Being able to timely access and share accurate information to all stakeholders that can influence a supply chain is referred to as supply chain visibility. Supply chain visibility is crucial for increasing the resilience of a supply chain as this helps to proactively plan and design interventions to undesirable events. Various methods are available to increase supply chain visibility, often by increasing the accuracy of estimating future events. Many of these methods are relianton (large volumes of ) data, which is problematic because data on supply chains is often not accessible, expensive to acquire, and difficult to process. Therefore, methods to increase supply chain visibility in situations with limited data availability are needed.

 Modeling and simulation can be applied to situations with limited data availability, but has a limited predictive power for complex systems. Data assimilation in discrete event simulation (DES DA) is a method to increase the predictive power of modeling and simulation. This method aims to approximate system states based on real-time measurements to increase the accuracy of simulation models. DES DA has not yet been applied to supply chains and the dependency on data availability still needs to be explored. The goal of this research isto develop a DES DA algorithm that can accurately estimate future events in supply chains and test this algorithm for different levels of data availability. This will give insight in the relation between data availability and the accuracy of the proposed DES DA algorithm. During the development of this algorithm the typical supply chain challenges of high dimensionality and future state estimation are addressed.The proposed DES DA algorithm shows to have a higher predictive power (accuracy) than the results of a simulation exercise without assimilation. To allow for maximal reproducibility and facilitate future work, all adjustments to the simulation algorithm are extensively described in the body of this thesis.

Analysis of the performance of the proposed algorithm under different levels of data availability shows that limited data availability does not necessarily result in less accurate estimations. Less observations can can result in similar or even more accurate estimations. However, this can only be the case if the missing data is not part of the set of crucial data sources. If the missing data points are part of these crucial data sources the  accuracy of the data assimilation algorithm detoriates and the algorithm starts to under fit. Under fitting occurs when a model uses input variables that are not significant enough to determine a meaningful relationship between the input and output variables. 

If limited data availability is the result of omitting data sources that are not part of the crucial data sources, the accuracy of the data assimilation algorithm can increase. Using less datapoints helps combatting the curse of dimensionality which results in more accurate estimations. This research has shown that crucial data points for supply chains are the data sources that serve as arrival sensors of entities at the system boundaries. The arrival sensors reduce the uncertainty of future state estimations by accurately estimating the number of the entities in the system and the moment of arrival of these entities. Especially the accurate estimation of the moment of arrival reduces the variation in the estimations of the particles and thereby increases the accuracy of the estimation of future events.