- Does your plant have capacity you are aware of but can’t tap into reliably?
- Can performance be measured comparatively across all production areas?
- Is there a bottleneck effecting the total plant performance?
When considering the efficiency of production resources one must consider at least machines, process and people. OEE (Overall Equipment Efficiency) is a powerful performance indictor that can be simply calculated based on Availability (Time), Performance (Speed) and Quality (Product Scrap).
OEE provides an easy to understand, single measurement of efficiency and is typically supported by tools that can assist with continuous improvement where required.
What is OEE?
OEE is an essential metric and basic methodology for manufacturers pursuing a 'lean' manufacturing strategy - that is ‘zero waste’ in their ‘value streams’.
OEE is simple and practical. It takes the most common and important sources of manufacturing productivity loss, places them into three primary categories and distils them into metrics that provide an excellent gauge for measuring where you are - and how you can improve! OEE isn’t enough for root cause analysis on its own but measures the effectiveness trend of improvement actions.
OEE is frequently used as a key metric in TPM (Total Productive Maintenance) and Lean Manufacturing programs giving you a consistent way to measure the effectiveness of TPM and other initiatives by providing an overall framework for measuring production efficiency.
OEE Factors
Analysis starts with Scheduled Time; the amount of time your site is open and available for equipment operation.
From Scheduled Time, you subtract a category of time called Planned Downtime, which includes all events that should be excluded from efficiency analysis because there was no intention of running production (e.g. breaks, lunch, scheduled maintenance, and holidays). The remaining available time is your Planned Production Time.
OEE begins with Planned Production Time and scrutinizes efficiency and productivity losses that occur, with the goal of reducing or eliminating these losses. There are three general categories of loss to consider – Unplanned Downtime Loss, Speed Loss and Yield Loss.
OEE = Availability% x Performance% x Quality%Availability
Availability takes into account Downtime Loss (Planned & Unplanned), which includes any Events that stop planned production for an appreciable length of time (usually several minutes – long enough to log as a traceable Event). Examples include equipment failures, material shortages, and changeover time. Changeover time is included in OEE analysis, since it is a form of Downtime. While it may not be possible to eliminate changeover time, in most cases it can be reduced. The remaining available time is called Production Time.
Performance
Performance takes into account Speed Loss, which includes any factors that cause the process to operate at less than the maximum possible speed, when running. Examples include machine wear, substandard materials, misfeeds, and operator inefficiency. The remaining available time is called Performing Time.
Quality
Quality takes into account Quality Loss, which accounts for produced pieces that do not meet quality standards, including pieces that require rework. The remaining time is called Productive Time. Our goal is to maximize Productive Time.
OEE Summary
Now that you have taken a look at how the factors that contribute to OEE Losses are developed we can quickly review the key points.
| OEE Loss | OEE Factor |
| Un-scheduled | Not part of the OEE calculation |
| Downtime Loss (Unplanned & Planned) |
Scheduled Time = Total Time - System Not Scheduled Time Availability=(Production Time)/(Scheduled Time)= (Scheduled Time-Availability Loss Time)/(Scheduled Time) Availability represents the percentage of time your process is available for |
| Speed Loss |
Performance= (Total Calculation Units Count)/(Theoretical Rate × Production Time) Performance represents the ratio of your actual production speed versus the theoretical production rate. The Theoretical Rate (Calculation Units per Second) is part of the KPI Calculation. |
| Quality Loss |
Quality=(Good Calculation Units Count)/(Total Calculation Units Count) |
Six Big Losses
One of the major goals of TPM and OEE programs is to reduce and/or eliminate what are called the Six Big Losses – the most common causes of efficiency loss in manufacturing. The following table lists the Six Big Losses, and shows how they relate to the OEE Loss categories.
| Six Big Loss Category |
OEE Loss Category |
Event Examples | Comment |
| Breakdowns | Downtime Loss |
• Tooling Failures |
There is flexibility on where to set the threshold between a Breakdown (Downtime Loss) and a Small Stop (Speed Loss). |
| Setup and Adjustments | Downtime Loss | • Setup/Changeover • Material Shortages • Operator Shortages • Major Adjustments • Warm-Up Time |
This loss is often addressed through setup time reduction programs such as SMED. |
| Idling and Minor stops | Speed Loss | • Obstructed Product Flow • Component Jams • Misfeeds • Sensor Blocked • Delivery Blocked • Cleaning/Checking |
Typically only includes stops that are under five minutes and that do not require maintenance personnel. |
| Reduced Speed | Speed Loss | • Rough Running • Under Nameplate Capacity • Under Design Capacity • Equipment Wear • Operator Inefficiency |
Anything that keeps the process from running at its theoretical maximum speed (a.k.a. Ideal Run Rate or Nameplate Capacity) |
| Start-up Losses | Quality Loss | • Scrap • Rework • In-Process Damage • In-Process Expiration • Incorrect Assembly |
Rejects during warm-up, start-up or other early production. May be due to improper setup, warm-up period, etc. |
| Defect Losses | Quality Loss | • Scrap • Rework • In-Process Damage • In-Process Expiration • Incorrect Assembly |
Rejects during steady-state production. |
Addressing the Six Big Losses
Now that we know what the Six Big Losses are and some of the Events that contribute to these losses, we can focus on ways to monitor and correct them. A key goal should be fast and efficient data collection putting the information to use throughout the day and in real-time.
Breakdowns
Eliminating unplanned Downtime is critical to improving OEE. Other OEE Factors cannot be addressed if the process is down. It is not only important to know how much downtime your process is experiencing (and when) but also to be able to attribute the lost time to the specific source or reason for the loss. Categorisation of downtime event codes will allow further detail to be captured regarding the initial downtime event. All events related to the primary reason for the loss should also be captured to allow Root Cause Analysis to be applied starting with the most severe loss categories. In some cases a good understanding of the impact of planned downtime may lead to systemic changes that can also lead to improved availability.
Setup and Adjustments
Setup and Adjustment time is generally measured as the time between the last good part produced before Setup to the first consistent good parts produced after Setup. This often includes substantial adjustment and/or warm-up time in order to consistently produce parts that meet quality standards.
Tracking Setup Time is critical to reducing this loss, together with an active program to reduce this time (such as an SMED – Single Minute Exchange of Dies program).
Many companies use creative methods of reducing Setup Time including assembling changeover carts with all tools and supplies necessary for the changeover in one place, pinned or marked settings so that coarse adjustments are no longer necessary (Poka-yoke).
Idling and Minor Stops
Short stops are typically less than 5-10 minutes and they are typically minor adjustments or simple tasks such as cleaning. They should not be caused by logistics.
Reduced Speed
Reduced Speed losses are caused when the equipment runs slower than its optimal or designed maximum speed.
Short Stops and Reduced Speed are the most difficult of the Six Big Losses to monitor and record. Cycle Time Analysis should be utilized to pinpoint these loss types. In most processes recording short stop data for Cycle Time Analysis needs to be automated since cycles are quick and repetitive events that do not leave adequate time for manual data-logging. By comparing all completed cycles to the Ideal Cycle Time and filtering the data through a Small Stop Threshold and Reduced Speed Threshold the errant cycles can be automatically categorized for analysis. The reason for analyzing Short Stops separately from Reduced Speed is that the root causes are typically very different, as can be seen from the event examples in the previous table.
Start-up Rejects and Production Rejects
Start-up Rejects and Production Rejects are differentiated, since often the root causes are different between start-up and steady-state production. Parts that require rework of any kind should be considered rejects. Tracking when rejects occur during a shift and/or job run can help pinpoint potential causes, and in many cases patterns will be discovered.
Often a Six Sigma program, where a common metric is achieving a defect rate of less than 3.4 defects per million “opportunities”, is used to focus attention on a goal of achieving ”near perfect” quality.
