Predictive Analytics – Future Forecasting via Commodity, Industry View (Part 2)

In Part 2 of an occasional series, I will consider some of the advances our partners are making in the area of predictive analytics (or as Peter Drucker says, “trying to drive a car down a country road while looking out the rear view mirror.”)  In Part 1 we made a case for the use of predictive analytics, in Part 2 we consider one dimension to future forecasting based on commodity price indexing and other base industry program drivers.

In 1986 I joined Northrop (now NorthropGrumman) on the top secret B-2 Stealth Bomber program.  We were nearing what would be the summit of Cold War defense spending and as a young aeronautic engineer the opportunity to work with a professional team blended of both venerable platform airframe mentors and young, energetic technocrats was intoxicating.  The plan was to build well over 130 aircraft, and as such procurement and human resource moved quickly to secure contracts for future materiel, talent, and facilities to drive the next century’s strategic bomber program.  The materials used were state of the art composites, something only Lockheed (now LockheedMartin) had ever used.  Lockheed talent streamed through the door instructing manufacturing engineering on new techniques to use hard to develop composites and tooling required to build the stealth-enabled airframe.

What, you don't want 138 of these? Woops...

Several years later, and even before first flight, signs of program downsizing became evident.  First, budget constraints at the federal level made taking a hard look at large programs (in those days you looked at everything, including defense programs).  Second the Soviet Union (remember them?) began its own dis-assembly into the Commonwealth of Independent States and glasnost where a large Soviet threat appeared more remote.  Finally it was simply too long a program to get the first several aircraft in the air to prove to naysayers that the technology actually worked (which it did, really well – hats off to all of my Northrop colleagues).

The ability to predict the down-cycles of the program, combined with the increase cost of the materials used (remember those early contracts?) drove the per item cost of each delivered airframe – when amortized with the hefty research and development costs – through the roof.  Business planners constantly drove new cost models and profit forecasts with each new contract amendment.  The rest is, as they say, history.

Fast forward to today.  While the largess of military spending continues, the commercial organization faces many more moving parts than even in the late 1980s.  Currency fluctuations, material commodity pricing (such as oil, precious metals, and core components), as well as internal risk measures make predictive chess moves look like child’s play.  Many organizations still operate their business planning like we did with the flying wing back in the days of Reagan and Bush 41.  However they don’t have to.

First, there are many technical advances that have occurred since the 1980s.  These will be the subject of future posts.  Second, the ability to integrate industry, commodity price fluctuations, and other inputs to actually create a spread of possible forward scenarios exists.  Companies like IHS have been working in multiple industries such as automotive, aerospace and chemicals for years to provide business planners with the opportunity to have a very accurate view of futue programs (and anticipated changes to those programs).  Companies can put their bets on the most likely scenario and hedge on the outlying possibilities based on what their risk appetite is like.  Third, the integration of so-called “black swan” or “fat-tail” risks – such as geopolitical unrest or catastrophic incidents like the Japanese earthquake and tsunami – can be used to create a risk-adjusted strategy and a field of new Key Performance Indicators (KPIs) which are dynamic versus static based on world reality.

In the next installments I’ll take a look at the horizontal approach to predictive analytics – how to drive performance estimation out of organization functions based on predictive models – as well as some of the new technologies moving the needle of how these approaches may be implemented.

Please contact me for more information on predictive analytics, including the use of our technology partner products and change programs to introduce these technologies in your organization.

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1 Comment

Filed under Change Management and Leadership, Enterprise Performance Management, Financial Management, Operations, Procurement, Program Management, Risk Management, Strategy, Supply Chain Management, Technology

One response to “Predictive Analytics – Future Forecasting via Commodity, Industry View (Part 2)

  1. Pingback: A Framework for Social Media in Sustainability Programs | The View from C-Level

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