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Big Data Sources for Supply Chain Management Apps

The scale, scope and depth of data supply chain technology applications are generating today is accelerating, providing ample data sets to drive contextual intelligence.


Machine downtime imposes a cost to firms due to forgone productivity and can be particularly disruptive in supply chains. The primary operational risk to firms is unexpected failures of assets. A wave of new data generated by the “internet of things” (IoT) can provide real-time telemetry on detailed aspects of production processes. Machine-learning models trained on these data allow firms to predict when different machines will fail (LaReviere, 2016).


Big data solutions that support integrated business planning are currently helping organisations orchestrate more responsive supply chains as they better understand market trends and customer preferences (KPMG, 2017):



The following graphic provides an overview of 52 different sources of big data that are generated in supply chains Plotting the data sources by variety, volume and velocity by the relative level of structured/unstructured data, it’s clear that the majority of supply chain data is generated outside an enterprise.

Figure. SCM Data Volume and Velocity vs Variety

(source: Rozados I.V., Tjahjono B. (2014), Big Data Analytics in Supply Chain Management: Trends and Related Research. Presented at 6th International Conference on Operations and Supply Chain Management, Bali)


Forward-thinking manufacturers are looking at big data as a catalyst for greater collaboration.



Analytics within different fields, including supply chain, has 5 levels

  • Descriptive – What happened?

  • Diagnostic – Why did it happen?

  • Predictive – What will happen?

  • Prescriptive – How can we optimise?

  • Adaptive – How do we learn?


  1. LaRiviere J., McAfee P., Rao J., Narayanan V.K., Sun W. (2016), “Where Predictive Analytics Is Having the Biggest Impact”, Harvard Business Review. – URL: https://hbr.org/2016/05/where-predictive-analytics-is-having-the-biggest-impact.

  2. [KPMG (2017)]: Simon Rowe, Mehrdokht Pournader (2017), "Supply Chain Big Data Series Part 1", KPMG, 16p. – URL: https://assets.kpmg.com/content/dam/kpmg/au/pdf/2017/big-data-analytics-supply-chain-performance.pdf.

  3. Robinson A. (2017), “The Digital Supply Chain: The Landscape, Trends, Types, and the Application in Supply Chain Management”, Cerasis, 85 p. – URL: http://cerasis.com/2017/06/05/e-book-digital-supply-chain/

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