The COVID-19 pandemic has pushed risk to the top of virtually every corporate agenda, according to research by McKinsey & Co. Nearly 60% of respondents to a November 2021 survey by the consulting firm say they’ve adopted new supply chain risk management practices over the past 12 months. An overwhelming majority say they’ve invested in digital supply-chain technologies during the past year, and the success of an organization’s planning during the crisis was strongly linked to its use of modern digital tools.
Over the past few years, we’ve seen a number of software-as-a-service (SaaS) solutions emerge to manage the heavy lifting for global supply chains. These solutions predict and alert users of potential risks, offer mitigation solutions, model “what-if” scenario planning and more. Cloud-based assessment tools utilizing artificial intelligence (AI) and machine learning (ML) can profile a company’s supply chain risk maturity, risk appetite and operational propensities, and then provide action items to boost resilience.
These technologies break down as follows:
AI: Deployed via software that learns from experience, AI adjusts to new inputs and performs human-like tasks.
ML: A subset of AI, ML describes the algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, and instead relying on patterns and inference.
Deep learning: A subset of ML, deep learning uses neural networks to understand vast amounts of unstructured or unlabeled data in either a supervised, semi-supervised or unsupervised way. The system reaches conclusions, determines if those conclusions are correct, and then applies that learning to new data sets.
We can expect more and more cloud-based solutions to emerge this year, leveraging predictive analytics and getting quite prescriptive or even cognitive. These will go on to support suppliers and customers up and down supply chains — and offer a promise of end-to-end resilience.
