Overall Equipment Effectiveness 101: How Does OEE Factor into Your Manufacturing Operation?

Widely respected management consultant Peter Drucker is often quoted as saying, “You can’t manage what you don’t measure.” You can use whatever version of this you like, such as, “You can’t improve what you don’t measure.” The idea is ensuring you are measuring the elements that impact your bottom line. These are your key performance indicators or KPIs.

Makeready & Downtime on a 17-in. Press

Chart 1
All data courtesy of Harper Corporation of America

Monitoring your KPIs is an essential part of production management and continuous improvement. These KPIs should have a direct tie to the bottom line if they are going to mean anything. Often, these KPIs are individually monitored, and include categories such as waste, run time, downtime or makeready. Improvements in any of these categories can be presented with false savings or “funny money” because of the effect on other categories. Each KPI should include one or more consequential metric to ensure that if one shows improvement, it does not adversely affect another.

Taking a hypothetical example, we have a 17-in. press and we are monitoring the makeready times on jobs of similar length for all 8-color presses over the period of a month (depicted in Chart 1). The team launches a project for makeready reduction with a goal of 20 percent. After the month passes, the team reports makereadies have improved by 25 percent.

On its own merits, it looks like a great improvement. However, if team members would have measured a consequential metric, they would have spotted that downtime was increased when the solutions used for faster makeready times were applied. The team would have realized the need to go back and find other means to reduce makeready without adversely affecting downtime.

Organizing Data

Overall equipment effectiveness (OEE) is a tool used to evaluate how well a manufacturing process is functioning. The idea of OEE is not a new one—it came about back in the 1960s, pioneered by Seiichi Nakajima. Seiichi based OEE off of Harrington Emerson’s idealisms on labor efficiency.

OEE calculates in a manner that allows you to see how a particular machine, shift or shop performed while keeping consequential criteria in the data. OEE, from a continuous improvement point, helps managers set goals that target three main measurements: availability, performance and quality.

OEE, from a continuous improvement point, helps managers set goals that target three main measurements: availability, performance and quality.

The intention of OEE is not to replace your current KPIs, but more to organize the data that is already being collected. When a drop in OEE is recognized, getting to the root cause is, in most cases, easily traceable through which multiplier dropped. Each of these measurements utilizes two indicators that can be broken down into smaller buckets for analysis.

As we go through OEE here, understand this is a 10,000-ft. view. There is a lot here that can go much deeper. There are versions of OEE that break down to OEE1, OEE2 and even OEE3; this discussion will only skim the surface.

Just like any KPI, both the data collected and how it is measured is completely yours and your interpretation of what should or shouldn’t be measured, but it must include the six key losses of planned and unplanned stops, minor stops and slow takt time, and production and start-up waste. One prime example is in the availability set, where one company may measure as availability accounting for 24 hours/day and 365 days/year, where another may feel availability only accounts for scheduled machine time which may include breaks and lunches if the press is not staffed during those periods.

The important thing, once you have determined how to measure the data, is to stay consistent.

OEE is derived from the following equation: OEE = Availability x Production x Quality. To break down OEE to its core, let’s look at each element.


Your availability is simply your run time divided by your planned production time, where your run time equals your planned production time minus your stop time. Within your availability multiplier, you have both planned and unplanned downtime.

Planned downtime includes things like makeready, matrix changes, roll changes, etc. Unplanned downtime, commonly attributed to machine failure, includes things like maintenance downtime (mechanical, electrical, etc.), unscheduled breaks or meetings, ink spills, color matching, and web breaks.

Each shift needs to record any downtime and makeready time. Unless the press is running every minute of the shift non-stop, those times will be recorded and identified as to why. This will include roll changes, ink adjustments and so forth. Stops that include web breaks, ink overflow/blowout, color matching, alarm stops and the like are counted as unplanned downtime. These examples should all be tracked as smaller buckets for deeper dives into their root causes or for continuous improvements.


Chart 2

Let’s look at an example of a shift:

Planned downtime = 120 minutes

  • 3 roll changes/hour @ 5 minutes each = 15 minutes x 8 hours = 120 minutes

Unplanned downtime = 32 minutes

  • 1 ink blowout @ 20-minute clean-up
  • 1 web break @ 12 minutes

480 minutes/shift – 152 minutes total downtime ÷ 480 minutes/shift = 68.3 percent Availability for the shift

Defining Downtime

It is critical to understand what downtime is. Downtime can be considered as anything that doesn’t make a physical change to the product. Using the letters in “downtime” as an acronym—as described by Taiichi Ohno, the father of the Toyota Production System—it is easy to sort downtimes. There are essentially eight types of waste:

  • Defects include product that does not meet or exceed customer expectations, to include makeready and production waste
  • Overproduction is the overrunning of a job that is not billable. Sometimes we overrun jobs because we know waste is high or to put into inventory. But you have to be careful, as both high waste and inventory are forms of waste if we do not have purchase orders (POs) or inventory agreements in place. It’s a calculated risk
  • Waiting is poor takt time where one process is faster or slower than the next; where one process has to stop to wait to perform the next task
  • Not utilizing employee skill or talent is not having the right people in the right place. This can show up in the form of poor training or not fully utilizing the talent of your staff
  • Transport is the moving of materials from place to place. If you are having to move your material all the way across the plant to perform the next step in converting, you may want to look at improving your workflow
  • Inventory can be both raw materials or finished goods. If we don’t have POs or agreements to purchase, carrying an inventory is a calculated risk, as graphics change and can render printed materials useless. Overpurchasing raw materials without solid forecasts is also a calculated risk. A customer may have always purchased a 1.5 mil white poly label, but now wants semi-gloss to reduce costs. If you loaded up on 1.5 white poly, you may be stuck with it for a while
  • Motion is the waste of movement. If you have to walk all the way to the end of your press multiple times a day to get a flathead screwdriver, think about where you need that flathead all the time and put it where it is needed the most. Doing a good spaghetti diagram identifies motion waste pretty quickly
  • Excess processing is usually caused by a lack of standards, excessive reports and human error. This can be the result of an overthought or poor process reacting to a previous defect

These wastes, like the OEE here, is covered at a very high level and can be broken down to finer detail.

Teams would track planned and unplanned downtime caused for continuous improvement projects to improve availability. Activities such as ink proofing using anilox correlation, 5S, lean manufacturing practices and robust maintenance programs will improve the availability element of OEE.