REDUCT APPROACH TO EFFECTIVE PROCESS MANAGEMENT
Modern process industries require timely information and data to meet the challenges of todays fast changing business environment. Management can meet these challenges and competitive forces by having the ability to respond with the right strategies. This requires reliable process data, as well as the ability to make sound decisions. Organizational management needs, therefore, effective tools to process a variety of information, and to identify and implement timely actions.
Todays plant information systems are very effective in collecting, summarizing, interpreting and displaying process data. The problem is, however, that there is often too much information: the amount of data is simply overwhelming. What is needed, therefore, is not more data, but more process intelligence.
Process Intelligence (PI) is a code-name for a range of technologies which unlock knowledge hidden in process data while telling how to use it more effectively. The overall objectives of PI are to improve productivity, product and service quality, and business profitability by making information and data more comprehensible and using process knowledge more effectively. The specific objectives are to leverage investments in instrumentation, PLCs, SCADA and DCS systems, and plant information networks through better process intelligence. In short, Process Intelligence helps to use process data for competitive advantage.
PI assists process engineers, plant superintendents and managers in making everyday decisions about processes. It uses intelligent software technologies for better, more optimal reasoning about process data. PI technologies apply sophisticated algorithmic methods based on advanced statistics, operational research, Artificial Intelligence, evolutionary algorithms, etc., for tasks such as data analysis, system control and optimization, knowledge acquisition, or adaptive scheduling. Process data can be gathered, interpreted and made available in real-time throughout the plant using Internet Web technologies, OLE for Process Control (OPC), On-Line Analytical Processing (OLAP), MS COM/DCOM, etc.
Systems integration technologies focus on integrating various equipment control functions, and information services focus on implementing business transactions. Process Intelligence, in contrast, focuses on process data, process information, knowledge, and all other business and operation aspects involved in scheduling , optimization, control, maintenance and management of the process. Process Intelligence provides tools to help process engineers and managers to make better process decisions.
Process Intelligence is a "bridge" between the machines' management and the business management:
Process Intelligence can:
The specific capabilities of Process Intelligence technologies are:
OLE for Process Control (OPC) allows access to process information in DCS, SCADA and PLCs in a standardized way, thus enabling open and more efficient communication in a heterogeneous computing environment. OPC offers an interface with speeds up to 10,000 points per second, high robustness and reliability, and advanced security on PC computers. OPC client applications can be built at half the cost of proprietary system architectures
Data mining enables understanding of complex processes and issues where the engineering and scientific principles are not well understood. It discovers how and why process experts and operators make decisions under diverse circumstances. The user can also learn about the complex strategies used by the best shift teams by mining their historical production records, and discovering how they reacted to various production events.
Advanced algorithm methods like Genetic Algorithms build real-time, adaptive scheduling systems that incorporate process economics into production schedules. They allow elimination of inefficiencies in utilizing production capacity by integrating inventory, current orders and production plans. These methods facilitate easy rescheduling, review of alternative schedules and quick assigning of production orders.
Virtual On-line Analyzers using technologies such as neural networks to model and predict process measurements that are difficult or costly to perform. They act as virtual instrumentation or sensors and can be easily updated as equipment ages, retrofits are done, or process operating characteristics change. They also help to explore the relative impact of process variables through what-if analysis and to analyze process economics.
Supervisory DecisionExperts perform multiple tasks in supervisory control, optimization and automation. They help evaluate and implement optimization strategies based on process knowledge discovered from process data. The operator can address varying objectives and constraints simultaneously while compensating for changing feed conditions and process dynamics. Since there is no need to develop process models, DecisionExperts are less costly and more profitable than other methods.
Intranet Web technologies make process information available plant-wide in a format that addresses employees needs. Whether it is the process accountant, process expert, or production manager, each receives up-to-date process information that is prepared for easy use.
Business Intelligence using technologies like OLAP allows management to gain insight into their business by drilling-down business data for trend spotting or using what-if analysis for scenario forecasting. BI technologies provide management with up-to-date information on vital business aspects of their operations.
Risks and Challenges
Implementing Process Intelligence requires some changes to how an organization works with data. These changes will create a variety of reactions including disbelief, skepticism, hesitation, or fear that a PI project will fail and reflect negatively on its champions. There is no simple answer to every possible fear of change, but there are effective ways to manage the risks and challenges associated with PI projects.
The foundation of PI risk management is a two- phase approach to its implementation: First a feasibility study is conducted where benefits and risks are identified and evaluated with actual process data. Then in a second stage, selected opportunities are implemented after all questions are resolved. The risk that a PI project may miss its targets and objectives is reduced by the formation of a project team consisting of company process engineers and management, as well as knowledgeable consultants. This team will manage all technical risks and staff training, and keep company management abreast of all significant milestones.
Finally, the possibility that a PI project will divert a large amount of staff time away from everyday urgent tasks is remedied by having consultants perform most of the detail project work, thus providing additional, temporary manpower and capabilities to the company management.
Both competitive business pressures and the uncontrolled growth of process data have contributed to the need for technologies that can assist process engineers and managers in dealing with the information overload and in using data more effectively for competitive advantage. Process Intelligence offers such technologies today. Process Intelligence systems provide vital business and process intelligence to industry, and PI technologies are within the reach of every plant.
|REDUCT & Lobbe
P.O. Box 800, 186 - 8120 No.2 Road., Richmond, BC, Canada V7C 5J8
ph: (604) 275-3711 fax: (604) 275-3715 email: firstname.lastname@example.org