The Power of Data

The episode contains a handful of genuinely useful ideas - using agentic AI to dynamically correct ERP lead times rather than waiting for clean data, and the counter-intuitive Unilever finding that focusing on value rather than cost actually cuts more cost - but these are interspersed with extended career biography, change-management platitudes, and broad strategic exhortations that add little density.

“you can use these tools to get your data clean. What I mean by that is creating agents for example that will look at the real lead time in terms of, to procure an item... tracking continuously how long from purchase order creation to goods receipt, uh, are these items taken? And dynamically updating, um, your data pool with real time data”

“when they were focusing on reducing costs, they were actually realizing they were destroying value. And then when they pivoted to actually focusing on delivering value, they actually realized they reduced more costs than when they were focusing on reducing cost”

There are a few memorable framings - the Copernican business revolution metaphor for customer-centricity and the talent-pipeline risk of automating graduate-level work - but the bulk of the episode recycles standard consulting positions (antifragility from Taleb, 80/20 on tier visibility, 'no silver bullets' on nearshoring) without meaningfully extending them.

“we need a Copernican business revolution, so we need to stop thinking that the customer will circle us when we need to put them in the center”

“the people that they brought, they would have brought in to train up are the people that will create the future for that organization. And they've just basically told them they're not needed here”

Sean Cooley is a genuine multi-sector practitioner - decade at Cadbury Schweppes on one of the world's largest SAP roll-outs, supply chain work for the UN and Gates Foundation, and hands-on agentic AI deployment - giving him real credibility. He is, however, a director at a research centre rather than an active C-suite operator at scale, and his current role limits the depth of live commercial examples he can cite.

“the near decade I spent the Cadbury Schweppes, uh, working on what was the world's largest SAP implementation at that particular point in time”

“I was sort of booked to work out in across Africa, um, with um, the Bill and Melinda Gates association working alongside the Global Fund to um, sort of basically sort out all of the supply chains for the malaria elimination program”

The episode earns marks for referencing the D&B Manufacturing Pulse Survey statistics, the Amazon-Kiva acquisition, the Unilever/Sigismondi example, and the early-2000s SAP APO failure at Cadbury's, but many of the most important claims - about antifragility, value networks, and AI opportunity - are argued entirely by analogy and assertion without concrete metrics, timelines, or named deployments.

“only 36% of manufacturers feel confident making informed decisions with their current data and almost half have experienced failed AI projects due to poor data quality”

“Amazon had just acquired Kiva Robotics. So the sort of concept that's inventory moving to the picker rather than the picker moving to inventory”

The host's questions are almost entirely pre-scripted and generic ('could you unpack that concept', 'what are some best practices'), with no meaningful pushback on any claim the guest makes; injecting the D&B survey statistics is the one structural bright spot, but follow-up questions rarely go deeper than 'have you got relevant examples of that?'

“Um, Sean, you've spoken and written extensively about how data and technology are reshaping manufacturing. Um, what inspired your focus on the theme and what do you see as the most significant shifts happening in supply chains today?”

“And from your career and experience, have you got relevant examples of organizations that have been able to, you know, get that visibility into the, into the secondary, secondary and Tertiary supply chain risks”