The Challenge of AI-Driven Supply Chains: Why This Is a Critical Moment
A new study by ResearchAndMarkets says that AI-enabled supply chains are 67% more effective than those which don’t utilize AI, due to lower overall cost and risks, according to this SupplyChainBrain report.
These types of reports and headlines are becoming more common as industries see the benefits of data-driven, efficient supply chains. And, with the current spotlight on AI solutions thanks to the popularity of ChatGPT and other tools, leaders are reexamining the role that AI and machine learning can play in creating more efficient, intelligent, and sustainable supply chains.
The challenge you can’t afford to miss at this crucial moment is understanding not just the outcome you want to achieve, but the process you need to follow and the role that AI will play in getting your company to that end state.
Finding the Right Problems for AI to Solve
We recently co-authored a report with BCG where we examined the source of companies’ struggles to maximize value from AI in supply chains. What we found was that the issue generally doesn’t lie with AI technology itself, but with where and how companies have tried to apply it.
In many cases, the focus has been on using AI for analysis and prediction – for example, to create production plans or demand forecasts. What we weren’t seeing was companies focusing on how AI could transform decision-making processes by leveraging its strengths: analyzing and harmonizing data, finding patterns that people wouldn’t otherwise see, and leveraging those insights to generate real-time recommendations.
As more companies take a fresh look at AI solutions, the time is ripe for companies to deploy AI-powered systems that are integrated across functions, with the ability to learn from decisions and their outcomes.
What is needed is not more data or dashboards – teams are drowning in data – but analytical engines that can help digitize, augment, and automate decision making. Instead of serving up passive insights to busy people who still have to analyze them to come to a decision, AI can take on the burden of analysis and even automate routine tasks according to predefined rules, opening up new opportunities.
What We Can Learn from History
This is an exciting moment when industries are once again looking at creative ways to apply AI to supply chain problems, from generating adaptive employee schedules for retail to optimizing feed and grain supply chains.
The history of AI deployments that didn’t live up to expectations, or provide the expected ROI, highlights the importance of understanding where AI can be used to its fullest potential – and why it’s critical to have a partner who knows your industry, and can help you map out the journey. A Decision Intelligence platform is the ideal way to apply AI to supply chain challenges because it focuses on decisions and their outcomes.
We take a deep dive into the pain points of supply chain AI deployments, and how Decision Intelligence solves them, in our Future Now webinar session, “AI in Supply Chain – Challenges and Opportunities.” Watch this presentation and you’ll learn how AI can help companies continuously learn and make decisions that optimize performance – and how you can make this transformation quickly and seamlessly.
