Can you improve the effectiveness of your automation with automation? DMS Powders, of Meyerton, South Africa, has shown that you can. The trick was to deploy information technology (IT) in a way that enabled management to employ a useful parameter called overall equipment effectiveness, or OEE. As the product of three percentages—availability, performance, and quality—the parameter gave DMS managers an objective index for judging the effectiveness of the company’s equipment and taking steps to improve it.
OEE helped the company to go beyond simply improving control over its processes for milling and atomizing ferrosilicon (FeSi), an important ingredient for separating minerals and scrap metals. Although improving process control is a laudable endeavor, it can consume a lot of resources for small, incremental improvements, especially if the process is already in control as currently configured. It does not evaluate the process itself.
Finding opportunities
OEE, on the other hand, provides that evaluation. Moreover, if the OEE score is below an acceptable benchmark, an analysis of its three components can direct the attention of managers toward much greater opportunities for improvement. OEE, therefore, is a tool that can help analysts to identify downtime and other indicators of poor performance, determine their causes and rectify them.
At DMS Powders, putting this tool in place was a task outsourced to ABB Asset Management Services, a unit of Zurich-based ABB that specializes in managing the maintenance and continuous improvement of other companies’ production processes. Experts from ABB sold DMS on OEE as the logical next step in the continuous improvement program. Although an extensive information network was already in place, DMS’s management and ABB’s experts still had a number of questions about the causes of downtime. They needed a way to isolate such problems.
ABB recommended a tool called DT Analyst software from Wonderware, a Lake Forest, Calif.-based unit of Invensys Systems Inc. Not only does the software calculate OEE, its components and other efficiency indicators, but it also fit easily into the other Wonderware factory management software already gathering, processing and disseminating information throughout the plant.
自安装软件,实时OEE figures have become a regular feature of morning production meetings. “The DT Analyst software is helping DMS Powders to map cause-and-effect scenarios and detect the root causes of problems at many levels,” says Piet van der Merwe, an automation specialist at ABB.
By drawing attention to the activities limiting availability and performance of the induction furnaces, for example, OEE has helped the staff at DMS Powders to reduce the eight- to 10-hour daily downtime to only five to eight hours. In the chipping plant, an OEE-based analysis of electrical current uncovered the fact that the motors actuating the activity there were idle 70 percent of the day. Now, the motors run 70 percent of the day, and production there has skyrocketed.
The evaluation of performance is not limited to downtime and productivity. “The software is also making DMS more environmentally friendly by continuously monitoring stack emissions and warning the operators when dust blown into the atmosphere reaches unacceptable levels,” explains van der Merwe.
Plant comparisons
Other large corporations extend the parameter beyond just individual machines and production lines to measure and compare the performance of their many plants. OEE ends most of the debate among managers on how to make these comparisons fairly. “This is one metric that is really facility independent,” says Jeff Nuse, Wonderware’s product manager for DT Analyst and OEE. “It bundles three important parts of plant effectiveness into one metric that can be easily used by managers at all levels.”
OEE is finding wider application on this level because the data already exist in the controllers running automatic equipment, and because calculating the parameters is relatively easy now. “Automation hardware is more enabled than ever to store historical data and make it available,” says Scott Teerlinck, director of marketing, customer support and maintenance at Milwaukee-based Rockwell Automation. Modern information technology can reach across computer networks into those controllers in real time and retrieve that data for use by software capable of computing the latest OEE values.
In fact, failing to use information technology to automate the calculation of OEE is a big mistake. “When data is entered manually, it is subject to human error, interpretation and manipulation,” explains Jim Feltman, vice president at Vorne Industries Inc., a manufacturer of real-time monitoring products based in Itasca, Ill. “Every time operators have to enter downtime data or log an event, it pulls their focus away from the process they are asked to manage.” Besides being fraught with inaccurate feedback, collecting the data and computing OEE manually is slow, reducing any action taken on it to reactive rather than proactive.
Lesson: automate calculations
A supplier of automobile door panels learned these lessons the hard way. According to Teerlinck, at Rockwell Automation, the lesson began when a customer, an automaker, required one of the supplier’s plants to produce a line of panels in more than 70 variations. To keep the business, the plant would have to change the production line making them from batch production to in-line vehicle sequencing so it could deliver them in the order that the automaker needed.
Needless to say, the change was a shock to the system, and there were problems. Scheduling became a nightmare, and quality was near the bottom of the supplier’s locations. These problems, however, had to remain transparent to the customer. The contract made shipping defects expensive, and not delivering just-in-time would shut down the customer’s line, which could cost the supplier the business altogether. So the supplier did what it had to do to make deliveries, and two 12-hour shifts at one of the plants quickly blossomed into three eight-hour shifts plus overtime.
Although the plant had used OEE in the past, the old manual method for calculating it simply did not provide the information necessary to schedule production efficiently and to control quality. Management had been relying on operators to collect the data, not only making it subjective, but also placing its accuracy and completeness at the mercy of each person’s memory and ability to record events. So when defects surfaced, finding the cause was difficult at best.
In some instances, for example, searching through entries in the handwritten shift reports was the only way of determining whether a vision system conducting in-process inspections of assemblies detected the problem in question and reported it to the appropriate operator. When such searches bore no fruit, whether man or machine was at fault would remain a mystery.
To correct the problem, management asked the integrator that installed the control system to automate data collection and its flow throughout the organization. The integrator responded with a generic data-collection package that required quite a bit of customization to fit the plant. When it failed to work, the engineering manager brought in Rockwell Automation and its RSBizWare PlantMetrics software, a package developed specifically for these kinds of analyses. In ten days, the Rockwell installation engineer installed the software and connected it to the work cells on the production line without a lot of customization, according to Teerlinck.
Once the line began reporting data directly to the software, the software was able to calculate the true OEE—which was less than 40 percent. Although the figure fell far short of the 90 percent minimum set by management, the good news was that the engineering staff had real data that it could analyze and act upon. Within six months it boosted OEE to a little more than 85 percent.
Better quality control was an important contributor to the dramatic improvement. Now, for example, the software not only tracks measurements from the vision system but also ties them to the appropriate operators. Consequently, it is no longer a mystery whether the operator or the vision system is to blame for any faults detected at the final inspection conducted before products are shipped. Rejects have fallen from more than 20 monthly to fewer than five, saving more than $20,000 per month.
自动化的计算OEE也给管理rs and supervisors the information that they needed to boost the efficiency of the operation. By increasing production, they not only reduced unit cost, but also eliminated the need for the extra labor that the company incurred after switching from batch production. Overtime has disappeared, and management is considering eliminating the third shift. If it does, the annual savings could be as much as $1 million. Making these savings even more remarkable is the fact that the customer had increased the complexity of the assemblies, causing cycle times to increase by 28 percent during this time.
Guarantee results
Getting these results is not as automatic as computing OEE, however. Management must act on the information that OEE provides, such as working it into a larger total productive maintenance (TPM) program. “Even in plants that are leveraging their automation control system for OEE data collection, many still manage their maintenance departments off schedules or budgets, as opposed to managing them off the key performance indicators,” notes Rockwell’s Teerlinck.
“And companies just beginning to develop TPM and continuous improvement programs should not be discouraged by a first measurement of OEE in the 25 percent to 30 percent range,” says Feltman, at Vorne. He urges users to consider such results as an opportunity to build momentum. In other words, emulate the door-panel supplier, and use the automation at your disposal to improve the effectiveness of your automation.
For more information, search keyword “OEE” atwww.myenum.com.
