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Overall Equipment Effectiveness: Guidelines for the CPG Industry And Its Suppliers

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DEVELOPED BY THE OPX LEADERSHIP NETWORK OPERATIONAL RELIABILITY SOLUTIONS GROUP

FEBRUARY 2012

Overall Equipment Effectiveness:

Guidelines for the CPG Industry

And Its Suppliers

© 2012 by the OpX Leadership Network; Reston, Va.

Facilitated by PMMI, the OpX Leadership Network is a dynamic community of manufacturing,

engineering and operations professionals dedicated to operational excellence. Through open

dialogue between CPG manufacturers and OEMs, the OpX Leadership Network provides an

exceptional forum where the best minds come together to identify and solve common operational

challenges, and apply best practices and innovative solutions to the real-world context of

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Overall Equipment Effectiveness: Guidelines for the CPG Industry & Its Suppliers

i

Intended Audience This white paper is intended for all consumer packaged goods, food and beverage, and pharma-

ceutical manufacturers and their suppliers interested in implementing a continuous improvement

initiative at the plant level. An Overall Equipment Effectiveness (OEE) continuous improvement initia-

tive allows companies to expose all losses within their operations to drive objective and informed

decision making for improvement plans.

This paper is particularly targeted toward companies that have no prior experience imple-

menting an OEE metric program, or that have been unsuccessful in completing an OEE initiative. CPG

organizations that have established an OEE program may also find utility in this white paper as a tool

for sharing OEE best practices with others in the industry.

ii

OpX Leadership Network Operational Reliability Solutions Group

Contributors This paper would not have been possible without the collaboration of those who truly value

operational excellence in manufacturing. The OpX Leadership Network would like to thank the OEE

team, and in par-ticular, Platinum Sponsor GE.

Bill Herbes, B & G Foods, Inc.

Robby Martin, Bush Brothers & Company

Phil Phillips , The Coca-Cola Company

Peter Hock (Chair), ConAgra Foods

Scott Butler, Del Monte Foods Company

Shawn Sassaman, Furmano Foods

Erin Bradshaw (Technical Writer) , GE

Robert Gates, GE

Tim Rich, The Hershey Company

Paul Dash, Inventure Foods, Inc.

Mark Hanley, Land O’Lakes, Inc.

Jeremy Kacuba, Leprino Foods Company

Ben Gibbons , Musco Family Olive Co.

Brett Davis, Musco Family Olive Co.

Leo Robertson , Pearson Packaging Systems

William Cooper, Nestle Purina PetCare Company

Terry Zarnowski, Schneider Equipment

Mike Pieper, Siemens Corporation

Scott Spencer, Snyder’s-Lance, Inc.

Bill Snow, SPX Flow Technology

Chad Sprinkman, WM Sprinkman Corporation


iii

Table of Contents

Introduction

Case Study 1:

OEE Delivers $5 Millioni in Annual Savings ........................................................................... iv

Business Drivers ............................................................................................ 1

Case Study 2:

OEE Enables 10 Percent Increased Efficiency ....................................................................... 2

Getting Started with OEE .............................................................................. 3

Case Study 3:

OEE Reduces Downtime by 39 Percent ................................................................................ 5

OEE Implementation Advice ......................................................................... 8

Measurements & Calculations ...................................................................... 8

Conclusion ..................................................................................................... 10

Case Study 4:

OEE Reduces Manufacturing Costs 15 Percent .................................................................... 11

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Join the discussion on OEE at www.OpXLeadershipNetwork.org

Case Study 1:

OEE Delivers $5 Million in Annual Savings A glass bottle manufacturer in the U.S. found the business need to grow sales and meet

quality, consumer safety, and environmental protection goals and implemented an OEE regimen to meet this sales growth imperative. Delivering results would not only affect their bottom line, but would also prove to customers that their company is committed to innovative, efficient, and environmentally friendly production practices. To accomplish their goals, they chose to imple-ment an automated OEE information system to collect and present data from all steps in bottle making, including batch, furnace, forming, finishing, inspection and packing. This system rec-orded equipment downtime events, equipment process data, and rejects automatically, and required operators to manually input defect observations into the system.

Additionally, they implemented an automated reporting process to provide visibility into their operations. Their implemented solution serves multiple users within the plant - from oper-ators on the lines to maintenance managers to senior managers who need quick, graphical re-ports via the web.

As a result of streamlining and standardizing basic OEE measurements across the plant, the

bottle manufacturer was able to increase production by 5 percent, decrease defects by 25 per-

cent, reduce plant downtime 25 percent, decrease energy consumption for their furnaces, and

decrease furnace emissions by 80 percent. All of these improvements add up to deliver savings

of $5 million annually.

The software program that the team

chose to implement interfaced with

the company’s installed base of equip-

ment — including sensing and inspec-

tion devices, PLCs, and scales and

timing systems — to deliver real-time

data on fill variance distribution (art)

and defects. This enabled the improve-

ment team to make more informed

decisions. This is the pinnacle of an

OEE program.


v

Introduction Consumer packaged goods (CPG) companies and their suppliers have been

steadfast in their pursuit of continuously improving their bottom line while de-

livering high quality products to customers. In support of these goals, opera-

tional measurement and reporting enables CPG companies to discern the best-

value opportunities, to assign accountability and to track progress. Since neces-

sity is the mother of invention, and there has been no single authorizing group

to set guidelines, operations measurement has sprung up with a variety of ap-

proaches. Lack of standard terminology and definitions has hampered efforts to

make comparisons between plants and industries, and leant confusion to dis-

cussions between vendors and suppliers.

Out of the noise and confusion, Overall Equipment Effectiveness (OEE) is gain-

ing the broadest adoption as the most powerful, systematic tool available to

help companies expose losses. OEE enables companies to view operations

through a consistent and objective set of measurements. Once opportunity are-

as are identified, management teams can make better data-driven decisions to

improve operations. Subsequently, it is critical that the OEE metrics be clearly

communicated and understood in order to sustain and build on great results.

This white paper will establish a roadmap for a systematic approach to measur-

ing and tracking OEE for any piece of equipment or process, and present stand-

ard guidelines for initiating an OEE program. The authors are members of a

team established by the Alliance for Innovation and Operational Excellence for

Overall Equipment Effectiveness, and will share best practices about how to re-

view the financial business drivers behind establishing an OEE metric regimen,

how to get started, and how to gauge the efficacy of OEE programs.

To accomplish these goals, the authors will share success stories from CPG com-

panies that are achieving operational excellence, as well as details about how

these companies started their journeys and moved through the phases of the

OEE roadmap. By establishing tangible user experiences within the framework

of good project execution, the authors anticipate that companies new to OEE

metric reporting can achieve faster implementation and broader workforce ac-

ceptance, accelerating their continuous improvement initiatives for equipment

effectiveness.

vi

OpX Leadership Network Operational Reliability Solutions Group

Figure 1: OEE: Pervasive Driver of Business Performance

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OEE Business Drivers Every CPG program manager must be able to justify the financial and manpower commitment of

undertakings such as an OEE metrics program. In addition, such a program is doomed to failure with-

out strong alignment to company-wide metrics and goals.

Whether ownership is public or private, the end goal of the business is to continually increase

value to the owners. One of the largest value drivers for enhanced operational efficiency is its impact

on cash flow. The three main financial components of cash flow are sales growth, cost and capital

investment. A thorough OEE platform will improve elements within each of these areas (Figure 1).

Sales Growth The three major performance areas within CPG plant-level operations that affect sales growth

are quality, service and innovation. An OEE regimen touches these metrics at a fundamental level.

Measuring OEE and eliminating production upsets will result in greater process stability and product

consistency. This, in turn, reduces rejects and rework, and allows for fewer out-of-specification prod-

ucts. The end result is consistent delivery of in-spec product to the customer. When it comes to inno-

vation, an OEE regimen ultimately leads to a deepened understanding of equipment capabilities,

enabling better utilization of technology for new product trials and deeper process knowledge at

new product launches.

Cost The most apparent advantage that is gained in implementing an OEE regimen is a reduction in

operating costs. In a production environment, operating costs stem from four key performance di-

mensions: safety, material usage, labor productivity and overhead. The first of these–safety–is a goal

in and of itself as well as a cost consideration. OEE’s connection to safety is obvious. An effective OEE

improvement program will reduce the number of human touches on a given machine, such as the

clearing of jams, thereby decreasing the risk for OSHA-reportable incidents. Because OEE exposes all

losses, it directs management to address the root causes of scrap and waste, thus enabling fixes that

result in decreased material costs. CPG manufacturers are able to drastically reduce the amount of

rejected material by targeting their largest losses, such as reductions in purge losses during sanitiza-

tion, and product or packaging defects that occur when a system upsets. OEE enables focus and pri-

oritization of activities to maximize uptime and drive out stops and non-productive time. Because

OEE affords a view of all losses at a very granular level, the causes of hidden inefficiencies can be

exposed. This leads to increased labor utilization, decreased unplanned overtime costs and de-

creased overhead expenses such as power and water utilities.

Capital Investment Since capital investment is usually a long-term, strategic decision, improvement teams often

overlook the interplay between investment decisions and OEE. Companies who achieve OEE im-

provement can generate higher output and flexibility with their existing asset base.

This enables them to avoid capital investments driven solely by productivity gaps. Capital avoid-

ance has the added benefit of MRO parts inventory avoidance. Additionally, exposing and reducing

failures prolongs the useful lifecycle of existing equipment, thus lengthening the amount of time

between capital expenditures. Finally, OEE has a significant impact on the amount of cash companies

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Case Study 2:

OEE Enables 10 Percent Increased Efficiency After making a decision to

drastically change the business model, a large gum producer found itself needing to increase its flexibility and efficiency to keep up with a growing demand. In order to solve this problem, the producer used an OEE process model to identify areas for growth and opti-mize production performance.

The project team chose to pilot an OEE program on a clearly underperforming packaging line that offered high-volume through-put with a mix of manual and auto-mated procedures, and chose a pro-duction management software suppli-er with experience in the OEE space as a partner in implementing this project. First, the team connected the islands of machines on the pilot line — creating a Profibus network to a new front-end PLC, which caused no disruption to on-going production. Next, an Ethernet link from the front-end PLC to the existing OPC Server provided a data route to a database. The software module the company chose accesses the data in the database and delivers a ‘Scoreboard’ to a plasma screen on the line. In the pilot – and today – the Scoreboard charts the shift productivity curve and displays key information such as the current productivity, the line stoppage time, total production, production per minute and waste. The software also produces reports showing the productivity curve and stoppage times.

This OEE dashboard captures product line, downtime event, and

waste information in a user-friendly timeline format. Additional-

ly, the system records detailed information for each downtime

event, allowing the improvement team to understand or analyze

the root cause for multiple events of the same type. With this

level of detail on production line status and loss data, the team

can quickly and easily identify problem areas and can drive im-

provements that have meaningful impact.

3


must tie up in inventory. Product changeovers are clearly a loss. Companies without an effective OEE

improvement program will try to minimize changeovers. As a result, production cycle times are long,

and inventories must grow to accompany them. OEE improvement enables companies to build the

technical competency to neutralize the losses from increased changeovers, and to achieve rapid and

successful restarts after each change. OEE improvement efforts which include focus on rapid change-

over provide a powerful means to generate a rapid cash infusion. In this way, decreased inventory

levels generate cash for investment in research, marketing, sales, and manufacturing improvement.

Closing Financial Thoughts When starting an OEE regimen, program managers need to secure buy-in from all major stake-

holders. These stakeholders are likely to see the value that an OEE-based continuous improvement

program adds to their bottom line when specific benefit impacts are broken down into financial per-

formance drivers. Additionally, their support will form the foundation for establishing project success

metrics. Oftentimes, OEE programs fail because they are “not delivering the value intended.” This

typically occurs when the safety, quality, cost, delivery and working capital impact of operations per-

formance is not clearly understood, or when a project scope and expected results are not clearly

defined and agreed upon. Program managers should meet with stakeholders to define OEE imple-

mentation success based on the metrics that the given group of stakeholders cares about most. This

will ensure that the program will be based on realistic program practices, thus enabling successful

implementation of a strongly grounded OEE regimen.

Getting Started with OEE Engaging stakeholders in an OEE program is not as hard as it may seem, as communicating a

plan to increase sales growth and cash flow is generally greeted with a positive response. The real

work comes with designing an OEE program that includes the right scope and scale. If an OEE pro-

gram is poorly executed, it may not be allowed to expand. Similarly, if the program is too large, it

may not show value quickly enough to gain momentum. To help avoid these pitfalls, the authors will

be very prescriptive on the OEE roadmap (see Figure 2).

Figure 2: OEE Roadmap

4


For this paper, the authors will focus on the first step–initiation–and will review the best practic-

es to help program managers achieve this crucial step in moving along the operational excellence

journey. Understanding how to be effective in this step will enable success in the other steps

throughout the OEE journey. The initiation step involves four consecutive stages: Plan, Do, Check,

and Act. Executing each of these phases in order will provide integrated transitions from planning to

implementation.

Plan Like all great projects, a successful, sustainable OEE regimen starts with a clearly defined scope

and articulated deliverables that engage the key stakeholders. The success metrics chosen for the

OEE regimen should be impactful, easy to track and easy to understand. During this development

phase, it is best to limit the first OEE scope to one area that can be reasonably expected to succeed;

over-promising and under-delivering will immediately disengage stakeholders, resulting in failure. To

avoid this, it may be advisable to start with a smaller pilot project that enables the program to estab-

lish a foothold. The lessons learned here will prove valuable as the OEE metrics are rolled out to larg-

er areas.

It is best to start on a department or line that is critical to business performance and has signifi-

cant improvement opportunities. In this phase, it is important to define the analytical and report

deliverables first, and then determine the primary data collection elements required for these deliv-

erables. The program can be started small and grown with success; this will enable program manage-

ment to shake out the bugs while still logging some gains from the process.

Many people start by driving down to a product line and logging:

Schedule time (both value added and non-value added)

Production potential at zero loss

Planned downtime (type, location, duration)

Unplanned downtime (type, location, duration)

Production rate

Scrap and/or rework amount

An OEE program startup cannot be handled by one person alone. This new metric will affect the

entire production team, from plant managers to equipment operators. It is of utmost importance to

ensure the appropriate level of engagement from the operators in particular, as they bear direct

witness to the results and issues that are now under greater scrutiny. Once managers, supervisors,

and operators align on the value of this data collection, in terms of its importance to the program’s

future success and impact on the health of the business, the program management team can more

easily ensure accountability.

The most successful OEE system designs are developed by cross-functional teams.

Collaboration between quality, engineering, production, floor associates and management will

promote engagement and uncover the most important organizational needs. However, it must be

remembered that too many “nice to have” features could doom the project. The key to a robust data

collection system is to collect only what is needed at the granularity that is needed. Two factors drive

the need for very tight scoping of data requirements: resolution and data management.

5


These reports show the same fault data

analyzed by shift (top) and by production line

(bottom).

This functional view enables the team to

prioritize improvement opportunities through

comparative analysis, validating and verifying

the business case for individual projects.

Case Study 3:

OEE Reduces Downtime by 39 Percent This OEE improvement program took place in a large brewery facility. Within its company, this

plant is the second largest brewing plant, producing over 2.7 million hectoliters annually. The

bottling line within the plant was already using a software program to record and track downtime

events. The in-place program promoted operators to record information on downtime events lasting

five minutes or more. This manual approach to OEE allowed them to realize significant capacity gains

but proved to be very time-intensive for the operators. For this reason, it was easy to develop a busi-

ness case for a more accurate, automated, and granular system. Additionally, the company wanted

their new system to automate the analytics and reporting process and to deliver these results on the

web.

They installed an automated system was installed that interfaced with sensors at various points

on the production line. An analytical software program modeled all machines on each line, giving a

detailed description the equipment’s current state. A corresponding electrical signal in the database

linked to each such description (stoppage, shortage, lowering/raising of forklift, etc.). Using this in-

formation, the software can determine if at a given time a machine has stopped, or released a faulty

product, or performed its operation incorrectly, or transferred to another machine a set number of

items. Additionally, the project team added functionality to allow operators to add more data on

production line events. In the last phase of the project, the brewer invested in a web-based solution

that provides reports in real time.

After project implementation, the brewery saw a 39 percent decrease in the total number of

mechanical and electrical downtime events and increased availability of human resources. They also

have active access to information on breakdowns and stoppages, enabling the continued elimination

of losses.

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Resolution is the ability of the data collector to discern and record the event accurately, and can

be thought of in terms of time and event description. For example, an operator cannot be expected

to give information on something he cannot see or does not understand. This lack of discernment

could be because the event happened too quickly, or the operator was otherwise engaged and did

not have enough evidence to determine what happened after the fact. Very often, technology can

open new vistas of data resolution, but we should never overlook the importance of operator en-

gagement and input.

Data management refers to the practicality of collecting, organizing, analyzing and reporting the

information that is collected. Highly granular data sets must be sifted for meaningful patterns, and

that requires much more automation than can be managed effectively with standard desktop ana-

lytics. The time spent understanding and optimizing the data collection process will pay off in the

first OEE implementation as well as every future OEE implementation. At the start of the OEE imple-

mentation, it is suggested that initial focus be on effectiveness. Once managers are satisfied with the

quality of data and analysis, focus can shift to improving the efficiency of the data system.

The final aspect to address in the planning phase is to ensure a mechanism to ensure the accu-

racy of the OEE data and to efficiently communicate results with all of the stakeholders. In order to

communicate results and trends, management should set up a cadence for data review and reporting.

Ideally, OEE discussions should be integrated into regular daily, weekly and monthly business review

& planning meetings.

In order to ensure the accuracy of the data, a system of ownership and oversight is needed. On

a daily basis, line data owners should scrub their data to correct errors and provide feedback to the

operators to teach or reinforce good data collection methods. At the plant level, a single owner

should be tasked to inspect the daily verification activities and to devise methods and systems to

improve the information value generated. This data verification process should be allowed to evolve,

but the frequency, accountability, recognition, and reinforcement of these reviews must happen

consistently in order to instill the value of the OEE regimen. When possible, the teams most affected

by the new process should be afforded the ability to refine the data collection method as well as the

look and feel of the reports. One approach would be for the program management to provide the

teams with a starting template, then afford the teams with the ability to provide input on what

works and what needs to be modified. Through this approach, the teams will gain ownership of the

report and the data, thus increasing data accuracy and accountability. After an effective skeleton has

been designed for the OEE regimen, program managers can act on the lessons gleaned therein.

Do In order to drive aligned action on the insight gained from the OEE implementation steps, man-

agers must ensure that the necessary stakeholders feel motivated and empowered to support the

OEE process and thus effect change. For this reason, it is important to put an organizational model in

place to direct and encourage sustained support of the OEE regimen at this phase of initiation. Em-

ployees at various levels of the organization will be expected to measure performance and guide

changes, so a formal model for driving behavior change allows for reinforcement and communica-

tion of the value that these change drivers add.

Two effective models that are widely accepted are the Hiatt ADKAR and the Galbraith Star. The

Hiatt ADKAR model expresses the need for awareness, desire, knowledge, ability, and reinforcement

of the initiative to all relevant stakeholders. The Galbraith Star organization model expresses the

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need for ongoing support for the various initiative tasks, people and skills, structure, information,

decision processes, and rewards. These popular models are two examples that the authors recom-

mend examining, but it is important that program managers employ a strategy that is commonly

accepted and understood throughout their organization.

The largest pitfall that many OEE programs fall prey to at this point in the initiation phase is

holding the wrong person or group of people accountable for the wrong metric in a way that dis-

courages accurate data collection and reporting. Employees at all levels will seek to exclude OEE

losses for which they do not want to be held accountable, and this can be detrimental to an OEE

program. Excluding important data elements will restrict visibility to the available areas for improve-

ment, leading to diminished and perhaps distorted decision making processes. Managers must limit

the scope to which accountability is assigned so as to include only data elements that are within the

spans of control of those collecting the data. A commitment from the stakeholders for open trans-

parency, and not punishment, is critical at this juncture. Misuse of the accumulated data will under-

mine trust and weaken chances for successful implementation of OEE systems and improvement

initiatives.

Check This stage of the program initiation for OEE is a holistic review and refinement of the program in

place. A review should be made of the gaps in the data collection process, including any gaps that

may exist in design, training, and daily execution of the OEE metric. It is important to review the data

reporting and analysis process to ensure calculations and reports are efficient, accurate, and timely.

Lastly, the most crucial element to address is the utilization of the OEE metric to inform activity,

track progress, and measure results. If the newly implemented OEE metric is not used to improve

business performance and results, all of the accumulated insight into the process is meaningless and

the OEE journey will quickly come to an end either through a lack of results or apathy.

Act Natural areas for improvement will fall out of the data collected, and this awareness must be

used to tackle large inefficiencies and improve overall performance. These improvement areas will

most likely require the attention of an improvement team to introduce necessary process, equip-

ment, or operating procedure changes. These improvements can be in the form of the corporate

continuous improvement process, but often fall into broad categories of improved communication,

planning, maintenance, or operations.

There are many processes and tools available for improvement. Whatever method is adopted,

simple, sustainable improvements can make large differences in OEE and productivity. Additionally,

measurement of OEE before and after changes will support continuous improvement throughout the

life of equipment and processes. It is at this point that program managers can explore expanding the

successes achieved in initial OEE programs to new lines, departments, and processing stages. Once

the OEE metric has been proven to drive improvement and performance, managers can grow the

OEE program to expose all operational inefficiencies. As the OEE program gains traction, program

management must move to prioritize changes for the largest impact, establish OEE as the foundation

for all plant-level loss exposure and subsequent decision making, and monetize the improvements

driven by the plant-wide adoption of OEE.

8


OEE Implementation Advice In order to better equip CPG managers to initiate an OEE programs, the authors have the follow-

ing advice from industry experts in implementing OEE programs:

Don’t be alarmed by initial drops in efficiency percentages. Proper calculation of OEE will

typically drop a plant or line from 90–110 percent “performance” to 50–80 percent OEE.

Overall performance has not dropped off; rather, the OEE program is measuring all losses so

that they can be eliminated. Educating the plant floor and first line supervision on how OEE

is calculated will help with buy-in of the new, “lower” metric.

Don’t make excuses. OEE only makes sense if all downtime and uptime are included in the

calculations. Plants or lines should not be permitted to take out events; automated data col-

lection and calculation mitigates this risk. Changeovers, sanitation, and maintenance are all

losses that can be minimized, if not eliminated.

Don’t underestimate the need for change management. Structured programs like the AD-

KAR Model will ensure that key people and steps are not overlooked in the process, and will

help ensure that gains are sustained.

Don’t underestimate the need to communicate. An OEE journey is predicated on changing

the culture toward one of honest root cause analysis. This must be demonstrated by the

entire leadership team to the plant floor. Visual controls like display boards near lines or at

meeting sites indicating performance and cost metrics are invaluable to show that leadership

cares about results.

Measurements & Calculations Thus far, discussion has focused on the implementation issues of an OEE regimen. In order to

drive corrective action and continuous improvement, certain detailed calculations and analytics must

be developed, as well. At a high level, OEE is a ratio of the acceptable units produced to the potential

units that could be produced over a specified interval. This metric is certainly very easy to under-

stand; unfortunately, it does not give us actionable data. We must break OEE down further to ex-

pose, prioritize and address improvement opportunities.

At a granular level, OEE is calculated as the product of three percentages: availability, rate per-

formance, and quality. All losses identified through the data collection process can and should be

attributed to one of these three categories in order to systematically drive change at the appropriate

level and magnitude.

OEE =

Availability (%) x Rate Performance (%) x Quality (%) Availability describes the losses that result from downtime, both planned and unplanned.

Some of the most common types of availability losses result from maintenance and clean-

ing activities, line changeovers, equipment failures, starvation due to missing input materi-

als upstream, and blockages due to outages in downstream conversion work centers or

handling processes.

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Rate performance describes the losses that result from running the equipment at speeds below

the target rate. Rate losses inherently occur during ramp-down and ramp-up around machine

stops, or from machine settings that differ from the target rate. The target rate is generally de-

fined as the best demonstrated instantaneous rate that an SKU can run through the chokepoint

of the production system. A machine may run different SKUs at different rates, but there should

be only one target rate for each SKU for that machine.

Rate is a very important metric that is used by many functions to plan capital investments

and to plan and execute the production schedule. Table 1 will help sort out the different

ways to look at rate. Each approach is valid; the important thing to remember is that only

the target rate is relevant to OEE.

Quality describes the losses that result from

quality defects. These losses must take into ac-

count all scrap and rework incurred over the

identified interval for the given process. Sanita-

tion defects, out-of-spec production, in-process

damage, and incorrect material usage are some

of the common examples of quality losses. For

process systems that deliver continuous flows or

bulk batches, the output may be graded based

on certain parameters such as percent purity,

percent solids and density. Where applicable,

these output parameters figure into the

“quality” component of the equation for OEE,

above.

Once it has been identified how all data ele-

ments fit into each category, the overall equip-

ment effectiveness may be calculated for the

process. It is important to note that continuous

and discrete operations should be calculated

separately , as these two types of processes tend

to operate at different baselines.

Calculating these values separately will ensure

that one area of improvement is not overshad-

owed by a high efficiency in another portion of

the larger process. A separate procedure must be used to roll up multiple OEE metrics at various

parts of the plant to obtain a larger OEE rating overall.

*Rates for each line can be SKU-specific; there should be

only one target rate for each SKU on a given line. Differ-

ent lines may have different rates for the same SKU.

Metric Definition

Ideal Rate*

The highest sustainable instantane-ous line rate on the line constraint that Operations, Engineering, and R&D believe can be achieved over the next five years that: (1) Has a technical basis for achievement, (2) Does not require a capital appropria-tion for capacity increase

Theoretical Max Rate (T-Max)*

The engineering or OEM-designed instantaneous rate at the chokepoint unit operation.

Locked Tar-get Rate (LTR)*

The maximum demonstrated pro-duction rate, expressed as units produced per uptime minute. LTR excludes losses related to input material deficiencies, production stoppages, production slow-downs, ramp-ups, ramp-downs, quality or rework losses, etc.

Planning Rate*

The rate used for production sched-uling based on a recent survey of throughput capability of the system, factoring in production efficiencies. Planning rates may or may not in-clude changeover or line set-up ime, depending upon the platform.

11


Case Study 4:

OEE Reduces Manufacturing Costs 15 Percent A Fortune 150 consumer packaged food company found it imperative to drive continuous man-

ufacturing improvement in order to generate cash flow that will fund marketing and innovation and

to grow sales and margin. Their strategy to meet this goal included developing and deploying a bal-

anced score card using OEE as a key performance indicator. In analyzing the current state of their

operations, the company was faced with consolidating performance metric terminology and calcula-

tions for over 200 production lines in 30 plants to gain enterprise OEE visibility.

The company identified a very specific execution plan which included the following steps: define

terms, calculation methods and system specifications; obtain capital approval; select an automation

vendor; and roll-out OEE system to plants iteratively. Each roll out included shop floor equipment

ERP updates, local configurations, operator training, and system validation. Because this process was

iterative, the company was able to incorporate learnings to succeeding roll-out plans.

Their OEE program roll-out was completed on time in less than 24 months and under budget.

Subsequently, OEE data visibility supported a 5 percent improvement in cumulative annual cost and

productivity, 15 percent reduction in manufacturing costs, a decrease in recordable incidents, and

improved quality and service. Additionally, they now have visibility to line, plant, and technology

platform performance and a robust system to product timely and accurate data and reports. Produc-

tion potential and productivity losses can be uniformly quantified to ensure inefficiencies are objec-

tively addressed. This OEE program enables the company to increase their profitability and lead the

industry in manufacturing excellence.

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Conclusion This paper has provided a foundation for plant-level continuous improvement initiatives on

which consumer packaged goods, food and beverage, and pharmaceutical companies can build. One

cannot improve what cannot be measured, and this is most true in the manufacturing world. Imple-

menting OEE initiatives in a structured and calculated manner ensures that results will be accurate

and actionable for real cost savings. Streamlining and standardizing OEE regimen initiation and adop-

tion enables efficient project management, objective decision making, and cost savings that are sus-

tainable and expandable, enabling growth and adding to the bottom line.

It is important to limit the scope of the OEE implementation project to a portion of the plant

where improvement is realistic and necessary. Most OEE programs fail when the initial boundaries

and goals for the initiation stage are not clearly defined or well thought out. Spending the majority of

time up front to understand how and why each data point is important to measure will lead to a

smoother execution and adoption by the most essential stakeholders; without this buy-in, long-term

success will not be supported or promoted. Once the initial process is rolled out, management must

take time to refine the data collection, analytics, and reporting processes with those most affected

to ensure that the program is sustainable. Rushing any portion of the design and refinement is not

recommended.

It is also important to note that in order to drive and maintain process changes, program man-

agement must secure employee support on multiple levels. This involves being clear and transparent

in the benefits and goals of an OEE program, enabling appropriate behavior, and providing continu-

ous feedback on progress and gains. Employees at all levels will attempt to omit data elements for

which they do not feel they are responsible. This will skew results, and can lead to uninformed deci-

sion-making. For this reason, it is beneficial to adopt an organizational model to guide the company’s

approach to OEE program promotion.

Copyright© 2016. PMMI. This publication was developed through the OpX Leadership Network, convened by PMMI. It may be downloaded, reproduced, and distributed for business or academic use, but not for license or sale, provided there is clear attribution to the OpX Leadership Network as the developer of the publication and PMMI as the copyright owner.

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