Supply ChAInge

About Supply ChAInge

Join supply chain expert Derek Aranda for a series investigating the challenges and opportunities of cognitive supply chains. We interview enterprise-level experts in AI, risk, change management, ethics, HR, and logistics to gain an understanding of the real-life implementation and impact behind intelligent supply chain management. Like all good investigations, our conclusion is not a foregone one, as complex systems require complex analysis, and the cutting edge of technology, well, it can be sharp.Join us on Supply ChAInge to ask the question behind the question: not just can a cognitive supply chain work, but what does it take at scale?

Publisher

Derek Aranda

Category

business

Language

en

Explicit

No

First episode

4 Jun 2026

Latest episode

26 Oct 2026

Latest episodes

11 episodes in the feed.

1. 

   26 Oct 2026

   Volatility Is an Optionality Problem (with Johnnie Moore)

   Johnnie Moore of The JR Moore Group on supply chain resilience, Bestshoring, and AI adoption in logistics: why volatility is an optionality problem rather than a forecasting problem, and why applying AI to a broken process only exponentiates the problems you already have. The conversation covers why standardized processes are the floor for any AI deployment, how to build governance that empowers people to act before a crisis instead of during one, why trust in AI should be built gradually like testing a flimsy lawn chair, and how the "watermelon effect" leaves companies hitting every KPI while their customers stay unhappy. 🎧 Episode Highlights [4:16] Bestshoring focuses on where logistics work and processes live, not where goods are sourced. [9:18] AI makes options cheaper and faster to exercise, but only on top of standardized processes. [12:00] Governance should be built around optionality, with signals and triggers embedded in the process. [19:14] Technology isn't the limitation; adoption and empowerment are. [24:28] Governance controls speed, while optionality lets you steer around obstacles when volatility strikes. [30:00] Test AI gradually, the way you'd test a flimsy chair before putting your full weight on it. [32:00] The "watermelon effect": every metric looks green but the customer is still unhappy. [41:53] If you don't know your stuff, you can't check AI. [45:04] Agentic AI: higher cost if wrong, enormous benefits if right. [47:36] Assess the workforce you have and acquire the right talent; AI supports that talent, not the other way around. 🔑 Key Takeaways •Standardized, documented processes are the foundation for any successful use of AI. Applying AI to a broken or unclear process doesn't fix it; it multiplies the problems that already exist. •The best way to build trust in AI is to start with customer outcomes, work backward to the most critical decisions, and test AI on those decisions gradually. Every KPI should tie to a real customer result, because green metrics mean little if the customer isn't satisfied. •AI is an enabler, not a replacement for knowing your business. Experienced people are needed to catch the small errors in AI's reasoning, and organizations that invest in their talent alongside the technology will benefit most. 🧭 Frameworks Worth Saving •Treat volatility as an optionality problem, not a forecasting problem. Instead of trying to predict the next tariff or disruption, build standard processes, clear roles, and governance that give you multiple ways to respond when it hits. •Plan continuity in tiers, not at 100%. Prioritize processes by importance and set recovery targets for each, such as 20% back online in 24 hours and 50% in 48 hours, rather than paying for full continuity across the board. •Design where work lives, who does it, and how it's organized. Bestshoring moves the question from where goods are sourced to where logistics work and processes sit, with governance as the dimension that carries the most weight. 💬 Notable Quotes Without a customer, you're not a company. Johnnie Moore AI is an enabler. It is not the solution. It is not the ultimate decision maker. It is not the fix-it-all. It is not. Johnnie Moore AI is gonna support the talent that you have. And if it's not in that order, I think we might be doing something wrong. Johnnie Moore 👤 About The Guest Johnnie Moore, The JR Moore Group Johnnie R. Moore, Jr. is the Founder and CEO of The JR Moore Group, Inc. He brings 38 years in logistics and supply chain, 28 of them at DHL Global Forwarding, where he scaled the shared services for the Americas to 1,300 FTEs and developed global frameworks adopted across a network of nearly 6,000 employees worldwide. He now does that work for freight forwarders, 3PLs, 4PLs, and organizations with large, complex logistics operations. He serves as VP of Membership of the CSCMP South Florida Roundtable. Website: https://thejrmooregroup.com 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected Supply ChAInge: https://supplychaingepodcast.com April 12 Advisors: https://april12advisors.com Produced by Speakerbox Media.

2. 

   8 Oct 2026

   Visibility Is Table Stakes: Grant Ritchie on Trust, Data, and the Next Frontier of Logistics

   Grant Ritchie of Freedom Trans USA joins Supply ChAInge to trace how "visibility" evolved from a pin on a delivery map into a full decision-support system spanning condition, upstream materials, and predictive resilience. Derek and Grant cover the "Amazon effect" that reset customer expectations, why most mid-size shippers are still far from a 20-screen command center, and why AI is more likely to absorb repetitive tasks like rate confirmations than to replace trust-based freight relationships. They close on the blockchain hype cycle as a cautionary tale, and why "we've always done it this way" is often justified inertia, not simple resistance. 🎧 Episode Highlights [2:07] Grant's career path from oil and gas trucking in South Texas and North Dakota to Freedom Trans USA. [6:37] What visibility means today across asset, asset-light, and platform-based logistics, and how the Amazon effect raised the bar for every mode of freight, not just parcel. [11:05] Why most companies, especially mid-size shippers, are still far from the "20-screen command center" fantasy, and how Grant helps customers figure out what they actually need instead of selling every tool available. [18:48] The AI-as-replacement fear, and why the blockchain hype cycle is Grant's go-to cautionary tale for evaluating today's AI claims. [22:32] The real AI tools that are already showing up in freight, including a bot built to sit on top of existing TMS platforms and handle repetitive tasks like rate confirmations and carrier vetting. [30:11] Visibility as decision quality — using real-time data to reroute around disruptions like a closed Strait of Hormuz, including shifting port strategy toward Mexico. [35:19] "Shipper of choice" and the cost of change — why an hour-and-a-half load time versus a 15-minute one is a wake-up call most companies need data, not opinion, to see. 🔑 Key Takeaways Visibility now spans condition, upstream material availability, and predictive resilience — the goal is better and faster decisions, not just faster information. AI is more likely to absorb repetitive tasks (rate confirmations, carrier vetting, tendering) than replace freight relationships. Blockchain's hype cycle is a useful reminder that "this changes everything" often overstates the change. Adoption is a trust and change-management problem before a technology one. "We've always done it this way" is often justified inertia — what moves people is showing true logistics cost, not a generic pitch. 🧭 Frameworks Worth Saving The visibility maturity ladder: (1) where is my shipment — the baseline; (2) what condition is it in — temp, humidity, quality; (3) what's happening across my network — tier 1–3 supplier visibility; (4) predictive and prescriptive — building contingency plans before disruption hits. Justified inertia: "We've always done it this way" is often a legitimate response from a company that's been successful. What moves the conversation is a true-cost picture and a concrete comparable example, not a generic pitch. The blockchain test: before assuming a technology will replace a function, ask whether the hype resembles blockchain's. The tool usually has real value, but total-replacement predictions tend to be overstated. 💬 Notable Quotes Visibility these days is really table stakes. If you don't have visibility, you can't operate. Grant Ritchie Logistics is a true collaboration from everybody involved, and all it takes is one bad apple to gum up the works. Grant Ritchie 👤 About The Guest Grant Ritchie, Freedom Trans USA Grant Ritchie has spent his career viewing logistics and visibility from nearly every angle in the industry. He started in oil and gas trucking across South Texas and North Dakota's major shale plays, then moved to the customer side importing oil country tubular goods, where his company became the largest importer of break bulk pipe into the Port of Houston. From there, he moved into operations and procurement at Ryder Integrated Logistics, then sales at XPO Logistics, and now works at Freedom Trans USA, which combines its own asset fleet with brokerage and managed transportation services across the lower 48, Mexico, and parts of Canada. 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: supplychaingepodcast.com Produced by Speakerbox Media

3. 

   25 Sept 2026

   Supply Chain Transformation: AI Can't Enforce Culture (with Sanchay Roy)

   Sanchay Roy on supply chain transformation: AI can't enforce culture, but it can capture frontline operator knowledge before the retirement clock runs out. Drawing on 34 years leading continuous improvement and manufacturing excellence at GE, Philips, and Shell, Sanchay joins Derek in the make pillar of the supply chain to unpack Lean's hardest lesson and why it applies directly to AI: culture, not tools, decides whether change sticks. They dig into the tacit knowledge MES and SCADA systems never record, why operators judge a new tool within weeks, and how turning AI-driven transparency into blame instead of learning can undo earned trust far faster than it was built. 🎧 Episode Highlights [5:41] Continuous improvement in the analog world: Gemba, shifts spent watching a process, and how leadership habit becomes culture. [8:36] The hardest lesson from Lean: one change agent doesn't change the culture. When the talent leaves, performance slides back. [11:23] The retirement clock: a large share of the Western manufacturing workforce is nearing retirement, and its knowledge will walk out the door. [16:08] "You are asking us to become robots": a Dutch plant manager's pushback on Lean, and how standard work freed creativity for real problems. [17:17] AI can't enforce culture, but it can keep continuous improvement from becoming episodic. [23:08] You can't buy operator knowledge off the shelf, and how AI can turn unstructured observations into shared enterprise knowledge. [29:20] Transparency cuts both ways: learn from the root causes AI surfaces, or turn them into KPI chasing and blame. [34:46] AI is not gospel truth: repeating Lean's commandments mistake versus embracing AI through rapid PDCA cycles. [36:00] Trust comes walking, but leaves galloping on a horse. [37:42] Closing thought: operators give a new tool two to three weeks, and they will price the change more accurately than finance. 🔑 Key Takeaways AI will not change your culture; leadership practice does. Leaders who expect AI to drive cultural change will repeat Lean's mistake, only faster, because AI amplifies whatever culture it lands in. Your systems know what happened; your operators know why. MES and SCADA capture data after the event. The operator knows the humidity is curling labels, the rubber flecks mean a belt is wearing, the motor's pitch means it needs lubrication. That knowledge can't be bought off the shelf. Transparency is a choice leaders make, not a feature AI provides. Once AI surfaces the root causes behind line performance, leaders can use that truth to learn or to assign blame. Turn it into KPI chasing and operators stop engaging. 💬 Notable Quotes Cultural change is probably the most challenging aspect of any transformation. Sanchay Roy AI cannot enforce culture. Nothing can. But what AI can do is prevent CI from becoming episodic. Sanchay Roy They always tell you what has happened. The operator knows why it has happened. Sanchay Roy The trust comes walking, but leaves galloping on a horse. Sanchay Roy 👤 About The Guest Sanchay Roy, Operational Excellence and Transformation Leader Sanchay Roy spent 34 years leading business improvement and operational excellence across energy, oil and gas, lighting, consumer goods, and process and discrete manufacturing. A GE Certified Master Black Belt, he spent 14 years at GE before leading business excellence for Philips' lighting business across 61 assets. At Shell, he drove continuous improvement for capital projects, improved the upstream oil and gas supply chain, and led manufacturing excellence for the Lubricants business across 32 sites. Transformations he led have been recognized as Best Run Supply Chain and with an EU Manufacturing Excellence award. He is co-author, with Stewart Dunbar, of Improving Supply Chains in the Oil and Gas Industry (Springer). Sanchay now works with startups, bringing Lean, Design Thinking, and fact-based problem solving to new ventures. 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: supplychaingepodcast.com april12advisors.com Produced by Speakerbox Media

4. 

   10 Sept 2026

   Implementing AI in Supply Chain: Stop Bolting AI Onto the Old Process (with Narahari Rao)

   Narahari Rao of SLB on implementing AI in supply chain: why bolting AI onto old processes is like plugging a motor into horse-and-carriage infrastructure, and why a successful supply chain should be invisible to the customer. The conversation covers the engineering mindset that makes for stronger procurement negotiators, why digitization doesn't require 100% clean data to get started, the case for narrow, edge-of-the-ERP use cases over full-process overhauls, and how to embed risk management directly into the workflow instead of leaving it stranded in a dashboard nobody opens. 🎧 Episode Highlights [4:03] How an engineering mindset makes for strong procurement negotiators. You don't need the deep technical expertise, but you need to know enough. [8:46] The digitization starting point. Harmonizing data across ERPs doesn't require 100% clean data. Roughly 70% good data is enough to begin. [12:40] Thinking backward from the customer instead of function by function, and why procurement too often feels disconnected from who it ultimately serves. [17:56] The Model T analogy. Why bolting AI onto an existing process just builds a better horse-drawn carriage instead of designing the automobile. [23:06] Dashboards are where insights go to die. Why risk data still needs a human to interpret it, and where AI can actually help. [29:33] Embedding risk management directly into the sourcing workflow, instead of asking buyers to jump between disconnected systems to connect the dots. [34:14] The supplier outage story. A single hidden raw-material dependency shut down a "backup" supplier too, and what that reveals about the illusion of multi-sourcing. [39:34] Building narrow, purpose-built AI agents for known risks. Hurricanes today, wildfires or cyber threats tomorrow. [52:28] Lean roots at Toyota and why continuous improvement is a cultural shift, not a one-time initiative. [55:22] The case for starting with front-line pain points. Small, fast wins build the ROI story before you attempt large-scale transformation. 🔑 Key Takeaways · A successful supply chain is invisible to the customer. The product shows up at the right time, cost, and quality without the customer ever having to think about how it got there. That outcome, not process optimization for its own sake, should anchor every transformation decision. · You don't need perfect data to start. Waiting for 100% clean, harmonized data before beginning a digitization journey is a common trap. Roughly 70% good data is enough to start building insight and momentum. · Multi-sourcing isn't real risk mitigation if your suppliers share a hidden dependency. A "backup" supplier that draws from the same raw-material source, region, or single point of failure as your primary supplier isn't actually diversification. The risk is just one layer removed. 🧭 Frameworks Worth Saving · Work backward from the customer, not forward from your function. Hari's approach to scoping any transformation: define who the customer actually is (internal or external), then work backward from what they need to experience through planning, sourcing, and logistics. The goal is resisting the pull to optimize each function in its own silo, disconnected from that end goal. · Look at the edges of the ERP before rebuilding the core. Where Hari sees AI use cases pay off first: start with a narrow, well-defined question, like "when will this product show up at my desk?", and use existing ERP data, stepping slightly outside the walled garden to answer it, rather than optimizing the whole sourcing process before proving value at the edges. · Embed risk management inside the workflow, not next to it. The pattern for making risk management usable at scale: build risk checks into the intake process itself rather than a separate tool buyers have to remember to check, avoid siloed systems that require manually connecting the dots across screens, and build a bespoke model matched to your company's policies and culture rather than tying yourself to a vendor's release schedule. 💬 Notable Quotes A successful supply chain is one which makes the consumer not think about how that product actually showed up at their door. Narahari Rao You don't actually have to have 100% clean data before you actually progress on this journey. Narahari Rao Lean is not something you do just at your workplace. It's a cultural shift. Narahari Rao 👤 About The Guest Narahari Rao, Global Procurement Risk and Resilience Director, SLB Narahari Rao is the Global Procurement Risk and Resilience Director at SLB, where he manages supply base risk across 30 global business units. With over 20 years of experience in supply chain, strategic sourcing, and engineering, he specializes in building resilient supply networks for complex global operations. He holds a Master of Science in Mechanical Engineering from Texas A&M University and is a Certified Lean Six Sigma Champion. Throughout his career, Narahari has led major digital transformations, SAP deployments, and process optimizations across global teams. He actively serves on the Editorial Board for the Lean Six Sigma review. Outside of his corporate role, he frequently speaks at industry conferences on supply chain strategy and operational excellence. He is a budding amateur astronomer and when he is not focused on risk management within Supply Chain, he is looking up at the stars and the galaxies that hold them. 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected · supplychaingepodcast.com · april12advisors.com Produced by Speakerbox Media.

5. 

   28 Aug 2026

   Agentic AI: Every AI Project Is a Data Project with Traey Hatch

   Traey Hatch of New Math Data on agentic AI: why every AI project is really a data project, and what agents need before they can run in production. The conversation covers the terminology blur between generative, physical, and agentic AI, the context work that decides whether a model is useful inside a specific enterprise, the cost logic behind frontier versus specialist models, and the identity, permissions, and audit gaps still keeping agents-supervising-agents out of production. 🎧 Episode Highlights [2:22] Traey's path from data warehousing and machine learning into a full AI practice, and why nearly every client conversation has shifted. [4:14] Reframing the McKinsey and MIT numbers on stalled AI projects. This is an experimental phase, not a failure phase. [6:45] The word AI now absorbs machine learning, workflow automation, and analytics. What that blur costs organizations trying to scope work. [10:51] Provenance as the adoption gate. People abandon a tool the first time it embarrasses them in front of someone else. [15:43] What a harness actually is, and why the hard part is structuring context rather than picking a model. [19:19] SAP means something different in every enterprise. Why tribal context has to be handed to a model explicitly. [23:26] Onboarding an agent the way you onboard an employee: credentials, permissions, roles, and where the data actually lives. [24:44] Choosing between frontier, specialist, and open source models as a cost decision tied to what the process is worth. [28:55] The shoebox story. Fifteen years of uncashed checks in a permit office, and what it says about process nobody designed. [33:34] The principal consultant approach. A negative proof of concept is a valid result, and off-ramps belong in the contract. [37:37] The age of the operations person, and why process fluency is the durable advantage. [39:37] Agents managing agents. Identity, permissions, and audit trails are the gaps, not model capability. [43:15] The agent that cancelled a leadership team meeting, and where the human has to sit in the loop. 🔑 Key Takeaways · Every AI project is a data project with a thin model layer on top. The visible work is the model. The actual work is information retrieval, data modeling, and structuring context so a language model can reach the answer you wanted. Organizations that scope these as model projects underestimate them by an order of magnitude. · Process discipline predicts success better than model selection. Companies that already design, document, and run their business processes rigorously do well, because the technology fills gaps in work they can describe. Companies that cannot describe their own process end up with overlapping agents, messy transitions, and handoffs that never happen. · Multi-agent systems are blocked on governance, not capability. Parallelizing work across sub-agents is well-trodden ground. Agents supervising other agents on real business decisions is not, because agent identity, permission propagation, spend thresholds, and audit trails are still being invented. 🧭 Frameworks Worth Saving Onboard the model like a new employee. The pattern Traey returns to throughout the conversation: · Terminology. Define your organization's specific language. A lane on a highway is not a lane in a transportation contract. · Location. Tell the model where the data actually lives: which systems, which APIs, which warehouse. · Permissions. Give it credentials and role-based access, the same way you would a new hire. · Provenance. Surface how it reached an answer, so the people using it can judge the result. Model choice is a cost decision, not only a capability decision. Route high-value, high-complexity work to frontier models. Route repetitive, well-defined work like document sorting or data entry to smaller, cheaper ones. The deciding question is what optimizing this process actually saves, and whether that justifies the engineering spend. Crawl, walk, run, and most organizations are in the middle. Single-agent workflows and parallelized sub-agents are established. Agents supervising other agents on real business decisions is early, held back by unresolved questions on identity, permissions, and audit trails. 💬 Notable Quotes It's a gigantic data project with a very thin layer of AI model use over the top of it. Traey Hatch A valid result of a proof of concept is negative. Traey Hatch I think that this is the age of the operations person. Traey Hatch 👤 About The Guest Traey Hatch, CEO, New Math Data Traey Hatch is a co-founder of New Math Data and a cloud and data engineering leader focused on AWS, AI, and scalable analytics platforms. He works with organizations to modernize infrastructure, unlock data value, and deploy production-grade AI systems with an emphasis on security, governance, and long-term operability. 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected supplychaingepodcast.com⁠ ⁠April 12 Advisors⁠ ⁠⁠newmathdata.com⁠⁠ Produced by Speakerbox Media.

6. 

   13 Aug 2026

   AI ROI in Supply Chain: Autonomy Is an Asset, Not an Expense

   Six episodes into Supply ChAInge, the definitions, the leadership, the culture, the data foundation, and the governance are all on the table. This solo episode takes on a question underpinning all of them: why pursue autonomy at all? Derek Aranda borrows Donald Rumsfeld's knowns, known unknowns, and unknown unknowns to map where supply chain value sits, and argues that measuring return through a cost lens alone turns an investment conversation into a headcount conversation. The case he makes is that autonomy is an asset, not an expense. Decision quality and decision velocity compound as you move up the autonomy pyramid, resilience and optionality cover the shocks you cannot forecast, and a fourth bucket of customers, markets, and products opens up once the first three are working. 🎧 Episode Highlights [0:00] A Churchill line frames the moment: not the end, not the beginning of the end, but the end of the beginning. [0:22] The ground covered so far. Definitions, leadership, culture, data, and governance. [1:59] Why the value conversation has to move past cost. [2:28] Channeling Rumsfeld. The knowns, the known unknowns, and the unknown unknowns of supply chain value. [5:02] The standard ROI numerator is just a change in cost profile. [6:36] Where the danger sits. Pilots get locked into showing a cost result fast. [7:13] The implementation hump. Parallel systems, training, and integrators before the cost curve bends. [8:31] Why headcount becomes the easiest lever to pull, and why that narrows the whole opportunity. [9:17] From a P&L mindset to a balance sheet mindset. Autonomy as an asset whose benefit accrues over time. [10:33] Decision quality and decision velocity, worked through a hurricane in the Gulf. [14:00] Better decisions faster. Why the two compound instead of adding. [15:35] Into the unknown unknowns, where optionality and resilience become the payoff. [18:13] The Panama Canal case. High value cargo pays to jump the queue and grain sits further back. [21:03] The fourth bucket. Customers, markets, and products that open up once the first three are working. [23:26] The leadership job. Reorienting what gets measured, what gets rewarded, and who gets credit. 🔑 Key Takeaways · Cost is a necessary lens and an insufficient one. Valuing autonomy purely on cost creates pressure to show savings inside a quarter, and the fastest way to show savings is to remove people. That reflex narrows the opportunity to the one bucket you already knew how to measure. · Autonomy is an asset, so value it on a balance sheet, not a P&L. The benefit accrues over time, and the journey includes false starts. A narrow payback window forces suboptimal choices before the capability has had a chance to compound. · Decision quality and decision velocity compound, and resilience is what covers the rest. Moving up the autonomy pyramid means better decisions made faster against the known unknowns, and a wider surface area of visibility against the shocks nobody forecast. The organizations that capture the most value are the ones that widen what they measure and reward. 🧭 Frameworks Worth Saving Three lenses, four value buckets. Map the investment case against what you can and cannot know. · Knowns produce the cost bucket. Inventory positions, transportation lanes, maintenance spend. Real, measurable, and the only one most business cases capture. · Known unknowns produce the decision bucket. A hurricane will hit somewhere this year. Value shows up as decision quality, the range and consistency of the trade-offs you can make, and decision velocity, how fast new signal reaches those decisions. · Unknown unknowns produce the resilience bucket. Optionality and response capability against events you cannot name in advance. · The fourth bucket is growth. Customers you can win, markets you can enter, products you can grow into once the first three are working in your favor. The test for any business case: what does this do besides cost? How does it help us decide better or faster? How does it help us absorb a shock? 💬 Notable Quotes That cost efficiency mindset, again, is not wrong. It's just not enough. Derek Aranda Autonomy, AI, digital is really an investment, not an expense, not a cost. Autonomy is an asset. And as an asset, the benefit accrues over time. Derek Aranda The opportunity mindset allows us to explore the full spectrum, cost plus, plus, plus. Derek Aranda 🎙️ About The Host Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected · https://supplychaingepodcast.com · https://april12advisors.com Produced by Speakerbox Media.

7. 

   30 Jul 2026

   AI Governance in Supply Chain: Rocket Ship, Not a Rulebook

   Most leaders treat AI governance as a leash. Derek Aranda sits down with LeAnne Coulter, founder of BlueWave Supply Chain and a 30-year veteran of designing solutions for Fortune 500 supply chains, to argue the opposite: governance is the rocket ship, not the rulebook. Drawing on a major outsourcing partnership the two worked from opposite sides of the table, the conversation reframes AI governance in supply chain as shared language, a trust framework, and a design discipline rather than a compliance checklist. LeAnne and Derek dig into why the word "pilot" invites pocket vetoes, why AI adoption is really an exercise in onboarding a new team member, and why the biggest return hides in the 30 to 60 percent of hidden work no job description ever mentions. 🎧 Episode Highlights [0:43] Why you don't get to autonomy without governance, and why skipping it gets you chaos instead. [4:15] AI as outsourcing: why bringing on an AI teammate demands the same trust-building as any new hire or partner. [17:33] Pocket vetoes: why the word "pilot" quietly signals a lack of commitment, and what to say instead. [27:40] Whether your governance structures are already archaic, and why decision points designed for 2020 may be the barrier in 2026. [30:39] Why AI breaks processes, and why those breaks were human workarounds all along. [39:35] The hidden 30 to 60 percent: how a 65-person email chain over a shipping document exposes where the time actually goes. [42:00] From roughly 50 percent to over 99 percent: what happened when one company put an agent on carrier visibility, and where the reclaimed capacity went. [43:32] The revenue case nobody makes: the customers you cannot serve today because your people are chasing a truck. 🔑 Key Takeaways Governance is a design conversation, not a control system. It starts with a shared vision and objectives, then establishes the ways of working that let people, and now AI agents, collaborate with confidence. Treated as guardrails rather than restrictions, governance is what lets organizations move fast without going rogue. Language is a governance tool. Words like transformation, integration, and platform mean different things across functions. Getting explicit about shared definitions, and swapping pilot for phase and project for journey, removes the ambiguity that breeds resistance and pocket vetoes. The real prize is hidden work, not headcount. Thirty to 60 percent of time in supply chain roles goes to invisible workarounds, like a 65-person email thread untangling a shipping document. One company put an agent on carrier visibility, moved from roughly 50 percent coverage to over 99 percent, and pointed the reclaimed capacity at OTIF work nobody had ever had time for. That is where the dollars are, and it is a different number than a headcount line. 🧭 Frameworks Worth Saving In the loop, on the loop, out of the loop. The promise sold to executives has been out of the loop: the system decides and the people go away. LeAnne's position is that human in the loop is not a training-wheels phase on the way to that. It becomes the governance structure itself. In the loop means you sit inside the decision. On the loop means you hold oversight of decisions the system makes. Out of the loop is where consistency and institutional knowledge quietly disappear. The word swaps. Pilot becomes phase. Project becomes journey. Pilot and project both carry an end date and an implied exit, and that exit is exactly what gives a reluctant stakeholder the pocket veto. Phase and journey carry commitment, and they tell people the direction is settled even when the route is not. 💬 Notable Quotes "There is not a supply chain silver bullet that is going to take care of your problems." LeAnne Coulter "When I think of pilots... it conveys almost a lack of commitment, because there's an element there that says, 'If I don't like the outcome, I can reserve judgment.'" LeAnne Coulter "It's a rocket ship that you're putting on the back of your people to go to places that we've never been before." Derek Aranda "You don't wanna lose that. You wanna take that curiosity and that problem-solving to then start to look at those process breaks that AI brings forward." LeAnne Coulter 👤 About The Guest LeAnne Coulter is a supply chain executive and technologist with 30 years of experience leading operations, strategy, and digital transformation across Fortune 500 supply chains. Founder of BlueWave Supply Chain, she helps organizations leverage AI and emerging technology to strengthen profitability, agility, and performance. She serves on the Wayne State University Global Supply Chain Management Advisory Board and the FourKites Strategic Advisory Council. https://bluewavesupplychain.com Stay Connected: https://supplychaingepodcast.com https://april12advisors.com Produced by Speakerbox Media.

8. 

   16 Jul 2026

   Perfection Is the Wrong Benchmark

   Everyone says garbage in, garbage out. Almost no one acts on what that actually demands. In this episode, Derek Aranda sits down with Brian Reed, an executive sponsor at Oracle who has run supply chains, advised the C-suite, and built autonomous planning systems across 56 countries, to pull apart what "good data" really means on the road to AI. The conversation cuts against the reflex to hold AI to a standard of perfection no human has ever met, and reframes data quality as a question of timeliness, location, and fitness for purpose rather than flawless numbers. It is a clear-eyed look at why so many AI pilots get written off as failures when they are actually doing their job, and what it takes to move from hand-holding to trust to genuine autonomy. 🎧 Episode Highlights [00:21] — Why AI's inaccuracies get all the scrutiny while human inaccuracy goes unquestioned. [12:31] — Why a "failed" AI pilot that exposes bad data was never really a failure. [17:41] — The "hands off the wheel" case study: 30 days of tracking that convinced a customer to trust the system over their own team. [24:11] — Who should own the data: resolving the IT vs. business tension and putting AI on the RACI chart. [35:24] — Teaching AI to "trust and verify": what it takes to move from human-in-the-loop to real autonomy. [52:42] — Brian's closing advice: experiment small, because "the quarters and the dimes" add up to millions. 🔑 Key Takeaways • Measure AI against humans, not perfection. The bar for a system is not flawlessness, it is whether it beats the fallible person it replaces on speed and accuracy. Brian's autonomous planning project proved the point: over 30 days of tracking, the humans consistently broke the system's answer. Perfection can stay the goal without being the benchmark you measure against to move forward. • A failed pilot usually isn't one. A program that reveals your data is awful just did something valuable, it found the problem you didn't know you had. The work isn't perfecting every field before you start; it's prioritizing which data actually needs bit-for-bit accuracy and which is good enough for the decision at hand. Eat the elephant one bite at a time instead of waiting for a fully stocked data lake. • Data ownership is joint, and the machine is now on the RACI. The business owns data creation and the value drawn from it; IT is the enabler in the middle keeping it centralized, clean, and usable. Those two groups belong in nearly every conversation together, and AI is now a third party on the chart, responsible for storage and processing, informed by inputs across the business. 💬 Notable Quotes "We talk all about the data and the inaccuracies of things about AI. We don't talk about the inaccuracy of humans." — Brian Reed "You didn't know your data was awful before you ran the program. Now you know your data's awful, so it has some value." — Brian Reed "Perfection can always be the goal, but it doesn't have to be the bar at which you measure to go forward." — Brian Reed "Small value adds up to big value. We call it the quarters and the dimes. We added up enough quarters and dimes, and they turned into millions of dollars." — Brian Reed 👤 About The Guest Brian Reed Brian Reed is a tenured supply chain expert with 20+ years of global experience spanning every aspect of supply chain, from customer service through final-mile fulfillment, across all industries and on both the business and IT sides of digital and physical supply chain solutions. Beyond technology and consulting organizations, he has led a high-growth supply chain organization focused on execution at a large U.S. CPG manufacturer. Brian has helped some of the world's largest technology and manufacturing companies transform their supply chains with technology as a tool for automation, insight, and resilience, including broad transformations delivering cost savings and revenue enablement measured in the hundreds of millions to billions of dollars. He was most recently the AWS Supply Chain and Pan-Amazon Supply Chain GTM Leader, working with Amazon's largest and most strategic customers and partners to develop and deliver new supply chain capabilities. He now advises the C-suite of Oracle's largest and most strategic customers, helping them deploy Oracle Applications and AI at scale. Outside of work, Brian and his family enjoy traveling internationally, food and wine, and hitting the mountain bike trails as often as possible, both at home in Southern California and while traveling. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com Produced by Speakerbox Media.

9. 

   2 Jul 2026

   Why Trust Is the Real AI Strategy, with Chris Pitre

   Episode Summary: In this episode, Chris Pitre of Softway makes the case that AI transformation is a leadership and culture journey, not a tools journey. Fear, loss of control, and concerns about being replaced only get resolved when leaders lead with vulnerability, curiosity, and psychological safety. Before AI can take root and scale, Chris argues, you have to till the soil of culture: trust, inclusion, and honest dialogue. The challenge he leaves leaders with is direct: be brave enough to admit you do not have all the answers, and run your AI journey as an ongoing, human-centered transformation. 🎧 Episode Highlights 03:21]: AI as a transformation, not just a tool, and why many pilots disappoint [05:43]: Psychological safety and trust: why people won’t speak up about AI fears [10:30]: Soft skills become critical as AI takes over repetitive, mechanical work [21:20]: “Everything moves at the speed of trust”: designing safe-to-fail AI experiments [29:10]: The IKEA case: retraining 8,500 agents into consultants and unlocking $1.4B in new revenue 🔑 Key Takeaways: AI transformation is primarily a leadership and culture challenge, not a technology rollout. The episode argues that fear of job loss, loss of control, and being “devalued” will derail AI efforts if leaders don’t first build trust, inclusion, and psychological safety. Embracing vulnerability, openly saying “I don’t know,” naming fears, and inviting help becomes a core leadership behavior that enables honest conversations about what AI really means for people and the business.Leaders must shift from cost-cutting and control to curiosity, experimentation, and empowerment. Instead of “go do AI” or chasing shiny tools, they should educate everyone on what AI actually is, surface real “migraines” from the front line, and run small, fast, safe-to-fail experiments. Failure is reframed as learning, with accountability separated from punishment, so teams feel safe to try, break small things, retrain the models, and iterate quickly.AI’s real promise is to unlock human potential and more joyful work, not just replace headcount. By offloading repetitive, mechanical tasks to AI, organizations can reskill people into higher-value, more creative roles as illustrated by IKEA retraining 8,500 call center agents into interior design consultants and unlocking $1.4B in new revenue. This requires leaders to rethink org design, role definitions, and the employee value proposition, placing “love as a business strategy” at the center of how AI is adopted and scaled. 💬 Notable Quotes: “AI is a tool, not a teammate.”“Everything moves at the speed of trust.”“Silence is the biggest cost, never on a balance sheet.”“Curiosity is above knowledge in the age of AI.”“If you can forgive a human, why can’t you forgive technology?” 👤 About The Guest: Chris Pitre Chris Pitre is the Vice President at Softway and co-author of the best-selling books Love as a Business Strategy and Love as a Change Strategy. He helps organizations lead AI and digital transformations by centering on culture, using trust, vulnerability, and psychological safety to unlock sustainable performance. Chris works with executives and frontline teams alike to redesign how work gets done so AI augments people, rather than replaces them. Learn more about Softway and Culture+: https://www.softway.com https://www.culture-plus.com Stay Connected: https://supplychaingepodcast.com https://april12advisors.com Produced by Speakerbox Media.

10. 

    18 Jun 2026

    Don't Lead AI Like a Project: A Leadership Guide to AI and Autonomy in the Supply Chain

    AI will not fix a broken supply chain and it requires committed leadership. In this solo episode, Derek Aranda argues that autonomy demands a fundamentally different kind of leader, one who treats AI as a continuous journey rather than a project with a finish line. Using lean as both a blueprint and a warning, he lays out the two strategic choices every leader has to make, the four things AI forces you to rethink, and why many failed AI pilots are leadership failures in disguise. The takeaway: get out of the boardroom, pressure-test what your dashboards claim, and build the conditions in which AI can actually succeed. 🎧 Episode Highlights [00:00]: Why today’s geopolitical shocks expose the need for AI in supply chains. [02:45]: Two strategic AI choices every supply chain leader must make.[08:40]: Lean as the blueprint (and warning) for your AI and autonomy. journey. [15:20]: Why many AI “failures” are actually leadership and culture failure [23:55]: Getting out of the boardroom: how real leaders make AI work on the front line. 🧭 Frameworks Worth SavingThe two strategic choices that come before any technology decision: • How existential is AI and autonomy to your operations? • Do you see it replacing your people or augmenting them? The four things AI forces you to rethink: •AI is probabilistic, not deterministic, so it needs the right context wrapped around it •AI does not stand alone, it lives inside a tech stack and your data flows •AI introduces new risk, so decide what stays with the human and what moves to the machine •AI requires orchestration across sales, finance, legal, suppliers, and customers, not just supply chain 🔑 Key Takeaways: •AI isn’t a shortcut, it’s a catalyst for a full organizational re-architecture. Leaders who treat AI as a plug‑and‑play fix will repeat the same failures we’ve already seen with lean and digitalization. The real work is root‑to‑branch: rethinking processes, decision rights, incentives, and org design so that probabilistic, AI‑driven decisions can actually stick. Without that strategic redesign from the C‑suite and board level down, AI will become just another expensive front‑end layered on top of manual, spreadsheet‑driven reality. • The real leadership challenge is marrying top-down intent with bottom-up autonomy. Durable AI is built by empowering the people closest to the work: planners, schedulers, operators, and trade compliance teams. That means leaders have to leave the conference room and get onto the floor, pressure-test "we're automated" claims, surface hidden manual work, and turn frontline judgment into system logic instead of letting it quietly override the tool. • AI only creates lasting value when leaders accept they will not get it right on day zero, or day 1,000. The shift is from project completion to the infinite game: continuously tuning data, stack, and process, recalibrating the risk line between human and machine, and iteratively earning trust in AI recommendations. • Two choices sit upstream of every technology decision: how existential AI is to your operations, and whether you treat it as replacement or augmentation. There is no off-the-shelf playbook for either. Derek's own bias is toward augmentation, but the point is that leaders have to consciously decide where they land, because those choices drive investment, org design, incentives, and risk appetite. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: • https://supplychaingepodcast.com • https://april12advisors.com A project by April 12 Advisors. Produced by Speakerbox Media.

11. 

    4 Jun 2026

    Welcome to Supply ChAInge: Exploring the Autonomous, Cognitive, Agentic Supply Chain

    What does it actually mean for a supply chain to run itself? Season 1 opens by defining the thing everyone is racing toward before agreeing on what it is. Derek lays out a five- level pyramid, from automation to analytics to cognitive to agentic to autonomous, so leaders can locate where they really are today and see how big the next leap is. No alphabet soup of technical terms and no vendor roadmap. The goal is a shared definition you can build against. 🎧 Episode Highlights [00:30]: How AI is transforming modern supply chain careers [01:45]: Why supply chain is now a strategic AI priority [09:30]: AI supply chain pyramid: automation to autonomous operations [15:10]: Cognitive AI for tariffs: smarter supply chain decisions [20:05]: Bounded autonomy: AI, dark warehouses, and human oversight 🔑 Key Takeaways: ● Supply chain moved from cost center to strategic lever. COVID, tariffs, and global conflict exposed how fragile and decisive supply chains are, pushing them into the boardroom. AI's rise came in the same moment, bringing real opportunity and a lot of hype. ● There's a five-rung climb, and the rungs aren't evenly spaced. Automation and analytics make work faster and more visible while humans still decide. Cognitive systems understand context and trade-offs and narrow the decision space. Agentic systems act within guardrails. Autonomous removes the human from the loop. The jump from agentic to autonomous is far larger than the jumps below it, and most organizations are still near the bottom. ● You don't fix process discipline with AI. Agents need defined rules, sandboxes, and decision criteria. If your SOPs aren't solid today, that gap becomes the dividing line between firms that can deploy AI fast and firms that can't. ● Bounded autonomy is a more realistic target. Fully dark, end-to-end autonomy is distant and maybe not even desirable. The richer goal is autonomy inside defined domains, with humans on the frontier as decision makers, coaches, and trainers Autonomy means hands applied differently, not hands off. 👤 About The Host: Derek Aranda Derek Aranda spent over two decades as a global executive operating across commercial, supply chain, and digital transformation roles at scale. That span across the full value chain shapes his lens: the decisions, incentives, trust, economics, and governance that determine whether technology actually works inside real supply chains. On Supply ChAInge, he pressure-tests the autonomous future and helps leaders shape the framework to build it on their own terms. Stay Connected: ● https://supplychaingepodcast.com ● https://april12advisors.com Produced by Speakerbox Media

Contact Supply ChAInge

Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.