Why Are A Few Profitable Processes Are Highly Reliable With Small Variations In Output While Many Marginally Profitable Processes Show The Opposite?

 

Background

Many industrial processes know they are marginally profitable because the see the financial performance number.  They disbelieve a few of their competitors are highly profitable because their production and financial performance numbers are not disclosed.  After all both the marginally profitable and highly profitable processes were designed by similar engineering companies using similar equipment and similar raw materials with the processes operated by competent and well indented people.  

Barringer process reliability plots provide metrics for the differences.  They tell when to search for the underlying causes for the real differences.    

1.      You must persuade your organization a chance is required from substandard performance to best in class performance.

2.      Barringer process reliability plots provide the graphics and quantification needed for selling that a change is required. 

3.      The process reliability plots provide metrics for the size of the hidden factory causing the marginal performance. 

4.      Associated with the hidden factory is a hidden need for improvement to achieve high profitability. 

5.      Your hidden 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.

First lets 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, the production losses for Table 1 and Table 2 the production goes to ~zero output (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 error is in the reliability loss data.

Small values of beta imply large variability in output (small beta is bad) while 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 is bad) while large values for process reliability imply few problems in output (large reliability is good) along the production line.

The characteristic value for output, eta, when small tell about the single point estimate of daily output (small eta is bad) while large etas show more output (large eta is good).

Reliability losses imply problems which have names.  These are gaps between the production line and the data points below the process reliability cusp which declares a loss of production consistency.  Big reliability losses are bad.  Small reliability losses are good.

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 resolved and removed from the issue list.

Figure 1: 1st Quartile Ammonia Process Reliability Plot

 

Figure 2: 4th Quartile Ammonia Process Reliability Plot

How Do Best In Class Resolve Issue To Become 1st Quartile



 

Other Process Reliability References:
You can download other articles from this site concerning process reliability:

·         Effective Exception Reports For Special Causes, March 2011

·         Use Periods Of Low Production Output to Improve Process Reliability And Consistency, February 2009

·         Special Cause Variations, Common Cause Variations, and Process Reliability Plots, October 2008

·         Summary of Process Reliability, June 2008

·         Process Reliability Punch List March 2005

·         Process Reliability Line Segments April 2004

·         Process Reliability Plots With Flat Line Slopes May 2001

·         Key Performance Indicators From Weibull Production Plots May 1998

·         Production Reliability Example With Nameplate Ratings April 1998

·         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 No-charge Downloads
   --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

Comments:

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. 


Download a PDF copy of this problem here.

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May 15, 2011
© Barringer & Associates, Inc., 2011