History Matching and Relevance Analysis
In many projects, it is hard to determine which historical data should be used as an analog for future analysis. For example, in many research and development projects, new projects may significantly differ from the previous projects. To improve the accuracy of estimates based on risk occurrence data the selection of analogs for the historical data should be done through an analysis using a Bayesian approach.
Many risks can be similar for different projects. For example, the risk “Budgetary Problems” may have already occurred in a different project within an organization. As a result, historical information about risks can be more comprehensive than information about the duration and cost of tasks as many research and development projects have unique activities. The selection of an event with its respective probabilities and impact from the historical data is based on an analysis of evidence regarding how relevant the event is to the current activity or project. Relevance analysis is performed using the different criteria. If an event is a full or partial match according to the selected criteria, it will contribute to the overall evidence that this event is relevant to the current activity. The selection criteria can include:
- Events and event chains that belong to similar projects (similar cost, duration, or objectives), managed by the same project manager, and performed by the same organization
- Events and event chains that belong to similar tasks or group of tasks (similar name, duration, and cost)
- Events and event chains that occur during work performed by similar resources
- Events and event chains which have similar names, probabilities, and impacts
Sometimes descriptions of current and previous projects, tasks, resources, and risks can differ slightly. Therefore, linguistic analysis can be applied for relevance analysis related to various descriptions. Sets of criteria and business rules for the relevance analysis are adjustable for different industries, organizations, and projects.
If the historical data has been properly collected, it may contain information about how particular events affected a previous project. This data can be used to calculate the probability and impact of the risk in the current project. In addition to getting evidence of relevance for risks from historical data, project managers may define a relevance coefficient or belief that the event is relevant to the current activity. For example, the event chain “Problem with supplier” has a 50% relevance according to the historical data. In addition, project managers may define that it has an additional 90% relevance based on own her understanding of the event. In this case, both numbers will be used for calculating the probabilities and impact of risks using the Bayesian approach.