success.

At Genpact, David Petrucci is Vice President, Sales and Business Development, Industrial Solutions, and Gaurav Agrawal is Assistant Vice President, Industrial Solutions.

Source: Genpact

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