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Measurement drives behavior. Unfortunately, most performance measurement initiatives overlook this fact. Implementations are performed top‐down with strategy as the starting‐point. There needs to be a better understanding of the cultural context of the metrics (What is driving the behaviors?) and a better understanding of what metrics are to define (How do we drive the right behaviors through measurement?). The purpose of this paper is to explore the notion of a context‐based approach to performance metrics – by examining an organization's negative values – and the notion of a content‐based approach – by introducing the concept of business interface metrics.

The article analyses business metrics.

The paper demonstrates the need to use interface metrics in order to better manager processes and deliver organizational values.

To get new insights, sometimes conventional wisdom needs to be challenged. Following best practices around metrics can prevent companies from reflecting on the effect of the metrics they are trying to put in place. By coming up with a different approach (business interface metrics and negative values), interesting insights can be gained. Moreover, taking a fresh approach ensures that new thinking takes place and that there are fewer conformist paths to fall back on.

The purpose of this paper is to provide industrial managers with insight into the real‐time progress of running processes. The authors formulated a periodic performance prediction algorithm for use in a proposed novel approach to real‐time business process monitoring.

In the course of process executions, the final performance is predicted probabilistically based on partial information. Imputation method is used to generate probable progresses of ongoing process and Support Vector Machine classifies the performances of them. These procedures are periodically iterated along with the real‐time progress in order to describe the ongoing status.

The proposed approach can describe the ongoing status as the probability that the process will be executed continually and terminated as the identical result. Furthermore, before the actual occurrence, a proactive warning can be provided for implicit notification of eventualities if the probability of occurrence of the given outcome exceeds the threshold.

The performance of the proactive warning strategy was evaluated only for accuracy and proactiveness. However, the process will be improved by additionally considering opportunity costs and benefits from actual termination types and their warning errors.

Whereas the conventional monitoring approaches only classify the already occurred result of a terminated instance deterministically, the proposed approach predicts the possible results of an ongoing instance probabilistically over entire monitoring periods. As such, the proposed approach can provide the real‐time indicator describing the current capability of ongoing process.