A Few Profitable Processes
Many industrial processes know they are marginally profitable because each plant sees their monthly financial performance numbers. The poor financial performers often disbelieve a few of their competitors are highly profitable and can set the price structure for sale of their products to which they must follow. Of course the leaders don’t publish their monthly financial details—they just keep the financial pressure on the less profitable competitors.
How can large financial differences exist between competitors? After all, both marginally profitable and highly profitable processes were designed by similar engineering companies using similar equipment and similar raw materials. Both competing processes are operated by competent and well intended people. What are the differences between the marginally profitable and the very profitable processes? Where do you commence the attack on hidden factories that represent waste?
What is a hidden factory? Factories and the processes that produce
saleable products have two types of factories:
A) The productive factory that is obvious and generate productive effort for saleable products.
B) The hidden factory is a waste factory consuming resources. It is not productive. It operates in stealth mode.
The sum of the two is the intrinsic factory of the whole. The hidden factory can be ~15% to ~40% of the intrinsic factory. You are already paying for the costs of the hidden factory but you get no positive results.
When you eliminate the hidden factory, the money received from the newly productive efforts sinks directly to the bottom line of the financial pages to make your process more profitable. Your competitive advantage derives from making the hidden factory productive to increase effectiveness. Highly effective plants and processes define the competitive advantage observed in best in class leaders.
Hidden factories have two components for
1) Losses that have names such as:
the cost of poor quality,
the cost of lost production specific from maintenance failures,
and other special cause events that you can enumerate for specific corrective action.
2) Losses that do not have names and represent the stealth factory such as:
and other common cause losses that do not have obvious names as they are built into the system.
When you eliminate these losses the enterprise becomes more productive and more profitable.
The reason best in class organizations rise like cream to the top is because they work hard to destroy the hidden factory. When you destroy hidden factories your competitors feel the completive pressure but seldom know how you became more profitable. This phenomena was well put by Sun Tzu in the 6th century BC book The Art Of War: Ch. 4, 人皆知我所以勝之形，而莫知我所以制勝之形。All men can see the tactics whereby I conquer, but what none can see is the strategy out of which victory is evolved. This means you must first develop an attack on the hidden factory by a well configured strategy and then by use of effective tactics you destroy the enemy robbing you of profitability.
Barringer process reliability plots provide metrics to show the differences. They tell where to search for the underlying causes for the real differences. For success these conditions must exist:
1. You must persuade your organization a change is required to move from substandard performance to best in class performance for a persuasive presentation this you need sales aids with graphics. You’ve got to see it to believe it!
2. Barringer process reliability plots provide the graphics and quantification needed for selling that changes are required.
3. The process reliability plots provide metrics for the size of the hidden factory causing the marginal performance.
4. Hidden factories grow unless hidden factories are reduced by improvements for achieving high profitability.
5. Your improvements will not be obvious to competitors but become part of your improvement culture.
6. Institutionalize the significant improvements to hold the gains for the long term. If you’re not improving you are back sliding!
First let’s look at some facts, then some Barringer process reliability plots, and finally, discuss how to resolve the issues.
What Is The Issue?
Consider the process reliability data in Table 1 for the best and worst process reliability performers in a few categories with the nameplate rating for production the same as the 1st quartile produces actual beta and eta values:
Where a reliability cusp is discovered on the Weibull probability plot, the production losses for Table 1 and Table 2 the production goes to ~zero output for simplicity in comparisons (since the production data will be plotted on a logarithmic scale, the value of 0.1 is used in lieu of zero output—thus a small, insignificant, error is in the reliability loss data).
Small values of beta imply large variability in output (small beta is bad). Large values of beta imply small variability in output (large beta is good).
Small values of process reliability imply many problems manifest in reduced output (small reliability is bad). Large values for process reliability imply few problems in output (large reliability is good) along the production line.
The characteristic value for output, eta, tell about the single point estimate of daily output (small eta is bad). Large eta values show more output (large eta is good).
Reliability losses imply problems which have names. Big reliability losses are bad. Small reliability losses are good. Reliability losses are the gaps between the production line and the data points below and to the left of the process reliability cusp. The cusp declares loss of production consistency.
Efficiency and utilization losses imply unnamed problems. Small efficiency and utilization losses are good. Large efficiency and utilization losses are bad. Efficiency and utilization problems are the gaps between the nameplate line and the production line. When the efficiency and utilization losses are identified with names, they go from common cause problems to special cause problems. In most cases these issues are a multitude of small issues you solve everyday as a never ending nuisance which really means the problem is not permanently resolved and removed from the issue list—thus they become daily fire fighting problems.
Figure 1: 1st Quartile Ammonia Process Reliability Plot
Figure 1 shows a first quartile production facility, for simplicity, the large beta values for the first quartile production facility are taken as the nameplate ratings signifying very small efficiency and utilization losses. It is clear that Figure 1 displays tight control of daily output of production.
Figure 2: 4th Quartile Ammonia Process Reliability Plot
Figure 2 shows the bottom of the barrel, 4th quartile, production facility. The small beta value shows much large scatter in daily production and much larger special cause losses with the low reliability values at the cusp. Now the problem becomes what will you do to fix the hidden factory problem? The hidden factory in Figure 2 is 158,829/35,225 = 4.5 times larger than Figure 1. This is dead weight in your saddle bags for the race to profitability!
How Best In Class Resolve
Performance Issues And Become 1st Quartile Performers
Harvard Professor Steven J. Spear, author of the 2009 book Chasing The Rabbit, ISBN 978-0-07-149988 provides real life examples from multiple industries. He tells how market leaders outdistance the competition and how great companies can catch up and win the race for cost effective performance. Figure 3 is an adaptation of Spear’s Figure 1-1.
Figure 3: The Difference Between The Fast Rabbit And The Lagging Pack
Spear argues there are four capabilities high performing rabbits demonstrate which the pack lacks:
Capture Existing Knowledge and Build-In Tests
To Reveal Problems-
● The fast running rabbits know how to make the enterprise achieve success.
● They build into the process a capability to detect failures when and where they occur (of course this requires a definition of all to know what a failure looks like and to measure the failure).
● They specify in advance what outcomes are expected and who is responsible for the work and the corrective action.
● They define the methods to be used on accomplish each piece of work.
● Before work commences they go into the specifications to maximize likelihood that people will succeed.
● They clearly specify the outcome desired from the process and reject “normalization of deviance” [as former Astronaut Mike Mullane explains: “normalization of deviance” is a long term phenomenon in which individuals and teams repeatedly “get away” with a deviance from established standards until their though process is dominated by this logic].
● They identify their pockets of ignorance and invest to eliminate the ignorance.
● They invest in significant upfront efforts to avoid handicapping the production efforts.
● They invest in ways to learn.
● This is a game changing core element for the fast running rabbits.
Swarm And Solve Problems To Build New
● The fast running rabbits are adept at detecting problems at the time and the place where they occur.
● They contain the problem before it spreads to contaminate the system.
● They want to diagnose and permanently remove the problem rather than solving the same problem every day.
● By literally swarming the problem with knowledgeable front line people the convert ignorance into knowledge.
● They want timely information to be contextual and not lost to memory or lost to evidence as the interaction of people, processes, equipment, and places unexpectedly converge to a deviation.
● They use the scientific method in a disciplined method to achieve the previously determined outcome.
● They fix the problem but also convert the information into gaining a deeper knowledge of how the process really functions.
● This is a game changing core element for the fast running rabbits.
Sharing New Knowledge Throughout The
● The fast running rabbit multiplies their new knowledge by making it available to those who discover it and to everyone else in the organization including the processes by which the discovery occurred including what was discovered and how it was discovered in a lessons learned library.
● They propagate discoveries whereas the pack allows problems to persist as information remains where found.
● They work to make sure discoveries become cumulative information for the organization.
Leading By Developing-
● The fast running rabbits management team works for delivery of products and services plus continual improvement of the process.
● The management team job involves teaching subordinates that continual improvement is part of their jobs.
● The management team avoids command/control and contrived metrics for evaluation by ensuring their organization is more self-diagnosing and self-improving by improving skills for detection and problem solving through out the organization.
● The management team does not plan it’s organization way to success but reward learning must occur when and where the problems exist while recognizing is both contextual and it spoils with time which drives the immediate need for immediately swarming problems to make their organization successful.
● The management team recognizes their complex systems are never perfect and continued improvement from the people who are on the spot and they are empowered to make the changes and they enable their people to make the changes as a key element of their work effort. The leader is the developer of the people. For details about empowerment and enablement see http://www.barringer1.com/dec07prb.htm .
Figure 4 is an adaptation of Spears Figure 2-1 comparing the changes in complexity with time.
Figure 4: Inclusion Of Nameplate Potential Trendline Including Complexity
The story of Figure 4 is the rich get richer and the poor get poorer as the hidden factory grows from waste which saps profitability.
For Figure 4, the bottom line is simple. You must make some substantial changes in your process to survive by improving the reliability of the process and eliminating the hidden factory to survive.
You can’t fight tomorrows war on waste by using yesterday’s tools. Improvements are necessary for survival.
People, Processes, and Equipment
In mature nuclear power plants the source of problems is considered to be:
1. People are the root of the problem for 38% of all problems
2. Procedures/Processes are the root of 34% of all problems
3. Equipment is the root of 28% of all problems ßEngineers only want to work on this and it’s the minor problem!
Compare the similarity of problems from the ASME National Boards Bulletin publication for the Summer of 2002 concerning “Ten Years Of Incident Reports Underscore Human Error As Primary Cause Of Accidents” failure of power plants and pressure vessels governed by the ASME Boiler Test Codes shows for the time period of 1992-2001:
1. 127 people died from pressure vessel and boiler accidents with 60% of the recorded deaths a result of human oversight or lack of knowledge
2. Of the 23,338 accident reports 83% were due to human oversight or lack of knowledge
3. The same reasons of human oversight or lack of knowledge occurred for injuries
Bottom line, enable and empower (see http://www.barringer1.com/dec07prb.htm for definitions of enable and empower) your workforce for “all hands” to concentrate on making improvements.
Permanent improvements will reduce errors and reduce variability in processes to save lives and reduce costs following the thought process of Chasing The Rabbit and using the techniques of Barringer Process Reliability technology. This is how you improve profitability.
Other Process Reliability References:
You can download other articles from this site concerning process reliability:
· Effective Exception Reports For Special Causes, March 2011
· Summary of Process Reliability, June 2008
· Process Reliability Punch List March 2005
· Process Reliability Line Segments April 2004
· Nameplate Capacity March 1998
· Coefficient of Variation February 1998
· Six Sigma January 1998
Production Output/Problems May 1997
Papers On Process Reliability As PDF Files For
--Process and Equipment Reliability May 2004
--Process Reliability: Do You Have It?—What’s It Worth To Your Plant To Get It? March 2002
--Process Reliability December 2001
--New Reliability Tool for the Millennium: Weibull Analysis of Production Data October 2000
--Process Reliability and Six-Sigma March 2000
Refer to the caveats on the Problem Of The Month Page about the limitations of the above solution. Maybe you have a better idea on how to solve the problem. Maybe you will find that I’ve screwed-up the solution and you can point out my errors as you check my calculations. E-mail your comments, criticism, and corrections to Paul Barringer.
Revised July 23, 2013
© Barringer & Associates, Inc., 2011