Actionable intelligence is what any manager craves, especially those who manage factories and lines. Nobody wants inefficiencies in their production, the problem is finding them and their cause. That is where Overall Equipment Effectiveness, or OEE, could help. It is not a new concept as a Key Performance Indicator (KPI) in manufacturing. A more widespread use of OEE seems to be gaining momentum, however. This is due both to increased interest in it and improvements in data gathering technology.
“OEE or Overall Equipment Effectiveness is a relatively simple metric that provides insight into how well your manufacturing process is working,” said Wes Blankenship, president of Factory Systems (Columbia, SC). “It can also be used to help one better understand the effect of various factors on productivity and track process improvements over time.”
There is a standard, simple definition of OEE (see sidebar) that seems intuitive and easy to calculate. “It can tell you an awful lot about your process,” said Blankenship. Many manufacturers also employ variants of OEE, according to Blankenship. Managers at times use other metrics implicit to OEE computations that better suit the nature of their specific operations. “Factory Systems works with our clients to develop related metrics that provide the best feedback and diagnostic information to monitor and improve their processes,” he said.
However, he had cautions. “While OEE seems simple, it can be difficult in a practical sense to implement,” said Blankenship. For example, in calculating Availability for a CNC milling machine, the actual uptime could be affected by being starved for parts from an upstream process or unplanned maintenance due to broken tools or jamming. “This often times leads to debate in a plant as to how to define [critical parameters] used in calculating OEE,” he said. Plants with older machines may find it hard to get Availability data. “You may or may not be able to interface with older controllers or proprietary data formats.” Using the right data is critical.
Karl Ritzinger, president of Production Process (Londonderry, NH), said that his company both recognizes the data problem and provides devices to help collect that data. He has seen that it is difficult to calculate OEE by collecting data manually and using a spreadsheet, at least to any degree of accuracy. Their devices collect data for downtime tracking that connect to “dumb” machines (as Ritzinger describes them) that are not automated and usually are legacy machines. “Although we are slowly moving into the CNC world,” he said. They offer simple devices that capture binary data—think go/no-go or on/off—through machine data transducers. “We even had one manufacturer who correlated the flow of cutting fluid as a good indicator as to when a saw was actually cutting,” he remarked.
The company’s line of MDT devices collects, displays and transmits real-time data to a remote Windows-based PC. They use existing electrical signals that operate the machine, such as switches or relays, photo or proximity sensors and voltages from 24V to 120V AC or DC. The company’s newest device, the MDT-20, is especially interesting in that it provides Ethernet, wired or wireless, in plant-floor communications with four machine inputs. Through those connections it provides OEE data, automatic parts, reject tracking, and end-of-line count. It calculates OEE in real time. The MDT-20 also has an optional tablet PC interface, following the general trend towards more portable computing. “Once we get that data off the machine, we also provide software to present it in any number of ways,” he said. He finds that OEE is especially useful to manufacturers of discrete products, from cars to toys.
Ritzinger also said that most people who contact him are initially only looking for data on machine downtime. “Ten to 20% I would estimate are not familiar with OEE, and many more are familiar but have not implemented it yet,” he remarked.
That is a sentiment echoed by Dave Biros, global product marketing manager, process automation service of ABB (Westerville, OH), the supplier of automation and controls systems. “I was surprised at how many customers were not tuned into [OEE],” he said. “Once we explained it and they understood it, the conversation became more meaningful.” He stressed that OEE can be an important tool in balancing tasks and responsibilities within a plant. “Most people think operations runs a plant and maintenance people fix the equipment when it breaks, but actually the maintenance department is important to Availability, while operations department has more influence on Quality,” he explained. “You need to look at machinery in the context of lifecycle, like employing technologies that can predict failures before they occur to increase Availability.”
To put it in context, he explained that there are eight typical causes for losses in a plant: equipment shutdowns, production adjustments, equipment failures, process failures, slowdowns, abnormal production, quality defects, and rework. Of these, the first four affect Availability and for which the maintenance department is directly responsible. “Do not treat the maintenance department as a second-class citizen,” he warned, and use OEE if need be to prove why.
OEE can also be a powerful tool for understanding how to increase profit through increased Availability, Biros added. He showed how increasing Availability from 77 to 83% increased OEE by 10% and drove up a Return on Capital Employed, or ROCE, calculation from 6.81 to 12.25%. “In this example, it was equivalent to a 6.2% price increase and no one is commanding that kind of price increase today,” said Biros.
How does ABB help customers? It is more than equipment and software, though that is important. “We help them understand what their baselines are through our Advanced Services group,” said Biros. Since an accurate, useful OEE is tricky to calculate, customers may not know where to begin. “A lot of data can be found through the control system, for example,” he said. “We can find where and when aberrations in production occurred using it for historical data purposes rather than in-process control.” After a benchmarking engagement, ABB offers its ServicePort system to track key KPIs that affect OEE. It resides at the customer’s site, providing local or remote access to views of KPIs, and diagnostics and data for use by the customer or ABB service experts.
Adam Moran, manager for Vorne Industries (Itasca, IL), provides another caution about data. “You can collect too much data. You need to align your strategy with capabilities.” He warned, “I have some customers who collect gigabytes of machine fault codes that no one actually uses. Why? Because it’s often technically easy to capture lots of data.” This is part of the natural human tendency to capture everything “just in case,” especially if it is easy and cheap to collect. How is this a problem? “It is waste,” he said. “It creates a muddled mess that makes it difficult to identify the real issues. So collect less data, do it really well, and then use that data to make decisions. Data is only so valuable as how it’s used!”
The context of OEE measurement also matters. High-volume manufacturing processes can naturally have long production runs with very short cycles and few changeovers, and therefore a high OEE. By contrast a low-volume manufacturing process may have very short production runs with many changeovers, and therefore a low OEE. “Lacking context, OEE does not have value and can drive organizations to focus on the wrong things, such as over-production, or overspending on labor to achieve a high OEE,” said Moran.
Vorne Industries manufactures the XL Productivity Appliance, a bolt-on hardware solution that calculates and displays OEE in real time on the factory floor. With over 16,000 installations across 40 countries, Vorne targets small-to medium-sized businesses that are currently capturing production information on pieces of paper, Excel spreadsheets, and in-house systems.
Vorne designed the XL Productivity Appliance to be easily installed in almost any manufacturing process by a union-level trained electrician. “My customers want equipment that can measure OEE on existing and legacy equipment, and they usually have a mix of different machines from different suppliers,” he said. “That is why we provide a complete hardware and software system that is simple and easy to install.”
For providers of software and systems that calculate OEE, variety is its own complexity. The makers of the Ignition software package, Inductive Automation (Folsom, CA), seem to welcome variety. They help customers that include waste water, oil & gas, automotive, and general manufacturing. Ignition is positioned as an MES platform able to collect and connect data using a variety of standards such as SQL, Ethernet and OPC. The company released its first OEE module in 2011. “We are going to upgrade our OEE module this year and we have been receiving all kinds of input on it since 2011,” said Tom Hechtman, president of Inductive.
One production scenario that he sees as important to address is the general-purpose workcenter, a group of machines and personnel that produce a variety of part numbers and lot sizes. “These are like job shops, they produce no predetermined volume and they are much more concerned about cycle time and idle time,” said Hechtman. They want to collect data about their downtime reasons, he said, which usually goes along with OEE, but in these cases the classic formulation of OEE provides limited insight. “It only tells you how you did during production on that equipment. It does not reflect times when you were not doing production,” Hechtman explained. “For example, in a bottling plant, if you ran it for one shift, it does not tell you how well you are using that asset. It only tells you how well you did while running that one shift.” That is why he also advocates using Total Effective Equipment Performance, or TEEP, implying that this calculation will be in the next release of the OEE module for Ignition. TEEP measures OEE against calendar hours, for example 24 hours per day, 365 days per year. It gives the bottom line assessment of utilization of assets. (A number of other people interviewed for this article also pointed to the value of TEEP.)
Hechtman’s discussion about workcenters and variety also points to an ongoing issue with OEE, the variety of ways it can be calculated. This might not be a good thing. “There is no standard out there on how to calculate it, no industry or governing body, like an ISO standard,” he said. “Just generally accepted practices and principles.” He described interacting with customers who want to change the OEE method, for example not tracking downtime because they did not have planned downtime and no mechanism for recording it.
“Fifteen years ago I learned about OEE and realized that this is a very useful tool, combining all of the key factors into a single number,” said Tim Kilboy, president of Capstone Metrics (Jackson, TN), a provider of OEE calculation software. Leaving capturing the data to others, the company’s OEE Management Software imports it either manually or as files, such as text or Excel formatted data that can be gathered either from machines or from MES or ERP systems.
“Automated data has obvious benefits, however manually entered data is worth the effort. Some have backed away from collecting electronic data,” he said. Kilboy went on to say that “there needs to be a human interaction to explain why something goes down. Simply recording may not be good enough. Human data collection is a small expense compared to the benefit of knowing the data is correct and valid,” he said. “It comes down to the credibility of the data.” When a manager or executive sees results they do not like, a first instinct is to question the data. That is where human data input contributes to the credibility itself, he believes.
From his perspective, about 80% of most manufacturers he works with—automotive and general manufacturing—use OEE and TEEP calculations “to some level.” As valuable as the statistic is, using it by itself may not be where the value lies. “You need more detail by doing a deep dive into the contributing factors and then understand what needs to be done,” he said. That is what Capstone Metrics’ OEE Management Software provides to users, a way to look at data in any number of ways. These ways include by workcenter, shift, even by part number. “You can drill down and understand where losses occur, even look at the tools per machine,” he said.
While agreeing that OEE is becoming more important to manufacturers, Daoxia Ding, management consultant at P3 Group, also cautions that human interpretation needs to remain a core element of any OEE study and calculation. “People know that the concept exists, but getting accurate data to reflect the actual OEE of the machine is the challenge,” he said. “For example, when a machine goes down, the data collecting software which is part of the line control system should be able to tell the status change immediately. In reality, this is not always the case. That is where we need to validate the data, correct the software setup, before we utilize it to calculate the OEE, in order to monitor the machine,” he said. Such validation requires humans to, if not collect the data, at least understand the contributing factors behind the numbers.
This article was first published in the September 2016 edition of Manufacturing Engineering magazine.
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