mellor consulting group approach toward a bkm oee deployment

Are You Satisfied with Your

Bottleneck OEE?

Deployment of Overall Equipment Effectiveness

Programs at Semiconductor Fabrication Facilities to

support the implementation of Total Productive

Maintenance and beyond…

© Mellor Consulting Group 2016

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Mellor Consulting Group

01. ABSTRACT: ------------------------------------------------------------------------------ 01

02. EFFECTIVINESS VS EFFICIENCY: ---------------------------------------------------- 02

03. WHAT IS OEE?: ------------------------------------------------------------------------- 05

04. OEE IN SEMICONDUCTOR MANUFACTURING FACILITIES: ------------------- 07

05. OEE SUCCESS FACTORS: ------------------------------------------------------------- 09

06. SUMMARY: ----------------------------------------------------------------------------- 11

TABLE OF CONTENTS

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01 - Abstract

Manufacturing processes become more complex every year, so does the equipment needed to

support them. Sophistication of the equipment adds new process capabilities and improves the

product quality, but it also increases the purchasing cost and the costs of ownership. It is well

known that the total cost of building a new semiconductor fabrication plant (fab) these days is

nearly $10 billion, while certain equipment (photo cells, for example) costs a total of $50 million

per tool. This, along with fluctuations in customer demand, forces manufacturers to carefully

plan the expansion of their capacity and to maximize the effectiveness of the existing assets and

their operational efficiency.

The purpose of this article is to discuss some aspects of Overall Equipment Effectiveness (OEE)

measurement on semiconductor equipment and to describe a typical OEE system and

deployment of this methodology in a fab.

02 - Effectiveness vs. Efficiency

Effectiveness is defined as a degree to which objectives are achieved. In contrast to efficiency,

effectiveness is determined without reference to costs and, whereas efficiency means "doing the

thing right", effectiveness means "doing the right thing" (www.businessdictionary.com).

Therefore, equipment effectiveness is the ability to achieve the desired performance goals while

efficiency deals with doing it at the lowest possible cost. As an example, achieving the same

output with two people performing the job instead of one results in an effectiveness of 100% in

a given period, while the efficiency of such a process drops to only 50%.

Another example is working on the "right" Work in Process (WIP), for instance, manufacturing

the orders following the schedule that improves on-time delivery rather than simple throughput

optimization. It is one of the most common conflicts in a high mix manufacturing environment

where equipment effectiveness sometimes has to be sacrificed to assure the committed delivery

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dates. Pure throughput optimization may be a highly effective, but potentially inefficient

approach that might lead to lower customer satisfaction (which might have a negative impact on

future revenue) and increased inventory cost.

Saying that, when asked to choose between effectiveness and efficiency, a world class

manufacturer (WCM) chooses both. Such facilities have all the required components to achieve

a balance between the two, be it well-trained personnel, a properly designed set of Key

Performance Indicators (KPI), robust decision support systems, and most important of all, a clear

strategy and objectives. WCM’s do not wonder very often what to do next, how to measure

progress, or guessing what else needs to be done to make things better. Their operational

management process is not a chain of brainstorming and crisis management events, it is a well-

planned and executed flow that eventually yields the desired results. Their operations are

prepared, trained, and fully fitted upfront to face the challenges and obstacles that might arise

during the execution of their strategy.

Their success then is not coincidence, it is a result of a hard work invested in defining their

strategic objectives, translating it to tactics, and adjusting their organization to support both.

Among others, one of the main issues a WCM should address is the effectiveness of its assets. It

is a MUST to know the effectiveness of each capacity-constrained asset at any given point in time.

Lack of understanding of how effective the equipment is, or how effective it could be, is a major

gap any manufacturer who strives to become a WCM should eliminate. Currently, the best known

method to measure the effectiveness of assets is an OEE tracking system implemented along with

an improvement methodology to support it.

03 - What is OEE?

OEE measures how effective the equipment is in manufacturing good products. The metric first

appeared in 1960 and was invented by Seiichi Nakajima, the father of Total Productive

Maintenance (TPM), to measure effectiveness of manufacturing equipment. The OEE measure

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plays a major role in the definition and execution of a TPM improvement strategy. OEE has

standards of 90% availability, 95% performance efficiency and a 99% rate of quality. An overall

OEE benchmark of 85 % is considered as world-class performance.

A classic approach towards measuring the OEE is to collect the availability, operational, and

quality performances and multiply them by each other to achieve a single metric that represents

the equipment effectiveness. Once a value is calculated, it is measured against the OEE targets

per tool or tool type and a factory can start using it to improve equipment performance.

As simple as it may sound, the design and deployment of an OEE tracking and improvement

system is not a trivial task. Some may suggest just going to the Gemba and measuring the

equipment effectiveness or asking to track it manually by documenting each status change. It is

true that conducting a multi observation study (MOS) to evaluate equipment performance is a

valid option that can provide a good sense of how well equipment performs. However,

continuous performance improvement and effective bottleneck management are challenging

tasks using manually tracked and calculated OEE. In addition, such an approach would consume

a lot of the staff's time and would be highly inaccurate due to variance introduced by the human

factor. Herein we are facing two major challenges, first to keep the continuous improvement (CI)

process going without investing an enormous amount of human resources, and second, to

continuously execute the principles laid out by E. Goldratt in his famous Theory of Constraints.

To enable the CI and performance tracking, an automated data collection and OEE calculation

system must be established. The desired end state for each factory aiming to track and improve

OEE should be an automated system which collects the data in the real time and calculates OEE

on the fly. The system should be able to present detailed drill downs into OEE components at

various resolutions (time or tool based). Such a system can be homemade or can be a shelf

product that is customized to specific factory needs. Prerequisites for such systems are the

existence of standardized processing time per process step and a Manufacturing Execution

System (MES) to collect the changes in equipment states.

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04 OEE in Semiconductor Manufacturing Facilities

OEE in semiconductor facilities is calculated differently than using the classic method presented

above. Due to the lack of ability to relate scrapped material to a specific asset immediately upon

process completion, the quality component in the calculated OEE is omitted. Therefore, the OEE

in semiconductor fabs is defined as the multiplication of the availability, operational, and rate

effectiveness, as presented in Exhibit 4.1

Exhibit 4.1: Time and OEE losses breakdown and description

The preparation of the OEE tracking infrastructure begins long before a first OEE calculation is

done. It includes the modeling of standardized processing times and the implementation of an

equipment state reporting logic defined by the SEMI E10 specification. These two items are

mandatory for being able to calculate an accurate OEE value.

Standardized times are set through a speed model of each process step at a tool level, where the

durations and frequencies of process recipe steps are combined to form a flow a product should

pass to accomplish the process.

Equipment states are then modeled according to the SEMI E10 specification in the MES and

certain rules must be followed while changing the tool states. For example, a change from “down

for repair” directly to a productive state must be prohibited to avoid mistakes. Worth to mention

that the equipment states reporting logic automated and embedded into the MES will reduce

the load on floor personnel and will assure data integrity and reporting accuracy.

Once the processing times and equipment reporting states are in place, an equipment cluster

modeling issue comes to the forefront. Because semiconductor manufacturing flow consists of

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different processes types, the tool types significantly differ one from another, while for the OEE

modeling purposes, they are divided into four major types:

• Simple - One process location with a single process type applied.

• Sequence - Tool hardware is configured to support a single process sequence.

• Parallel - Tool is configured to run the same process in parallel at multiple locations.

Next wafer is being dispatched to the first available processing location.

• Complex - Similar to Sequence, but the tool is configured to run a verity of sequences

in parallel or in a stand-alone mode.

These four logical types introduce different levels of modeling complexity and require a

differential approach to correctly track the equipment status. The complexity (mainly for

complex tools) in general can be described as (n-1) k + 1, where n is the number of tool modules

and k represents various equipment states. This may change in some cases where a certain

module, in addition to a mainframe, is defined as the critical one. This requires the

development of additional logic which will define the interdependencies and trigger the status

change in specific modules as a result of a change in one of the critical ones. Cluster modeling is

by far the most complicated task in OEE deployment, and once you model the complex tools,

expect to face some challenges trying to make it accurate. This poses an additional challenge

when trying to benchmark similar equipment operated at various facilities and using different

cluster logic because they might be incomparable due to variation in their modeling logic.

An additional point is the aggregation of OEE between non-related tool types. For example,

calculating factory OEE is a complex process and mostly meaningless compared to calculating an

accurate OEE for bottleneck equipment, which in my opinion better reflects the overall factory

effectiveness. As we are all aware, the Pareto principle and the Theory of Constraints both define

the need to focus on the most problematic resources that will provide the highest gains when

resolved.

Therefore, the general approach should be modeling the constrained equipment as accurate as

possible and all the rest to the extent that it is worth it. After all, the strength of a chain is

determined by its weakest link.

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05 - OEE Success Factors

It begins with training and then more training and then when you think it is enough, you do it

one more time, to make sure everyone gets it. Everyone should know how OEE is calculated and

how one can contribute to improve it. The second point is to emphasize why OEE is so important

by showing its impact on the company’s strategic KPIs. The third point is to clarify to everyone

that OEE is not owned by a specific person but rather it is a team metric. The training must be

done until each stakeholder understands the numbers and is able to challenge their accuracy if

needed. Another good practice would be to establish an OEE champion in each group whose role

would be to train his teammates, answer their questions, help with analysis and conclusions, and

address any data integrity issues. It is important to realize that OEE is a tool, and like any other

tool it is not perfect and should be maintained and improved over the time.

The next step would be setting up the OEE goals. To become an effective improvement driver,

each OEE component must be measured versus a goal and gaps will act as improvement triggers.

Setting the OEE goals can be a topic by itself, but as already mentioned, try to align it with

corporate objectives. In general, you may start by creating a baseline, measuring OEE, and adding

improvement increments. Other good ways to start include using availability goals provided by

original equipment manufacturers (OEM), operational goals derived from the capacity model,

and setting the rate goals based on equipment capabilities. I would strongly advise to set goals

for each OEE subcomponent, such as setup, idle time, scheduled and unscheduled downtime,

and more. This will enable a more structured approach towards performance improvement, and

if added to a factory capacity model, it will improve the accuracy of the capacity planning process.

Each loss will then be measured versus its target and will make it easier to define the activities

needed to avoid capital investments, but this is another topic.

Once the system is in place and teams are trained and goals are set, you are good to go, and any

performance improvement approach will work, be it Lean Six Sigma (LSS) (DMAIC and DMADV

cycles), Kaizen (PDCA), 5 Whys, 8D, or any other improvement approach. I would recommend the

LSS tool box, but it is not mandatory, anything that works for you is just fine as long as you are

making progress. Just be persistent and do not give up. The OEE improvement process is similar

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to any problem solving process in that it requires practice, practice and more practice to master

it. Be patient and passionate because it takes time to change the current mindset and more than

just talking to trigger such a change. Grit is the key word, do not give up when criticized if the

calculation is inaccurate, the progress is slow, the goals are too high, or because of something

else. Just keep going, when it will kick in you will know the difference, and equipment

management will become a whole different story.

Avoid making OEE an absolute KPI such as on-time delivery, cost, cycle time, and more. This will

just diminish the initial idea of OEE and defocus the personnel. As was previously mentioned, OEE

is mainly to be used as a performance improvement driver and to the extent that it serves the

overall company strategy. Remember, OEE is not an absolute metric because it allocates the same

weight to availability, operational, and rate components when calculating OEE. In reality, a

percent of availability is rarely equal to a percent of operational efficiency in terms of output,

cost, or other fundamental KPI’s. The deterioration or improvement of different OEE components

may have different impacts on the company’s bottom line performance. The same applies when

comparing the same equipment having exactly the same OEE values at two different time

periods, but actual output, cycle time, manufacturing costs, and quality might significantly differ

due to a difference in product mix for example.

An additional point is that OEE is not valid for benchmarking different equipment or processes.

OEE is a relative indicator of a specific equipment and its effectiveness should be compared to

itself over a period of time. OEE, to some extent, can be used to compare equipment producing

similar products and operating in similar environments in terms of loading and automation levels.

Another issue with OEE is that it accepts the standardized processing time as the absolutely best

possible speed the process can perform, which in many cases is false. Process recipe optimization

may significantly reduce the processing time and improve throughput while OEE remains intact.

This might create another benchmarking issue where tools running like processes and performing

at identical OEE levels yield different throughputs because one factory uses a more efficient

recipe for the same process. This once again proves that OEE is a relative metric and is an

inaccurate metric benchmark. When it comes to OEE, in my opinion, the focus should be on

benchmarking best methods and tools rather than a metric. I will cover the difference between

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benchmarking metrics and methods in one of my next articles to emphasize the gap between

organizations who are just measuring and those who are already learning.

06 - Summary

OEE is essential to have a full control over equipment effectiveness. However, it is not an absolute

metric that can assure factory effectiveness and efficiency. Additional metrics should exist, but a

balance between OEE and others should be maintained. An unbalanced set of KPIs will lead

nowhere but to unfocused personnel, reduced performance, ineffectiveness, and increased

costs.

The OEE improvement process is fun if the numbers are reliable, goals are SMART, and people

understand how to improve performance. In addition to higher effectiveness, there is another

benefit in having a robust OEE improvement process. People like to feel engaged and successful

in what they do, and a properly deployed and driven OEE process will give such a feeling. Those

who will be dealing with OEE improvement will have endless opportunities to refine their

problem solving skills and almost immediately see the impact of their work on the OEE and other

related company KPIs.

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Bringing all together

Anything you do should serve a purpose of achieving your company objectives, otherwise

it is a waste of time and resources.

Avoid using OEE as an absolute KPI and trying to maximize it by all means. OEE should

only be used to support the achievement of the company’s strategic objectives.

Stick to a classic definition of OEE as much as possible. Be consistent, avoid excessive

customization of the reporting, modeling, and data collection logics.

Clusters modeling complexity should be kept at an adequate level – over-engineering will

not add value, but will complicate the analysis and improvement processes.

Model the bottleneck tools as well as possible, and all the rest as much as worthwhile.

Make sure all inputs are accurate. After all, as the saying goes – garbage in, garbage out.

Automate the system and create a user-friendly GUI, make it easily available to everyone.

OEE benchmarking between different facilities is mostly a waste of time due to potential

differences in multiple parameters – loading, mix, process design, tool differences,

automation levels, and even factory layout, among many others.

Make sure everyone in the organization knows what OEE is and how it can be used to

improve overall factory performance.

Make OEE a positive metric, and never criticize the missing percent. Understand the

reason why it was missing and celebrate the improvements.

Never give up and have fun improving your factory’s performance!

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Michael started his semiconductor journey as a Production Team Leader who managed

equipment operators and technicians. During his career, he has filled various positions with

different semiconductor manufacturers, including Production Manager, Project Manager and

Director of Industrial Engineering. His academic background in industrial engineering and

management has been enriched with practical experience in leading manufacturing teams. This

enables him to develop comprehensive and long term improvement strategies, design and

implement sophisticated methods and tools.