Third wave portfolio management: using market mechanisms to prioritise projects

Written by Bryan Fenech, Director - PPM Intelligence.

The textbooks tell us that there are 2 portfolio management approaches to evaluating and prioritising projects:

1. Quantitatively using financial return measures – e.g., Net Present Value, Payback, etc or
2. Qualitatively using a multi-attribute scoring model or algorithm.

There is, however, a third option emerging which offers exciting new possibilities – harnessing the power of market mechanisms to select portfolios.

Markets versus hierarchy

In a market both the price of goods and services, and the distribution of information and resources, is a function of the transactions between buyers and sellers and the relative balance between supply and demand involved in these interactions. At a macro-economic level it is recognised that this mechanism is more efficient, and less prone to corruption, than a “command and control” model based on bureaucratic directives by “experts”.

The power of markets has many applications, from moderating debilitating price fluctuations through futures markets to reducing dangerous pollutants, such as atmospheric lead and CFCs, using cap and trade systems.

The internal workings and governance of business and government organisations, however, has tended to be a domain of hierarchical decision making and centralised control of information and resources. The market is for the most part excluded.

Until recently, that is. Organisations are starting to explore how they can harness the power of markets to solve a range of complex business problems.

Ideas futures

In his book The Future of Work Professor Thomas Malone of MIT describes an experiment at Hewlett Packard where a market was set up that allowed employees to buy and sell predictions of future sales in any given month. Employees received $1 for each “futures contract” they had acquired that correctly predicted sales. The result – the markets repeatedly predicted sales more accurately than the official sales forecasts of HP’s expert analysts!

How is this possible? Because of the distributed nature of knowledge the dispersed workforce will collectively always possess more wisdom than central planners. Further, in a hierarchy there is always an incentive to make biased judgments – e.g., choosing a number to keep the boss happy.

Supply chain allocation

Similar experiments have been conducted where market simulations have been set up to complement, or as an alternative to, supply chain planning, budgeting and scheduling. In these experiments plant managers, sales representatives and other staff can buy and sell futures contracts for specific goods in the supply chain, for which there is a degree of price volatility. The objective for each participant is to maximise their personal profit margin but the overall result for the organisation is close to perfect allocation of plant capacity and sales.

These approaches offer much potential. Traditional, centralised supply chain planning and allocation is cumbersome at the best of times. In particularly volatile business and economic environments it is nearly impossible to respond effectively to change. The cycles of data gathering, analysis, approval and syndication cannot happen quickly enough.

Portfolio selection

Some organisations are now experimenting with applying these ideas to the problem of portfolio selection. 

There are many ways that this can be done. One way is to establish an ideas futures market in which employees are given $100 of virtual money to invest in a mix of projects that they think will have the best return. The market will in effect evaluate and prioritise projects and select a portfolio based on the distributed knowledge of the workforce rather than a small number of portfolio analysts working with biased business case cost and benefits estimates. The results can be very interesting. And challenging!

The next step here is to conduct a study comparing methods: which approach selects the most optimal portfolios – traditional centrally controlled quantitative and qualitative evaluation and prioritisation techniques or market mechanisms. Such an experiment would be difficult, because of the length of time required to collect the data required to do the comparison, but not impossible.

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