3 steps to getting started with supply chain AI

3 steps to getting started with supply chain AI

The modern global supply chain is defined by scale — billions of transactions and terabytes of data across multiple systems, with businesses generating more every moment. Traditional supply chain management (SCM) practices are quickly becoming outmatched by the ceaseless onslaught of information.

When a problem arises in inventory carrying costs or availability, financial and demand planners dive into Excel or legacy SCM tools in an attempt to pinpoint issues. It’s like looking for the proverbial needle in the haystack. The sheer volume, velocity, and variety of data defy human efforts to understand dynamics and right the ship.

That mismatch is why AI has emerged as a hot topic in supply chain management. Innovative organizations are applying artificial intelligence and machine learning against vast data sets of supply chain data to unearth insights into problems and performance that are effectively beyond the reach of even the most skilled planning professionals.

AI holds tremendous promise to optimize processes. In fact, Gartner has found that 25 percent of organizations had begun AI initiatives through 2017, up from 10 percent two years earlier. Firms in pharmaceuticals, consumer packaged goods, manufacturing, and other industries are looking to move beyond relatively simplistic SCM tools built on static business rules that inhibit the ability to optimize and scale.

A common question I hear is, “How do we get started?” I’d like to offer three suggestions.

For a first project, it’s best to identify a specific supply chain issue that could be solved with AI. That helps focus efforts and resources on a single problem, rather than throwing spaghetti at the wall. Naturally, you’ll want to select a significant pain point with implications for your supply chain efficiency, customer satisfaction, and bottom line.

For instance, let’s say a global CPG company has challenges in meeting service level agreements with its retailer customers. The company can face stiff penalties under its SLAs if stock is not delivered on time and in full. Applying AI to that specific issue has the CPG company on the fast track to resolving its service level fulfillment issues.

You may have a dozen potential projects for AI across your supply chain, from planning to production, packaging, warehousing, distribution and logistics. Targeting one in particular positions you for the best results, while minimizing the risk that ill-defined experimentations end up on the back burner. By selecting a discrete project, you can build on initial successes and learnings to apply AI in other areas.

Data is a critical ingredient of AI readiness.

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