Artificial Intelligence Supply Chain Applications of Advanced Analytics

Line Art

It’s well established that artificial intelligence supply chain systems and advanced analytic cloud platforms stand to improve performance at all points, from upstream raw materials suppliers, to finished goods makers, to distributors and merchandisers. That’s a shared competitive advantage for all partners in the system.

If you’re interested in implementing such collaboration, even if it is only internally, a big question is where to begin. With an artificial intelligence supply chain analytic platform? With the right people in place who are committed to exploring ranges of possible improvements? Both steps simultaneously? Whatever your right answer is, the process for building a more collaborative value chain will bring substantial change (even upheaval), which will require a top-notch change-management program.

That said, let’s get back to our focus on technology by highlighting briefly how the effective use of AI and advanced analytics can raise the performance of your organization, and potentially your shared value networks.

Shared strategies for shared benefits

The supply chain is a continuum of both risk and opportunity. To minimize risk and maximize opportunity,  that tomorrow’s supply chain tool set should include advanced data analytics, cognitive computing, and smart sensors (such as embedded RFID technology). Continued advances in each of these fields will help supply chain organizations automate routine tasks and forge better supplier networks. The ultimate goal is to share end-to-end data and information loops that connect entire chains in real time: raw materials to point-of-sale and everyone in between. These connections and collaborations will not only save time and cost from the system, they will improve forecast accuracy, multi-point planning, and profitability.

Automating routine, manual tasks

From picking and packing to sorting and shipping, the effective management of resource-intensive steps is integral to bottom-line outcomes. In some cases, this means developing more efficient processes. In other cases, it should mean full-on automation. Either way, variables such as weather, demand/supply variability, and more will pose a constant challenge.

While some planners remain reluctant to introduce automation and optimization through more advanced systems, Forbes notes that these investments are essential to maximizing supply chain efficiency and are essential to growth.

In short, AI and the use of predictive analytics can help organizations optimize plans, levels, and policies, such as for production and delivery scheduling, inventory levels, and much more.

Transport and fulfillment

 that by 2020, 50 percent of mature organizations and networks will be using AI and advanced analytics for supply chain.

Applied to transport and fulfillment, these technologies are converging with developments in other fields, such as RFID shipment tracking and mobile computing, to improve visibility and transparency like never before.

With so much more data being created, machine learning models will become increasingly relevant, able to accurately interpret unstructured data, simulate outcomes, and suggest best possible solutions.

The warehouse floor

Gartner predicts that by 2021, autonomous mobile robots will replace one in 10 workers. Amazon, for instance, was an early adopter of this type of technology. In 2014, they started utilizing Kiva robots in their warehouses, as they work faster than humans and are more cost-effective.

For planners, AI and advanced analytic solutions that emphasize robotic automation (armed with decision automation) can perform menial or repetitive tasks not only correctly, but optimally. This further minimizes human error, reduces employee risk, and slashes downtime.

Self-driving vehicles

With IoT advances, self-driving vehicles utilizing advanced analytics can help supply chains with lifting heavy equipment and transportation. Software can monitor traffic, weather, and ETAs, as well as reduce transportation costs. AI-operated vehicles won’t tire, can work 24/7, and can reroute in bad weather to reduce delays.

The artificial intelligence supply chain future

Advanced analytics that include future technologies add value to supply chains. Decision makers are increasing AI and analytics budgets as they understand that optimized efficiencies maximize future ROI.