Integrated Plant Performance Management as a Means to Improve Profitability | Automation.com

Integrated Plant Performance Management as a Means to Improve Profitability

Integrated Plant Performance Management as a Means to Improve Profitability

By Yasunori Kobayashi, Senior Manager and Executive Consultant, Yokogawa

In today's competitive environment, process industry companies need to balance multiple objectives including safety, profitability and delivering value to customers. However, these objectives are often conflicting goals not aligned across departments, plants, or enterprise boundaries.

Most companies establish C-level key performance indicators (KPIs) such as energy cost, gross margins and incidents. Unfortunately, the KPIs used by C-level executives (CEOs, CFOs, etc.) frequently do not match up with those used by engineering and operations personnel. These groups typically have their own set of KPIs, often generated independently to maintain safe and stable plant operations within the constraints they face daily. Adding to these challenges, the experience and competency needed to manually align with high-level objectives are declining due to retirement of key personnel, especially control room operators.

What if KPIs could be structured systematically across an organization from management through engineering to operations to unify all the employees' capabilities, motivate them to do better, and cultivate the knowledge in the DNA of the organization?

 

Creating a Performance Metrics Methodology

One effort for such a structured KPI framework was made by integrating Yokogawa’s expertise in industrial automation technologies with the domain knowledge of  Yokogawa subsidiary KBC, which works in global analysis and consulting with major energy and chemical plants.

Yokogawa collaborated closely with KBC industry consultants to first define and then systematically structure several hundred metrics, henceforth referred to synaptic performance indicators (SPIs). These are collected at the plant operations, engineering, and top management levels in process industry operations, based on deep knowledge of how they affect plant performance.

At the operations level, a typical midsized refinery has about 377 SPIs, and a typical ethylene plant has about 77 SPIs. Approximately 60 percent of SPIs indirectly correspond to control variables, and 40 percent directly correspond to control variables. A midsized refinery will have about 93 high-level SPIs for daily viewing and use by management, 357 SPIs for hourly viewing and use by engineering personnel, and 377 SPIs for real-time viewing and use by shift operators (Figure 1).

Figure 1: Conceptual framework of how operations, engineering, and top management synaptic performance indicators (SPIs) are structured to align with high-level plant management objectives

SPIs in Action – Improving Operations

Operations SPIs, the main topic of this article, can mainly be determined using DCS data. Each of the 377 operation SPIs is aligned with one of the high-level plant management objectives (production achievement, profitability, energy conservation, asset reliability, or safety & environment), and displayed using an intuitive scoring-style dashboard (Figure 2, left side) integrated into the DCS interface. These dashboards help control room operators analyze their operational performance and find areas for improvement, motivating them to achieve enhanced operation.

Plant control systems continuously receive a vast amount of data from sensors and devices, but the utilization of this big data collected in real time by the DCS has mainly been limited to front-line plant safety and line control. For this new methodology, in-house domain knowledge has been applied rigorously to first select the appropriate indicators impacting high-level management objectives, and then determining from where to collect the data to create the indicators. For SPIs not measured due to a lack of sensors or analyzers, process simulation is implemented to calculate soft sensors for estimating unmeasured process variables from measured variables using rigorous process models.

Process variables are optimized by setting an optimal range for each critical set point. Analysis of historical data by process engineers and experts with extensive domain knowledge can determine the correct range for each set point, a complex task because many control loops interact with each other. Further complexity is added by also considering the effects of set points on the supply chain. All this data can be combined to provide deep insight into the behavior of a process at any point in time. These SPIs data can also be used to trigger expert guidance in the form of messages when a variable goes outside of the ideal range, thereby supporting prompt action by even inexperienced operators (Figure 2, right side). By optimizing SPIs, operator actions mimic those of a multivariable controller, which is one of the categories of advanced process control, resulting in optimal control of related process variables.

The dashboard tracks the performance of each control room operator during his or her shift by checking the uptime of an SPI (time during which the SPI is in the ideal range or without alarm). Operators can check their performance with respect to the high-level objectives, and improvements can be visualized by comparing performance of operators across shifts.

Figure 2: Example of performance dashboard for operations (left side of diagram), and automated expert advice on specific indicators (right side of diagram)

SPI's for  Process Plant Engineering

SPIs, alert information and operators' scores are stored in the DCS. Plant engineering and third-party personnel can use this information for benchmarking, root-cause analysis or expert consulting for continuous improvement. SPIs and related dashboards can thus help engineers and operators transform their work from event-driven to profit-driven.

The control room operator SPIs are used to inform the engineering team of possible improvement opportunities, creating aligned targets for the operations and engineering teams. The engineering team can use structured SPIs and stored data for analysis of plant performance, for improvement opportunities and for analysis of other issues related to production.

 

SPIs for Plant Management and Performance

SPIs for management give a clear picture of high-level performance metrics such as facility operations, the difference between planned and actual production, and energy use. The balanced performance metrics associated with carefully-crafted SPIs lead to improvements in quality and efficiency, reduce inventory, ensure compliance and increase flexibility—ultimately leading to greater profitability.

Some of the management and engineering SPIs also leverage manufacturing execution system data (e.g., production plan data, crude assay data, laboratory data) along with data from external sources such as utility and feedstock pricing. In addition, managers and engineers often prefer to check SPIs using a laptop, mobile phone or tablet—which can be done from anywhere in the world.

To provide this functionality, management and engineering SPIs are provided via a digital twin in the cloud (Figure 3). The digital twin in the cloud gathers data from the plant's distributed control systems, historians and labs, as well as from other sources such as feedstock and energy pricing. It uses this data to calculate relevant management and engineering SPIs, and securely distributes this information to users worldwide.

Figure 3: Digitally replicating live plant operating data and economic data in the cloud allows KBC, a division of Yokogawa, to distribute SPIs to management and engineering personnel worldwide

Integrated Plant Performance Management

It is easy to fall into the trap of functional silos of information, with each person optimizing his or her area of responsibility to meet objectives—but with overall effectiveness of the organization, operation or value stream suboptimal. This happens when the engineering team and management personnel use a set of KPIs different from and nonaligned with the control room operators' KPIs. This problem can be addressed by having aligned targets for engineering, operations and management

Integrated plant performance management is a fundamental platform that uses digital transformation to achieve profitable operation, with digitization and creation of an integrated visualization tool the first steps. The SPI framework is an approach that converges deep industry knowledge with automation technology, in line with Yokogawa’s Synaptic Business Automation concept.

SPIs are systematically and seamlessly connected from the management level to engineering personnel and then to control room operators, and vice versa. They help management to drill down to issues related to operations. Improvements in control room operator SPIs also ultimately contribute to better management SPIs.

SPIs aligned from operations to engineering to management provide a common goal for all plant personnel, letting them transform their work from event-driven to profit-driven.

About the Author

Yasunori Kobayashi, is a senior manager and executive consultant at Yokogawa. He has 30 years of consulting experience in process automation, and over 10 years of management experience in R&D, marketing, sales, operations, and services. He has led projects developing new solutions, including procedural automation using AI (Exapilot), software sensors using Neural Networks (Exaneuro), real-time alarm browsers in DCS (CAMS), automation benchmarking (CEA), knowledge-based graphics using ergonomics (AOG), and remote monitoring and consulting using cloud technology (KBC Co-Pilot Program).

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