Artificial intelligence and machine learning technologies are behind a host of emerging applications, especially for manufacturing demand planning and warehouse applications.
Like most everything in 2020, global supply chains were thrown into disarray during the pandemic. Demand plans and equipment maintenance schedules went out the window with U.S. e-commerce volume growing 44% as consumers stayed home to buy everything, they needed to sustain life. In response, many supply chain managers increased safety stocks to hedge against increased volatility. Now, as we begin to emerge from lockdown, everyone is looking for ways to boost supply chain velocity and efficiency.
Artificial intelligence (AI) and machine learning (ML) technologies seem a natural fit for helping wring out greater efficiency and better decision-making in this area. Steve Banker, vice president of supply chain services at ARC Advisory Group, wrote recently about a host of AI-driven supply chain use cases, ranging from those that are still hype-stage to those with established return on investment. Banker cites, in order from most hypothetical to most mature: blockchain, autonomous trucking, ML for warehouse management, robotic shuttle optimization, ML for transportation, ML for demand planning, real-time location services, and IoT for transportation.
Vendors are rushing to add AI/ML capabilities to their software. But there is real value to be had, now, for use cases like demand planning and warehouse management.
“There’s overwhelming proof that ML algorithms outperform the classic models that were used for building a forecast.”
The main inputs and contributions are:
Demand planning, the low-hanging fruit
- For many companies, demand planning is a good place to begin with AI/ML. ML enables auto-tuning, or automatic adjustment, that is especially useful in so-called “black swan” events like the pandemic.
- The ML capabilities inherent in demand-planning applications can spot patterns and trends in all types of data (structured and unstructured, internal, partner, published sources, public) long before a human planner would. ML is extremely useful for helping optimize pricing and promotions, too.
- Other use cases are using ML to analyze complex sales behavior for costumers and to develop apps for daily forecasting, which was useful for companies that make products that expire quickly, such as fresh meats, produce, and newspapers.
- The next step will be to offer adaptive supply chain planning in a ERP application (a “supply chain digital twin”). The companies will digitally simulate the supply chain, automatically refreshing it with data like bills of material, the run rates, the yields, procurement and supplier lead times to be able to make real-time adjustments to plans and inventory levels. If you can take your lead times from 14 days down to seven, you can make your supply chain much more agile, reducing the amount of inventory and thereby holding onto more cash.
Optimizing warehouse management
- Order streaming involves coordinating and optimizing all the aspects of the distribution center, the people and the automation equipment, as well as the shipment windows and requirements for fulfilling orders. The core of order streaming is the work release engine, which gets inputs from all over the distribution center. The ML capabilities predict how long the work will take, which helps determine the sequence of work and who it should be delivered to. Companies are currently using order streaming to drive lower order cycle times, better picking and packing efficiency, and an elimination of the number of orders that need to be sent with upgraded shipping, which takes a bite out of profit.
- Others are using AI to enable more accurate predictive maintenance of equipment used in order and parcel fulfillment, another application that became more important with the e-commerce boom last year. Companies need to know when the components are going to fail so they can replace them in advance of downtime. Equipment sensor data, such as vibration and temperature, along with other data including hours of operation and scheduled maintenance is used to build the AI model.
- E-commerce demand will continue to reshape supply chains, making efficiency and optimization more important than ever. Toward that end, AI/ML will play an increasing role.
“You can improve things like demand planning, but what really drives value is when you realize what data you have. Investing in that data is just as important as what you want to do with it.”