From the December 2024 print edition
It’s an extraordinary time for the supply chain and commerce worlds.
In the wake of a pandemic, there has been dramatic uncertainty in demand and supply, trending inflation fluctuations, labour shortages and rising shipping costs.
If that’s not enough, e-commerce put the pedal to the metal, consumers want shorter lead times, better service and effortless refunds and returns, and they want all this along with a company’s strong commitment to sustainability. Supply chain professionals have a hyper-focus on warehouse management and are leaning on emerging technologies such as artificial intelligence (AI), robotics, and advanced analytics to enhance and expand on their management systems.
“What COVID did was create a lot of challenges and opportunities, especially with labour,” says Ramu Pannala, vice-president of supply chain technology, Penske Logistics. “The trend that we see within warehousing in general is that both warehouse management and warehouse management systems have a heavy focus on automation – whether it’s robotics within the warehouse or automating tasks in the office. And we’re also seeing a lot of machine learning and AI-based tools that automate tasks in the warehouse.”
Penske uses Blue Yonder, a tier-one warehouse management system (WMS) which is designed to unify data, the supply chain and commerce operations. And working with Blue Yonder, Penske is bringing in AI and machine learning to work alongside its WMS.
“Two big trends are automation and incorporating AI and machine learning within the WMS platform. We have done a few projects where we have an Each-Picking Robotics solution that was implemented at one of our larger regional distribution centres (RDCs). We have implemented machine learning AI-based tasking or prioritization of tasks within the warehouse, such as tasks based on the due date,” says Pannala. “The process itself has the intelligence built in that will eliminate the need for someone to manually go in and change the priority. The third thing we’re working on is automating the yard, which closely relates to automating the warehouse.”
Walmart Canada is a people-led, tech-powered omnichannel retailer, according to Matt Kelly, the company’s vice-president of supply chain planning and design. “We continue to invest in and expand the use of technology throughout our supply chain to get products into the hands of our customers faster,” he says. “We are leaning into AI and machine learning across our supply chainto support inventory management, forecasting, storage profiling and routing optimizations.”
“The use of unified WMS solutions supports seamless omni-integration across the supply chain and enhanced integration with automation and robotics providers, including picking optimization, multi-channel inventory management, and faster order processing,” adds Kelly. “Other technologies that work alongside our WMS include blockchain, integration layers for robotics and automation warehouse control systems (WCS), microservices platforms, and the Internet of Things (IoT).”
Ensuring automation works
Bard Critoph, a professional engineer with over 35 years of supply chain industry experience and a managing partner of Critoph Consulting Inc. (CCI), says that he has seen companies invest
in automation and can’t seem to get it working properly. It’s quite common and definitely the kind of thing you want to avoid.
In the past year, CCI launched a real-time 3D simulation platform that brings warehousing automation design and optimization projects to life. It’s designed to help companies improve efficiencies, add to a production line, or build a new facility.
For those who invested in automation that isn’t working for them, Critoph says, “It might be worth doing a simulation to see if you can fix the issue before tearing it out and starting from scratch. Without a simulation all you have is drawings and some data, which makes it hard to visualize. But when you see the pieces moving and integrating, it opens your eyes to what’s really going to happen.”
This simulation platform uses real data. Along with the warehouse design, which can be provided by the client or designed by Critoph, data on production peaks and surges, schedules, sequencing logic, and inventory movement to and from a facility will all be built into the simulation for evaluation.
“We will look at how the sequencing logic impacts the size of the equipment. There can be pick waves, and how will you generate those? How are you doing the slotting of the product? There are all kinds of options to test,” says Critoph.
“If there’s an expensive proposal for automated warehouse operations, this platform can simulate
it and help senior management with the decision of approving such a capital investment.”
According to Pannala, software companies are making it easier to integrate new solutions within an existing WMS. “It’s considered an integration hub, or soft hub. Not one solution will be able to
do all the things you want it to,” he says. “When it comes to incorporating AI and machine learning, task automation was the first one we saw. The other big one is dynamic slotting, which is based on seasonality and fast and slow movers. It can enable you to find optimal storage space for specific SKUs to meet demand. It optimizes warehouse processes by eliminating travel time, et cetera.”
AI is also being used for office automation for shipping and receiving tasks. Based on previous learnings and the demand AI sees, it can automatically prioritize and disseminate tasks. And Penske is using drones for cycle counting.
“The drone can read the label, but it needs to understand how to read it, so there are variations that you tweak with AI so that it’s more accurate in reading the data,” says Pannala. “To automate the yard, we’re using cameras. When the camera reads the trailer number it needs to know where it’s located, how to read it with text processing and then what to do with the data. This is all based on AI.”
Gains in efficiency
With all of this technology generating copious data, companies can leverage it for unprecedented efficiency gains. It not only gives them insights into the operation, but using such data helps predict future demands, maintain inventory levels, and optimize storage layouts.
“What Penske has done is we use a cloud-based system. We take all of the data in real time from our WMS and we have a robust analytics layer that is deployed in the cloud so that people can get a near real-time visibility to what’s happening on the site and how labour is performing,” says Pannala. “We use a labour management system that’s integrated into the analytics platform, and it has been a game changer for Penske. On day one we got our KPIs up and running—you’re not spending so much time in the office gathering data from different sources. By getting the data directly from the analytics platform layer, you can manage productivity and provide real-time performance visibility to the customer.”
Kelly says, “Advanced data analytics and business intelligence tools have provided us with actionable insights that help optimize our warehouse processes, predict demand, and improve our overall decision making.”
As more companies invest in automation, robotics, AI and predictive analytics, warehouses are becoming more efficient with faster delivery times, lower costs, and decreased dependency on human labour. As these technologies continue to evolve, the future of warehouse operations is bright as they continue to reach new heights.