Beyond OEE: Hidden Opportunities Within Your Plant

Beyond OEE: Hidden Opportunities Within Your Plant

Collecting control-system data beyond OEE can help determine root causes of downtime and inefficiencies leading to serious savings

When it comes to measuring and improving productivity, the question of whether a plant needs an overall equipment effectiveness (OEE) system is top of mind for many plant managers. OEE -- the ratio of good parts produced versus what could have been produced under ideal conditions -- is a simple performance indicator to which all managers can relate. Because it shows a machine's actual performance compared to its theoretical maximum, OEE can be applied to accurately compare any machine or any line, in any industry, anywhere in the world.

But using an OEE score alone to reduce costs is like using a credit score to reduce monthly expenses. Certainly, a credit score can help measure a consumers' future likelihood to pay back creditors, or to compare machine performance in the case of OEE. However, the data alone shows only a historical view of what's occurred. It doesn't provide any specifics in terms of actual performance and root causes.

To truly measure creditworthiness in a way that could lead to improvements, a consumer would need detailed data about spending habits and the specific spending behaviors that impacted their credit score. Likewise, an OEE score alone doesn't offer the detail of data required to understand the root causes of inefficiency and downtime, which makes it difficult for managers to identify and implement solutions that ultimately help reduce costs.

The good news is that the detailed data necessary to identify actionable areas for improvement already exists within control and human machine interface (HMI) systems on the plant floor. A performance management software system can collect this data and put it into context that will help the plant manager establish meaningful production metrics that go beyond an improved OEE score to achieve significant cost reductions.

When OEE Isn't Enough

Line Dashboard -- summarized OEE and components by shift with Top 5 Reasons for downtime, cycle time analysis and scrap percent.

There are three general ways to reduce the cost of an automated process: to reduce unproductive machine time (availability), to reduce cycle times (performance), and to reduce waste or scrap (quality). OEE certainly can help determine whether machines are producing at their maximum capacity, but a major drawback of tracking OEE alone is its inability to offer data that can help determine how to make a machine produce more.

So, rather than asking whether a plant should implement an OEE system, perhaps a better starting point is to determine whether the plant needs to reduce costs. If so, the next step is to identify immediate priorities -- such as decreasing downtime, reducing cycle time variation, improving quality or reducing overtime -- that can help achieve this goal. In most cases, measuring OEE alone will not help a manager determine how to meet these goals.

In addition to availability, manufacturers need detailed, machine-specific tracking at all times, including both downtime and unproductive time, along with contextual data that shows reasons behind these states. Beyond performance, accurate cycle times by product, shift and operator are needed. And rather than simply assessing quality, manufacturers should measure data on scrap reasons and counts.

In some cases, a goal like increasing production may be counterproductive to cost reduction. A large pet food manufacturer, for example, identified nearly $400,000 in potential savings simply by using performance management software to gather detailed data from one machine for one hour. The manufacturer began collecting data on the weight of every bag produced and quickly found that the machine was overfilling every bag by five percent.

This finding would never have been identified within the context of a single OEE calculation. The manufacturer actually could have improved its OEE score by producing more bags per cycle (performance), but in this case, higher "performance" in OEE terms would mean giving away even more product.

Seeing is Believing

In some cases, disparate data points alone are not enough to compel a manufacturer to take action, especially when operators can't physically see the problem the data has identified. Rather, it's combining data in a way that defines root causes that offers compelling enough evidence to drive actionable improvements.

When a performance management software system at a large beverage manufacturer pointed to a large amount of downtime in a tunnel heater machine, operators simply didn't believe the data because they weren't seeing the machine stop. Investigation into the process revealed the machine was stopping several times per second because the machine's heaters were undersized; an issue identified by monitoring the temperature inside the heaters. The heaters were shutting due to overheating, stopping and then starting up again after cooling down -- all within a split-second.

At other times, it may be easy for operators to identify that action is needed, but it's unclear where the priorities should be. This was the case for a large petroleum and petrochemical manufacturer in the Northeast. One of the producer's winding machines was extremely complex, operating in dozens of different states of running versus not running. When the machine would go down, operators had no way of identifying which state of not running the machine was in.

OEE Water Fall -- shows the cumulative effect that Availability, Quality, Performance have on OEE / TEEP.

The standard procedure during downtime was to start at the beginning of the line and check for potential problems until the problem part was identified. After installing a performance management software system, the manufacturer was able to log a specific event for each state, allowing operators to pinpoint the specific cause of downtime. Instead of floundering in the dark trying to identify a root cause, operators could go right to the area of the machine causing the issue, saving large amounts of time (and cost) in the process.

In both cases, a single OEE point was not enough to spark change on the plant floor. Instead, leveraging additional objective, fact-based data points from the control system and using them to identify root causes and specific improvements that would reduce costs were eye opening.

How to Improve Efficiency

Implementing a system that will help improve machine or line efficiency -- and reduce costs -- requires not only collecting and storing the right data, but also analyzing it in a way that offers actionable, and measurable process improvements. Following these easy steps can set up a manufacturer for serious savings:

  • Collect and store performance data -- Data should be collected from the control system and stored within a performance management software system database. Many manufacturers rely on clipboards and Excel spreadsheets to house this data, but leveraging an off-the-shelf software program will allow for deeper analysis and more impactful long-term insights.
  • Collect the right kind of data -- It's clear that collecting solely OEE data points doesn't provide a manufacturer with deep enough information to adequately determine root causes. In addition to OEE data, manufacturers should collect data that measures production counts, scrap rates, machine cycle times, downtimes and causes, unproductive time, machine states and quality problems.
  • Analyze the data -- In general, OEE calculations lead managers to compare machine performance in order to determine whether machines are producing as much as possible. They don't, however, answer the question of how to produce more. By analyzing a deeper level of control system data collected by a performance management software system, manufacturers can start to answer questions such as:
    • How are we doing?
    • What are the real problems?
    • What should we focus on first to make things better?
    • What's happening right now?
    • What else to we need to measure?
  • Act on the data to make process improvements -- While a performance management software system can generate easy-to-understand dashboards that make it easy to identify areas for improvement, it cannot physically take action on the plant floor. That responsibility ultimately falls on the plant manager.
  • Measure the impact and repeat -- After implementing changes, its important to measure the outcomes of the improvements in order to determine the greatest areas of impact and pinpoint places where further improvements may be required.

In addition to identifying weaknesses and analyzing opportunities for improvement, many manufacturers are using data collected by a performance management software system to justify new technology investments. Often, they are finding that making production lines more efficient allows them to add new lines without additional hiring. Or they discover that additional planned lines are unnecessary -- as clearly demonstrated through data collected in the control system.

Seeking opportunities to proactively reduce inefficiencies while preventing losses and errors should be a focus for any company looking to optimize its operations. A comprehensive, performance management software system can help reach these goals and provide the data needed to support making tangible business improvements.

For more information about manufacturing performance and OEE, visit:

Sponsored Links

Hide comments


  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.