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Dimensions of data sparseness and their effect on supply chain visibility

Published: 18 July 2024 Publication History

Abstract

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review of supply chain visibility and data quality literature, this study proposes to characterize data sparseness as a lack of data quality across the entire supply chain, where data sparseness can be classified into three dimensions: noise, bias, and missing values. The quantitative analysis relies on a stylized simulation model of a moderately complex illicit supply chain. Scenarios are used to evaluate the combined effect of the individual dimensions from actors with different perspectives in the supply chain, either supply or demand-oriented. Results show that when a data sparseness of 90% is applied, supply chain visibility reduces to 52% for noise, to 65% for bias, and to 32% for missing values. The scenarios also show that companies with a supply-oriented view typically have a higher supply chain visibility than those with a demand-oriented view. The classification and assessment offer valuable insights for improving data quality and for enhancing supply chain visibility.

Highlights

Classification of data sparseness for improving supply chain visibility.
Simulation-based assessment of the impact of the data sparseness dimensions.
Low data quantity (not quality) leads to the steepest decrease of visibility.
Demand-oriented companies have less visibility of the supply chain than supply-oriented ones.

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Published In

cover image Computers and Industrial Engineering
Computers and Industrial Engineering  Volume 191, Issue C
May 2024
999 pages

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Pergamon Press, Inc.

United States

Publication History

Published: 18 July 2024

Author Tags

  1. Data sparseness
  2. Supply chain visibility
  3. Data quality
  4. Supply chain
  5. Classification
  6. Simulation

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