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Build human performance management into automation strategies

10.01.2013  |  Martin, P.,  Invensys , Houston, TexasTeo, P.,  Invensys, Houston, Texas

HR professionals who use these new approaches into their development platforms will be well-positioned to help their firms weather the current industrial environment, which is increasingly becoming real-time.


Industrial companies are facing a significant human resource (HR) challenge. Over the recent tumultuous decades in the industrial markets, industrial companies have been downsizing labor forces and limiting hiring. This has led to other job markets becoming more attractive to potential employees. The result is that a major portion of the hydrocarbon processing industry’s labor force is nearing retirement, and there is a huge experience gap looming. As refiners face this gap, they also see performance demands on their operations continue to increase. The need for new, more effective approaches to human performance management has never been greater.

A subtle barrier to a more comprehensive and effective human performance management process for operations personnel is cultural. Engineers tend to perceive operators as laborers and try to protect their processes from them rather than advance the capabilities of this critical talent base. This attitude dates back to the industrial revolution, when frontline personnel were largely unskilled and uneducated and companies needed only their manual labor. But those days are well behind us. Today’s operators are much more academically advanced than their predecessors, and, because they have been trained on automation technologies, they bring a totally new and unique perspective on the operation. This provides an unprecedented level of experiential education, potentially elevating the operator from the position of laborer to performance manager. Engineers stand to gain significantly by supporting this transition.

Likewise, HR teams must adapt to this new culture. They tend to approach human resource management as a traditional academic process and may not appreciate the role that automation and information technologies can play in advancing human resource management, especially at the operations level. But HR professionals who can incorporate these new approaches into their development platforms will be well-positioned to help their companies weather the current industrial environment, which is increasingly becoming a real-time environment.

The demanding new industrial environment

The oil and gas industry marketplace has always been challenging, but recent trends are making it even more so. Most significant, perhaps, is electric power deregulation, which has led to real-time electricity price variation. This has had a domino effect on pricing in other markets that consume electricity, including petroleum exploration, refining, natural gas production and raw materials input, impacting a refined product’s final value. No longer are monthly business analyses adequate. Sustaining and increasing profitability has become a real-time control problem requiring real-time control solutions. Just as operators have always applied manual real-time systems to control the efficiency of plant floor operations, real-time systems must now also be integral and essential to successful business control strategies.

Automation-based management

Meeting the challenges of the emerging real-time industrial business environment, coupled with the growing human resource dilemma, requires a new perspective on human performance management. Fortunately, the solution is quite compatible with what research shows is the way that people learn best, which is through a feedback control mechanism in the brain. Trial and error, for example, is a classic effective feedback control learning process. Based on this principle, automation and information systems can provide effective real-time decision management learning environments, which can enable operators to make better and quicker business decisions while simultaneously learning their trade more effectively. Real-time automation and information technology can be effective with formal, informal and real-time, on-the-job, performance support.

Formal learning systems build a foundation and context for the student. Formal learning has traditionally often been associated with classroom training, which is an essential aspect of the learning process. Although classroom training is important, learning theory has demonstrated that experiential training is much more impactful than academic training for functions such as industrial operations. In the case of process operators, formal learning processes can be significantly enhanced through the utilization of offline experiential training provided through operator training simulation and virtual reality learning environments.

Simulated and virtual environments for formal learning are valuable because, while operators tend to learn the common repeatable tasks fairly quickly and quite well, they tend to have more difficulty with infrequently occurring aspects of their work, whether expected or unexpected. Using off-line training simulators, the operators can practice the infrequently occurring events in a safe environment and become much more proficient at responding to these events. This is particularly important for operators in refineries or other process settings, because these infrequent events can also be dangerous. Utilizing the combination of classroom training and offline real-time training has been shown to reduce the time required to get a new operator to an effective experience level by as much as 50%.

Understanding that humans learn by feedback control also provides a sound basis for the development of informal learning environments. Every operator engagement with an automation system is an opportunity to reinforce learning through real-time decision support, which enforces manual feedback control. By offering real-time dashboards and scorecards, we can provide operators with immediate performance feedback on the impact of their activity, so they can adjust their actions in real-time to achieve pre-established business or performance objectives. It is absolutely essential, then, that the performance measures presented on these screens are correct for the business performance of the operation and that they are prioritized according to the manufacturing or production strategy. If otherwise, operators would still use the information to guide their actions, but it could lead to learning of destructive, rather than constructive, behaviors. People perform to their measures. It is essential that those measures are right and in alignment with the business.

A second aspect to the informal learning can occur when the automation system provides online real-time guidance to operators as they perform complicated or infrequently occurring procedures. Today’s automation systems can provide such by embedding real-time workflows into the system and setting them to trigger on identification of an event requiring a response, such as a process anomaly causing excessive energy consumption. Such workflows provide a feedback control mechanism that can reinforce learning gained in the offline training simulation environment. This will contribute to proficiency and effectiveness of response for the operators increasing their effective experience level in shortened time frames.

The third component of effective learning systems is online performance support, in which automation system technologies can also play a valuable role. Learning from performance feedback in traditional manufacturing settings is difficult because time constants in manufacturing and production processes are so long that, by the time the impact of an action occurs, the operator may be off shift. Operator training simulators overcome this because the simulated process is not tied to the physical and chemical constraints, and can be run in a fast-forward mode, which would show the result of an action in very short order. In this way, real-time decision feedback can be provided even on very slow responding processes.

Finally, automated solutions facilitate involvement of subject matter experts (SMEs) as performance support. SMEs are often the most effective providers of performance support, but many industrial companies have too few SMEs who may not always be where they are needed. Fortunately, the open architectures of modern enterprise control systems can bridge the distance by enabling SMEs to connect right to the operators through the geographically distributed systems. SMEs become available where and when required, further increasing the learning levels of the operators.

Aligning enterprise control system technologies to human performance management tasks can be an effective way to bridge the experience gap and should actually result in higher performing operators. The small number of available, experienced operators will be able to accomplish much more and become greater contributors to their employer’s success in real-
time business and operational environments. And applying concepts across the enterprise—to managers, supervisors and others, as well as operators—will close industrial performance gaps further and lead to greater success. HP

The authors
  Peter Martin is vice president for business value solutions for the Software and Industrial Automation Division of Invensys plc. He has spent four decades in the automation industry, culminating with the development of commercially applied dynamic performance measurement technologies and methodologies. An established author and industry speaker, Dr. Martin received the ISA Life Achievement Award in 2009 for his work in performance measurement. He has an undergraduate and a MS degree in mathematics, a MS degree in administration and management, a MS of biblical studies degree, a PhD in industrial engineering and PhDs in biblical studies and theology. 

  Peter Teo is director of learning for the Software and Industrial Automation Division of Invensys plc. He has more than 30 years of experience working in services and engineering in the oil and gas and automation industries. Mr. Teo works closely with Invensys clients and engineering and services staff in developing technical learning solutions that align with business needs. His main focus is in on-demand information, processes and perspectives that are integrated with daily tasks to guide planning and action. He has a BS degree in mechanical engineering from the University of Texas at Arlington. 

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