Just 20 years ago there was a distinct separation between business systems underpinned by information technology departments and automation systems supported by engineering. Business systems were designed to report business results to management level personnel and as such were commonly referred to as Management Information Systems. These systems were typically designed to deliver output based on human schedules with many of the business functions executed monthly. Automation systems, on the other hand, were designed to control the manufacturing and production processes to maximise efficiency and had to operate in a time frame relative to the operation of the physical process – real-time. This separation and these distinctions served industrial operations quite well as long as the time frame of fluctuations in key industrial business variables occurred on a greater than monthly basis.
Over the past decade a number of key business variables of industrial operations have started to experience more frequent fluctuation rate, often fluctuating multiple times a day. This transition began with the opening and deregulation of electric power grids. The price of electricity, which had been stable for months at a time started to fluctuate multiple times daily. This caused a domino effect, first to other energy sources, such as natural gas, and then to some energy intensive raw materials. Traditional monthly business measurement systems proved to be inadequate for providing the necessary information to guide effective business decisions required at all levels of industrial organisations. Business decisions needed to be executed when the input data changed. Getting access to that data at the end of each month was insufficient and the business of industry started to be out of control.
Developing a real-time profitability perspective
Today, although many industrial business variables such as labour costs do not tend to change within a month, others can change multiple times daily. In reality, only those variables directly impacted by the frequent price fluctuations associated with the deregulation of the electricity power grids are experiencing real-time variability. These real-time business variables typically include those that comprise the production value of the operation as well as the energy and material costs. Since these three categories of business measurements tend to be the primary ones that experience real-time variability when they are measured together, the resulting model is typically referred to as the real-time profitability model of the operation. It is important to mention that there are a number of other variables used to determine the true profitability of the operation. But since those variables are essentially stable throughout monthly periods they do not need to be specifically considered when trying to control the real-time profitability of the operation.
The real-time profitability model shows that the objectives for any profit-based industrial operation should be to increase the production value vector to the greatest extent while reducing energy and material cost. Yet, in operating plants, there are limitations such as equipment, safety and environmental constraints. Since the safety of people, equipment and the environment tend to be of prime importance in most industrial operations, experience has revealed that in many instances these safety constraints are encountered even before most equipment constraints. To make matters more interesting and challenging, these safety constraints often change during different phases of operation and with conditions within the operations. This means that the safety constraints may exhibit real-time variability in a very similar manner to the variables of real-time profitability. This provides an even more challenging scenario.
The challenge for industry is to maximise the profitability of operations in real-time while ensuring the safety of people, equipment and the environment while all of the key variables are fluctuating fairly frequently. This is a daunting challenge that requires industrial companies to bring together different areas of expertise that have traditionally been completely independent. The key to success is in applying control theory to both efficiency and profitability. Previously control theory was only applied to efficiency.
Bringing real-time profitability under control
Industrial companies have invested significant resources to bring the efficiency of their factories and plants under control. Process control systems have been designed and used for this purpose for decades. When all of the key business variables of the operation were stable over extended periods of time, controlling the efficiency of the operation directly translated to profitability. But as key components of profitability started to experience real-time variability, the efficiency of the operations might still be well controlled while the profitability was out of control. For example, an efficiency-based control strategy may be aimed at reducing the overall energy consumption, which is a valid objective. But even as consumption declined, the cost of energy might actually increase because the energy that is consumed may be consumed during high price periods.
This trend for profitability to be out of control while the plant efficiency was well controlled caused great frustration for industrial management because the solution to this problem was not apparent to them and the monthly information provided by their MIS system was way to infrequent to help. From a control theory perspective though, the solution was obvious. The real-time variability of key components of profitability and safety presented a classic control problem. The solution is to develop a cascade control strategy in which the control of real-time profitability cascades to the efficiency control of the operation.
Process control applied to the efficiency or industrial operations has been successfully implemented for many decades and must be effectively applied to control the efficiency of the operation. Controlling efficiency is a prerequisite to controlling profitability. Bringing profitability under control can be accomplished through a simple three step process.
Since what is not measured cannot be controlled, the first required component of any successful control strategy is the measurement of the variables to be controlled. In the case of process control this includes physical measurements, such as flow, level, temperature, pressure, velocity and composition. In the case of profit control it includes the component variables that comprise production value, energy cost, material cost and safety as described above. Not only do these variables need to be measured for effective control, but they need to be measured in the frequency that they may change. Since each of these variables typically changes multiple times daily and in many cases multiple times each hour they each must be measured on reasonably frequent basis. In most industrial operations measuring the business variables is considered to be within the domain of ERP systems. Unfortunately ERP systems are not designed to gather the data at sub daily time frames and, therefore, do not provide an adequate data source for profitability control.
If the ERP databases do not have the source data necessary for profitability control, a different source is required. This has presented a daunting challenge to industrial managers, but this challenge can be readily overcome by utilising the primary real-time data source available in industrial domains, the sensor-based data used for process measurement and control. Most industrial operations have thousands of sensors measuring a wide variety of physical, mechanical and chemical variables throughout their manufacturing and production processes. These measurements continually convey what is going on in the plant second-by- second. This database is available in real-time, but is in the wrong form. Before it can be used it must be transformed into real-time accounting information that conveys the components of real-time profitability. Since process control systems are designed to provide real-time, sensor-based analytics, they are ideal domains for modelling the required real-time accounting data.
The second requirement for a real-time profit control system is to empower the operational employees with the exact information contextualised to their specific job responsibilities so they can make better decisions on how to perform their activities in a manner that drives profitability improvements. This information should be provided to every employee whose actions impact operational profitability from frontline personnel all the way through management. Providing this information to empower frontline operations personnel tends to be very anti-cultural in industrial operations. The tradition has been to provide process operators only with the information required for them to manage alarms and other abnormal conditions in the process and to let the control system do the real work. This attitude must change for real-time profit control to be effective. Operators make more decisions each day that drive profitability than anyone else in industrial operations. Decision, such as changing a temperature, either creates or destroys profit – it cannot be neutral. Each decision may only have minor impact on profitability, but since they may make hundreds or even thousands of such decisions each day their overall impact on profit can be very significant.
Empowering operators with contextualised real-time profitability information enables them to learn how to close the loop on profitability over time. Experience has demonstrated that the positive impact of this type of empowerment produces surprising profit improvements often providing a 100% return on the investment required to implement the system in under six weeks.
The third and final requirement for an effective real-time profit control system is identifying and implementing specific improvement actions that will drive incremental increased profitability. These improvements can be identified by executing a theory of constraint (TOC) analysis throughout the operation. The real-time accounting measures developed in the first step can be used both to accurately estimate the increased profitability that can be generated by freeing up a specific constraint and to measure the value of the improvement once implemented. This results in an operational environment in which every initiative that is implemented to drive business improvement can be measured in terms of true profitability gains or losses. This allows management to focus on the specific initiatives that drive the most value and to stop investing in activities to result in little or no value improvement. It also contributes to a new type of continuous improvement environment focused on continuous profitability improvement – which directly ties into management objectives. This tends to make continuous improvement programmes mainstream business processes rather than just on the side initiatives.
Conclusion
The real-time dynamics of today’s industrial businesses are presenting huge challenges to industrial companies continuously trying to improve the profitability of their operations and enterprises. Business systems are not designed to deal with real-time variability and have come up very short in meeting the challenges. Fortunately, the real-time sensor-based automation systems already implemented in most industrial plants have what is required to control real-time profitability in much the same way they have been utilised to control manufacturing and production processes. What is required is an open mind on how and where business solutions are implemented and the combined business, accounting and engineering talent to build the real-time profit control strategies. The initial results of applying real-time profit control systems have exceeded expectations returning many times the initial investment in the first year of operation with ongoing cash flow improvements.
For more information contact Jaco Markwat, Invensys Operations Management, +27 (0)11 607 8100, [email protected], www.iom.invensys.co.za
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