opportunities and challenges of the oee numbers game

September 2016 | AdvancedManufacturing.org 71

process improvement

Opportunities and Challengesof the OEE Numbers Game

Bruce MoreyContributing Editor

The issues around OEE are not its

utility but the ease and credibility of

the data used to calculate it Actionable intelligence is what any manager craves, es-

pecially 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 improve-

ments 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

An ABB service engineer checks out Key Performance Indicators (KPIs) on the ABB ServicePort Service Delivery Platform. Remote-

enabled, software-supported tools such as ServicePort automatically scan KPIs related to equipment or process availability, and

proactively alert service people via text or e-mail messages.

72 AdvancedManufacturing.org | September 2016

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 Blan-

kenship. 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.

Data Collection

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 col-

lecting 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

process improvement

T he basic equation for OEE is:

Availability x Performance x Quality, where Availability, Performance and Quality are metrics expressed as a number

from 0 to 100%.

Availability represents the percentage of scheduled time that a particular workcenter is available to perform work.

Thus Availability = Up Time / Planned Production Time. For example, if there was 40 minutes of unscheduled down time

out of 430 minutes of planned production time, the Availability would be 90.7%.

Performance represents the actual speed that a particular workcenter runs as a percentage of its designed speed.

Thus Performance = (Parts Produced x Ideal Cycle Time) / Up Time. For example, if a workcenter is designed to produce

two parts per minute, the ideal number of parts produced would be 780 over the 390 minutes, but if only 759 parts were

produced, Performance would be only 97.3%.

Quality represents “Good Parts” versus “Rejected Parts” as a percentage of the total number of parts produced.

Quality is a pure measure of process yield that excludes Availability and Performance. For example, if a total of 759 parts

were produced, but 12 were defective, Quality would be 98.4%.

Using these numbers, OEE = 90.7% Availability x 97.3% Performance x 98.4% Quality = 86.8%

(Courtesy Wes Blankenship, Factory Systems)

OEE Explained

Manufacturers often use statistics that include OEE and its companion, TEEP, such as these

displays from Capstone Metrics.


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 Ether-

net, 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 manufactur-

ers of discrete products, from cars to toys.

Familiarity and Use

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 balanc-

ing 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.”

process improvement

Capstone’s MDT devices collect, display and transmit real-time data to a remote Windows-based PC, including OEE calculations.


To put it in context, he explained that there are eight typical

causes for losses in a plant: equipment shutdowns, produc-

tion adjustments, equipment failures, process failures, slow-

downs, 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 histori-

cal data purposes rather than in-process

control.” After a benchmarking engage-

ment, 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.

Data Versus Utility

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

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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 deci-

sions. Data is only so valu-

able as how it’s used!”

The context of OEE

measurement also matters.

High-volume manufactur-

ing 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 change-

overs, 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 over-

spending on labor to achieve a high OEE,” said Moran.

Vorne Industries manufactures the XL Productivity Appli-

ance, a bolt-on hardware solution that calculates and dis-

plays 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 pro-

duction 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 hard-

ware and software system that is simple and easy to install.”

Variety, OEE and TEEP

For providers of software and systems that calculate OEE,

variety is its own complexity. The makers of the Ignition soft-

ware package, Inductive Automation (Folsom, CA), seem to

welcome variety. They help customers that include waste wa-

ter, 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

process improvement

Vorne Industries manufactures the XL Productivity Appliance, a bolt-on hardware solution that

calculates and displays OEE in real time on the factory floor.


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.

New Automation Versus Trusted Data

“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 Met-

rics (Jackson, TN), a provider of OEE

calculation software. Leaving captur-

ing 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 ben-

efits, 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 interac-

tion 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 ques-

tion 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 manufactur-

ing—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

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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 chal-

lenge,” 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 ma-

chine,” he said. Such validation requires humans to, if not

collect the data, at least understand the contributing factors

behind the numbers.

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process improvement

?ABB973-448-0634 / http: www/new.abb.com

Capstone731-610-3399 / www.capstonemetrics.com/files/oee-software.html

Factory Systems803-754-0090 / www.factorysystems.net/

Inductive Automation916-456-1045 / https://inductiveautoma-tion.com/

P3 248-792-2277 / www.p3-group.com/en/industries/automotive/

Production Process603-434-2300 / https://productionproc-ess.com/contact-us/

Vorne Industries630-875-3600 / www.vorne.com

opportunities and challenges of the oee numbers game

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