“Does my plant need an overall equipment effectiveness (OEE) system?” That question is top of mind for many plant managers intent on measuring and improving productivity. 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. But using an OEE score alone to reduce costs is like using a credit score to reduce monthly expenses. A credit score doesn’t provide any guidance in terms of actual performance and root causes.
To truly measure credit worthiness in a way that could lead to improvements, a consumer would need detailed data about 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 to achieve significant cost reductions.
When OEE Isn’t Enough
There are three general ways to reduce the cost of an automated process: reduce unproductive machine time (availability), improve cycle times (performance) and reduce waste or scrap (quality). Rather than asking whether a plant should implement an OEE system, a plant manager first should determine whether the plant needs to reduce costs. If so, the next step is to identify immediate priorities — such as decreasing downtime, stabilizing 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. Manufacturers need detailed, machine-specific tracking capabilities at all times, including both downtime and unproductive time, along with contextual data that shows the reasons for these states.
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 5 percent.
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 aren’t enough to compel a manufacturer to action, especially when operators can’t physically see the problem identified by the data. 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 significant downtime in a tunnel heater machine, operators simply didn’t believe the data because they didn’t see the machine stop. Investigation into the process revealed the machine was stopping frequently because its heaters were undersized, an issue confirmed by monitoring the temperature inside the tunnel. The heaters could not quite keep up with the flow of product entering the tunnel, so the conveyor stopped until the temperature increased — all within a split-second. Leveraging these objective, fact-based data points from the control system allowed the manufacturer to identify root causes and specific improvements to reduce costs.
How to Improve Efficiency
Implementing a system that will help improve efficiency and reduce costs requires not only collecting and storing the right data, but also analyzing it in a way that offers actionable, measurable process improvements. Following these easy steps can set a manufacturer up 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 –OEE data points alone can’t provide a manufacturer with adequate information to determine root causes. Manufacturers should also 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 to determine whether equipment is achieving maximum production. They don’t, however, answer the question of how to produce more. Analyzing a deeper level of control system data via a performance management software system allows manufacturers to begin answering questions such as:
- How are we doing?
- What are the real problems?
- What should we focus on first to make things better?
- Act on the data to make process improvements – A performance management software system can generate easy-to-understand dashboards that identify areas for improvement – but it cannot physically take action on the plant floor. That responsibility ultimately falls on the plant manager.
Bringing efficiencies into focus is a business imperative for every forward-looking manufacturer. Together with a sound strategy, a comprehensive performance management software system can reveal data needed to make informed — and measurable — operational improvements.
