Let's Talk Supply Chain

Scott Jennings of Anaplan talks about operationalizing AI in planning, eliminating latency tax, the shift to autonomous multi-agent systems, & your next steps. 
IN THIS EPISODE WE DISCUSS:
[02.52] Scott's role at Anaplan, what they do, and their ideal customer profile.
"If you're not collaborating, you're going to get stuck in siloes - and that's what we hear from companies time and time again. It's that ability to collaborate with your peers, and with all the different technology that's available, that determines success or failure."
[04.38] From use case selection and success criteria to cost, why – despite huge investment in AI experimentation – many organizations are still struggling to demonstrate tangible business value, and why 2026 is the year of change when it comes to businesses moving from AI experimentation to proving ROI.
"Companies are beginning to ask questions, about trade offs – did they pick the right use cases to experiment with AI? They're not sure. Did the experiment work, and can it be scaled?  They're not sure. And then the bill comes, and it turns out that tokens are expensive."
[08.10] What 'AI ROI' actually looks like in planning, and the metrics executives should be looking at to know whether an AI investment is genuinely paying off.
"Finance talks a lot about working capital, but the moment you move to supply chain you're talking units. Bringing those two concepts together, so you can understand the financial impacts across the board instantly, is what's been driving most of the AI projects I've seen."
[12.18] Why planners are often reluctant to rely on AI recommendations, and what organizations need to do to move from a 'black box' to a more explainable, transparent approach.
[15.56] Why 'AI for AI's sake' can actually create more problems, and why it's critical to ensure your data is ready for AI use.
"When you think about AI, you want to think about what's going to drive the big metrics that are important to your organization, and then work backwards. What you don't want to do is just start running AI projects… sometimes the cost is higher than running the exact same project with humans!"
[20.17] The importance of real-time planning, the cost of the 'latency tax' when decisions are delayed because data, teams and systems aren't connected, and how significant that impact can be for large organizations.
"You can lose 5 cents on every dollar due to decision delay. And that's millions of dollars for a large company."
[23.43] Why a common business ontology and semantic layer are critical in helping AI understand how demand, supply, inventory, capacity and financial outcomes are connected to drive more effective results.
"Ontology is a map of your business that AI can use to better understand how it operates, because AI needs to be told a lot about your business to be effective."
[26.40] How an AI system can move from identifying a change in demand to understanding its implications for things like bills of material, plant capacity, lead times, inventory buffers and ultimately the P&L.
[30.09] How organizations should think about combining probabilistic AI – for things like demand sensing and pattern recognition – with deterministic calculation engines for financial math, rules and governance.
[34.48] How the role of the planner shifts when AI stops simply answering questions and begins continuously monitoring the business, generating scenarios and routing exceptions for people to act on.
[37.23] Multi-agent AI systems – what a world of specialized AI agents look like in a planning environment, and where humans should remain in control.
[41.11] What organizations should be looking for in a planning platform, and the importance of capabilities like an embedded calculation engine, business-owned models, connected data and native AI.
"From an agentic AI perspective, you have to have a deterministic and scalable calculation engine with purpose built supply chain planning models that are connected to all relevant internal and external sources."
[44.05] How organizations can identify a high-value planning use case, prove ROI quickly, build trust and scale AI across the enterprise.
 
RESOURCES AND LINKS MENTIONED:
Head over to Anaplan's website now to find out more and discover how they could help you too. You can also connect with Anaplan and keep up to date with the latest over on LinkedIn or YouTube, or you can connect with Scott on LinkedIn.
If you enjoyed this episode and want to hear more from Anaplan, listen to 514: Turn Strategic Planning Into Retail Profitability, with Anaplan or 499: Navigating the EV Revolution, with Anaplan.
Check out our other podcasts HERE.

Bryan Keller of Keller Logistics Group talks about what they do; preserving a family-business culture; & the challenges & innovations defining logistics. 
IN THIS EPISODE WE DISCUSS:
[02.22] Bryan's journey in the Keller family business, from summer internships to CEO.
[07.07] Keller's near 50-year history, how and why the company was founded, and the biggest turning points that transformed Keller from a trucking company into a full logistics provider.
[11.47] An overview of Keller – who they are, what they do, and how they help their customers.
"I've looked at customer diversification, service diversification, geographic diversification – all for us to remain a privately-owned company for the next 100 years."
[15.27] The ideal client profile for Keller Logistics Group.
[17.29] The challenges customers typically see day-to-day, and how Keller supports them through disruption.
"The reasons for the disruptions are different, but they cause the same issues… You've just got to know how to react to that volatility, and create solutions for your customers where they can have a variable cost structure. Because no-one wants to pay for a truck that's not working or empty concrete."
[18.47] Keller's approach to working with customers to tackle challenges and design the right solutions.
[20.16] A case study exploring how Keller helped a key client find the right balance between spot price and dwell time to save them $800,000.
[22.15] The most exciting logistics innovations changing the landscape right now, and in the future.
"What's exciting is the data we now have… It's taken a lot of noise out of the equation, so we can now really work on making the supply chain more efficient. Where we need to go next – dynamic scheduling."
[25.41] What differentiation looks like for Keller and how that, in turn, creates competitive advantage for their customers.
"Our go-to-market strategy is: how can we save our customers money? How can we create that raving fan?!... Some of our competitors are looking to generate margin, looking at each deal as a one-off, not as a lifecycle."
[28.43] Why Keller's mission centers their people and community just as much as their customers, and how they preserve a family-business culture as the organization grows.
"It's the Keller 'Circle of Life' – we deliver for our people, they deliver for our customers, and we give back to the community to enrich as many lives as possible."
[33.07] The challenges and potential technology innovations that will shape the future of logistics.
"What's AI going to bring to the table?"
RESOURCES AND LINKS MENTIONED
Head over to Keller Logistics Group's website now to find out more and discover how they could help you too. You can also connect with Keller Logistics Group and keep up to date with the latest over on LinkedIn, Facebook and X (Twitter), or you can connect with Bryan on LinkedIn.
Check out our other podcasts HERE.

Michael Rentz of Gnosis Freight talks about where organizations are quietly losing money & moving beyond visibility to turn logistics data into cost savings. 
IN THIS EPISODE WE DISCUSS:
[02.50] Why logistics technology is still treated as a cost center, rather than something that can directly improve margin, and how to make that shift.
"If you have the right teams, mindsets and technology partner, you can get to a point where it's not a cost center, it's a growth center."
[07.49] What changes when a supply chain leader starts looking at logistics data through a P&L lens rather than an operational one.
"During the pandemic, for the first time probably ever, we had CFO's asking: "What the hell is demurrage and detention?!"
[11.27] Where companies are typically losing money in their logistics operations.
"A lot of enterprises have had to make up a for a lack of reliability, and that translates into carrying more inventory… How much it costs to move goods is also at a premium."
[15.07] How you connect operational events – like a delayed container, missed free time or an inefficient dray move – to their actual impact on landed cost, the value of data, and the problem with the 'flaw of averages.'
[17.58] How, from documentation to communication, small operational issues can easily multiply to become significant margin problems.
"A best in class standard for an international logistics team is 'Did we hit budget?' I'm not familiar with people asking 'Did we beat budget?'
"Your most valuable assets – your people – are being asked to do the exact same task in a slightly different way 50 times a day. How much better could the system be running if you freed up that headspace for them to think critically on behalf of the business?"
[19.51] Why we need to think differently about KPIs and budgets if we're going to see supply chain as a value driver not a cost center, and where team priorities fit in.
"You have to get teams off the back foot, from a defensive to offensive posture; get them back into doing the work that's actually in their job description."
[27.46] How better data can help resolve invoice errors and help companies identify and recover money they're currently leaving on the table.
"Invoice discrepancies shouldn't be a driver for profitability or growth, you should always get what your bargained for… AI has unlocked a huge amount of capability when it comes to reconciling matching invoices. The key unlock for us is having the underlying data."
[30.41] The key metrics you should put in front of a CFO to prove that better logistics data is delivering a real financial return, and how Gnosis helps customers, through data science, tooling and service, with an outcome-first approach.
[40.26] Finding the balance between AI and tribal knowledge, why it's important to think about how dynamic your data really is, and why context is critical.
RESOURCES AND LINKS MENTIONED:
Head over to Gnosis Freight's website now to find out more and discover how they could help you too. You can also connect with Gnosis and keep up to date with the latest over on LinkedIn, Instagram, Facebook and X (Twitter), or you can connect with Michael on LinkedIn.
If you enjoyed this episode and want to hear more from Gnosis Freight, check out 559: From AI Hype to Business Value: The Future of Supply Chain Intelligence, with Gnosis Freight.
Check out our other podcasts HERE.

Geoff Tamman of Shipium & Dean Freson of ODW Logistics talk about reframing peak season as a margin question; simulation; network redesign; & demand surcharges. 
IN THIS EPISODE WE DISCUSS:
[03.08] Why peak planning conversations are almost always about capacity, labor, carrier commitments – but much less often about margins.
"It's an overlooked topic, until it's too late… A small rounding error in August is multiplied exponentially in December – that one penny becomes millions."
[05.25] People, systems, or carriers – the first thing that breaks on your worst peak day.
"Peak is a four letter word, but the real four letter word is cost. Those peaking surcharges, volume tiers… How are you managing those components, to ensure you're putting the right capacity in the right carrier in the right method, to manage cost structures and margins?"
[07.52] What you can still influence in October, and what's already locked in.
"If you haven't already, take a second look and audit your contracts."
[11.20] When you hit peak, the network runs hot and volume triples; the cost per parcel changes first, and what often surprises people.
[12.52] Managing demand surcharges, and what cost can look like at different times of year.
[16.44] How you can determine whether you're making money during peak, instead of waiting to find out in February.
[18.25] The signals to look at in the first week of peak that tell you whether the network is still economically healthy, rather than just keeping up with volume.
"Start to pay attention to the lanes, the origins and destinations."
[22.36] How much a 'good' routing or rating decision matters when you're processing millions of parcels, and what happens when that decision gets slower or less accurate.
"'Good' isn't good enough. You need precision, alerts and alarms and all those things. But, more so, you need a system and services that are looking at all at these micro decisions and asking: If you're doing a million units a day and one is off, why is that one unit off?"
[25.54] The peak decisions Dean would simulate now before committing to them, rather than discovering the answer later down the line.
"Having the ability to simulate helps us make a business decision rather than a gut reaction, and understand the impact financially."
[30.44] The operational improvement that has had the biggest measurable impact on cost per parcel at ODW, and how they identified it.
[31.34] What last peak taught Dean that changed ODW's network design.
[33.35] Which parts of last year's peak network Geoff and Dean would redesign today with everything they know now.
"Understanding exactly how the carrier volumes and mix plays into the peak surcharge, and how it's calculated. Last year we didn't have all the same tools we have available today, so we're in a better place now with Shipium as a partner."
RESOURCES AND LINKS MENTIONED:
Head over to Shipium's website now to find out more and discover how they could help you too. You can also connect with Shipium and keep up to date with the latest over on LinkedIn, or you can connect with Geoff or Dean on LinkedIn.
If you enjoyed this episode and want to hear more from Shipium, check out:
502: Current Success vs Future Potential: Finding the AI Balance, with Shipium
491: 2028 – Shipium Counts Down to the AI Revolution
299: Modernize Your Ecommerce Supply Chain, with Shipium
Check out our other podcasts HERE.

Matt Elenjickal of FourKites talks about AI: key roadblocks to keeping exec buy-in; focusing on outcomes, metrics & shifting from automation to execution.
IN THIS EPISODE WE DISCUSS:
[02.52] The mindset shift needed in AI and new technology, and why the real question isn't "What can AI do?" but "What outcome am I accountable for?"
"In my role, I talk to a lot of supply chain leaders. And every leader is accountable for an outcome, not a capability. So a VP of supply chain isn't measured on whether or not they've adopted AI – they're measured on whether the product is arriving on-time, in-full, and at the right cost."
"Most conversations start with technology and work backwards."
[05.40] The difference between optimizing an individual task and optimizing an end-to-end business outcome, and why an outcomes-first approach is so important as companies invest more heavily in AI.
"Teams are optimizing in siloes, and that doesn't add up to C-Level outcomes."
[11.00] Why applying AI to internal data often hits a ceiling, and the role external data plays in making AI genuinely effective.
"The ceiling is the context. So when you work with your internal data, you're only optimizing what you know. You have no idea what's happening outside… But there are a lot of external factors that are impacting your supply chain."
[14.26] Why the conversation is shifting from AI that automates to AI that actually executes, the difference between the two, and how executives should rethink their AI strategy if they want measurable business outcomes rather than isolated productivity gains.
[16.51] Why, as AI takes on more execution, traditional business metrics don't tell the full story, and why 'invisible success' is a real challenge for leaders.
"When you think about traditional metrics, they count what happened. But when you think about prevention, it's measuring what didn't happen. And it's very hard for a CFO to credit something that didn't happen."
[21.03] The new metrics organizations should be looking at to understand AI's real impact.
[23.43] What a successful AI-enabled operating model looks like when everyone is measuring the right outcomes.
"Start with the people. With the right model, they'll stop firefighting and start leading."
[28.32] From 'the pilot trap' to data readiness, the biggest barriers preventing organizations from realizing the full value of AI, especially when it comes to buy-in, and how FourKites tackle them.
[32.50] How unrealistic expectations or poor implementation lead to loss of executive sponsorship.
"Sponsorship survives when you set the floor, not the ceiling."
[34.56] Matt's advice for executives trying to maintain board confidence as they scale AI initiatives.
[37.28] What the most innovative organizations are doing today that others haven't caught up with yet, and what's next.
"They've stopped talking about tools."
[40.22] Matt's big predictions for the future of enterprise AI.
 
RESOURCES AND LINKS MENTIONED:
Head over to FourKite's website now to find out more and discover how they could help you too. You can also connect with FourKites and keep up to date with the latest over on LinkedIn, Facebook or YouTube, or you can connect with Matt on LinkedIn.
If you enjoyed this episode and want to hear more insightful content from FourKites, check out:
550: Discover The Blueprint For Your Autonomous Supply Chain, with FourKites
536: Prevent Supply Disruptions and Protect Revenue, with FourKites
235: Use Real-Time Visibility To Transform Your Entire Supply Chain, with FourKites
Check out our other podcasts HERE.