In this episode, we’re speaking with Lucas Schorer, Co-Founder at Kestrel Insights, on how geofencing powers our world and what it means for our...
Episode 1 of What's Up Dock? Podcast
In this episode, Keith Moore, CPO at AutoScheduler.AI and Supply Chain Technologist, shares his perspective on the current trends and future of workplace automation.
Automation is revolutionizing almost every industry, but algorithms still need people to point them in the right direction.
Keith Moore, CPO at AutoScheduler.AI and Supply Chain Technologist, works with some of the largest companies in the world to orchestrate a balance between automation and existing supply chains. In this episode, he shares his perspective on the current trends and future of workplace automation.
Join us as we discuss:
- Designing automation to fit large-scale projects
- Tech development and maintenance
- Human elements of automation
Designing automation to fit large-scale projects
It’s no secret that companies everywhere have a surplus of data — knowledge that could help guide their business to new heights if they only knew how to leverage that data appropriately:
- What data do we have?
- What data do we need?
- What does a good project look like?
- How can I use artificial intelligence?
As nice as it sounds, once data is collected, it won’t just tell the business what to do. Think of it like a raw material that has to go through several processes before it’s ready to be consumed appropriately.
For data strategy, it can be separated into 3 tiers:
Visibility: Can you use the data to find connections?
Predictability: Can the data help predict the future of the business?
Prescription: Can the business change direction using the predictive data?
Think of a supermarket that collects data and finds out that customers tend to purchase bananas and grapes together — The business could then predict that, over the next year, bananas and grapes will continue to be purchased at the same time. But what can be done with this information from there to influence future sales and increase profits?
Tech development and maintenance
It becomes clear that leveraging data in a productive way is difficult for any business; but what happens when it’s a large-scale project? While many companies are coming around to the idea of automation, some still have a long way to go.
Keith’s biggest surprise through the COVID-19 pandemic was seeing just how many warehouses still hadn’t made the transition to the cloud — often seeing a server under a desk hold their management system instead.
“We break every site into a series of actors, actions, constraints and flows. We drop all that in a massive configurable algorithm, and then the optimization can run.” – Keith Moore
Working with the warehouses, Keith needs to pull in a substantial amount of data in order to map the total layout into AutoScheduler.AI. Only then can the site use its data to its full optimized potential. A process that supercharges the organization but requires a lot of trust.
Why then, with so many benefits to the new AI system for warehouses, do so many organizations resist the change? Compare it to ripping out all of the plumping in your house for a new optimized system. The company must truly understand why this change is so crucial to their future success and not just a minor improvement.
The only way to build that trust and willingness to change is to break down how AutoScheduler will not only manage the warehouse, but act as a brain, looking for the smallest inefficiencies and correcting them.
“You have to be in some of the first engagements with customers. So we can get a real feel for the operation and draw out all the different inventory flows through rebuilding.” — Keith Moore
Human elements of automation
Once AutoScheduler is in place, however, that’s not the beginning and end of automation. Going back to data, the system can only be as smart as the information it receives. As such, Keith emphasizes how important it is for organizations to understand their needs exactly if they want to find true success with the AI technology.
“If you can't explain it as a person, there's no way you can expect any automated machine learning system to do it well.” — Keith Moore
A visibly simple process might be much more complex when you break it down into every step. Think of the process of unloading crates from a truck. Who will transport those crates to where and at what time will all this be done?
At the end of the process, there might be 100 different steps to take just to unload the product. Make sure that, as an organization, you’re getting the most out of your investment.
Connect with Keith at https://www.linkedin.com/in/keithdmoore13/
Keep connected with us by subscribing on Apple Podcasts, Spotify, or anywhere you get podcasts. Find all the episodes here.