For the past few years, we have been hearing about Industry 4.0 (or the fourth industrial revolution), which promises to improve productivity, flexibility, quality, customer satisfaction and employee well-being. To assess whether these goals are achieved, it is necessary to implement a performance management system (PMS). However, a PMS must take into account the various challenges associated with Industry 4.0, including the availability of large amounts of data. While it represents an opportunity for companies to improve performance, big data does not necessarily mean good data. It can be uncertain, imprecise, ambiguous, etc. Uncertainty is one of the major challenges and it is essential to take it into account when computing performance indicators to increase confidence in decision making. To address this issue, we propose a method to model uncertainty in key performance indicators (KPIs). Our work allows associating with each indicator an uncertainty noted m, computed on the basis of the theory of belief functions. The KPI and its associated uncertainty form a pair (KP I, m). The method developed allows calculating this uncertainty m for the input data of the performance management system. We show how these modeled uncertainties should be propagated to the KPIs. For these KPI uncertainties, we have defined rules to support decision-making. The method developed, based on the theory of belief functions, is part of a methodology we propose to define and extract smart data from massive data. To our knowledge, this is the first attempt to use this theory to model uncertain performance indicators. Our work has shown its effectiveness and its applicability to a case of bottle filling line simulation. In addition to these results, this work opens up new perspectives, particularly for taking uncertainty into account in expert opinions and in industrial risk assessment.