Episode: AI in the Warehouse
Curious Why, When, and How You Should Be Using AI in Your Warehouse or Other Supply Chain Operations?
Mark Butler, Global Partner Manager at Zebra Technologies consulted Ansgar Thiede, Vice President, Data Science, and Justin Velthoen, Product Director, Warehouse Management Systems at Körber Supply Chain Software, to give listeners all information required to make the right decision for their people, processes, and profit targets.
Blog Post by Mark Butler, Zebra Technologies
Are you someone who could use the help of an AI (or just more help, period) to keep things running more smoothly across your supply chain? If so, then you’ll want to listen to the discussion I just had with Ansgar Thiede, Vice President of Data Science for Körber Supply Chain Software, and Justin Velthoen, the Product Director of Warehouse Management Systems at Körber Supply Chain Software.
They have been looking at ways that AI can be applied in the warehouse and broader supply chain a lot lately, and they’ve been involved in the training of some AI tools. So, they have a great grasp on what role AI is already starting to play in these environments and appreciate the larger role it will soon need to play for you to be able to do your job effectively.
Hear all about:
- The most prevalent uses of data science and AI in warehouses and other supply chain environments today, and the types of AI techniques used most often to support these use cases.
- What you and your operations managers, engineers, IT teams and other colleagues will need to do to ensure any AI tools you utilize deliver their promised benefits.
- Why you should consult with data scientists as you’re developing your AI strategy, even if you ultimately utilize low-code/no-code AI models.
- What you can do to make AI feel manageable to your team and drive AI tool acceptance by your management, IT, and front-line teams.
- Where it might make sense to leverage generative AI models throughout the supply chain, whether in a warehouse or distribution center or perhaps even over the road or in a factory. (The potential value proposition might not be what you expect.)
- How easy it can be for shift workers in warehousing and supply chain environments to learn some of these AI applications, and what contributes to fast adoption.
- How much humans will need to remain in the loop when AI is used for forecasting or labor planning.
- What types of decisions could/should be fully automated, and how much risk there is when automating macro decisions versus micro decisions.
Toward the end, we did a deep dive into how AI and data science can be used to adjust your slotting logic to improve velocity, safety, and overall operational planning. Ansgar and Justin also shared their thoughts on whether an AI model used for slotting could be applied to other functions, such as picking.
Right before we wrapped up, they called out one big thing that could hinder you from deriving benefits from AI and explained what to do about it so that you can extract value from AI.
Click here to see the watch or listen to the full podcast.
