Accomplishing antifragility with AI decision intelligence technology

07/30/2024

by Bret Farrar and Bret Farrar

The good news? We’re able to ideate, create, and purchase more products and goods more quickly than at any point in human history. It’s a testament to our collective ability to innovate across all sectors—technology, medicine, CPG, the list goes on. This speed to market and ability to specialize is made possible through an interconnected and interdependent supply chain.

The bad news? There’s an inherent fragility that comes from relying on such an interconnected supply chain, particularly when that supply chain becomes hyper-lean in the spirit of optimization. In order to mitigate risk and, ultimately, realize the benefit of today’s global supply chain, organizations need to go beyond resilience strategies to become antifragile.

Defining Antifragility

Earlier this year, I attended the Gartner Supply Chain Symposium, where industry leaders divided modern supply chains into the following categories:

Fragile: When there’s uncertainty in the marketplace, a fragile supply chain will suffer a relative loss due to resource constraints
Resilient: A resilient supply chain can weather uncertainty without experiencing severe loss or gain
Antifragile: An antifragile supply chain is able to not only cope with uncertainty but realize relative gain in an everchanging marketplace

Becoming Antifragile

Antifragility is the new supply chain gold standard. So, how can an organization achieve it? True antifragility comes into focus when an organization is able to account for and optimize a multitude of changing factors. It’s a moving target, but AI-enabled decision intelligence can help a company meet it.

For example, think of a company that manufactures and distributes goods across the country. Perhaps this company has a warehouse full of ice scrapers in Indiana. In the same week, severe snow storms are predicted in the Rocky Mountains and on the East Coast. Plus, one of the company’s retail partners in the Midwest is planning to run a promotion on winter gear. What’s the optimal way for this company to distribute their products to maximize sales? What happens if the snow storms are expected to impact transportation on the East Coast—is there an alternate shipping method that can be used? Is demand likely to be higher in the Midwest due to the promotion or in the Rocky Mountains due to immediate need? An AI-powered decision intelligence model can answer those questions using current and historical data, responding to changing forecasts, and accounting for the company’s current supply.

In this example, the need for AI might not be immediately apparent—a supply chain analyst for the company could develop a sound recommendation based on the information above. The value of AI comes into play when additional variables are introduced: more products, more retail locations, more nuanced weather patterns, and more specific customer data to forecast demand. On top of those variables, there’s near-constant change in the marketplace that can be triggered by something as minor as a delivery vehicle not running or as major as a global pandemic. What makes artificial decision intelligence technology useful in a supply chain is the fact that it can account for more variables than humanly possible.

Leveraging decision intelligence in supply chains allows companies to identify and capitalize on hundreds of additional revenue opportunities as well as many incremental cost-saving opportunities. This provides the basis to achieve an antifragile state in an exceedingly complex and interconnected global supply chain—which is a huge competitive advantage for any company that takes this step.

The Application of AI Decision Intelligence Technology

Artificial intelligence is one of the defining technologies of the decade, but businesses are still struggling to identify the most effective applications. Using AI decision intelligence technology for supply chain antifragility is an example of an effective use case because it:

1. Solves an existing business problem. There’s a need to optimize supply chains to maximize profits and minimize waste, and this technology can help companies find that balance.

2. Acknowledges the need for future flexibility. The global landscape is changing, and companies that are positioned to adapt have the highest chance of long-term success.

3. Realizes the true value of the technology. AI won’t replace humans, but it will empower humans to achieve more at a faster pace. This type of use case leverages AI for what it does best—mass data analysis—without trying to overstate its present capabilities.

4. Has the potential to strike a balance of decision intelligence, decision support, and decision automation. A good solution with long-term potential will not only be able to suggest actions based on aggregated data, but will also have the capacity to automate decision-making. An example of this might be a supply chain tool that automatically processes change orders within a certain set of parameters, like a high-confidence score or a low-cost differentiator. Because an AI model will learn from previous decisions, more data will enable more automation—and a faster, more dynamic supply chain—over time.

From healthcare to utilities to manufacturing and CPG, adopting decision intelligence into existing supply chain processes is one of the key ways to gain and maintain a competitive edge.

If you’re interested in learning about what this technology might look like for your organization, fill out the form below to connect with one of our consultants.