Uptime

Looking Up

What To Do After an Economic Crisis
by Ricky Smith, CMRP

Most companies have reduced costs during these tough economic times in order to survive – shutting down equipment, laying off or furloughing staff, extending time between rebuilds and preventive maintenance – without thinking about the preparation they should be taking in order to take advantage of the economic turnaround.  There will be winners and losers once the world economy turns around.  Companies that are prepared will win by shutting down their competition and demonstrating even higher profits than before the economic downturn.  This can be accomplished by focusing on “optimizing asset integrity at optimal cost”. 

Do You Want to be a Leader or a Laggard When the Economy
Turns Around?

Many industries have advanced maintenance and reliability in their operation and have taken great strides toward managing asset integrity.  By applying known maintenance and reliability best practices, they have been able to optimize asset integrity, reduce total cost and, at the same time, reducing overall risk.  But, most organizations are still trapped in the old way of thinking, and do not follow the true definition of asset integrity.  One good way to know where your organization stands is to answer these questions:

1.  Have your assets been ranked based on criticality?

The definition of critical equipment may vary from organization to organization. In fact, if it is not formalized, there may be several interpretations of equipment criticality within a single organization. The assumptions used to assess what equipment is critical are not technically based. As a result, when different individuals are asked to identify their critical equipment, they will likely select different pieces of equipment. Often we are told, “all of our equipment is critical!” Selections are based on individual opinions, lacking consensus or even basis in functional relevance to achieving business goals. The potential for equipment failure having significant safety, environmental or economic consequences may be overlooked.

A consistent definition for equipment criticality needs to be adopted. The definition used in the context of this article is:  Critical equipment is that equipment whose failure has the highest potential impact on the business goals of the company.

Each asset is ranked based on environmental impact, customer impact, cost, and more.  Remember 20% of your assets utilize 80% of your resources.  Criticality ranking is influenced by all key function leaders in an organization and their decision impacts which equipment to focus on with a strong maintenance strategy, equipment replacement strategy, PM/PdM compliance and planned and scheduled maintenance work.

 2.  Can you measure Mean Time Between Failure (MTBF) of your critical assets? 

This is a simple metric of measuring emergency or urgent work orders divided into time.  If your maintenance department cannot provide this information in a matter of minutes for the total operation or a critical asset then there are two problems:

A)   The maintenance software is not being used to its fullest potential, even though you
       may have spent millions to install and maintain it
B)   The maintenance department has not been educated in basic maintenance
       management principles

3.  Do you have 100% PM compliance and continue to have equipment breakdown?

It’s not too hard to figure this one out.  Take a sample of your current PM inspection sheets and if they do not prevent a failure mode, detect the presence of a failure mode or aren’t regulatory in nature then you have a problem.

4.  Are your total maintenance costs going down or are they flat?

If your maintenance costs continue to go up and you are adding staff or contractors, you are in what’s known as reactive maintenance mode.  Education of and implementation of best practices is the solution.  Having knowledge of common best practices will likely increase the frequency your maintenance staff and senior leadership chooses to apply in a given scenario.  The maintenance process is like a puzzle; if you want to see the whole picture, then all elements must be in place and applied at the right time.

In this article, we will discuss what are known as best practices in Asset Integrity and how they are applied to prepare an organization for the economic turn around.

Let’s look at Shell’s Asset Integrity and Risk Management Statement:

Asset Integrity and Process Safety Standards
We are committed to preventing incidents that put our people, neighbors, the environment and our facilities at risk.  Asset integrity and process safety means making sure our facilities are well designed, safely operated and properly maintained.

Webster’s Dictionary contains the following definitions:  Asset: anything of value that a corporation owns; and Integrity: firm adherence to a code.  So, it follows that the definition of Asset Integrity is:  anything of value that a corporation owns that is held in firm adherence to a code.

The Codes can be defined as ISO, ASQ, etc.  The question is to what level of adherence is your company meeting these codes?  Of course, you can say we satisfy environmental or safety codes by meeting PM compliance every month.  However, is this the intent of a true PM Program?  A preventive maintenance program is to prevent or predict a specific failure mode. All PM compliance tells us is that the PM is completed within some schedule, but it does not tell us if the PM is effective or not.  If something is of value then a corporation should want to ensure it is well protected from failure, especially if the failure results in unacceptable costs.

Let’s look at how this entire Asset Integrity process works.  Asset Integrity impacts safety, cost, and service to the customer.  It also impacts your competition.  If we look at the Market Survivor Model in Figure 1, it tells us a lot.

Any company will own a certain portion of market share.  The market price drives profits and your future.  With company “A” their market share is high, costs are low, and Asset Integrity is high, thus, they are prepared for economic turn around.  Company “B” has a large part of market share, however, their cost is higher than Company “A” and this puts them at some risk.  Their Asset Integrity is not optimized and thus their cost, along with risk, is higher.  Company “C” has the smallest market share and the largest cost.  A good example of a Company “C” would be Alcatel Telecommunications in 1999. The fiber optic cable business was growing at a rapid rate.  By assessing the current level of their Asset Integrity program, we found key areas of improvement and closed the gap quickly.  They went from being a “C” Company to an “A” Company within 6 months with a Return on Investment (ROI) of 8:1.  This involved the application of known best practices in maintenance and reliability.  Best practices are measurable and do impact the customer’s need in quality, delivery and price as seen in Figure 2.

Some of the Known Best Practices are:

Using the right metrics or Key Performance Indicators (KPIs) to manage asset health effectively and efficiently –  such as:

1.  Mean Time Between Failures: Most companies don’t measure Mean Time Between
     Failures (MTBF), even though it’s the most basic measurement that quantifies reliability.
     MTBF is the average time an asset functions before it fails. MTBF should be used for:

       •  Overall Operation
       •  Area
       •  Asset Type

So, why don’t they measure MTBF?  We will discuss those reasons a little later.

2.  Percentage of Assets with No Identifiable Defects – This means you can Identify a
     component defect early enough in the failure cycle where work can be planned and
     scheduled effectively and without interrupting operations or customers

In this case everyone must understand the P-F Curve and how it impacts asset reliability (Figure 3).

“P” on the P-F Curve is the point at which failure begins on a specific component which will lead to catastrophic failure of an asset.  Once a failure begins, we call this a defect and the severity of the defect and criticality of the asset determines how quickly we respond to the problem.  If the defect severity is low and asset criticality is low, then there is no panic.

Tracking the percent of assets with “NO Identifiable Defect” is key to knowing the current health of your assets.  When the percentage of Assets with No Identifiable Defect is over 80%, there is no longer a need to track Mean Time Between Failure (MTBF) because we are now in a proactive, not reactive mode.

An asset that has an identifiable defect is said to be in a condition RED.  An asset that does not have an identifiable defect is said to be in condition GREEN.  That is it.  It is that simple! There are no other “but’s”, “what if’s” or “if then’s”.   Yellow is an unknown and must be determined to be either Green or Red within 72 hours.  Figure 4 is an example of this metric.

Applying the right maintenance strategy at the right time – When an organization is focused on preventing and predicting failure modes, they can “Optimize Asset Integrity at an Optimal Cost” along with managing associated risks.  My question to you is…

“Do you know the failure modes of your critical assets, and if so, are you applying the right maintenance strategy at the right time?”

An example of a failure mode would be pump bearing failure which could have been caused by poor alignment practices.  If an inspection checklist for alignment had been followed, the problem would have been found.  A failure modes analysis would identify Vibration Analysis as the most cost effective maintenance strategy for detecting alignment problems.  Vibration Analysis will indicate if the shafts are in alignment, if a bolt is loose on the motor, if the pump has bearing stress, etc.

Failure of critical assets is unacceptable, as is spending too much money on reliability.  The focus should be on applying the maintenance strategy, which predicts or prevents a failure mode in the most cost effective manner.

Identifying the Most Dominant Failure Pattern in your Operation – Many organizations today are focusing their resources on the most dominant failure pattern in their operation instead of reacting to problems.  Identifying the most dominant failure pattern allows a company to focus on the common thread which has the largest impact on Asset Integrity.  The US Navy conducted a study of their assets and found the most dominant failure pattern was infant mortality and considered the findings to be unacceptable.  They put forth an effort to reduce infant mortality of their assets.  Focusing on the dominant failure pattern allowed them  to identify commonalities between different types of assets.  They were successful in doing so as the percentage of Infant Failures dropped from 69% to 6%, which had a significant impact on their overall Asset Integrity. 

The failure patterns shown in Figure 5 were conducted back in the 1960s by United Airlines.  These failure patterns have been found to be the same across different industry verticals.

Causes of Infant Mortality has been found to be the same in most industries.  Here are a few examples of Infant Mortality:

Number 1: Lack of effective preventive, corrective and lubrication work procedures.

Number 2: Employees fail to follow the steps for a given procedure because either they do not agree with them or there is a lack of leadership follow-up.

Number 3: No contamination control – personnel apply grease into perfectly good bearings with contaminated grease on the end of lube fittings on the motors or the lube guns.

Number 4: Not removing the relief plug on large motors to relieve oil grease to purge out of the motor.  Walk out to a 30-100 HP motor and look for a plug under the motor bearing and see if the plug has ever been removed.  My guess is that is hasn’t.

 

Number 5: Welding on equipment without attaching the ground lead within 6” of the weld.   Most construction and maintenance personnel attach the ground for welding as close to the welding machine as possible, thus allowing current to flow to the path of least resistance (aluminum conduit, etc.).  We do not want arcing between bearings, motor stators, electrical circuits, etc.  If the welding lead and ground lead on your (or your contractor’s) welding machine is not the same length, then it will cause either infant mortality or random failures.

This list could go on and on.  Just remember that if you could reduce the occurrence of this failure pattern by 50%, you will make a large impact on both asset reliability and cost.

The ultimate goal is to extend the life of the equipment without seeing “P” on the P-F Curve.  You can accomplish this by applying Precision Maintenance, which includes training craft personnel, effective work procedures, etc.  Look at the I-P Curve in Figure 7, and decide on which curve your maintenance staff is currently working.  This is exactly what the US Navy did, and their results speak for themselves.

I am sure that some people will say, “we do not have infant mortality”. Well, I hope that after reading this article, you actually investigate for yourself.  If you would like a longer list, send me an e-mail and tell me which industry you are in, and  I will share industry specific issues which cause infant mortality.  The long I-P will never be obtained as long as the focus is not on the reduction of infant mortality by everyone from Project Engineering to Operators.

What a Successful Asset Integrity Manager Does

A successful Asset Integrity Manager focuses on providing reliability to an operation at the rate which its customers demand.  However, we know this is not easy.  Many organizations have spent millions of dollars performing Reliability Centered Maintenance (RCM) on their assets with what seems like very little Return on the Investment (ROI).

Doug Plucknette, a noted RCM expert and author states in his book Reliability Centered Maintenance using RCM Blitz, “The key to RCM was abandoning the philosophy of “preserve-equipment” in favor of “preserve-function”.  Simply put, equipment became the means to an end, not the end in itself.”  Doug has performed RCM on thousands of assets world wide and is noted among the best in the business.

Past studies have concluded that a maintenance policy based on operating age would have little, if any, impact on failure rates.  Thus, applying time-based maintenance on equipment which has no “wear-out” pattern is futile.  This conclusion forced a change in philosophy from, “It wasn’t broken, but we fixed it anyway” to “If it isn’t broken, don’t fix it”.  These studies also concluded that:

1.  Time-based maintenance works only for a small percentage of components, and then only
     when there is solid information on their “wear-out” characteristics.

2.  Condition Based Maintenance (CBM) is the preferred option.  That means monitoring,
     observing and taking non-intrusive actions, such as lubricating and cleaning, until a
     conditionsignals that corrective action is necessary. This means striking the balance
     between PM and Condition Monitoring (CBM) / Predictive Maintenance (PdM).

We know it to be a fact that the cost to repair increases the longer we wait to correct a known defect, and that the time in the x-axis on the P-F Curve is an unknown.  We can’t determine how long that interval is, nor can we tell when something is going to fail (Figure 8).

If you hear people say, “I think it will last a little longer,” when a defect has been identified using one of the Condition Monitoring technologies (IR, UE, Vibe etc.), then you are taking a great risk if you leave that equipment in service.  You should be using asset criticality and defect severity to determine when to make the repair.  Have you ever seen a large pump fail after the bearing defect has been identified 6 months earlier?  It is all about risk and the consequence of that risk.
 
3.  Run-to-failure is a viable tactic in situations where there is little economic and no safety
     or environmental impact.

4.  In a significant number of situations, the very act of maintenance itself causes subsequent
     failure of the equipment.  This should be more clearly defined.  This suggests that even if
     we do maintenance correctly, we still cause problems.

5.  Non-intrusive maintenance tasks should be used instead of intrusive maintenance
     whenever possible.  In other words, don’t do any maintenance, except monitoring and
     non-intrusive sustaining actions, until condition directs intrusive corrective action. 

A simple example is v-belt tension.  The PM states “Check tension of Belt”.  The equipment is stopped, the belt checked and found to be “loose”, so the mechanic tensions the belt using his hand as the tension gauge.  It is highly probable he has over tensioned the belt because if he under-tensioned it you would hear the belt squeal when it returns to full speed and load.  Over-tensioning of the belt will cause bearing failure.  Why not use Infrared Inspection and look for belt slippage while the equipment is running (Figure 9)?

You must move from being a PM Centric organization to a truly PdM Centric organization if you ever want to obtain “Optimal Asset Integrity at Optimal Cost”. PdM is the most non-intrusive and precise method for conducting inspections while the machine is running under full load.

In his book, Plant Engineer’s Handbook, Keith Mobley links the following benefits to PdM:

  •  Maintenance costs - down by 50%
  •  Unexpected failures - reduced by 55%
  •  Repair and overhaul time - down by 60%
  •  Spare parts inventory - reduced by 30%
  •  30% increase in machinery Mean Time Between Failures (MTBF)
  •  30% increase in uptime

Now these numbers may seem high. But even if you take only a fraction of these benefits the financial impact of an effective PdM program at most organizations can easily reach into millions of dollars.

Despite what you may have heard, the foundation of a successful, comprehensive inspection program is simple: a detailed equipment list.

Why?  Because your equipment list is the foundation for all of the key steps that follow. For example, a good list is essential for:

  •  Identifying how your equipment can fail (identifying failure modes)
  •  Choosing the right PdM technologies to apply to the asset
  •  Determining the ideal amount of PdM coverage for your operation
  •  Ranking the criticality of each piece of equipment
  •  Building databases for each PdM technology
  •  Determining PdM staffing levels

So if your list is incomplete or incorrect, everything that’s built from it will be flawed. Any shortcuts or inaccuracies will be exposed as big problems later.

Figure 10 illustrates a sample of recommended technologies by equipment type for a specified environment.

In the last 40 years, no better method than RCM has been found for determining what maintenance should be performed to increase asset integrity.  Four statistically significant studies have confirmed the validity of RCM.

In a survey conducted by Reliabilityweb.com in 2005, many companies offered the following excuses for the failure of their Reliability Centered Maintenance Implementations.

“Organizations want results right away, not in 6 months or a year.  The classical RCM process
is too time and resource intense.”

“RCM is a great tool but very resource intensive.”

“100% reliability is extremely expensive, difficult to attain, and not necessarily the right answer.”

“RCM is misunderstood to be software.”

“In the beginning, it was hard.  And, it is still a challenge to steer the mind-set toward more
condition-based maintenance than time-based.”

“We always ran into the problem with implementation.  In the few places where we
implemented it successfully, it was at the maintenance level.  And recognition
for it was non-existent.”

“The system is very strong but too high level“

The truth is, there are many pitfalls in RCM.  But few get revealed when an RCM project fails.  You see, nobody wants to write an article or present a paper at a conference that reveals how money was wasted or about great visions that were never realized.

In order to ensure RCM works in an organization, you must focus on the systems that will give you the best Return on Investment (ROI).  Simply put, RCM is a slam dunk when it comes to ROI for critical assets.

Begin your RCM effort by identifying the top 10% of your most critical assets.  Once this list has been identified, you should now begin to measure Reliability on these assets; performing RCM analysis on those critical assets that have equipment-based operational, speed and throughput losses and thus reducing total cost of maintaining these assets by reducing contractors, overtime, maintenance parts, and fines.  If you have selected a critical asset, your implemented RCM maintenance strategy will show measurable improvements with added improvements in Health, Safety and Environmental performance as well.

As a general rule, the success of your first RCM analysis will build the business case to complete RCM analysis on the remainder of your critical assets.  In the simplest terms, RCM is a decision-making process which calls for answers to questions such as:

  -   What is this system supposed to do?
  -   How can it fail to do that?
  -   What causes it to fail?
  -   What happens when it does fail?
  -   Can we predict or prevent that from happening?

Getting the Tools in place - Funding, Staffing, Practices, and Reporting

Step 1 – Do not accept excuses for why you cannot transition an organization to the optimal maintenance model.  Steven Covey once stated the following: “Your sphere of influence is very small, your area of concern is very large, focus on your sphere of influence and not on your area of concern and you will find your sphere of influence will grow.”

When an organization transitions from their current maintenance model to a more proactive one, you will find staffing is not a problem, costs are lower, and emergencies become a rare occurrence, however, you must take it one step at a time and follow the industry proven best practices.  There are no excuses for following the wrong path.

Step 2 – Develop a business case for the reliability improvement process.  Identify costs and Return on Investment based on known data. Involve senior leadership and the financial team in the business case development.  You must have everyone sold on this idea of change or it will never happen.  Too many organizations have had many false starts. You should never start a journey without everyone knowing success is the only option.

Step 3 – Develop a master plan that focuses on results and moving an organization to having a balanced PM and PdM program using the Proactive Work Flow Model (Figure 11).  In this plan you want financial targets and metrics to ensure you are receiving the benefits expected.  You want short term results with long term gains.  Note the Work Flow includes Management of Change (MOC) procedures to ensure consistency of processes and data.   In addition, a process for a Failure Report Analysis and Corrective Active System (FRACAS) is identified for ensuring failure data is analyzed and used for decision making.  This means implementing known best practices at the right time and not trying “new ideas” which are not proven.

Step 4 – Ensure you have identified Key Performance Indicators (KPIs) and financial targets.  Make sure you have leading KPIs and lagging KPIs developed which equal the expected financial Return on Investment (ROI).  These metrics and financial targets must be aligned with the process changes being made.  In financial targets we want “hard dollar” savings and not “soft dollars”.  Here is an example of what I mean: While working at the Pentagon with the US Army I was in a meeting and a Lean Six Sigma Director was reporting to his General officer (3 star General) and he stated they he had exceeded their expected goal of $70 Million Dollar savings and the General looked at his comptroller and ask if he had seen the savings and, if so, he wanted to use this money for a critical project.  The comptroller stated he had not seen any monetary savings.  Needless to say, the General was not too happy about this situation.  Remember only “hard dollar”, verifiable savings can reported. 

Figure 12 shows some known benchmarks in  the chemical processing industry.

Benchmarking can be deceiving, but it is a great way to know where you stand in relation to other companies.  The metric is the amount of money spent annually maintaining assets, divided by the Replacement Asset Value (RAV) of the assets being maintained, expressed as a percentage.  This metric allows one to compare the expenditures for maintenance with other plants of varying size and value, as well as to benchmarks.  The RAV as the denominator is used to normalize the measurement given that different plants vary in size and value.  Here is what we know:

  •  Companies who are leaders have an RAV of less than 3.5% for Maintenance
  •  Companies who are in a reactive state have an RAV between 3.6-14%

Where does your organization you stack up?

The definition has been defined by the Society for Maintenance and Reliability Professionals (SMRP) after years of research.  Here is SMRP’s definition:

Guidelines provide additional information or further clarification on component terms used in SMRP Best Practice Metrics. This guideline is for Replacement Asset Value (RAV).

A. Definition:  The Replacement Asset Value (RAV) is defined as the cost that would be incurred, in today’s dollars, to replace the facility and equipment in its current configuration. It is intended to represent the realistic value to replace the existing assets at new value.

B. Purpose:  RAV is used as the denominator in a number of calculations to normalize cost performance of facilities of various sizes within a given industry. These calculations are used to determine the performance of the maintenance and reliability function relative to other facilities within its industry.

C. Inclusions:

  •   Building envelope
  •   All physical assets (equipment) that must be maintained on an ongoing basis
  •  The value of improvements to grounds (provided these must be maintained on
       an ongoing basis)
  •  Capitalized engineering costs

D. Exclusions:

  •  Value of land on which the facility is situated
  •  The value of working capital:
    –   Raw material inventory
    –   Work-in-process inventory
    –   Finished goods inventory
    –   Spare parts inventory
  •  Capitalized interest
  •  Pre-operational expense
  •  Investments included in construction of the facility that are not part of the facility assets
  •  Mine development

Replacement Asset Value Formula:

Best Practice Leaders typically have an Annual Maintenance Cost per RAV in the 2%-3.5% range, while Reactive Organizations tend to run anywhere from 4%-14%.

Look at this example of the cost a company incurs by not following codes and standards identified to optimize Asset Integrity.

This is not including increase equipment performance and output which can be 2-10 times these losses.

Step 5 – Educate everyone from top leadership to operators on your plan and what your organization’s future state looks like.  You want operations to understand they own reliability integrity as an equal partner with maintenance and of course engineering.  This is a three legged stool which will fall if not supported properly.

Step 6 – Execute the plan and always meet targets and goals set by the plan.  Ensure everyone sees the KPIs and financial targets which were established, and thus always knows the “score in the game”.

In summary, organizations can be successful if they apply best practices at the right time and tie all changes to “hard savings”. Remember, the ultimate goal is “optimal asset integrity at optimal cost” by following best practices.

Ricky has been involved with maintenance for over 30 years as a maintenance manager, maintenance supervisor, maintenance engineer, maintenance training specialist, maintenance consultant and is a well known published author.  Today he is a Principal Advisor at GPAllied, LLC.  Ricky has worked with maintenance organizations in hundreds of facilities, industrial plants, etc, world wide in developing reliability, maintenance and technical training strategies.  He has worked for Exxon Company USA, Alumax (this plant was rated the best in the world for over 18 years), Kendall Company, and Hercules Chemical providing the foundation for his reliability and maintenance experience.  Ricky Smith is Vice Chairman of the Oil, Gas, and Petrochemical SIG for the Society of Maintenance and Reliability Professionals (SMRP), is the Reliability Engineering Discipline Manager for PetroSkills and is well known as a Reliability Advocate in Asset Reliability and Process Integrity in the Middle East and around the world.   Ricky is the co-author of “Rules of Thumb for Maintenance and Reliability Engineers”, “Lean Maintenance” and “Industrial Repair, Best Maintenance Repair Practices” and “Planning and Scheduling Made Simple”.  Ricky holds certification as Certified Maintenance and Reliability Professional (CMRP) from the Society for Maintenance and Reliability Professionals (SMRP) as well as a Certified Plant Maintenance Manager (CPMM) from the Association of Facilities Engineering (AFE) and is also a Six Sigma Black Belt.  Ricky lives in Charleston, SC with his wife.  Aside form spending time with his 3 children and 3 grandchildren, Ricky enjoys kayaking, fishing, hiking and archaeology.  He can be reached at rsmith@gpallied.com

Positioning For An Economic Recovery.pdf

Using Process Reliability Analysis for Strategic Business Decisions

by Ron Moore, John Schultz & José Wilkins
Uptime Magazine August September 2008

Editor’s note:  This paper is based on the experience of the authors and is an amalgam of that experience.  Therefore, the information below bears no particular correlation, real or imagined, to any company, but rather, a fictitious company will be used to illustrate certain principles based on the composite experience of the authors working with many different companies. 

Background


“ABC” Company is growing and currently sold out in one of its major businesses.  Market demand is expected to increase substantially in the coming 5 years.  Current production capacity is limited and not expected to be able to meet the increasing demand within the next 12-24 months.  The long range capacity and demand profile is outlined in Figure 1.

The company is reviewing its options for supporting this anticipated growth in its markets.  These are summarized as follows:

1.       Option one, currently in progress, is to invest considerable additional capital into building new capacity.  The current estimated investment required to build a new plant or expand existing operations has been determined to be approximately $300M.  The company is proceeding with conceptual design work and planning using a budget of ~$1.5M to improve its understanding of the business requirements and capital budget estimates for the additional capacity.  This effort thus far has indicated that $300M is within a +20% band, depending on: a) whether the plant is new, and closer to some of its developing markets, 2) added to existing operations, but farther from those same markets, 3) whether they pick up more advanced process and control technology in the new plant. 

2.       Option two, now being considered, is to acquire some of its competitors’ assets.  The company has recently been advised that one of its competitors is in financial difficulty and in need of cash, and thus may be willing to be acquired or to have some of its assets acquired.  The company also believes that some additional capital will be required to convert some of the technology in their assets into the processes more compatible with the company’s.  A “due diligence” review will be necessary to better evaluate this option, including the current state of their assets and their historical performance.  If they proceed with this, it is expected to cost ~$1-2M. 

3.       Option three, not seriously considered until now,   The company had recently had a review done at its Turkey Creek plant, one of its five plants, and it was concluded that the Turkey Creek plant was currently only operating at 56% of its maximum capacity, and that it could, with minimal additional capital or operating expense (e.g., $1-5M), perform in a sustained manner at >80% on an annualized basis of that maximum demonstrated production capacity.  Members of the management team, who comprise former plant operating managers and/or capital project managers, are skeptical of this option.  They don’t believe that the plant is performing at 33% less than its inherent capability; or alternatively that they have invested 33% more capital than necessary to produce at current levels.  Very intrigued by the possibility of higher capacity -especially if it turns out to be true that not just at the one plant reviewed, but at the other plants as well, they could get all the additional capacity they need for the next 3-5 years without having to invest some $300M in new capital equipment.  The initial assessment of the Turkey Creek plant, including the development of an action plan for achieving 80% annualized % production rates cost some $150,000.  In spite of their skepticism, they have decided to proceed with having the other four plants reviewed as well. 

Option one is in process and is adequately described above for purposes of this paper.  Options two and three are discussed in additional detail below. 

Buying a Competitor

When buying a competitor, a due diligence review must be performed which includes the usual management, market and financial review of the competitor, subject to the appropriate state and federal laws and proprietary protocols.  The management team acknowledges their thinking has been challenged about the Turkey Creek plant’s performance and they agree they must review whether or not the competition’s assets are performing at a comparable level to theirs.  It is suspected that the competitors’ assets may be performing even worse, given their current financial difficulty.  So, they elected to ask the review team for the Turkey Creek plant to be part of the due diligence team to assess the performance of the competitors assets and the results are shown in Figure 2.

The results of this review were surprising to everyone.  The competition’s plant is reporting actual performance better than Turkey Creek, but they were typically only operating at ~50% of true facility design capacity.  The competition had consistently cut fixed costs over the past 5 years and also lost many of their resources due to retirement and attrition. 

It was painfully obvious, that this plant would have a very hard time pushing to meet the original rated design.  The plant had identified the Dryer System as deficient with a preliminary estimate of $25MM in capital for a new Dryer system.  This company had already reported being strapped for cash, so they have been unsuccessful in selling this Dryer Project to the Engineering Director to secure the capital investment approval. 

As a result, a business decision was made several years ago with the business team to reduce the rated capacity to more of a contractual arrangement of 110 Tonnes/Day vs. 200 Tonnes/day design capacity to better reflect true capability of the current plant limited operations. This decision also helped the plant to meet their capacity and availability performance metrics.  The existing Capacity Utilizations for the comparisons are also shown in Figure 2.   

After discussing the production profiles with the personnel from Turkey Creek, they reported that they could not do any better because the highest volume product at this plant runs at a slower output vs. rated design capacity.  The customers have convinced the marketing team that this slow running product is much better for their process operations.  The marketing team recognizes this value to the customer but has been unsuccessful in negotiating a higher price for this product.

The above data was presented to the “ABC” Company Asset Productivity Champion during an executive leadership meeting.  The champion was puzzled with this big disparity between the competitor and Turkey Creek, so now the challenge was to figure out WHY.

The “ABC” Manufacturing Director and the champion had recently been exposed to some newly developed statistical approaches to better characterize production output performance by a Leading Service Provider (LSP).  These methods are referred to as “Process Reliability Plots”.  These managers began to have dialog to see if this approach could help to figure out WHY.

Process Reliability Plot methods were developed in the mid-1990’s and are now just gaining significant acceptance throughout the manufacturing community. Utilizing the abundant data found in a plant’s assets and operating environment, these non-traditional statistical plots can be used to translate the data into an easy to understand language of improvement opportunity.  Most production output data is riddled with special causes leading the data to behave in a non-linear fashion and common causes that are not very well understood. 

Daily production output data is always available because it defines the overall production performance of an operating asset.   Typically, most companies look at traditional time-series data to analyze performance.  Personnel spend countless hours reviewing the performance in an attempt to quantify the potential for improvements.  It is very time-consuming to quantify efforts to improve performance simply from looking at time series data and they typically result in requests to spend major capital dollars to address improvement efforts.

After importing the daily production output data into the statistical software, many important characteristics to improve operational performance tend to stand-out more readily.   At this point, the data is plotted in rank order on log scale for the “x-axis” vs. a log-of-log scale for the “y-axis”.  This data has been disconnected from time-series effects and show distinctive patterns of performance.   When the data is presented in this fashion, straight line segments and definitive cusps will appear making it easier to more accurately characterize the overall “hidden plant”. 

These plots show data plotted in rank order rather than in time series using a method called “Bernard’s Median Rank”.  They provide the user with a quick, visual, assessment of the true process performance and potential. Steep slopes, commonly called “Beta or Production Slope” are desirable and display small variability in process output from small common cause variation. Flatter slopes display large variability from larger common cause variation which is built into the process or from special causes.

The plots will also establish a single point estimate of typical daily production.   This single point estimate always occur at the CDF = 63.2% or the R = 36.8 on the plots and will be the best characterization of the typical production output.  This characterization is commonly called “ETA” and the magnitude is determined by the size of the physical facility and show how it is managed and operated relative to rated design capacity.

Many plant cultures typically point to equipment failures or asset optimization issues when capacity losses occur.   It is a daily no-win battle to genuinely understand the true causes because no formalized, proven plan exists to determine the origin of ALL the loss. All of this compounds the uncertainty on how best to deliver asset and process optimization and keeps a business in constant flux when trying to target the hidden capacity of their operation, the “hidden factory” that lies beneath.

Over the past several years, a Leading Service Provider (LSP) has refined this method, to understand the potential, reduce both asset and process optimization losses and improves overall profitability for clients.  See Figure 3 for an illustrative example of how LSP has recently incorporated this concept into an improvement offering called “Transformational Analytics Plots”.

Note that because it is on a log plot, most the losses tend to be under the process optimization area, not the asset (equipment) optimization area.

The LSP has invested countless hours in the marketplace enhancing this approach.  Using this model to analyze:

·         Asset Optimization Losses - (e.g. equipment failures, raw material supply shortages, etc.) typically includes all major special cause or reliability issues. 

·         Process Optimization Losses - (e.g. yield issues, late starts, early quits, poor management decisions, etc.) typically includes efficiency and utilization or common cause or variability issues. 

·         Variable margin for a lost unit of production is then utilized and applied to both categories of losses to define $COUR – Cost of Un-reliability and $COV – Cost of Variability to dollarize the total improvement potential.

“Transformational Analytics” will help to challenge a company’s status quo in driving strategic and tactical decisions to:

• Improve Safety
• Improve Production Output Effectiveness
• Improve Process Reliability
• Reduce Production Variability
• Reduce Costs
• Improve Business and Capital Planning
• Create a Proactive and Can-Do Work Environment

The “ABC” management team is concerned about the amount of time it will take to actually make the acquisition, and to integrate the two company cultures.  While “ABC” is arguably not performing at a World Class level, it is seeking to become better and is open to new concepts, notwithstanding the occasional healthy dose of skepticism.  Its competitor appears to be intensely driven by cost cutting, and the state of the assets is poor.  Years of cost cutting have left the assets in visibly poor condition, and safety and environmental issues appear to be a major risk for any such acquisition.  Moreover the competitor is seeking $500 M, and appears unwilling to be acquired in a stock swap, but is seeking mostly cash, and would require “ABC” to take on some $300 M in debt. 

Leadership within “ABC” have decided to assign a team leader to work with the LSP experts to utilize “Transformational Analytics” to perform the analysis on the data from Turkey Creek and the competitor site to see if this approach would help to provide more compelling evidence to pursue buying this competitor’s facility. The results of the analysis are shown in Figures 4 - 5.

In this analysis, and based on Figures 6-7, it appears that the competitor’s site has left $70MM to $75MM profit loss on the table for a 73MM Tonne capacity plant. The analysis also yielded an $80 MM profit loss for the 131MM Tonne capacity at Turkey Creek as shown in Figure 4.   

Both facilities are operating at <60% OEE and missing many opportunities to increase the reliability and reduce the operating variability that impact their overall customer dependability.  With the clarity of these results, the leaders instructed the team and the Leading Service Provider (LSP) to complete the “Transformational Analytics” study for the remaining four operating plants in the “ABC” Company.   It is anticipated by the leaders, that results of the study will show a clearer short range plan for “ABC” to address capacity needs from their existing assets for the next 2-3 years.  This will also allow the Engineering Team to use the results of the competitor study and factor that into a decision to build a new plant or buy the competitor’s plant.

Improving Existing Assets

Given the above situations, both of which would require $100’s of millions in capital, the option of improving the performance of existing assets has become increasingly attractive in spite of the initial skepticism.  “ABC” thus decided to proceed with a 2007 review of all five plants in its system with results shown in Figure 6 as a simple production vs. capacity profile.  It is clear for this plot that production at Turkey Creek and Red River are falling well short of their expected capacity needs from the business.

Also in the chart in Figure 7,Big Bend, Eagle Cliff and Marsh Point all appear to be more stable and able to sustain higher production rates when they are up and running. Marsh Point installed new moisture extraction technology to help with more uniform packaging at this facility in late 2006.  This process has definitely shown increased production output since this implementation in 1Q2007.

Lastly, it was noted by the team that Red River, Eagle Cliff and the competitor’s plant in the previous analysis, have exactly the same process technology as Marsh Point.  The team also acknowledged that Marsh Point, Big Bend and Turkey Creek have some similar technologies but they are so competitive relative to each other that they spend very little time leveraging ideas across these plant sites.

The team utiltilized “Transformational Analytics” and the same data for the analysis in Figure 7 to better characterize the actual and entitlement performance of all the plants to define the overall hidden loss profit potential for the “ABC” Company.  The combined results are shown in Figure 8.

You can see that the “Transformational Analytics” analysis identified over 106,000 Tonnes in improvement potential across all five plants.  This combined total represents enough capacity potential that is bigger than rated capacities at Red River, Big Bend, Eagle Cliff or Marsh Point on an individual plant basis.  In fact, this potential is also larger than the total rated plant capacity of 73,000 Tonnes for the competitor site. 

At this point, the team displayed extreme excitement and alerted the sponsoring leaders to this finding.  The WOW factor prompted these leaders to ask the team, with the guidance of the Leading Service Provider (LSP), to drive “Transformational Analytics” down to each individual plant site and develop a business level improvement plan. 

In a proactive manner and parallel path, the “ABC” Asset Productivity Champion began to have discussions with the Leading Service Provider to inquire if they offered any services to could help “ABC” to identify specific improvement projects they could implement to remove at least 70% of the identified improvement potential gap.  LSP quickly responded with a detailed approach that utilizes a few proven Six Sigma, Lean Manufacturing and Reliability tools that they have successfully implemented to help clients improve their overall bottom-line profitability. The Transformational Analytics approach is outlined in Figure 9.
 

The “ABC” Asset Productivity Champion was so intrigued with “Transformation Analytics” approach that she requested the Leading Service Provider to provide workshops and utilize some of the improvement tools in the LSP’s arsenal to help her develop a business level improvement plan. The scope of this plan focused to deliver at least 75% of the identified loss potential previously identified.

The design of the workshops included representatives from all five plants to encourage increased leveraging and quick implementation of ideas that were beneficial to multiple sites.   After conducting the workshops, the business level improvement plan incorporated into the results are shown in Figure 10.

 

The results of this work quickly helped the “ABC” Business Leadership Team to set clear direction for the capacity growth and priority for the entire company.  They decided to:

1.                   Engage the Leading Service Provider to help the improvement teams to fast track top priority items identified from the workshops

2.                  Defer any plans to invest in building a new production line

3.                   Deploy a small team to complete a more detailed acquisition study of the competitor’s facility and consider if the current “ABC” improvement plan would minimize the safety, environmental or economic risks of purchasing this facility.  This would defer acquisition of the competitor’s facility until at least 2010.

4.                   Focus Engineering Team to complete the Front End Loading for a new production line within the next year.

The analysis in Table 1 can be translated into a simple table of current performance against future performance as measured by OEE, these OEE figures and losses being adapted from the Barringer Charts above to more accurately reflect maximum demonstrated sustainable rate.  Note that the increase in output is anticipated to be 106,000, going from 236,000 tonnes per year to 342,000 tonnes per year over the coming 4 years.

Note that material and other variable costs do not increase linearly due to anticipated improvements in yield and quality with a more stable process.  Likewise, labor and other fixed costs are actually decreased slightly because of improved productivity in the labor force.  All in all, the company will more than double gross profit contribution, with existing assets, avoiding the need to purchase a competitor, as well as the need to invest additional capital immediately.  It now has options for its future demand requirements and will likely use internal capital vs. taking the risk of acquiring a competitor. 

Action Plan/Next Steps

Now that ABC sees the possibility of meeting market demand with their current assets and accepts that there is only a limited need to invest additional capital, the following questions must be asked: 1) Where should we start with our improvement effort?, and 2) What improvement tools best apply to our business?  Most companies look for relatively easy ways to identify the biggest opportunities that are the easiest to do for improvement.  But, most middle managers are not “systems thinkers”, that is thinking of their manufacturing plant as a complex system that must be fully understood as to inputs, outputs and variables.  Rather, most tend to be “event thinkers”, wherein one reacts to an event without fully appreciating all the actions and circumstances that led to the event occurring. 

All ABC’s plants have a site strategy and improvement plan, and the tools they are applying are numerous.  They’ve been bombarded with promotional material and success stories regarding the use of tools and methods such as Lean Manufacturing, Six Sigma, Supply Chain Management, Total Productive Maintenance, Reliability Centered Maintenance, Root Cause Analysis, and Kaizen, just to name some of the more common ones.  They’ve used these in one form or another, and it seems that when properly applied, they work well. The “best” one seems to depend on the particular business situation, and given that, there may not be one “best”.  The question that many managers ask is how do I know which one to apply and where to start?  Below is a model for applying Process Mapping to analyze the details of the losses discussed above, and to develop an action plan specific to those losses to focus the management teams on the value to be achieved in the coming 3-4 years. 

The Manufacturing Plant as a Business System

Any manufacturing plant should be thought of as a business system, and management and shop floor must understand the events or defects (anticipated or otherwise) that occur resulting in the loss of capability of that system.  In a perfect world our plant would run perfectly all the time.  We’d have perfect quality, maximum production rates, no unplanned downtime, and product changeovers would be instantaneous.  Of course no one has ever seen that world.  With that in mind, we should understand those events or defects that cause us to lose capability, and ask if they are acceptable to the business or not.  At times they will be perfectly acceptable, e.g., rapid product changeovers, capital project upgrades, planned maintenance and so on.  At times they will not.  We must understand the causes of lost capability, and then take appropriate action to minimize them.

As noted above, one of the more common methods for identifying important problems is to measure Overall Equipment Effectiveness (OEE), and particularly the losses from ideal that goes with this measure.  The losses from ideal are typically due to planned and unplanned downtime, rate losses, quality losses, changeover losses and other losses, some unique to the business.  These are then used to select and apply the appropriate improvement tool to reduce the losses being incurred.  However, suppose we’re not measuring OEE or its equivalent, or don’t have confidence in the measure itself (as is the case with ABC demonstrated above), what then?  Or suppose we don’t have a good understanding of the underlying causes of the losses discussed in the Process Reliability analysis above.  What if we’d like to understand the system level interactions and be able to make better decisions on that basis.  Neither OEE, nor the above analysis will provide all the details of production losses at a system level.  Below is a model for better understanding the system interactions and prioritizing the work to be done, and then selecting the tools to be used. 

Business Level Failure Modes and Effects Analysis (FMEA)

ABC’s costs are too high. How do we know where to focus the resources to lower those costs?  Or, perhaps that’s the problem, ABC has “too many resources” with their attendant costs, so we cut costs, with the expectation that costs will come down, only to see our production capability suffer.  Things are often not what they seem to be.  For example, when maintenance costs are too high, companies often cut them, to their detriment.  Rather than cut maintenance costs, perhaps they should understand the source of the defects that are resulting in the failures and the subsequent need for maintenance.  The model below provides a step by step process for understanding where the “defects” are, that is, those things that cause us to lose production capability, or incur additional costs, so that we can eliminate the defects from our business system.  It’s called a business level Failure Modes and Effects Analysis (FMEA). 

1.  Assemble a cross-functional team from each production area (Area Teams), plus a support function team.  The cross functional team from each area typically consists of a senior operator, an operating supervisor, a senior mechanic, electrician, or technician, and a maintenance supervisor.  It may also include others, such as the area engineer or a vendor for certain critical equipment. The support team is typically composed of a stores supervisor, a utilities supervisor, a human resource and/or training manager, a purchasing supervisor, and a capital projects engineer. 

2.  Have each Area Team draw a simple block diagram of the production process.    

3.  Next, and this is the heart of the analysis process, we define a failure in the business system (the production process) is defined as anything that results in the loss of quality production output, or in extraordinary costs.  Loss of production is pretty obvious, but let’s give some examples of extraordinary costs.  Suppose we have in-line spares for several of our pumps.  So, when one fails we don’t lose production, but if they fail often, we can incur extra costs.  Or, suppose we routinely have yields that are below, or even well below, our ideal target that represents an extraordinary cost.  Or, suppose we are using excess additives or catalyst to assure proper reactions in a chemical process.  That represents extraordinary costs as well.  Or, suppose our energy consumption is 20% above the ideal state for conversion of our products.  These are all extra costs that we might be able to manage better, and minimize, with a more stable process and reliable plant. 

4.  Next, cross-functional team reviews each step of the production process, identifying failures in the production process at each step of the production process.  It’s usually best if each area does its analysis independently, and then presents its findings to the other teams at the end of the day.  For example, you would ask – What your biggest functional failure in Area 1?  Let’s say it’s a problem with raw material, both quantity and quality.  Next question – How often does this happen?  Let’s say weekly.  Next question – What’s the consequence of each event? Let’s say it’s reduced rate, or lost quality, or literally no production.  Next question – How many hours of equivalent production is typically lost for each event?  Let’s say it’s ~2 hours at ~100 tons per hour.  Next question – What’s that worth, in gross profit contribution?  Let’s say it’s $500 per ton of production.  Now we have an estimate that poor raw material quality/quantity is occurring once per week, typically resulting in 2 hours of equivalent lost production at 100 tons an hour at $500 per ton, or $100,000 per week in lost gross profit (presuming we could sell all we could make).  Or, it’s costing the company over $5,000,000 per year in potential gross profits.  Two hours a week doesn’t seem like much, until you add it up. 

5.  The support functions must also be reviewed for failures in our business system, e.g., poor quality or quantity spares, insufficient utilities, etc.  How often does it happen?  What’s the consequence to the business in production or additional costs?  What’s that worth, over a year’s time? 

6.  As discussed above, during the review, the value of each business level production failure is analyzed and estimated/calculated – Type of Failure, and its Frequency Per Year x Losses per Failure x Value per Loss to estimate the dollar value, or quantity of product “lost” per year. 

The process is shown in a simplified form in the Model for Performing a Business Level Failure Modes and Effects Analysis (FMEA) in Figure 11.

Each Area Team asks the questions described above and summarized in the Business Level FMEA Questions graphic in Figure 12.

For example, supposed in step one of our production process we identified supplier quality as a major problem.  That is, once a week the supplier provided raw material of substandard quality.  This quality problem required adjustment of the process and resulted in lower yields and/or extra costs.  Suppose we calculated that this lower yield resulted in the loss of the equivalent of 10 tons of product (or 1000 units or whatever appropriate unit), with each ton having a gross profit of $500.  This would result in a loss of $260,000 per year.  Further suppose other disruptions for accommodating this poor raw material were valued at $2,500 per event - lost productivity of people, plant re-configuration, and so on.  Overall the value of this failure in our system would be estimated at ($260,000 + $130,000) or $390,000 per year, a fairly substantial sum.  We continue with this process until we’ve identified all major losses in each production area, as well as those attributable to the support functions.  Next we prioritize those losses that provide the greatest opportunity for improvement, and select the appropriate tools for eliminating or minimizing them.

Note also at this point we do NOT try to solve any of the problems we identify.  We will prioritize the results of our analysis using the model in Figure 13.  For example, after we analyze our business level failures and place a nominal value on them, then we’ll “plot” them on a chart similar to Figure 13.  Tasks that are easy to do and have the most value, get done first; tasks that have less value, and are easy to do get done next; tasks that have high value, but are difficult to do, for example, changing a major technical process or getting capital funding, are done next.  The last category, tasks that have low value, and are difficult to do, may not get done at all, at least not in this round of improvements.   For example, at one of ABC’s plants, task no. 1 of the Decision Making Model in Figure 13 required a major design change to the process, including considerable capital.  It provided high value, but was also difficult to do.  Whereas task no. 4 provided high value, but was relatively easy to do.  This had to do with getting operators trained to follow a particular procedure, one that they had not been doing because of a lack of training and understanding of its importance. 

A word of caution in doing the analysis relates to identifying preliminary or potential causes.  Very often the team will want to begin solving the problem after they’ve suggested some potential causes.  This should not be done at this time.  The purpose of this question is to get people thinking about potential causes, but not to solve the problem.  We won’t begin problem solving until after we’ve plotted the opportunities in the decision making model chart.  After that we can select those problems that provide the highest return for the least effort given our resources, and we can select the right tool for the problem solving.  Too often people want to immediately begin problem solving, particularly engineers who like to develop engineering solutions.  Hold that until after the analysis is completed.  The process is also a bit iterative, that is, you’re guessing at value and difficulty, and need to refine your analysis and plans as you get additional detail and validation of the initial review. 

One of the key benefits of this approach is that people are working as a team, using a common strategy that’s focused on the success of the production line – it has a business system focus for the teams.  And, the team develops an action plan to improve the overall system performance.  That in itself is invaluable.

Typical Results

ABC has found the following to be fairly common problems at almost all its plants when doing a business level FMEA.  Case studies will be provided later that illustrate these problems: 

• Lack of understanding of the pacing or bottleneck unit for setting production requirements; and/or not fully understanding the impact of upstream and downstream area performance on each other, and the plant’s overall performance 
  
  
• In batch and discrete plants – changeover time and setup/startup problems; in process plants – transition losses due to changes in products and/or raw material
  
  
• In discrete plants – production stops for breaks and lunch; and short stops that do not get counted
  
• Raw material problems – poor quality and/or insufficient quantity
  
  
• Erratic production planning, primarily driven by erratic sales forecasts
  
  
• Equipment failures
  
  
• Spare parts unavailability or poor quality
  
  
• Design/layout features making maintenance difficult
  
  
• Poor power quality resulting in electronic problems and failures
  
  
• Operator inexperience and lack of training resulting in inconsistencies and failures
  
  
• Inadequate lubrication resulting in machinery failures; or lubrication delegated to the operators without adequate training, or buy-in
  
  
• Mechanics were in need of training on critical equipment and/or precision skills
  

Selecting the Right Tools

Using this approach to better understand and prioritize production losses and extraordinary costs, we can now do a better job selecting the appropriate tool and approach to solve or mitigate the problem.  Our strategy will be based on a system level review of our production process.   For example:

1.  If supplier quality was a top priority, we could work with purchasing to improve their supply, perhaps even applying Six Sigma in an effort to reduce the variability of their supply; or go to an alternative supplier. 

2.  If the next biggest opportunity was inconsistency in our production process due to a lack of training in our operators, we would probably set up a process conformance model to measure our non-conformances and characterize those; and ask human resources or training to develop a plan to address their skills in operating the system. 

3.  If the next biggest opportunity was related to a specific machine and its un-reliability, we might apply Reliability Centered Maintenance (RCM) initially, to better understand the machine’s functional requirements and the failure modes that are resulting in loss of functionality.  Next, and if appropriate, we might apply certain Total Productive Maintenance (TPM) principles, e.g., operator care, or better preventive and predictive maintenance to better manage these failure modes.  If the problem with the machine was particularly difficult, we might use Root Cause Analysis (RCA). 

4. If the next biggest opportunity was to reduce the erratic nature of production planning and frequent changeovers, we might work with sales and marketing to analyze our product mix, sales and gross profit by product.  It may be helpful to enlist key customers’ views to better understand, and rationalize, our product mix, and sometimes even our customers.  We would probably also want to implement a quick changeover capability, and work to level our production flow, even at the risk of modestly increasing inventory in the short term. 

5.  If the next biggest opportunity was to reduce spares un-availability, we would probably want to apply RCM methods to understand our most common failure modes, or our highest consequence failure modes, and then work with purchasing and accounting to make sure we had critical spares on hand. 

Using the Business Level FMEA to analyze a particular production system at the system level seems to work well in helping quantify the opportunities and prioritizing them for further action.  We need to understand the business consequence of each of the major failures in our business system, and then select the appropriate tool(s) or strategy to address these opportunities. 

Summarizing:
   

1.       It’s essential to look at the production process as a system, and fully understand the effects that the different areas within the system have on each other, and have within themselves.

2.       It’s essential not to assume that you know where the biggest problem is until you’ve objectively evaluated the losses in each area and their respective interactions at the system level.  Some of the data you have may lead you to the wrong conclusion. 

3.       It’s essential to “go and see” the problems at the shop floor, getting them to help you in your observations as to specifically what is happening in the process and equipment.
 

4.       It’s essential to look at your production system as just that, a complex system with many interactions.  A good method for doing this analysis is the Business Level FMEA.
 

Doing a Business Level FMEA should result in:

• Better teamwork
  
  
• Lower failure rates and downtime
  
  
• Improved output
  
  
• Lower operating costs
  
  

It’s essential that we view our manufacturing process as a highly complex and variable business system, and that we understand the failures in that system. Viewing anything that results in a loss of production capacity or extraordinary costs as a failure in our business system provides a good start for working as a team in quantifying those losses.  Quantifying those losses and then applying the appropriate tools on a priority basis will minimize those failures, assuring the success of the business. 

However, the tools themselves are not sufficient.  Management must provide continuous leadership for creating the environment that supports this approach - the time, tools, training, measures of success, and the sense of teamwork and common purpose needed.  Management must align the organization to that purpose and follow up on the findings and action plans, assuring that they are done, measured, and rewarded on a continuous basis.  These issues in themselves are major topics unto themselves, but for another day.  Finally, it’s essential that we engage the entire workforce in improvement, not just a few focused on the biggest problems.  If we don’t resolve the little problems, some of them will become big ones in the future.  So, it takes a combination of efforts for world class performance.  The business level FMEA helps us sort out the biggest problems, while we begin the process of engaging the entire workforce in solving the little ones. 

Summary

When ABC began the process of reviewing the options for meeting an expanding market, its initial thoughts were to either borrow a tremendous amount of capital to build more plant capacity; or to buy a competitor, also an expensive proposition.  After reviewing its current performance using the techniques described above, it concluded that it had adequate capacity in its current assets to meet demand for the next 3-4 years, if a “Best Practice” manufacturing process were implemented to maximize the capability of the existing assets.  Improving existing assets will provide essentially all the capacity needed in the coming 3-4 years.  Analyzing and addressing the improvement opportunities will be done specifically for each plant using a combination of Process Reliability Charts, OEE data, and the Business Level FMEA also described above.  The potential for the business is an additional $180M in gross profit contribution with minimal additional capital, e.g., $20-40M per year in sustenance and improvement capital.  The investment required is likely in the range of $2-3M per year in both internal and external resources to facilitate the analysis and the implementation of the improvement process.  Every company should be as fortunate as ABC.