Elephants in the clouds – an affirming but cautionary tale

Not long after publishing my last post on cloning Dell’s finished goods supply chain, I came across a May 2010 slide show presentation on measuring supply chain performance. Prepared by Joseph Francis, the executive director at the Supply Chain Council, it included a chart on slide four that caught my eye: Total Supply Chain Management Costs Expressed As % of Revenue.

The chart caught my eye because of data (credited to PRTM, now part of PWC) that looked familiar – the 4.2% for best in class and the 10% for the median in the computer industry. I went back to the numbers reported in my last post. I was right – the total supply chain costs expressed as a percentage of revenue for the 2011 Air Scenario were 4.4% and the 2011 Sea Scenario were 9.28%.

Real world validation of our Dell case study?

With the Dell case study, we showed we could distill results quickly by:

  • using limited input from the Dell 2011 Annual report ( hundreds of KB );
  • using our tooling to construct representative DNA ( tens of MB ); and then
  • cloning the DNA ( hundreds of GB ) using OperationalCloning.

The data in Francis’s 2010 chart was suggesting that OperationalCloning’s case study results corresponded to actual industry benchmarks. I admit that I was excited. Could I be looking at real-world validation of our case study results?

Cautionary clouds

I pulled myself up by remembering the words of caution in Nassim Nicholas Taleb’s excellent book, Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Taleb argues that we have a propensity to overestimate causality, “seeing elephants in the clouds instead of understanding that they are in fact randomly shaped clouds that appear to our eyes as elephants”.

The results of cloning the two Dell scenarios (Sea and Air) for the years 2011 and 2016 hinted that an increase in total supply chain costs expressed as a percentage of revenue was inevitable. If that’s right, the past numbers for total supply chain costs expressed as a percentage of revenue would show a similar pattern because similar conditions have existed for the last decade.

I decided to do some of my own sleuthing.

Testing the Dell case study’s predictions against real world data

Google was my starting point to track down earlier PRTM benchmarking studies similar to the one cited in Francis’s 2010 presentation. It wasn’t as straightforward as I had expected. I found:

  • A presentation dated August 2009 with the same PRTM data that was in Francis’s 2010 presentation, suggesting that the PRTM data was from 2007/8 or earlier.
  • A set of similar PRTM benchmarking data from 1998.
  • A set of similar PRTM benchmarking data in a February 2010 UPS presentation that I guessed to be from 2008/9.

I took a stab at comparing the three PRTM data sets, combining values for chemicals and pharmaceuticals to do so.

Industry

Estimated
Year

Best-in-Class
(BIC)

Median

Chemicals
& Pharmaceuticals

1998?

4

9.8

2007?

4.45

9.9

2008?

4.5

9.65

Computers

1998?

4

9.1

2007?

4.2

10

2008?

3.7

8.3

Consumer
Goods

1998?

5.3

11.2

2007?

4.8

10.7

2008?

3.4

8.5

Telecom
Equipment

1998?

3.3

8.5

2007?

3.6

7.4

2008?

2.8

10.4

Nothing in the data suggested the degree of change I expected. Although the 1998 and 2007 numbers for BIC and median show a minor increase across three of the four industries, they are remarkably similar. 2007 to 2008 shows only a minor drop.

I kept digging with the oracle’s help and found an October 2006 presentation by PRTM’s Mark Hermans called Supply Chain Benchmarking. It included the same set of data from Francis’s May 2010 presentation – the one that had kickstarted this whole caper – which I now guessed to be as old as 2005. My guess was confirmed with Hermans’ next slide, titled “US supply chain costs are rising”, with a graph that shows total supply chain management costs expressed as a percentage of revenue across industry for the years 1997 to 2005.

I tried to match the averages of the original datasets for what I thought was 1998 and 2007, and found they matched 2001 and 2002 best. Although the graph shows the steady drift upwards that Operational Cloning’s Dell cloning exercise predicted, I had to admit that I had, perhaps, just been seeing elephants in the clouds.

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