Feature
Artificial intelligence (AI) is being used to improve processes across the logistics industry, from predicting demand and managing inventory to optimizing transportation and increasing visibility. More than half of logistics companies believe that AI usage will grow, saying that most companies will use AI in the next few years. Here, we look at several examples of companies applying AI to inform and power logistics.
1. BMW
BMW car buyers love to customize their ride. They buy 2.5 million cars a year and 99% of those are tweaked before purchase to match the buyers’ dreams, according to a case study. This is a logistical challenge for manufacturing.
To help get it right, BMW used NVIDIA Omniverse to capture data in real-time and use AI to create a realistic, accurate digital twin of the factory they use to design the workflows to create new vehicles. This virtual process let engineers pull all the relevant data from disparate sources and work together in a virtual space while they plan the tasks that a total of 57,000 humans and thousands of intelligent robots perform as well as optimize material flow during production.
“Omniverse greatly enhances the precision, speed and consequently the efficiency of our planning processes,” says Milan Nedeljkovic, board member for production, BMW.
Results
- Reduced time and cost training robots
- Improved updates and controls of robots
- Used millions of generated images to train robot movements
2. Best Home Furnishings
Best Home Furnishings was spending $1.2 million annually on shipping. When it was time to evaluate the company’s parcel carrier agreements, the Best Home Furnishings leaders felt they could find savings, according to a case study.
Best Home Furnishings turned to AI-powered logistics company Sifted for help analyzing their spending. Sifted used AI to help review invoices, ratings and discounts to quickly audit transactions and find opportunities. Though the largest carrier they used told the team the shipping agreement was already competitive, Sifted told Best Homan Furnishings otherwise. The company also sought a better deal from another major carrier.
“Sifted’s comparative analysis of the two quotes was one of the most helpful things they did,” says Steve Wahl, CFO and treasurer, Best Home Furnishings. “The amount of additional work on our end was minimal, because Sifted was working behind the scenes to make it easier for us.”
Best Home Furnishings used Sifted’s intelligent analysis to guide their negotiations with the major shipping carriers, garnering significant discounts.
Results
- Identified a 10%-12% savings opportunity in shipping carrier contracts
- Saved nearly $500,000 over a 36-month term
- Saved 15% on gross parcel spend
3. Poloplast
Austrian pipe manufacturer Poloplast struggled with demand planning, according to a case study. Every month, the team pulled out spreadsheets, data and estimates to calculate how much raw material it would need to meet customer demand for its products. A great deal of money and customer satisfaction rode on the estimates. But they had to pull data from a system of inaccurate, disjointed systems to make them.
“The production planners made decisions based on institutional knowledge and work experience, rather than a connection to forecasting,” says Holger Kreisel, head of enterprise resource planning, Poloplast.
Poloplast moved its data and process to Microsoft’s Demand 365 Supply Chain Management as part of its digital transformation and move to the cloud. The transformation was intended to improve the demand planning process and connect all the systems. The migration gave the team a single source of truth for data related to their supply chain.
The app used AI to help support precise forecasting and improved accuracy.
“We are all operating with a shared understanding and asking informed questions now,” says Siegfried Wögerbauer, head of supply chain management and sales logistics, Poloplast.
Results
- Increased demand prediction window from one month to 18 months through collaboration
- Saved significant time on demand planning
- Improved forecasting collaboration by centralizing data
4. Emerson
Emerson wanted to better understand its supply chain to react to events, prevent disruption, control costs and cut carbon emissions, according to a case study.
Emerson turned to Oracle Transportation Management to better access and understand the data around its supply chain. The tool enabled the supply chain team to more intelligently select the right carrier and service level to achieve goals while improving delivery times — despite global disasters and weather events.
“We were able to reroute freight to different modes around volcanoes, hurricanes, floods,” says Don Sorg, director of supply chain systems and solutions, Emerson.
Even when there are no disasters, Emerson improved its on-time delivery and cut costs, and it is optimized all of its transportation to cut emissions.
Results
- When a hurricane approached the Gulf of Mexico, re-routed at-risk products that were already in transport
- During the pandemic, consolidated freight coming out of China and into the U.S. for domestic distribution
- After volcanoes erupted in Iceland, disrupting air transport, Emerson re-routed freight
5. IBM
IBM’s supply chain was running on legacy systems spread across global organizational silos even while the company itself was busy developing emerging technologies, such as AI, the internet of things (IoT) and edge computing.
“We saw the advances IBM was making in all these new technologies,” says Ron Castro, VP of supply chain, IBM. “So we asked, ‘Why not leverage our own technology to move our own supply chain forward?’”
IBM set out to build a cognitive supply chain that used data and AI to lower costs, exceed customer expectations, reduce labor and improve the experience for everyone working on the supply chain team. IBM Consulting helped design the supply chain transformation. The process is powered by IBM Cognitive Supply Chain Advisor 360, which runs on IBM Hybrid Cloud and Red Hat OpenShift.
The cognitive supply chain provided the company with real-time, intelligent supply chain visibility that can respond to changes in demand as they happen. It used Watson as an interface, allowing users to use natural language queries to get answers.
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“Guaranteed supply is important, but many of our clients are also looking for predictability of supply,” Castro says. “The tools we have now help us address both issues. They enable us to manage the demand side to meet the right client expectations.”
Results
- Provided employees with immediate access to the information to mitigate disruptions
- Fulfilled 100% of orders byre-sourcing and re-routing parts as necessary, despite dislocations during pandemic
- Reduced time to answer supply chain questions from hours to seconds
