AI is just now coming into its own, and it just so happens it’s at the same time there’s more adoption in the global logistics and supply chain fields. According to Adobe, 15 percent of all organizations are currently using AI, while 31 percent have plans in place to implement the technology by 2019.
The acceleration of AI is being driven by two critical elements: the advancement and growing capabilities of the technology, and the widespread digital transformation that’s taking place across most industries. The same can be said for its adoption in the supply chain. One characteristic stands out even more, though: efficiency.
AI is being used to create a more efficient and reliable supply chain, which includes real-time insights and accurate decisions. You could even make the argument for predictive solutions, which allow providers to act before an event while knowing how things are going to play out.
Developing a Smarter and Leaner Supply Chain
As data becomes more intrinsic to day-to-day operations within the supply chain, so too will the ability to ingest, process and extract insights from said streams. The handling of this data is key to building a smarter, more efficient world. Data will be used to build predictive models, inform and improve existing operations, discover new opportunities and build stronger automation tools.
If data is the key to a more productive environment, then AI is the tool that will be used to deal with such a massive influx of information. It would be incredibly difficult for human analysts to keep up with the demand, no matter how much experience and talent they have. For instance, MIT’s Auto Tune Models, or ATM platform, can complete tasks 100 times faster than human data scientists. This is not theoretical — it’s something that’s happening right now.
AI and machine learning technologies are not just able to handle such large amounts of data, but they can also do it at unprecedented speeds. What may have taken months or years to sort through in the past can now be done in a matter of hours.
Here are a handful of tasks AI can accomplish in the supply chain:
- It can build precise demand forecasting models.
- AI powers many of the robotics and automation hardware used in manufacturing, order fulfillment and storage.
- It can analyze and suggest improvements for existing operations.
- It not only creates contextualized solutions, but it also speeds up client and customer interactions.
- It can digest supplier-related information including delivery performance, audits, credit scoring, evaluations and collaborative processes.
- It can take over customer service channels to increase client loyalty. Chatbots, voice assistants and automated outreach are just a few examples of this in action.
- Both production planning and factory scheduling are improved for every player connected to a supply chain.
- Driverless or autonomous vehicles, which are starting to creep into enterprise fleets, are made possible thanks to AI.
AI can also be used to power specific tasks for each player. In development and design, for example, the technology can help with pr-compliance EMC testing by identifying potential points of failure. This is huge, considering 50 percent of products fail their first round of EMC testing. Imagine discovering major contention points — like radiated emissions outages — early on in the process?
Alternatively, and in distribution, AI can help build accurate demand profiles to deal with market changes, whether it’s related to materials availability, consumer demand or something else entirely.
Time Heals All Issues
AI isn’t perfect, however. While it can certainly improve the efficiency of the supply chain, enhance logistics tools and platforms, and present new opportunities, there will be some growing pains. If it’s implemented too early, and by those who do not understand it properly, it’s possible those benefits may never be realized. Worse yet, there’s always the potential for catastrophic productivity issues if the system in place fails.
The best way to prevent such a thing from happening is to honor a careful, calculated and systematic rollout, not one that is instant. As with most things, time can help alleviate most of these issues — especially as the industry learns how best to utilize the technology.
