Digital transformation will not happen overnight, but it will happen quickly. While the IoT is quickly expanding, many have yet to feel the benefits, as organizations struggle to merge existing operational frameworks with new systems and capabilities.
The global economy is undergoing rapid shifts in resource demand, infrastructure, and desired skill sets in the workforce. As technology and innovation continue to shape industry, a significant opportunity to ignite economic revival is digital productivity through asset performance management (APM).
When organizations digitize power plants and factories by connecting them to the industrial Internet, global productivity improves. Insights that operators derive from data analytics create new energy efficiencies and expand capacity. As industrial companies transition into digital businesses, the next wave of competitiveness will drive up productivity and enhance the global economy by removing waste and creating a more efficient response to customer needs. Industrial organizations must prepare for this change in steps to bring both workers and systems into the digital age successfully.
The journey begins with APM. Today, industrial managers, engineers, and operators can use data from their operating environments to significantly improve asset reliability and maintenance effectiveness while optimizing processes. This approach relies on APM—a system that uses big data and risk-based strategies to identify critical assets and areas of improvement across thousands of assets to eliminate failures, reduce production losses, and address poor performance. When a single unproductive day on a liquefied natural gas platform can cost as much as $25 million, for example, minimizing unplanned downtime due to equipment malfunction and human error is a top priority.
Industrial companies that analyze and optimize their machines are positioned to succeed in the era of the industrial Internet. The steps—consolidating disparate data, executing a risk-based criticality assessment, and empowering the workforce through a culture of reliability—sound simple, but they deliver big results.
Consolidating disparate data
Today many data projects are set up, but only a small percentage of those projects realize a return on investment. Data projects can be overwhelming and ineffective without the right tools and buy-in from users within an organization. When implemented properly, APM programs help organizations reimagine their asset strategy and drive significant performance improvements. By relying on sophisticated algorithms for gathering data, building integrated data modeling, and performing advanced condition monitoring across a variety of techniques, APM systems help industrial companies develop intelligent asset strategies and realize value.
Industry sensor technology gives visibility into machine health and performance across all types of assets. This information accounts for factors, such as temperature and vibration, that help direct maintenance and workflow decisions. Using machine sensors to monitor equipment performance generates a constant stream of in-depth data, which can be consolidated and used to derive meaningful insights. Operators with this capability gain actionable information that increases asset reliability and enables more precise maintenance actions, reducing the cost of ownership. Industrial companies are feeling the urgency to get connected and gain insights from their machine data. In fact, 87 percent of manufacturing and oil and gas executives stated that big data and analytics are in their top three priorities. That number soars to 94 percent for power generation. The urgency to connect and act on machine data is widespread—and the opportunities are well recognized.
When it comes to asset management and maintenance practices today, however, most organizations are still relying on time-based maintenance strategies, using separate management systems and a host of smaller independent solutions. The majority of these systems were custom built to enable the unique work processes within companies, but they require a huge amount of ownership and create a siloed system with limited communication across reliability and maintenance teams. To become a true digital business, organizations must get rid of aging systems that generate disparate data streams and focus on the value of a single platform to incorporate data from multiple tools across the enterprise and provide a clear view of asset health. A single, secure operating system that gathers data from a wide variety of assets and systems enables operators and leadership to easily access a comprehensive and validated data repository for more informed maintenance decisions.
Criticality assessment of assets
Many companies are only focused on the reactive rather than on the proactive approach to maintenance incidents and reliability. Both are critical components of a reliability-centric culture, but there should be greater emphasis on proactive methodologies. Digitally driven proactive methodologies focus on generating and implementing asset management strategies by optimizing the assets’ total cost, risk, and performance impact. Reliability will become an ingrained practice rather than a function when a business shifts its operating processes and adopts APM technology to proactively address critical assets before they fail.
To strategically roll out an APM framework, organizations should first determine criticality and rank assets according to which ones require the most focus and are a priority for continued operations. Engineers must complete equipment criticality assessments for all assets, as well as reliability-instrumented system studies for safety and instrument-critical functions. Once assets are defined, engineers can conduct reliability-centered maintenance studies for all critical, high-priority systems. As a last step, conduct root-cause analysis on all incidents related to production, environment, health, safety, security, quality, and customer complaints. APM systems manage data on these critical assets to help operators and leadership prioritize failing and poor-performing systems and avoid unnecessary maintenance on healthy assets.
As industrial operators and engineers know, even the smallest incident can cause a chain reaction that remains unnoticeable until there are large-scale ramifications. To avoid costly losses over time, organizations should develop daily plans against specific assets and measure performance against plans. When these plans are maintained and automated in an APM system, operators can first identify common failures and issues across sites at a global or regional level, and then narrow in on incremental losses and incidents at individual sites to prevent major problems in the long term.
Reports that are automated, digital, and standardized across the entire organization give full transparency into asset performance and leave little room for failure. A reliability-focused organization should consistently track key performance indicators, recommendations, and performance improvements. This involves standardizing the practices of monitoring production deviation triggers, classifying incidents, identifying performance killers, benchmarking and measuring compliance and data quality, analyzing data and trending procedures, and finally developing recommendations and closure for the loss-accounting process.
One mining company had massive quantities of historical data in its SAP plant maintenance system—about 1.5 million records of functional locations and equipment units, 10 million records of work history, more than 300,000 task lists of repetitive operations, and more than 35,000 measurement points. With such huge volumes of data, a single APM system helped the company seamlessly integrate data from its SAP system, prioritize data from critical assets, and develop strategic recommendations for maintenance. By automating its reliability processes and developing intelligent asset strategies, the company began to achieve major operational efficiencies and cost savings.
Digital workforce and reliability culture
Technology is only as effective as the workforce that uses it. If employees are not inputting data or pulling the right reports, then the system will not provide real value. In a 2014 global survey, 44 percent of oil and gas companies in the Americas said a skills shortage is the biggest threat to their industry— higher than capital costs, labor costs, or even economic stability concerns. This is largely related to training issues, with many companies citing the lack of quality candidates and skilled employees available to train. As a company undergoes full digital transformation, the industrial environment will change dramatically. Training programs need to evolve with it.
Simplifying what have become extremely intricate company cultures across industry will help reliability and digitization truly take root. By implementing a training program for all employees, leadership fosters the values and behaviors necessary for organizational reliability and also helps support talent retention and company growth. Training should review the fundamentals of reliability, detail the relationship to maintenance and operations, and teach how to use APM tools to support sound asset management decision making.
Forward-thinking organizations use lean tools and training to promote a reliability culture, as well as the long-term viability of an organization. New synchronous learning processes, for example, are highly interactive and are a platform to share and develop challenges, solutions, and ideas for employees. This type of training can take the form of virtual instructor-led training, social learning opportunities, and traditional instructor-led classroom training—either on or off site.
Operators and engineers should expect and seek training opportunities that involve participation across multiple disciplines from information technology, operations, and marketing to engineering. This cross-pollination approach to training will drive and ensure the success of corporate change and APM initiatives in an organization.
With the increased availability of these new data sources, engineers and operators are challenged to think beyond a single discipline, and today must consider both operations and competition from more of a marketing perspective. The goal of training, therefore, should be to acquire a big picture perspective of asset and system reliability and the competitive landscape—capabilities encompassed in APM methodologies.
Becoming a digital industrial
The industrial Internet will transform traditional industrial sectors with digital, data-rich services. But until now, only 5 percent of companies have succeeded in this transformation.
With APM, companies can streamline the process of connecting to smart assets, collecting all the data from those assets, and monitoring equipment for emerging threats. Weaving all that information together lets companies model various scenarios for evaluating the risk and cost of making changes to the asset management strategy. APM can compare the results and effectiveness of those new asset strategies, using machine learning capabilities to constantly learn and improve.
APM enables organizations to accelerate their path to a digital business model. Organizations gain control of asset decisions with broad-reaching data that reflects resource availability, operating impact, and real-time condition reports. When industrial organizations require assets to operate at all times, APM helps focus on the assets that need repair to lower the total cost of ownership and reduce the risk of unplanned downtime of mission-critical assets.
If they want to survive, industrial companies need to recognize that they are in the information business, and change extends beyond the assets themselves. Employees must embrace the digital industry they now work in and adopt APM tools for reliability and safety in today’s highly regulated environments.
About the Author
Jeremiah Stone is the chief technology officer at Catasys, and previously served as general manager of GE Digital’s APM business. Before taking over the APM business, Stone was the chief technology officer, software, for GE’s Energy Management business unit. Before joining GE, he was vice president, natural resource industries and sustainability solutions, at SAP. Stone started his career as a programmer and systems administrator with the National Center for Atmospheric Research, helping to develop systems to predict clear-air turbulence. He is a graduate of the University of Colorado’s mathematics program (summa cum laude), an inventor or co-inventor of multiple U.S. patents and several publications, and a founding member of the NextGen advisory board at the Computer History Museum in Mountain View, Calif.
A version of this article also was published at InTech magazine.