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Plant and Equipment Wellness, Part 1: Observing Variability

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Mike Sondalini: Enterprise Asset Management Best-Practices Powered by Lifetime Reliability Solutions.
B Eng (Hons), MBA, CP Eng.  In an engineering and management career spanning 25 years he has held project engineering and maintenance management positions at the Swan Brewery and at Coogee Chemicals, a national Australian industrial and mining chemical manufacturer.  He is also a qualified mechanical tradesman.   Along with authoring numerous maintenance and industrial asset management publications sold on the Internet, he developed the UPTIME training series for chemical and process plant operators and maintainers.  His consultancy 'Lifetime Reliability Solutions' ( specialises in identifying manufacturing and production wastes and losses and solving them using proprietary optimization solutions known as ‘ACE’ (Accuracy Controlled Enterprise), 'DOCTOR' (Design Options and Costs Total Optimization Review) and ‘DAFT Costing’ (Defect and Failure True Costing).  He is a past Chairman of the WA Chapter of the Maintenance Engineering Society of Australia.  Mike is based in Perth, Western Australia. You can contact Mike by email, phone or fax using the details on his website contact page
Published January 8, 2008

Plant and Equipment Wellness:
Part 1 - Observing Variability

Example 2.2:  The Hidden Factory

Here is an example of the value of identifying causes of variability in a business.   In this case the production from a mine is
trended on a simple bar graph.  Figure 1.5 shows the graph of the hourly production rates of a 24/7/365 mining operation during eight consecutive weeks.  It provides a lot of valuable information about the operation’s capacity as well as a clear indication that the business is suffering wild fluctuations in its production throughput.  An examination of the graph provides an insight into the facility’s dilemmas.

asset5_5.gif (11030 bytes)
Figure 1.5: Product Rates

The eight weeks of production shown on the graph represents 1344 production hours.  For 275 of those hours there was no production, which means that for 20% of possible production time the plant was standing still.  The plant design capacity is 1500 units per hour.  For 615 of the remaining hours it was running at under design rate.  This means that for 57% of the time that it was operating it was delivering less than it was designed to produce.  The actual average production rate for the entire eight weeks is 1000 units per hour, which is two-thirds of design duty.  This facility is suffering severe production problems and investigations need to be made as to why it is not consistently producing at its design capacity.

There is additional information to be garnered from the graph.   It is clear that for a significant number of hours the plant ran at above its design rate.  The implication is that the plant can comfortably run at more than its design duty.   There is a good chance that with minimal engineering changes the plant could run consistently at 2000 units per hour, which is 33% greater than design capacity and 100% higher than current average production.

There are obvious questions to ask of a plant with such variability of performance.  Such as, ‘What is causing below design throughput so often?’  And, ‘If the plant can produce at higher rates by accidents of circumstance, then what could be produced if it was done intentionally?’  It would be sensible to identify the causes of both the disastrous production losses so they can be solved and the fortuitous accidents of the past so then can be applied intentionally.  The total ‘lost’ capacity represented by the stoppage time and slow throughput, plus the ‘hidden’ capacity available from higher production rates, means that this operation has plenty of opportunity to deliver a large production increase without significant capital investment.

This company’s decision to spend $250,000,000 on a major capital upgrade to boost production 50% may not have been necessary.  If the downtimes and low production rates were recovered, and the causes of the higher throughputs were made standard practice, the extra 50% capacity was been achievable with the old plant.  It was only necessary to conduct root cause investigations on why the production losses occurred and engineer them out.  Combined with an analysis to understand why higher than design production rates occurred and re-engineer the process to deliver them consistently.  The financial return on such an investment could be unbelievable.  All these options became clear simply by measuring production variability.

To construct a graph like that in Figure 1.5 requires collecting the hourly production figures for a sufficiently long period of time so that the full range of variability affecting the process can be observed.  The figures will show a range of performance around a mean value.  The extent of the spread below the mean, from average to lowest, will indicate if there are production problems hampering throughput.  The spread above the mean, from average to highest, will indicate if there is spare capacity available.  If the spread is tight about the mean production rate then the operation is running well and it is performing as it should.  But if, as in Figure 1.5, the spread is wide then the plant has ‘hidden’ opportunities to improve its production performance and efficiencies.

When production throughput graphs have a wide spread of production rates there is potential to increase plant capacity by removing the causes of operating losses with minor engineering upgrades and removing variability by adopting improved procedures and extensive training.  Before investing more capital to expand plant capacity, investigate the variability of current production because there may already be a ‘hidden factory’ within the existing plant.


< Inventory Replenishment Mayhem

By: Mike Sondalini, Enterprise Asset Management Columnist for


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