Production scheduling using production feedback data; an illustrative case study

Industry 4.0 is providing unprecedented opportunities for the capture and use of data into production planning and control (PPC). The accuracy of such data for PPC has been found to have a direct positive effect on operational performance. This study builds on a dynamic approach where production feedback data is used to improve the accuracy of master data used in tactical planning. The study applies a model-based approach using data from a real case. Two illustrative sensitivity analyses indicate that even small deviations in the accuracy of master data have an impact on the production schedule in terms of job sequence and makespan. The paper's main theoretical contribution is the development of six propositions on this relationship, where in short, the sequence appears to be sensitive to the accuracy of both changeover time and processing time. The paper illustrates how sensitivity analysis can be used in investment decisions about which production feedback data to capture and use for PPC purposes. Further research should test the propositions in more real cases and other production environments and carry out sensitivity analyses with more types of master data, variables, and combinations.