Data Science and Supply Management: Trends
Some examples of current trends in data science and supply chain management include increased adoption of Blockchain technology, Artificial Intelligence, and Machine Learning, increased use of advanced analytics, and more IoT adoption and use cases.
Blockchain and Artificial Intelligence/Machine Learning
- Although the adoption of Blockchain technology and Artificial Intelligence in supply chain management is still at the nascent stage, experts state that "interest has accelerated significantly during the past year, making blockchain a top trend for supply chain leaders to watch in 2019."
- Companies in the supply chain sector are increasingly gathering/generating data (such as data that are being generated with the adoption of beacons, IoT solutions, and RFID technologies in supply chain) that are under used and AI and machine learning can leverage such data to make processes more efficient.
- According to analysts, through self-learning from available data and natural language technology, "AI solutions can help automate various supply chain processes such as demand forecasting, production planning or predictive maintenance. Along with automation comes augmented human decision-making, because the human is then no longer involved in the decision-making."
- Experts also state that "drones, autonomous intelligence and robotic automation will eventually transform warehousing and transportation, which will create networks that may look and operate very differently from those of today."
- More companies in the supply chain industry are increasingly adopting advanced analytics to better understand their data and increase their effectiveness.
- McKinsey states that advanced supply chain analytics "expand the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems and applies powerful statistical methods to both new and existing data sources. This creates insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models."
- Advanced analytics are being increasingly deployed in real-time or near real-time in supply chain processes such as product quality testing, dynamic pricing, and dynamic replenishment.
- It will also "improve organizations’ ability to gain visibility on the real-time status of their supply chain network, thus giving them the ability to not only rapidly respond to problems but more importantly, anticipate and prevent them more effectively."
- Many companies in select supply chain industries are increasingly adopting Internet of Things in their supply chain management process and the trend is expected to continue.
- The adoption is expected to grow in the future as businesses continue to assess more ways IoT can be used beyond the current use cases. Retailers are currently using it to track stock levels, analyze customer data, and strengthen customer relationships.
- According to analysts, "IoT could have a broad and profound impact on the supply chain in areas such as improved asset utilization and higher uptime, improved customer service, improved end-to-end supply chain performance, or improved supply availability, supply chain visibility and reliability."
To understand current trends in data science and supply chain management, we extensively searched for industry trends detailed in market research reports and industry reports. We were able to find reports from Gartner, McKinsey, Supply Chain Management Review, and Absolute Reports. We then analyzed the trends mentioned in the independent reports and selected trends that appeared in at least two of the four reports. We have detailed our findings above.