Our desire to see history through the lives of great men blinds us to the real complexity of politics, business and finance, and leads us to find intentionality and design where there are only chance and improvisation. The philosopher, Alasdair MacIntyre, put it acerbically: “When imputed organisational skill and power are deployed and the desired effect follows, all that we have witnessed is the same kind of sequence as that to be observed when a clergyman is fortunate enough to pray for rain just before the unpredicted end of a drought!” He also said: “One key reason why the presidents of large corporations do not, as some radical critics believe, control the US is that they do not even succeed controlling their own corporations.” That was the experience of Chuck Prince, former Citigroup chief, and Stan O’Neal, former head of Merrill Lynch.

Reality is too complex for simple causal links…

Industrial Engineer’s dream: a nice Shadow Board (via Evolving Excellence). Sometimes, factories get lost in buzzwords - Lean/TPS, Six Sigma etc., attempt to implement them using a top-down approach, but neglect the basics! The “shadows” show the designated place for each tool and give instant feedback on what’s missing!

Evolving Excellence: Examples of Excellence: Shadow Board

Sequoia Venture Capital Warning to CEOs - Get more Business Plans

 “It’s just as well that in-flight food isn’t about gustatory greatness, because meal service isn’t easy when you’re five miles high. Even coffee is problematic because it’s harder to bring water to proper temperature at 40,000 feet. When McDonald’s did a Happy Meals promotion with a major carrier in the 1990s, the burger giant had to reformulate the cheese so it would melt rather than liquefy in flight. And forget about gourmet dining. How do you create a four-star meal when open flames are verboten, prep space is nonexistent, knives have rounded edges, and flight attendants must serve dozens of passengers at once using a convection oven?

1. Lack of constancy of purpose
2. Emphasis on short term profits (Overreaction to short term variation is harmful to long term success. With such focus on relatively unimportant short term results focus on constancy of purpose is next to impossible.)
3. Evaluation of performance, merit rating or annual review
4. Mobility of top management (too much turnover causes numerous problems)
5. Running a company on visible figures alone (many important factors are “unknown and unknowable.”
6. Excessive medical costs
7. Excessive legal damage awards swelled by lawyers working on contingency fees

What is interesting, is the fact that the path to mediocrity  is quality > low cost, rather than the other way round:

The company either loses its actual quality advantage and is forced to compete on cost, or the product becomes a commodity so there is no perceived quality advantage. The company tries to compete on cost but usually is not equipped adequately (manufacturing- and culturewise).

It seems that in order to achieve constancy of purpose a long view is essential. But with high turnover this can be elusive…

Crocs are declining in popularity. If you were their head of Supply Chain what would you be doing now?

The question is interesting because the product was very likely a fad, with high early approval and equal backlash (a Facebook group titled “I Dont care How Comfortable Crocs Are, You Look Like A Dumbass.” has 930,000 members). Did Crocs managers evaluate all scenarios: e.g. fad vs stable demand? Their decisions point to them being optimistic:

Crocs made the decision to exit a 3PL distribution relationship and open their own “owned and operated” distribution center (this is well documents in their 10K reports). They “sunk” capital into a distribution facility in the Denver area. Now that volume is down, there has to be big unused capacity in that facility. That facility has ongoing fixed expenses that have to be paid, and are now a millstone around Crocs’ neck. Oh, and I don’t think that there is a lot of demand for a distribution facility in Denver.

Anderson believes we have overcome a critical point were the computing/storage power we have is enough to do this:

At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later. For instance, Google conquered the advertising world with nothing more than applied mathematics. It didn’t pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right.

The problem is that when the model’s causality is unknown (since we didn’t design it in the first place; the data did), we can never be sure when it will misfire: the black swan problem. While such models may work in domains like marketing, biology etc., where the cost of mistakes is low, it cannot be trusted in mission critical functions (see quant funds and subprime crisis).