This commentary appeared in the print edition of the Jan. 6, 2020, Journal of Commerce Annual Review and Outlook.
Combined with artificial intelligence (AI) and machine learning, data is the driver for predictive capabilities — with it, future performance can be optimized based on historical results. This powerful data has the potential to positively affect every aspect of the supply chain, from sourcing and compliance to production and quality control.
AI and machine learning are both essential to get the most out of natural language processing (NLP). The complexity of human language requires smart algorithms and self-teaching systems to parse and understand language input and provide appropriate responses and actions. NLP could provide many benefits to the supply chain, including understanding and mitigating potential risks with supply chain stakeholders, ensuring compliance, monitoring reputations of supply chain organizations, and reducing language barriers.
We are at a significant inflection point in the adoption of AI-enabled solutions. Linking domain expertise and data with technical innovation is necessary for AI to reach its full potential to deliver measurable, effective results.
To successfully implement AI into the supply chain, organizations need high-quality supply chain datasets, properly trained employees, and buy-in from all stakeholders. Not only can this upheaval become costly, but it takes time to integrate the technology properly without causing enormous disruptions to current initiatives — these are just a few of the factors that can hold back progress or contribute to slow adoption rates.