Model for Extracting Information from Production Schedule Data

Data collected from production is available in computerised production control systems, but its current utilisation could be improved. To introduce one way to use production data, this paper studies automatic post-analysis of the schedule data of a single-machine system. This kind of estimation is extra information
that can be used to strengthen planning and reporting systems. The approach in this paper is different from previous studies as, instead of just considering e.g. bottlenecks or the critical path, our method estimates different causes of tardiness for a tardy job. In the model we automatically find a job for which the finish time is after the deadline. After selecting the target job, our model finds out what the likely causes of the tardiness are. The following five causes of tardiness are studied: bad scheduling, a rush job, a long job,
unavailable capacity and bottleneck congestion. For each cause and each job, there is an index that estimates how significant the cause is. The indices are combined to calculate how much the other individual jobs affect the tardiness of the target job. The paper provides an example where the model is used. Using the model extra information is revealed from the existing schedule data. The model is easy to understand and implement and the computational complexity of the model is low.