AI and Machine Learning: The Key to Managing Data in the Supply Chain

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AI and Machine Learning: The Key to Managing Data in the Supply Chain

I think we all know how frustrating it is when traffic lights aren’t synced together during our morning commute. It leads to a constant flow of jams, delays, and clusters—not to mention a lot of frustration. The same thing happens in the supply chain when we aren’t properly synced with our suppliers, production team, and customers. Only in this case, it also leads to lost production time—and lost profit. Luckily, artificial intelligence (AI) and machine learning are offering a smarter way to connect our supply chain via big data. And the best part is, you don’t need to understand big data to take advantage of it.

The Great Supply Chain of Data

 The truth is, we have reached peak data overload. Whether or not you already have machine learning in place to process your data, it’s likely you’ve been collecting mounds of it for at least a few years now. If needed, you can pull everything from peak temperatures in your key markets to the average efficiency on your production floor. It is no longer an issue of having enough data. It’s an issue of having so much that no human could possibly make meaningful sense of it. That is where machine learning and AI come in.

Supply chain tools like Watson’s Metro Pulse are bringing supply managers even greater control of their connected chains by using cognitive learning to find insights that are generally “locked away” in the data pool. It doesn’t just read the data for key words or trends. It interprets it—everything from traffic, weather, local holidays, and news—and puts it into meaningful context. In effect, it merges hyper-local data with your own company’s data to make you even more efficient, productive, and profitable.

Creating Smarter, Connected Supply Chains 

So how does local data make a difference to my global supply chain? Lots of ways. Take a look:

  • By understanding the weather of a certain community, you can better target supply—and eventually marketing—of your company’s goods, be it warm drinks or umbrellas
  • By knowing the special events, such as a marathon or a parade, happening in the local community, you can better gauge how demand for your supply may be impacted—perhaps cutting off customer access to your local retail partner, or creating an even greater opportunity to sell your good to a wider audience.
  • By staying on top of traffic patterns, you can better plan customer or supply deliveries so that you always have the items you need when you need them—and I’m not just talking in the United States. I mean around the entire world.
  • By being aware of a local victory—or tragedy—you can be more helpful and emotionally aware of the communities’ experience. For instance, if a flood makes product delivery to a certain community challenging, you can contact customers in that region, let them know you’re aware of the problem, and even offer a certain discount or free item to help them through their difficulties. This type of partnership between supply chain and marketing will make customers even more loyal customers over time.

The most exciting part: with machine learning, all these insights can be gleaned instantly, for every market you serve. With Metro Pulse, you can become a local expert of every community you serve, better anticipating their needs and wants, while also finding the best, fastest, and smartest way to provide it to them.

Machine learning and AI have already proven their value in the marketing realm, but I think it’s time for them to burst the industrial and supply chain sectors wide open. When partnered with the vast amounts of information that can now be gathered via the Internet of Things (IoT) there is truly no limit to the insights and knowledge you can gain your customers and their local communities. (Check out my article IoT Driving Another Industrial Revolution for more about that.) In my view, machine learning will become an absolute staple to the supply chain sector. It will become one of those factors that determines which companies succeed—and which companies die out—in digital transformation.

Additional Articles on This Topic
IoT Driving Another Industrial Revolution
How Machine Learning can Give Us Greater Customer Insights
Taking Machine Learning to the Next Level

This article was first published on IBM Retail Industry Blog.

Daniel Newman is the Principal Analyst of Futurum Research and the CEO of Broadsuite Media Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise. From Big Data to IoT to Cloud Computing, Newman makes the connections between business, people and tech that are required for companies to benefit most from their technology projects, which leads to his ideas regularly being cited in CIO.Com, CIO Review and hundreds of other sites across the world. A 5x Best Selling Author including his most recent “Building Dragons: Digital Transformation in the Experience Economy,” Daniel is also a Forbes, Entrepreneur and Huffington Post Contributor. MBA and Graduate Adjunct Professor, Daniel Newman is a Chicago Native and his speaking takes him around the world each year as he shares his vision of the role technology will play in our future.

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