5 Ways Artificial Intelligence Is Improving Supply Chain Management

The computer age is incredible. Artificial intelligence (AI) and machine learning (ML) are making possible what we once only dreamed of in TV shows like The Jetsons and Futurama. But aside from robot maids and galactic space travel, the greatest everyday impact of real futuristic technology can be felt in practical terms. AI makes everything from Amazon Prime to the delivery of a new iPhone every year possible. It has also made much of the continued manufacture and distribution of critical everyday items possible amidst the largest global health crisis the world has endured in a hundred years, and we’ve only really scratched the surface.

AI has caught on in the supply chain, and with great reason. Sixty-three percent of executives surveyed reported revenue increases with 61 percent reporting cost savings as a result of adopting AI into their supply chain management (SCM). Meanwhile, 61 percent reported revenue gains with 64 percent reporting cost savings from AI adoption in manufacturing. The upside is definitely enticing.

5 Ways AI Is Improving Supply Chain Management

How is AI being implemented in supply chains to improve SCM and manufacturing? Here are just five optimizations a business can achieve.

1. Warehouse Efficiency
   On average, businesses burn about 6,500 hours per year processing paperwork (about 55 hours per week), adjusting purchase orders (about 39 hours per week), and responding to supplier inquiries (about 23 hours per week). These are tasks that can and should be offloaded to automation software.

   In 2016, Google and Baidu spent $20 billion to $30 billion on AI, 90 percent of which was spent on research and development. Amazon and Alibaba have employed automation in the form of machines and robots that box customer orders at their warehouses. Considering the massive scale at these companies operate at and the potential to reduce stocking time by as much as 30 percent using autonomous vehicles, it’s no wonder AI has earned such enormous budgets.
   
   

2. Supply Chain Planning
   Traditionally, supply chain managers have burned countless hours of labor into gathering data from a multitude of disparate systems, attempting to manually make sense of it all. AI reduces the leg work by orders of magnitude. It’s become not just a “nice to have,” but a necessity considering the mountains of data at hand. Once a business graduates past a certain threshold of data, applications, and variables, it’s simply not feasible for the human mind to account for everything.
   
   Extremely scalable AI that is capable of processing terabytes and petabytes of data can do the heavy lifting now, delivering deep analysis on thousands of SKUs. This opens up extraordinary accuracy in forecasting inventory, supply, and demand. Coupling AI with ML, supply chain planning and decision-making is more agile and optimized than ever. The result: more balanced supply and demand along with faster product delivery, all with minimal human intervention.
   
   

3. Next-Level Material-Centric Operations
   Advanced AI and ML enables a material-centric view of operations. Deliverables in this phase include advanced visualizations, cause-and-effect identification, industry benchmark reporting, and cross-plant KPIs. The ThinkIQ manufacturing platform can identify previously unseen correlations and root causes in your supply chain through internal manufacturing processes. Our proprietary software incorporates AI and ML with four unique elements: a next-generation historian, a semantic model, TIQQL (ThinkIQ’s query language), and our material ledger. The combination of these elements leads to a transformative view of manufacturing data to the point of fully automated smart manufacturing, including material flow and dwell times, expressions and valuation scripts, material flow diagram (MFD), material process analyzer (MPA) frequency and scatter plots, and more.
   
   ThinkIQ Insight services include material model configuration, material movement detection module building, custom algorithm writing, baseline performance metrics, specialized dashboards, material-centric reporting, and a plan to achieve the next level. This results in transformative data, alerts, and notifications that bring supply chain problems to immediate attention, mitigate of recall risks, and provide significant yield gains.

4. Chatbots
   Imagine how much time could be saved cutting out the trivial chit-chat with customers and vendors. Chatbots are already capable of handling as much as 80 percent of all customer engagements, such as simple package tracking requests. The value of chatbots goes well beyond helping customers on websites; they can be utilized in non-critical conversations with suppliers, for placing purchase requests, answering internal questions on procurement, and receiving/documenting invoices. Remember those 23 hours a week lost to supplier inquiry response? This is how you reclaim that time.
   
   
5. Autonomous Shipping
   Despite being a bit of a sexy concept (as much as logistics and shipping can be considered sexy) thanks to Elon Musk and Tesla, autonomous trucks do possess the potential to solve a major problem in the supply chain. Two years ago, the American Trucking Association estimated a 60,000-driver shortage in the over-the-road truckload market. The problem has only gotten worse with about 80,000 fewer available drivers than a year ago due to new regulations and other pandemic fallout. Also considering strict restrictions that limit drivers to no more than 11 hours per day without an 8-hour break, a driverless truck would present a boon to the industry and the supply chain, effectively doubling the output of U.S. transportation at 25 percent of the cost.

To learn more about the future of how AI can make a significant difference in your supply chain management, get in touch with a ThinkIQ expert today or you can download our new eBook titled "Advanced Material Traceability Revolutionizes Digital Transformation"