Increasing data integrity for improving decision making in production planning and control

Manufacturing companies gather a vast amount of data in order to track production, assembly and logistical processes. Among others, these data are used for updating production schedules, deriving process interventions and calculating key performance indicators. However, the integrity of these production feedback data is regularly impaired by various inconsistencies and errors, which negatively affect the value of these data for any decision-making processes. In this paper, a new approach for increasing the integrity of production feedback data based on an adapted Data Mining algorithm is proposed to compensate for a typical data error in production feedback data.