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 www.feedforward.com.au UPTIME training series for chemical and process plant operators and maintainers.  His consultancy 'Lifetime Reliability Solutions' (www.lifetime-reliability.com) 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 http://www.lifetime-reliability.com/howtocontact.html.
Published January 8, 2008

Plant and Equipment Wellness:
Part 1 - Observing Variability

To understand variability and why it is a problem there is a simple tabletop game to play that is a great introduction to the variability within processes.

In Figure 1.1 two lines are drawn crossing at 90o with a 2mm circle drawn around their intersection.  The game is to sit at a table and drop a pen into the two millimetre diameter circle from a height of around 300 mm (one foot).  Getting a hit within the circle is the outcome required from this ‘process’.  Repeat the targeting and drop process at least thirty times.   After each drop measure the position of the new mark to an accuracy of half a millimetre.  Record the horizontal distance from the vertical line (the ‘x’ distance) and the vertical distance from the horizontal line (the ‘y’ distance) in a table like that of Table 1.1.

asset5_1.gif (2029 bytes)
Figure 1.1: The Crosshair Game

 

Table 1.1: Record of the Crosshair Game Hits
 

Hit No

Distance X

Distance Y

Hit No

Distance X

Distance Y

Hit No

Distance X

Distance Y

1

8.5

16

11

1.5

5

21

1.5

5.5

2

7

9

12

1.5

20

22

3

3

3

4

16

13

3.5

3.5

23

3.5

0

4

3.5

2.5

14

2.5

12

24

2.5

6

5

5

24.5

15

3

24.5

25

0.5

2

6

5

16

16

4.5

6

26

1

2

7

7

10.5

17

4

12.5

27

3.5

10.5

8

5.5

9.5

18

5.5

5

28

1

9

9

2

3.5

19

1

9

29

4

14

10

3

2

20

6

4.5

30

0.5

3.5

 

 

 

 

 

 

 

 

 

 

 

 

Average

X = 3.48

Y = 8.90

 

 

 

 

 

 

Spread

X = 0.5 - 8.5

Y = 0 - 24.5

 

 

 

Observe the average and spread, of the ‘X’ and ‘Y’ results.   In Table 1.1 no hits are within the two millimetre circle; some are on or near the edge while most are well away.  Even though great effort was made to control the ‘process’, the results were across a wide band of outcomes.  This same problem occurs in all business and operations processes.  The outcomes of a process are spread across a range of results.   That is variability.  Variability becomes a problem for a business when the results from a process are not consistently within their required boundaries.

If the aim of the game is to have every pen-drop fall inside the 2mm circle, then we have a very poor process for achieving that outcome.  To get better results requires changing the process.  The game can be repeated using a different process.  The results in Table 1.2 were from a process where the pen was dropped after aiming it at the circle from above, much like dropping a bomb from an aeroplane using targeting sights.

Table 1.2: Record of the Crosshair Game Hits Using a Sighting Process
 

Hit No

Distance X

Distance Y

Hit No

Distance X

Distance Y

Hit No

Distance X

Distance Y

1

8

10

11

5.5

6

21

3.5

0

2

5

6

12

2

4.5

22

2

5

3

4

3.5

13

0

1

23

0.5

1

4

3

4

14

5

2

24

6.5

0

5

2.5

1

15

4

7

25

3.5

3

6

2

0.5

16

3

1

26

0

8.5

7

13.5

7.5

17

3.5

5

27

6

1.5

8

10.5

9.5

18

4

0

28