CIM for the process industries: A framework

CIM for the process industries: A framework

Computers ind. Engng Vol. 17, Nos 1-4, pp. I-6, 1989 Printed in Great Britain. All rights reserved 0360-8352/89 $3.00+0.00 Copyright © 1989 Pergamon ...

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Computers ind. Engng Vol. 17, Nos 1-4, pp. I-6, 1989 Printed in Great Britain. All rights reserved

0360-8352/89 $3.00+0.00 Copyright © 1989 Pergamon Press plc

CIN S Y S ~

CIM FOR THE PROCESS

INDUSTRIES:

A FRAMEWORK

Terry C. Jackson America PO Box 471 Sandersville, GA ECC

31082

ABSTRACT Computer Integrated Manufacturing techniques for discrete manufacturers have received much attention in recent years. However, due to inherent differences in the nature of operations, process industry CIM requires special considerations. In process operations maximum utilization of expensive processing equipment lines is more important than inventory minimization. Key quality control information is generated in laboratories and needs to be readily available to Production personnel. Process control is typically performed by dedicated real-time minicomputers. Process data acquired needs to be available to other "business" systems. A strategic advantage exists for process industry companies who can integrate these diverse systems and databases to allow application of "decision support" technology. Plantwide Local Area Networks provide the backbone necessary for this integration. INTRODUCTION Stereotyping an operation as either "discrete" or "process" is a theoretical exercise since most contain elements of each. However, these generalities are useful for focusing on the differences between the two and pointing out the unique information technology needs of process industries. Discrete product manufacturers are typically fabrication and assembly operations traditionally concerned with control of direct labor, cost tracking for individual orders, and managing in-process and finished goods inventories which represent the bulk of the company's investment. Products can be made up of hundreds or thousands of individual parts, either purchased from outside vendors or produced in-house. Capacity issues, though important, are usually secondary to concerns for managing the sea of materials. Adding capacity is often as simple as adding labor to operate an additional shift. Process industries tend to fall into two categories: "batch" or "continuous flow". Small operations are typically batch processes, but as these operations grow, there is an economlc need to develop larger scale continuous-flow processes. Examples of large scale process operations are chemical plants, paper mills, paint manufacturers, pharmaceutical plants, mineral processors, and food producers. Operations such as these often involve elements of discrete manufacturing in the packaging and handling of finished product. SPECIAL CHARACTERISTICS

OF PROCESS

INDUSTRIES

A major factor in successful process operations is the maximum utilization of dedicated processing equipment. The investment in plant and equipment is usually much greater than that in inventories, therefore attractive return on investment is largely a function of "uptime" and "throughput" rates. Key factors involved in maximizing utilization are: -Optimized scheduling to increase the length of production runs and reduce costly setups between grade changes. -Aggressive application of preventive maintenance techniques to minimize downtime. [email protected] an item based on its known life or based on direct monitorlng of key parameters (temperature, vibration, etc.) BEFORE it fails is much more economical than waiting until a total breakdown occurs. CAI£ 17-1/4--B

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Proceedings of the 1lth Annual Conference on Computers & Industrial Engineering

-Using in-process and raw material inventories as buffers between processes with different throughput rates. -Using finished goods inventories to cope with surges in demand so that premature grade changes are not needed to meet "rush" orders. (Process industry products are often ordered with short notice.) However, product shelf lives often limit the time that material may be stored. Figure 1 shows a simplistic, hypothetical processing operation flowsheet. Process plant flowsheets are relatively fixed (i.e. not flexible for quick rerouting) and often split from few raw materials to many "co-products" (i.e. inverted Bill of Materials). Likewise, losses typically occur at various process ste~s so "yield" tracking is important. When the value of the material belng processed is high, (such as pharmaceuticals), more attention is paid to controlling yield. Some material is inevitably removed in processing that can be sold "as is" or processed further into a lower quality grade of product ("by-products") In addition to normal by-products, process upsets often result in the downgrading of material to a lower quality grade. The maximization of yield and the efficient use of by-products often determine whether or not a plant will be successful. Key quality control parameters usually relate to quantitative physical properties (such as mean particle size, % moisture, etc.) and chemical properties (such as ppm of impurities, pH, etc.). As a result, this information is usually @enerated in laboratories. Each raw material, chemical, and product Wlll have several key attributes that must be monitored, not just a simple release status. Often raw materials to the plant are natural resources which inherently exhibit wide variations in key ~/ality parameters. In these cases, extensive raw material inventorles and careful blending techniques are used to level out the variations in feed qualit[. Lot tracking of materials from feed through to finished product is needed to comply with government regulations and for quality problem troubleshooting. Many lower-value process products are shipped in bulk forms requiring a dedicated fleet of railcars and trucks. The cost of transportation is high relative to the value of the product. In these cases, lowest cost distribution and fleet management are critical to profitability. In processing plants, the key cost elements are usually: Capital(depreciation), electricity, fuel, chemicals, and raw materials. Though indirect labor for support groups can be substantial, direct labor in the traditional sense is relatively small.. Most modern processing plants employ sophisticated instrumentation and control systems. Computers and/or Programmable Logic Controllers monitor electronic signals from processing and test equipment on a real-time b a s i s Under manual control, when a parameter (such as temperature or pressure) goes beyond predefined limits, an alarm is given to the operator. If the parameter continues to stray beyond a second, wider set of limits, an automatic, orderly shutdown of the process is begun. Automatic control involves comparison of the parameter with a desired setpoint at regular intervals. If the measured value strays too far away from the setpoint, the computer/PLC calculates and communicates to the equipment instrumentation to make an adjustment automatically. (For example: If a flowrate into a reactor exceeds the desired rate, the system partially closes a valve in the feed line until the flowrate returns to the desired setting.) PROCESS

CIM R E Q U I R E M E N T S

In general,

the application

of CIM should provide the following basics:

-Interconnected s[stems of hardware and software with a common database to elimlnate redundant, conflicting information and multiple data entry. -All systems would be interactive and user friendly. systems need real-time update, some very frequently control) and some less frequently (accounting).

All (process

Jackson: CIM for the process industries

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-Distributed processing techniques would be used to share the processing load. Analysis and compute intensive applications would be offloaded to microcomputers. -Local Area Networks would link user terminals and computers. Networking technology allows an end-user to access any connected computer "seamlessly". -All end-user terminals would be "intelligent", supporting standalone computing for "personal" use and logging onto the network to access other computers. In addition, to address some of the unique challenges faced by process industries, CIM implementation should focus on the following: 1) To support efficient utilization of processing facilities while meeting customer needs. This includes specialized software in the following areas: -Order Processing software to perform the traditional check of product inventory for "available to promise". Also, for orders to be produced, the software should track commitments against capacity targets to provide immediate feedback to customers on possible delivery dates. -Production Planning software to project needs for all key resources, particularly capacity requirements. Traditional MRPII systems perform material requirements analysis first, assuming infinite capacity. Later full capacity requirements analysis is performed. New software placing equal or greater emphasis on capacity is needed and is becoming commercially available. More sophisticated materials requirements modelling is needed to accurately reflect inter-related material balances between products (co-products, by-products, etc.). -Production Scheduling software to generate optimum (or near optimum) schedules by minimizing setup times and grade chan~es subject to capacity constraints, in-process inventory capaclties, and customer demand. Options include mathematical programming approaches, finite capacity loading routines, and manufacturing simulation software. Many standalone packages are currently available and some MRPII system houses are now adding finite loading capabilities to traditional Master Production Scheduling modules. -Maintenance maintenance

Management software to support the preventive function (commercially available).

-Fleet Management software to tie into railroad systems movement data daily to give timely information on the location and status of railcars. Truck fleets can also be monitored. This software is commercially available. 2) To support controlled blending of highly variable feedstocks. More and more customers are redefining "quality" as "minimum variation from target". Customers are now demanding that suppliers give proof of stable, controlled processes before ~urchase agreements are made. Mathematical programming approaches that Integrate with inventory information and laboratory results are needed to select highly consistent feed blends. 3) To support distributed process control and process data acquisition for use in "business" systems applications. For example: -Upload of latest throughput rates, downtime status, and inventory levels (automaticall[ monitored tank and silo levels) for use in production schedullng routines. -To provide more meaningful usage information for better product costing. Present practice often involves overhead allocation of "utilities" costs using a meaningless factor such as direct labor hours. Electricity and fuel consumption can be measured directly and fed to cost accounting for more accurate costings. Distributed control systems typically include minicomputers and PLC's. Lower level minicomputers/PLC's perform real-time process control while the supervisory minicomputer stores key data for presentation to operators and managers in concise reports and SPC charts. Blend recipes, feed s t o r a g e locations and key parameter setpoints can be downloaded from other systems to allow automatic computer control of grade changes and production.

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Proceedings of the 1lth Annual Conference on Computers & Industrial Engineering

4) To meet the needs for laboratory information management. Commercial packages (called Laboratory Information Management Systems, or "LIMS") are now available which allow easy collection, storage and retrieval of all test results, in addition to providing lab management support. These packages provide SPC charts for use by production personnel and SQC charts and Certificates of Analysis ("COA") for customers. The presence of a LIMS package interfaced to a plant database allows easy correlation of key operating parameters with quality results for troubleshooting and process Improvement studies. 5) To meet the needs for lot traceability from finished product back through raw materials. This is commercially available in most MRPII packages but needs to tie into the LIMS for quality parameter details pertaining to each lot. Figure 3 gives an overall systems integration chart showing the key interrelationships between subsystems. Note that the objective of the whole is to provide better service to the customer, faster and more efficiently. This should be the driving force behind CIM. Figure 2 gives a hypothetical plant CIM network based on LAN technology and interconnection of all components. Computers communicate to one another over the broadband LAN. PC's are used as standalone local processors and serve as terminals for the larger systems, "logging on" over the network. This example assumes order processlng and external warehouse inventory control are handled at a remote, corporate site interconnected to the plant "MIS" system via phone lines. DESIGN AND IMPLEMENTATION CONSIDERATIONS Though there is no set recipe for success, there are several good "rules of thumb" to follow in the design and implementation stages. i) Derive a well-defined plan and get consensus agreement up front from upper management and all departments. Key decisions in the process: * Multiple hardware vendors vs single suppliers? * What software should be integrated and what is suitable as standalone? * For the software to be integrated, how will it be developed? -Commercially available "integrated solutions" (quicker but lower goodness of fit) -Multiple end-user packages selected individually and integrated after the fact (longer time but better fit) -Custom developed software (longest time but best fit) * In-house programming vs contract programming? 2) Divide the total project into manageable sub-projects implementation. "Don't bite off more than you can chew". 3) For each sub-project, create multi-disciplinary by end-users (non-MIS) to agree the design details.

for phased

project teams headed

After these ste~s are completed, the project teams can oversee the acquisition/programmlng of the final systems, ensuring the plan is followed. REFERENCES Bolander, S.F., et al., 1981, MANUFACTURING PLANNING AND CONTROL IN PROCESS INDUSTRIES, APICS Publications. Connor, S.J. and Thompson, O.W., 1986, "Manufacturing Systems in Four Parts-Part Three: Systems Management in Process Manufacturing", NEWS/34-38, July 1986. Eads, G., 1989, "Manufacturing Systems for the Process and Repetitive Industry", PRODUCTION AND INVENTORY MANAGEMENT REVIEW, Volume 9, Number 2, Feb. 1989. Hordeski, M., 1988, COMPUTER INTEGRATED MANUFACTURING- TECHNIQUES AND APPLICATIONS, TAB Books. IBM, 1972, COMMUNICATIONS ORIENTED PRODUCTION INFORMATION AND CONTROL SYSTEM, Volumes IV, V, VI, IBM Technical Publications. Lefkon, R.G., 1986, SELECTING A LOCAL AREA NETWORK, AMA Membership Publications.

Jackson: CIM for the process industries

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