When I think of Artificial Intelligence (AI), the first image that comes to mind is Tony Stark’s “J.A.R.V.I.S” computer. J.A.R.V.I.S could answer any question thrown at it. History, math, computational problems… it was what we wished Siri would be when we pulled our new iPhone out of the box and engaged Siri for the first time. This type of AI is called General AI. Currently, we are years away from general purpose AI at the level of J.A.R.V.I.S, although IBM’s Watson is pushing the boundary. What we experience as AI is just an algorithm (or in Watson’s case a collection of algorithms) that acts on data to give the effect of simulating human intelligence.
For the supply chain, AI holds the promise of being able to optimize every action, every expenditure of time, fuel, resources and labor, to maximize throughput in ways that humans would never intuitively do through better optimization.
ERP does not think
The supply chain generates a huge amount of data. In the beginning, this data was manually cataloged (anyone remember inventory cards?) and retrieved. Electronic automation later added speed and scale to what could be logged and processed for the retrieval of the human overlords. Electronic general ledgers developed into huge business scale calculators called enterprise resource planning systems (ERPs).
When the humans have a question, ERP can pull everything together to answer that question very quickly. Today, ERP systems have evolved into huge knowledge centers, and the ERP itself has a perspective of the supply chain that no human could possibly hold in his head. The scale of ERP is beyond human capacity for accuracy and speed of recall… but the ERP does not think. It just responds to the rules and demands placed upon it by the human.
Here come the machines
Enter Artificial Intelligence. AI loves data. And AI can pull meaning out of data that humans can never pull out. In addition to being faster than humans at optimal decision making, the real benefit of AI is in its ability to find solutions to problems that wouldn’t be apparent to even the most skilled analytics experts. We call this unintuitive optimization. It means making improvements that you didn’t even realize were possible or needed.
Have you ever noticed that at the grocery store the beer is by the diapers? That seems like an odd place for it. Wouldn’t it make more sense to have the beer next to other adult products, or complimentary food items like snacks or meat? Well, an analysis of data from a 1992 study that examined 1.2 million super market purchases identified about 20 common product pairings like beer and diapers, as well as fruit juice and cough syrup. This conclusion took years of data mining and analysis from the best data experts. With AI, this unintuitive optimization would be recommended based on data without anyone even posing the question.
In modern warehouses, there is a lot of waste: wasted movement, wasted space, and cash sitting in inventory. What if every warehouse was optimally placed, stocked with the most efficient mix of product and was laid out to absolutely minimize movement in put-away and retrieval? AI holds the keys to driving down costs in ways that were previously impossible. Automation is coming, but automation holds only part of the future. The robots need a brain, and AI will start to appear at every level from the global supply chain to the optimization of a single action within a single warehouse.
For these intelligent warehouses, the AI connected to their business systems will be thinking about their business every second of every day. By analyzing every data point across the entire warehouse, the AI has far greater visibility into the comprehensive operation than any team member ever could. For pennies, it identifies and solves critical operational problems.