Predictions of time (e.g., work hours) are often based on the aggregation of estimates of elements (e.g., activities, subtasks). The only types of estimates that can be safely aggregated by summation are those reflecting predicted average outcomes (expected values). The sums of other types of estimates, such as bounds of confidence intervals or estimates of the mode, do not have the same interpretation as their components (e.g., the sum of the 90% upper bounds is not the appropriate 90% upper bound of the sum). This can be a potential source of bias in predictions of time, as shown in Studies 1 and 2, where professionals with experience in estimation provided total estimates of time that were inconsistent with their estimates of individual tasks. Study 3 shows that this inconsistency can be attributed to improper aggregation of time estimates and demonstrates how this can produce both over- and underestimation—and also time prediction intervals that are far too wide. Study 4 suggests that the results may reflect a more general fallacy in the aggregation of probabilistic quantities. Our observations are consistent with that inconsistencies and biases are driven by a tendency towards applying a naïve summation (2+2=4) of probabilistic (stochastic) values, in situations where this is not appropriate. This summation fallacy may be in particular consequential in a context where informal estimation methods (expert-judgment based estimation) are used.

The field of benefits management gives guidelines on how to plan and realize benefits  throughout the life-cycle of a system. However, realizing benefits from information systems projects has proven to be challenging in practice. In this paper, we investigate specific benefits management challenges as perceived by practitioners involved in information systems engineering projects. We conducted 22 interviews with respondents representing nine public sector projects, where challenges in managing benefits were elicited and identified. We elicited six specific benefits management challenges: A - Identifying and describing benefits, B - Alignment of work with planned benefits, C - Reception and acceptance of the planned benefits, D - Organizational issues, E - Alternative or competing solutions, F - Measuring and evaluating benefits. Overlaying these challenges with current normative models on benefits management, we find  that: 1. Normative models on benefits management lack sufficient guidance on operative work on how to create information systems fit for realizing benefits and how to introduce these solutions to ensure benefits realization, and 2. Normative models on benefits management do not explicitly leverage the  rapid project learning promoted by modern engineering methods. We conclude that more specific benefits management models should be elaborated, which are integrated into modern information systems engineering practices. This will enable best practices on the continuous adjustments of cost and scope according to evolving knowledge in projects to also be adapted to the management of benefits.