Dimensions of Data Sparseness and Their Effect on Supply Chain Visibility

Supply chain visibility is the ability to track parts, components, or products in transit from supplier to customer. However, the data to improve supply chain visibility is often sparse for many organizations. Actors within a supply chain are reluctant to share (correct) data for various reasons like competition, high costs of data solutions, or because of illegal behavior in case of fraudulent supply chain organizations. Dealing with data sparseness is challenging as no clear and concise formulation of data sparseness in the context of supply chain management is present. Consequently, little insight into the true impact of data sparseness on supply chain visibility exists. This paper proposes a classification of the dimensions of data sparseness using a literature review for supply chain visibility. Moreover, this paper numerically assesses the impact of these dimensions on supply chain visibility for a moderately complex supply chain using a stylized simulation model on counterfeit Personal Protective Equipment. The results provide a classification of data sparseness as: “lack of data quality across the entire supply chain for the quality dimensions: noise, bias, and missing values, where a certain proportion of data sparseness is intentional”. Analysis of the impact of the individual dimensions on supply chain visibility shows that when more sparseness in the data is present, a decrease in supply chain visibility up to 31% occurs. The stylized scenarios show that when companies are only demand-oriented, it is more likely that a low global supply chain visibility exists. This clear classification and assessment of data sparseness dimensions on supply chain visibility leads to new insights for academics and practitioners to improve their data, visibility, and efficiency