London Underground Implements Massive Maintenance Using System Dynamics
Case summary
The London Underground carries up to 4 million passengers a day, and required a major upgrade involving new trains and signalling, plus much additional engineering work. A working system must be maintained during the upgrade, requiring a complex migration process to be planned and implemented. The service operators receive financial penalties for poor system performance against constantly rising targets.
The system dynamics project captured progress on train introductions and engineering work, their impact on system performance, and the resulting financial implications. The model enabled management to assess different options for work scheduling against changing conditions of access to the system they were improving.
Client | London Underground |
---|---|
Authors/Consultant | Steve Curram, David Exelby, and Jocelyn Lovegrove |
Links to articles, presentations or models:
Keep on rolling – understanding the migration dynamics of a large rail improvement project Link to the Paper |
For further information, contact Steve Curram at DAS Ltd: www.das-ltd.co.uk |
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Northrop Grumman Develops Super Hornet Selling 500+ Units at $50bn
Northrop Grumman Case: Part 1
(Narrated by Ken Cooper) 8:27 minutes
Northrop Grumman Case Part 2 – The Back Story
(Narrated by Ken Cooper) 2:31 minutes
The Issue You Tackled
After experiencing some large program cost and schedule problems, Northrop Grumman hired us to help proactively on a new, yet to be started program–the development of the F/A-18 E/F Super Hornet fighter aircraft. Extensive model usage contributed to an award-winning performance on this development program, and led to new corporate policy beyond that program as well.
What You Actually Did
We developed a model of the project dynamics for the design and prototype production of the F/A-18 E/F Super Hornet. Starting our modeling work even before the development program started, we spent extensive time with senior corporate leaders and the entire top management team on the program. In addition to the pure modeling work, we undertook an extensive series of sessions discussing the Rework Cycle and productivity dynamics. The Program Manager credited the SD modeling work with instilling an important understanding of the unintended impacts of different actions and of design changes (secondary impacts).
“Undiscovered rework” and “the rework cycle” became common elements of the lexicon of the program, changing the culture and thinking among the management.
Following that assignment, Corporate requested an analysis to support new company policies regarding the use of overtime.
This is discussed in the accompanying video, and in the paper “The 00 Hour”.
The Results
Today the F/A-18 E/F Super Hornet remains the premier carrier-basedaircraft for the US Navy, flying over 1100 mph in multiple combat roles. It is widely regarded as an extremely successful procurement in DoD, with over 500 aircraft produced in the past 20 years, with a value near $50 billion.
Name | Northrop Grumman and the $2000 Hour |
---|---|
Modelers | Kenneth Cooper |
Client/Participant | Northrop Grumman |
Client Type | Corporation |
The Official Website | For added information, or with any questions, see CooperSDNetwork.com, or contact Ken via email: Ken.Cooper@CooperSD.com |
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Hughes Aircraft Turns Around on “Lost Year” in Engineering Program
Hughes Aircraft Case: Part 1
(Narrated by Ken Cooper) 9:37 minutes
Hughes Aircraft Case Part 2 – The Back Story
(Narrated by Ken Cooper) 3:51 minutes
The Issue You Tackled
Hughes faced what seemed to be stalled progress and out-of-control costs on their most important aerospace program, the AMRAAM missile development. The future of the program and relations with DoD and Congress were in jeopardy. We needed to define the true scope of the problem and seek effective corrective action.
What You Actually Did
We designed and built a SD model of the AMRAAM (Advanced Medium Range Air-to-Air Missile) development program. This was the first SD model of an aerospace program, altered from our early shipbuilding program simulations. For example, we built additional sectors to portray prototype production and flight testing (not a big issue on ships…).
The senior program manager related that one year earlier the program was estimated to be 70% complete. After the past year, with 1000 engineers at work, the program seemed to be at …70%! He called it “The Lost Year”. The Company Board, auditors, Defense Department, and Congress were all demanding answers to what seemed to be rapidly escalating costs. Our objective was to identify where things really stood and find corrective action.
The Results
We were able to identify the causes of “The Lost Year” and explain them to the program, Corporate, and other reviewers. New initiatives were identified and implemented. The program went on to progress as simulated.
In the end, the AMRAAM program became one of the most successful programs in DoD and for Hughes.
After that uncertain start, the AMRAAM has been in production for over 25 years.
It flies at Mach 4 from most advanced fighter jets today, the F15, F16, F18, F22 and more. The AMRAAM is still known as “the world’s most sophisticated air dominance weapon”.
Name | First Aerospace Use of SD Project Modeling |
---|---|
Modelers | Ken Cooper |
Client/Participant | Hughes Aircraft Company |
Client Type | Corporation |
The Official Website | For added information, or with any questions, see CooperSDNetwork.com, or contact Ken via email: Ken.Cooper@CooperSD.com |
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Modeling for Impact in the Era of Big Data
Modeling for Impact in the Era of Big Data
On the occasion of the field’s 50th anniversary, in 2007, Jay Forrester spoke to us (SDR 23:359-370) about prospects for the field of System Dynamics—his view of the situation summarized by the phrase “aimless plateau”. He lamented our “slight impact” in government and placed the blame primarily on our failure to ask the big questions, to write books to influence the public (in the tradition of Urban Dynamics, World Dynamics, and The Limits to Growth), and to maintain rigor rather than “dumbing down” our models with “unwise simplification”.
In calling for courage and rigor in SD modeling, Jay was echoing the words of Dana Meadows and Jenny Robinson more than 20 years earlier, in “The Electronic Oracle” (Wiley 1985).
Meadows and Robinson added a third component that they felt was critical for having influence in the public sphere, namely working collaboratively with other researchers and influencers.
Jay, Dana, and Jenny all hoped and believed that, if only we acted with quality and high purpose, the unique attributes of SD modeling would emerge and ultimately be recognized as more powerful than the intrinsically limited modeling paradigms of statistics, econometrics, and input-output analysis.
I have to admit I’m not so sure. Looking at the world of modeling today and who has influence, I’m struck by how statistical approaches still seem to have a strong hold on much of public policy—whether the subject is climate change economics, sustainable development goals, or even the (obviously dynamic and nonlinear) COVID-19 pandemic. There are some exceptions, such as the Forrester award-winning work by Thompson and Tebbens on polio eradication, but such examples of strong SD public policy impact in recent years are few and far between.
Consider, for example, climate change and its likely damages for the global economy. The prominent Yale economist William Nordhaus (a vocal opponent of SD since the 1970s) first published work in this area in 1991, and in 2018 was awarded the Nobel Prize for it. Yet, this econometric work is surprisingly narrow in its outlook, with its damage estimates biased downward, as described by the Australian economist Steve Keen in his recent article, “The appallingly bad neoclassical economics of climate change”.
It doesn’t necessarily require an SD model to overcome some of the shortcomings of the Nordhaus work. An influential study from 2015 is also statistical, but looks at data not only cross-sectionally across many geographical locations (as Nordhaus does) but also longitudinally across 50 years (1960-2010). This study’s estimates of economic damages over the next several decades are at least ten times greater than those of Nordhaus. This longitudinal analysis does not shy away from data but dives further, and more productively, into it.
Conclusions? First, it seems to me that if one is hoping to impact public policy these days, it is important to draw from a large number of cases across diverse locations—the more the better. This is the era of Big Data, after all, and your model needs to be calibrated and applied to many separate cases to prove its worth if you want to influence the public conversation.
A good recent example is a study of the effect of weather and air pollution on COVID-19 transmission. This collaboration between SD modelers and quantitative health scientists drew from data on more than 3,700 different locations around the world, and it garnered significant public attention.
Second, I think we should emphasize our ability to work with longitudinal data. We know (more than most statisticians and economists do) how to move from longitudinal data to properly estimated parameters, and how to situate these parameters within robust dynamic models.
Of course, we should always remember the importance of courage, rigor, and collaboration in modeling. But, these days, to be heard widely in the public forum we must also employ both cross-sectional and longitudinal data and calibrate our models to many individual cases when possible.
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