Information Quality Dimensions for Identifying and Handling Inaccuracy and Uncertainty in Production Planning and Control

The production planning and control (PPC) is an important part of the factory automation. The PPC develops plans for an efficient operation of the machines and thus determines their configuration, but needs feedback data and information from the machines to react on deviations from the initial plans. The consideration of the information quality of the feedback data is a crucial factor in PPC. Various influencing factors impair the quality of used information in the decision-making processes of the PPC. If this influence is not considered, false or suboptimal results might be the consequence, esp. when planning results are close to critical decision limits. In this paper, granularity, actuality and accuracy are identified as important information quality dimensions, which should be considered when assessing the information quality. Often information are not deterministic, containing a certain degree of uncertainty, which has to be considered. Fuzzy logic is applied for modelling uncertainty in information as well as for their consideration in further decision-making-processes of the PPC. This is illustrated at an example: assessing information quality by applying quality dimensions and handling with fuzzy logic.