It is refreshing to see when developed countries take note and learn from the success stories of developing countries. In particular, Transmilenio, Bogota’s Rapid Transit System, is an example of what E.F. Schumacher called “Appropriate Technology”
Michael Bloomberg, New York City’s mayor writes on The Economist
…we drew on the experiences of Berlin for our renewable-energy and green-roof policies; Hong Kong, Shanghai and Delhi for our innovative transit improvements; Copenhagen for our pedestrian and cycling upgrades; Chicago and Los Angeles for our plan to plant 1m more trees; Amsterdam and Tokyo for our transit-oriented development policies; and Bogotá for our plans for Bus Rapid Transit
The simplified market adoption model I described on previous postings (1,2) is an Excel implementation of a kind of logistic function. The Bass model is one of the most popular models used in marketing, and management of technology to think about product introductions. (See Wikipedia article). From a mathematical perspective, when the parameter p is 0, the Bass model reduces to the logistic function.
What is most interesting, from a business perspective, is how you arrive to each of those functions by modeling real-world interactions. On both models, you can conceptualize the world as two different pools of people (or stocks, in the system dynamics terminology). One is the pool of potential adopters, and the other is the pool of adopters. The flow between these two pools is controlled by the adoption rate, a variable that models how probable is that a potential adopter becomes “infected” by a current adopter. On the logistic model, it depends solely on how much they interact, how big the total population is, and how “contagious” the product is. On the Bass model, an additional parameter accounts for external factors, the most common being advertising. The Bass model overcomes what is called the startup problem of the logistic model: how a initial base of zero adopters can spread “infection” of the product.
There are more refinements that can be done to the Bass model: accounting for changes in the total population over time, learning and experience curves, etc. For projects where the sensitivity of the model to these factors is high, I definitely recommend to spend more time calibrating your model, understanding which of the different available curves fits better any data you may have, and most critical of all, whether the chosen coefficients for any of the functions have strong impacts on the critical business issues you want to model — capacity planning, pricing, profitability, etc.
For many projects like business plans, revenue projections, etc. I’m willing to sacrifice the ability to fine tune parameters in a model like the BDM for the clarity provided by a model like the Excel logistic function I described. I can generate more tangible conversations with executives by discussing what they believe will be the takeover time, when they believe it will be the start of the fast growth, how much share they believe will be reached in steady state, etc.
Tagged with: .
Without being able to show actual graphics from the data I’m working on, due to confidentiality with my employer, this post is a note on a good technique I’ve found that could be helpful to others in the portfolio management space
I’m finding PivotGraphs a powerful tool to visualize and communicate portfolio interactions. One example would be how different vehicles in a manufacturer’s portfolio interact with each other. Through proper aggregation, it provides insigths that a traditional segmentation will miss.
Here is how a PivotChart looks like, linking to an IBM research article on the topic:
PivotCharts are not related to Excel PivotTables at all