Planning projects accurately is whether it is publicly or privately funded difficult especially in domains like technology, infrastructure, pharma, construction. A survey conducted by PMI found that only 50% of the projects were completed on time and 55% within budget during the period 2011 to 2018. A major source of risks is inaccurate forecasts of durations, costs, resources and benefits.
Daniel Kahneman with his collaborator Amos Tversky noted that humans tend to suffer from planning fallacy i.e, they over promise and u der deliver and the project objective forecasts are unrealistic. They suggested an external view to developing more realistic project plans. They proposed a technique called reference forecasting where project duration
or costs were predicted by comparing the project to a set of similar projects executed in the past. This removes the bias to a large extent where the inside view where the planning is done with little regard to the historical performance and its ability to meet set targets.
Today, changing attitudes towards data collection and data-based decision making offers a great opportunity in the field of project planning. Using historical data on the projects initial estimates with the actual data, accuracy estimates can be established. This can be used for forecasting and setting project goals.
There is a huge body of data available now across the world that can help appraise the accuracy of the estimates. The appraisal includes an explicit adjustment to account for systematic optimism which is an overestimation of benefits and an underestimation of costs and durations.
Most of these analyses to date are identified by human judgement. Artificial intelligence could help perform this role quickly and accurately and within a fraction of time that what the human judgement takes. Access to data-driven artificial intelligence can help take the reference class forecasting to the next level. Deep learning and artificial intelligence can identify patterns of similarity among project tasks, hierarchies and precedent relations. The AI algorithms learn which patterns are useful to predict delay, thereby improving the forecasts.
This it is predicted that the availability of data and AI intelligence would introduce a seismic shift in project planning and help in overcoming the planning fallacy.
Use Data to Revolutionize Project Planning
by Yael Grushka-Cockayne February 26, 2020
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