The Link Between Automation and Enterprise KPIs

Long-term, enterprise key-performance indicator (KPI) goals are directly correlated to short-term, process-and-control system KPI-goals. Case studies clearly demonstrate that improved control system KPIs directly drive improved corporate KPIs. Managing process-and-control-system KPIs has the advantage of immediate response to problems, identified and solved quickly at the plant floor level wit...


Long-term, enterprise key-performance indicator (KPI) goals are directly correlated to short-term, process-and-control system KPI-goals. Case studies clearly demonstrate that improved control system KPIs directly drive improved corporate KPIs. Managing process-and-control-system KPIs has the advantage of immediate response to problems, identified and solved quickly at the plant floor level without waiting for monthly management reports to drive action. A performance supervision system is essential for tracking, analyzing, prioritizing, and improving the control-system KPIs.

Enterprise KPIs measure the business goals and help management allocate resources. Process-and-control KPIs measure the effectiveness of the process and the control system and help plant-floor personnel allocate their resources. When the process is running well, control system KPIs and enterprise KPIs improve. Driving automation KPIs to their optimum in turn drives enterprise KPIs toward management goals.

Enterprise KPIs, while meaningful, reflect longer-term results, and cannot be manipulated directly. Control system KPIs are shorter-term indices that can be managed daily. Maintaining a smooth-running process by managing control system KPIs drives unit-operation, plant, and enterprise KPIs to new highs.

Maintaining short-term KPIs is similar to maintaining performance of an automobile or airplane. What you desire is high fuel efficiency and high reliability. However, you cannot easily make direct adjustments to these KPIs. Instead, you can make adjustments to compression, fuel mix, operating procedures, and tire pressure to optimize short-term process performance. Managing these factors ensures better performance of the long-term measures.

Enterprise KPIs

The longest-term enterprise KPIs drive long-term company success. Most typically, these include company reputation and customer satisfaction.

While these KPIs are critical, they are difficult to measure and come too late. It may take years to address the root cause of a problem. Consequently, many companies look at performance measures with a shorter time frame.

Medium-to-long term enterprise KPIs may include:

  • Profits;

  • Quality;

  • Total costs;

  • Throughput;

  • Uptime; and

  • Operating costs.

These KPIs offer distinct advantages. They are relatively easy to measure, they can be measured at shorter time-scales (typically monthly), and they are scalable to the entire enterprise. Scalable means that the same KPIs can be applied to the corporation, division, site, plant, and even unit-operation levels.

Without sustained long-term enterprise goals, the longest-term goals would not be possible. The company must create good quality products, at high throughput, in efficient processes, while generating a profit, to have satisfied long-term customers resulting in a good company reputation.

While these KPIs provide a good measure of progress toward company goals, they are not always directly actionable. If performance of a plant or unit operation is suffering, what improvements can be made? To answer that, look at short-term performance measures.

Process, control-system KPIs

Achieving medium- to long-term enterprise KPI goals requires achieving short-term process-and-control system KPIs. These KPIs include:

  • Efficiency;

  • Variability;

  • Reliability;

  • ExperTune index;

  • Harris index;

  • Oscillating;

  • Time in abnormal mode;

  • Noise;

  • Output at limit;

  • Valve travel;

  • Robustness; and

  • Settling time.

Each of these short-term KPIs meters certain aspects of plant performance. In many ways, they are like the gauges on a car's dashboard, or the heart rate monitor on a patient in a hospital bed. They provide an immediate indication of any problems with process performance. When a problem is noted, an immediate action can be taken to correct it.

Modern DCS- and PLC-systems are collecting the entire base of data needed to complete these assessments. By properly supervising results, the process can be quickly optimized.

Universal process KPIs

Of the KPIs discussed, several have universal appeal. These are KPIs that everyone in the company from operations to plant management can easily understand and interpret. These KPIs can be directly related to unit and plant performance KPIs. Three simple KPIs for this purpose are output at limit; time in abnormal mode; and oscillating.

Rationale for these three simple KPIs is this: everyone can focus on the same easily understandable measures that have direct impact on corporate KPI goals. Other performance measures may be equally important, but require more process knowledge to interpret. Let's look at each control-system KPI and see how it directly links to corporate KPI goals.

Direct links

Short-term KPIs can directly drive long-term KPIs, such as throughput, energy cost, and uptime.

Output at limit. Controllers whose output is at a limit are indicating a bottleneck in the process. Corporate KPI of throughput improves when the control system KPI of output at limit improves. In fact, even when it is wide open, a control valve can restrict flow.

If the controller is at a limit, one degree of freedom is lost in the process. If a supervisory model predictive controller is being used, it has lost a degree of freedom and has become constrained. The model predictive controller has less ability to move and the corporate KPI of profitability is degraded.

The root cause of output at limit is typically a valve-sizing problem. This may have been an original design problem, or it may have happened after capacity was increased, but this particular valve was not considered.

Time in abnormal mode . Control loops operating in manual override or with manual overrides of any sort are loops with time in abnormal mode (TAM). This is an excellent measure, because a large TAM means that the controller is not working as it is supposed to. If it is consistently high then either the:

1) Controller is unnecessary; or

2) Design of the control system is incorrect, with the controller in the wrong place.

For example, if the loop is designed with a valve that is too large or too small, operators may not put it in automatic. Since there is a work-around, the control engineer may not notice.

Another example is a valve placed or measurement taken at the wrong location. This could be temperature control of a distillation column where a non-sensitive temperature measurement is used for control. This loop would probably not be in automatic, as it could not react to disturbances.

Another example is controlling a pressure with a valve when it would be better to control it with cooling media flow. This loop would not be in auto because it is not using the correct pairing of manipulated variable and controlled variable, in this case pressure.

Operators usually inform maintenance about troublesome loops. However, if after a certain time it does not get fixed, the work-around is to just keep it in manual. Under normal operating conditions, the plant will run fine, but in an upset condition, this work-around can cause problems. Tracking this metric uncovers hidden issues. Repairing these issues may have a direct effect of increasing the corporate KPI of uptime.

Oscillating. Loops that are oscillating typically result in either a quality problem or high-energy costs. To solve quality problems, remove oscillation to move closer to quality limits. Improving the control system KPI of oscillation increases the corporate KPI of profitability by allowing you to crowd specifications.

It is slightly harder to understand how energy consumption is affected by oscillation. Consider automobile fuel efficiency when oscillating between slow and high speeds, or by causing the air/fuel ratio to swing up and down once per minute. It is not hard to see that this would degrade performance.

The very same thing happens in process plants. Oscillation in boiler feedwater leads to boiler inefficiency. Furthermore, this oscillation may carry through to steam pressure and create more expensive, downstream process-inefficiencies.

For example, oscillating steam pressure can cause pressure oscillations in a distillation column. This oscillation in differential pressure (DP) affects efficiency of separation. Correcting this oscillation lowers the control system oscillation KPI. This results in increased efficiency of separation, which increases the corporate KPIs of throughput and quality.

Short and fast

The beauty of short-term KPIs is that they are directly actionable. When loops are oscillating, controllers may be tuned and valves fixed to eliminate the oscillation. Focusing on loops in abnormal mode will uncover a host of process, equipment, and control configuration problems, many of which can be resolved in one day. Addressing valves at limit usually requires slightly more time to resolve, since it may involve replacing the valve with a spool piece, adding a bypass, or installing a larger valve.

Several case studies illustrate the effectiveness of performance supervision software in improving control system KPIs. These improved control-system KPIs resulted in measurably improved enterprise KPIs.

Chemical manufacturer . A large, U.K. chemical manufacturer applied a supervision system to oversee the performance of 2,000 loops. The system identified interacting loops and many valve problems for the manufacturer. Engineers there used oscillating and valve-at-limit KPIs to identify problem areas. After valves were repaired and control loops were tuned, these short-term KPIs showed results. Oscillation, variability, and valve travel were greatly reduced. This, in turn, reduced operating costs $300,000/year. With this kind of return in an enterprise KPI, the payback is very fast.

Kruger Paper. Kruger Wayagamack, in Trois-Rivieres, Quebec, Canada, applied performance supervision software to 500 loops at the commissioning of a new paper machine. During startup, many stability problems were uncovered early. Using a KPI weighted with economic importance, the mill kept a daily focus on problem areas throughout start-up and commissioning. For example, high variability in flow loops pointed to mechanical issues with a pump.

The result of improving the control system KPIs during startup reduced the startup time by three months. Kruger estimates that its enterprise-cost KPI has been reduced by $1 million annually due to improved control system KPIs.

Refinery in Washington . A refiner applied a performance supervision system to a fluidic catalytic cracker. The system was part of a pretest before applying a model predictive control (MPC) system. The system diagnosed a plant-wide oscillation. Investigation revealed an interaction between control loops as the source of the problem. Through tuning and changes to control strategy, the oscillation was eliminated. Without the oscillation, the plant could run closer to specification limits. Operating set point was raised, resulting in reduced operating costs. Total cost KPI was cut $300,000 annually.

Inside Process Products

Real-time, Windows-based package

DAP 840 board and DAPstudio software combine to form a development and runtime package that provides a real-time product in a Microsoft Windows-based product. DAPstudio simplifies development of PC-based measurement and control systems with one or more data acquisition processor (DAP) boards. Each DAP board gives a system an additional processor running a real-time operating system controlled from a Windows application. Added resource frees an application from system delays and applies computing power when and where needed. Two or more DAP boards in the same PC or on a network can work together as a single synchronized system using DAP-to-DAP communications. Microstar Laboratories Inc.

Expert systems software tool

Exsys Corvid development tool and expert system software based on artificial intelligence now offers enhanced multi-language features and an improved interactive user interface. Easy-to-learn general-purpose development environment can be used to build any type of expert system project. It helps distribute problem-solving knowledge over the Web using interactive sessions that emulate a conversation with a human expert to provide situation-specific advice and recommendations. Applications include quality control, customer relationship management, data analysis, compliance, and research. Exsys Inc.

Author Information

John Gerry, P.E., and George Buckbee, P.E. are of ExperTune Inc.,

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