Supply Chain Analytics Trends and Practices
With the availability of vast amounts of data, supply chain leaders continue to turn to analytics to help make business decisions. APQC has watched how organizations have steadily incorporated analytics into their business operations, as well as the technologies they have adopted to support their analytics efforts. APQC recently conducted research across the entire supply chain on the use of analytics. Its survey of 202 respondents across 16 industries gathered insights on the types of analytics used and in which parts of the supply chain, how the efforts are structured, and how organizations measure performance.
APQC conducted its last major survey on supply chain analytics in 2016, and some trends have remained constant. Supply chain professionals in both 2016 and 2019 indicated that their organizations have increased investment in analytics over the last three years. In APQC’s most recent study, it categorized supply chain analytics into five types:
- Descriptive: what is happening or has happened
- Diagnostic: why it is happening
- Predictive: what will happen
- Prescriptive: what should be done
- Cognitive: using machine learning, tells what should or could be done
APQC’s research in 2019 found that many organizations have made great strides in their supply chain analytics efforts since 2016. However, they should keep in mind key enablers and barriers to analytics success.