S&OP MasterClass

Managing supply and demand comes with ever-increasing complexity. With thousands of part numbers, hundreds of suppliers and a market that constantly changes, supply chain and operations management is a real challenge. In this S&OP MasterClass™ podcast series, we take a head dive into the complexity, and give you concrete advise on, how to better manage your sales and operations planning. We’ll talk about automation, forecasting, inventory planning and integrated business planning – and everything S&OP-geeky in between. Your host is Søren Hammer Pedersen, CCO of Roima – Perito IBP, who will be interviewing supply chain experts, automation professionals and exciting people who have their S&OP figured out. For more information on this S&OP MasterClass™, go to our website. This podcast is brought to you by Roima . This podcast is produced by Montanus .

S&OP MasterClass is a business podcast hosted by Unknown Host, with 27 episodes on record and a Required Pod Score of 80.

Unknown Host hosts S&OP MasterClass, a business show with 27 episodes published.

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  Episode #27

  Embedded AI vs bolt-on AI in Supply Chain Planning | Ep. 27

  Sep 2, 202630 minS5

  "Why don't we just pour all our data into Claude and use that as our planning tool?" If you work in supply chain planning, you have probably heard that question in your own company. It sounds reasonable. The general AI assistants are impressive, everyone is already using them, and they will happily answer any question you throw at them. This episode takes that question seriously and answers it properly. Søren Hammer Pedersen hosts this session of the S&OP MasterClass and brings in Benjamin Obling, CPO of Perito IBP at Roima Intelligence. Benjamin spends his working life embedding AI agents into integrated business planning software, which makes him exactly the right person to explain where the general assistants end and dedicated planning AI begins. The surprising starting point is that the two are built on the same raw material. The LLMs behind an embedded planning agent and behind Claude or Copilot can be identical. What separates them is instruction, context, and governance. An embedded agent knows which page you are on, which step of the process you are in, which of your six different forecast versions you actually mean, and which data your role allows you to see. A general assistant knows none of that, and it will still give you a confident answer. By the end of the conversation you will understand where each kind of AI belongs in your planning setup, why verification is the difference between a useful alert and a dangerous one, and why the real test of any AI analysis is a simple question. Did we make a better decision? In this episode Why "just pour the data into Claude" keeps coming up, and what that question misses What actually separates embedded AI from bolt-on AI when the underlying LLMs are the same How role-based governance keeps planners inside the data they are allowed to see When a general AI tool is still the right choice for a planner What changes for planners and executives in the monthly process, and what stays the same Chapters 01:10 Welcome and the question every planner hears 02:04 Bolt-on and embedded AI defined 05:05 Instructions, context and the six forecasts problem 09:17 Security, roles and data governance 11:10 The cost of iteration and pre-prepared analysis 14:18 Sharing best practice through one data model 16:38 When to use which tool 19:26 Same monthly process, different division of labour 21:47 The shadow AI pitfall and proving the answer 25:01 How planners actually receive embedded AI 27:32 Less is more and the decision test Contact and follow Questions, topic ideas, or guest suggestions: podcast@roimaint.com Find more episodes at: https://www.roimaint.com/en/catalog/node/insights-webinars-events-and-podcasts If you want to know more about what we do in Perito IBP, we are here to help. Production This podcast is brought to you by Roima. This podcast is produced by Montanus .

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  S&OP MasterClass

  Agentic AI in Operations Management – from insight to intervention | Ep. 26

  Aug 4, 202629 min

  Every shift on a modern factory floor throws off more signal than any team could ever read. Machines, quality checks, maintenance logs, operator comments, all of it lands in systems of record, and almost all of it goes unread. Managers make do with a handful of dashboards while the answers to their most expensive problems sit in the data, waiting for someone with the time to go looking. Most of the time, nobody does. In this S&OP Masterclass from Roima, host Søren Hammer Pedersen sits down with Rafael Amaral to look at what changes when agentic AI is pointed at that data. Rafael has spent around 20 years in manufacturing technology, split between supply chain planning and manufacturing execution systems. He is CTO and co-founder at TilliT, now part of Roima, where he leads the engineering team behind the TilliT stack, a cloud native MES application. The conversation moves from planning, the subject of the previous episode, onto the shop floor, where the data is densest and the losses are most expensive. Rafael explains Aura, an agentic AI toolset that reads factory data, forms its own hypotheses, writes its own queries, and returns findings a manager can act on. Think of it as a small team of data scientists that never sleeps, correlating things a human would rarely think to compare. You will come away understanding how the analyst bottleneck really works, why the most valuable factory insights are the ones nobody has time to find, and what it looks like to walk in on a Monday morning to a board of evidence-backed recommendations rather than a fresh round of firefighting. In this episode The "data is gold, so where is my shovel" problem with systems of record like MES and ERP How Aura forms its own hypotheses, writes its own queries, and refines its analysis with no human in the loop The agent swarm explained as a mini factory, a plant manager agent directing maintenance, quality and operations specialists The CFO agent that adds your labour and utility costs so findings arrive with the financials already worked out Why the second wave of AI in supply chain is one professionals cannot afford to sit out Chapters 03:18 What agentic AI and Aura are 04:40 The problem of siloed factory data 06:41 A team of data scientists on demand 12:43 Correlating machines with operator processes 14:38 The agent swarm as a mini factory 19:08 What you get on Monday morning 20:22 Concrete findings from the floor 24:15 The CFO agent and the financials 27:32 The second wave of AI in supply chain About Rafael Amaral Rafael Amaral is CTO and co-founder at TilliT, now part of Roima, where he heads the engineering team behind the TilliT stack, a cloud native MES application. He has worked in manufacturing technology for around 20 years, with the first half of his career in supply chain planning and the second half in manufacturing execution systems. He has spent that time close to the shop floor, from his first TilliT customer's plant manager to the breweries and wineries he happily admits a soft spot for. His current focus is Aura, the agentic AI toolset that he describes as changing the game for how much value teams can pull out of their own manufacturing data. Contact and follow Questions, topic ideas, or guest suggestions: podcast@roimaint.com Find more episodes and get in touch through the Roima website. Production This podcast is brought to you by Roima. This podcast is produced by Montanus .

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  Episode #25

  Agentic AI in Supply Chain – Beyond the Buzzword | Ep. 25

  Jul 2, 202637 minS5

  The word "agentic" is everywhere in supply chain right now, and rarely defined. In this S&OP MasterClass from Roima, host Søren Hammer Pedersen sits down with Rafael Amaral, CTO and Co-Founder of TilliT, to cut through it. They trace the shift from the first wave of AI, which made our existing forecasts and plans better, to the agentic wave, where systems can take a goal and work towards it on their own. Rafael offers a genuinely usable definition of agentic AI, explains why giving a model tools changes everything, and is candid about why so many projects fail, from the 10,000-bottle order nobody approved to the temptation to dump an entire dataset into the context window. The conversation then turns practical: where agentic AI is already creating value in planning, how optimisation is being democratised beyond PhD-level data science, and how Roima's Aura connects the data silos across IBP, MES, WMS and PLM to surface insights teams have been missing. They close on the question everyone asks: what does this mean for the people doing the work? Essential listening for supply chain managers, Heads of S&OP, IBP managers and anyone weighing where to place their next bet. Key takeaways There are two waves of AI in supply chain. The first made existing tasks better; the second, agentic wave takes on goals and works towards them. "What is my OTIF?" is a request; "how do I increase my OTIF?" is a goal the system works towards on its own. A model becomes agentic when you give it tools to query data, run code or take action, and let it navigate them in a loop. More power means more risk, so guardrails and gates are non-negotiable. Projects fail on missing basic assertions and on dumping too much raw data into the model. Success comes from pairing the right use case with the right methodology. Aura gives AI the power of a developer, writing and running its own code across the full dataset, security-built from the ground up. It is like hiring a team of specialists you could never afford, and Roima's digital thread lets it connect insights across IBP, MES, WMS and PLM silos that never spoke to each other. Waiting for 100 per cent accuracy is the wrong test, because humans make mistakes too. The real question is the cost of an error and the gates around it, and so far these tools empower planners and keep them in control. Chapters 03:37 Why supply chain professionals need to care now 04:53 From the first wave of AI to the agentic wave 08:29 What "agentic AI" actually means 14:11 The big pitfall: dumping all your data into the model 15:18 Where we are now, and the tipping point 19:41 Democratising optimisation without an army of PhDs 24:30 Real use cases: from chatbots to Aura 32:11 The digital thread: connecting the silos 33:44 What this means for the humans, and wrap-up Guest and host Guest: Rafael Amaral, CTO and Co-Founder of TilliT (Roima Intelligence). More than twenty years in manufacturing technology across supply chain planning, optimisation and execution systems. Host: Søren Hammer Pedersen, CCO, PERITO IBP at Roima Intelligence. Production This podcast is brought to you by Roima. This podcast is produced by Montanus .

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  S&OP MasterClass

  Monthly S&OP | Balancing and Executive Review: How to Stop Strategic Decisions Being Made on the Warehouse Floor | Ep. 24

  May 13, 202646 minS0

  Most supply chain organisations can produce a demand plan. <br />Many can build a supply plan. <br /><br />Where the wheels tend to come off is in the two weeks that follow, the balancing week, where you reconcile what the business wants with what it can actually produce, and the executive S&OP, where senior leaders are asked to commit to the trade-offs. <br />Skip these two steps, and strategic decisions quietly slip to whoever shouts the loudest in the warehouse or the sales region. Keep them, and your planning starts connecting to your strategy.<br /><br />In this second ins...

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  Episode #23

  Monthly S&OP | Get chaos or get the first weeks right | Ep. 23

  Apr 14, 202640 minS4

  <b>Most companies know they need an S&OP process. Few have one that actually works</b>.<br /><br />The gap between having the meetings and having a functioning integrated business plan is where most organisations quietly struggle, and where the biggest operational and commercial risks accumulate. <br /><br />In this episode of the S&OP MasterClass, <b>Søren Hammer Pedersen</b> sits down with <b>Benjamin Obling</b> – a practitioner with 16 years of focused S&OP experience across industries and organisations – to work through <b>the first two weeks of a monthly S&OP cycle</b>. Not the theory...

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