Wednesday, 6 December 2017

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 constant jams and delays—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 teams, and customers; however, in this case, it also leads to lost production time—and lost profit. Luckily, artificial intelligence (AI) and machine learning (ML) are offering smarter ways to connect our supply chains via big data. And the best part is that you don’t need to understand big data to take advantage of it.

The Great Supply Chain of Data

The truth is that we have reached 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 you needed to, you could pull data about everything from peak temperatures in your key markets to the average efficiency of a process 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 ML and AI come in.

IBM Tutorial and Materials, IBM Guides, IBM Certifications

Solutions like IBM 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. This tool doesn’t just read the data for keywords or trends. It can interpret it—everything from traffic and weather to local holidays and news—and put it into meaningful contexts. In effect, it can merge hyperlocal data with your company’s data to give you the potential to be even more efficient, productive, and profitable.

Creating Smarter, More-Connected Supply Chains

So how does local data make a difference to your global supply chain? Lots of ways. Take a look:
  • By understanding the weather in a certain region, you can better target the supply—and eventually the marketing—of your company’s goods there, be they warm drinks or umbrellas.
  • By knowing the special events happening in local communities, such as marathons or parades, you can better gauge how they may affect the demand for your products—perhaps, negatively, by cutting off customer access to your local retail partners, or by creating an even greater opportunity to sell your goods to a wider audience.
  • By staying on top of traffic patterns, you can better plan deliveries of goods and supplies so that everyone always has the items they need when they need them—and I’m talking not just in the United States, but around the world.
  • By being aware of a local victory—or tragedy—you can be even more helpful and emotionally aware of a community’s experience. For instance, if a flood makes product delivery to a certain community challenging, you can let customers in that region know you’re aware of the problem, and perhaps 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 over time.
The most exciting part: with machine learning, all these insights can be gleaned instantly, for every market you serve. With IBM Metro Pulse, you can become a local expert on every community you serve, better anticipating its needs and wants.

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

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