An executive’s guide to AI in supply chain management

Faster, smarter, more profitable supply chain decisions

Transitioning from hype to reality, artificial intelligence (AI) is gaining momentum across industries thanks to an explosion in computing power and storage, the emergence of IoT (Internet of Things) and big data, and algorithmic advances. While there have been numerous examples of how AI can boost profits in supply chain execution—most notably in the form of autonomous vehicles and smart robotics—the benefits related to supply chain planning have largely been ignored. But that’s starting to change as future-focused executives see the potential to improve profitability and productivity by making faster, smarter supply chain planning decisions. The first step in achieving those benefits is understanding AI and the other technologies associated with it, and where they can help improve your supply chain planning initiatives.

Emerging technologies and how they relate to supply chain planning

People often use AI as a catch-all term for any kind of technology that involves smart machines. And while many of those fields do fall under the AI umbrella, it’s still important to know some of the key differences between the terminology. Here are three simple definitions of AI and related technologies, plus how they apply to supply chain planning.

1. Artificial intelligence

The ability of a machine to perform human-like functions such as perceiving, reasoning, learning, interacting with the environment, problem solving and exercising creativity to form plans, make decisions and achieve goals. Use in supply chain planning:

• Amplify the value of your existing processes and people with machine-assisted planning, which can help you bridge the knowledge gap between experienced and inexperienced planners, and gain real-time recommendations based on historical and current data analysis.
• Improve your supply chain visibility and risk insight by using AI to track and predict possible supply chain disruptions based on inputs and correlations across multiple data sources, including weather forecasts, news and even social media.

2. Machine learning

A subset of AI, machine learning is a method of data analysis where machines use algorithms to detect patterns, learn to make predictions and make recommendations by finding hidden insights in your data without being explicitly programmed where to look. Use in supply chain planning:

• Unlock new sources of revenue savings by implementing a self-healing supply chain that can continuously observe, monitor and correct out-of-tolerance lead times for all related products based on historical data and slope.
• Increase customer service levels with more accurate demand behavior for new products by using algorithms based on early-sell signals to optimize inventory levels and replenishment plans.

3. Deep learning

A subset of machine learning, deep learning leverages neural networks to understand vast amounts of unstructured or unlabeled data in either a supervised, semi-supervised or unsupervised way, drawing conclusions, learning if those conclusions are correct and then applying that learning to new data sets. Use in supply chain planning:

• Save time and money with an always-on automated planning agent that automatically handles low impact exceptions as they arise, delivers detailed reports on its observations and the corrective actions it took, and send alerts to the right people when larger issues arise.

Companies are making big strides in developing and deploying real, practical applications of AI in supply chain management and planning. Before jumping on the bandwagon, ensure you’re not getting caught up in the hype and truly understand what you’re signing on for, because while AI is helping pioneer a new future, it still has its limitations. How are you exploring AI in supply chain planning? Let us know in the comments section.

Additional Resources

• Supply chain management frequently asked questions