S&OP MasterClass

About 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. (https://www.roimaint.com/da/content/perito-ibp-podcast) This podcast is brought to you by Roima (https://www.roimaint.com/). This podcast is produced by Montanus (https://montanus.co).

Publisher

Roima – Perito IBP

Category

business · business · technology

Language

en

Explicit

No

First episode

23 Mar 2022

Latest episode

7 Oct 2026

Latest episodes

28 episodes in the feed.

1. 

   7 Oct 2026

   Vibe Code Your Own IBP Solution? Here are the Traps | Ep. 28

   Most production companies have already done the hard work upstream. The demand forecast is in place, inventories are optimised, and the MRP is running on better input. Then the production order proposals land on the planner's desk and the whole thing drops into Excel. But why Excel? Software could automate and optimise the job has existed for years. The short answer is that advanced planning software is built to be configured for any factory in the world, so configuring it for yours becomes a very expensive project. That’s why Excel wins by default. But that raises a question many is asking right now. Why not just vibe code an IBP system ourselves? Ask Claude or ChatGPT for an optimisation algorithm, describe your machines, orders and constraints, and let it run. In this episode, we discuss this question, and what traps you need to watch out for if vibe coding is your plan. By the end of the conversation you will know what that building block needs from the platform around it, where the planner and the embedded AI fit, and how to test the whole idea in four weeks before you commit any real money. In this episode How the same advanced planning software makes both a steel foundry and a food producer pay for complexity they do not need Why vibe coding the whole scheduling tool recreates the Excel problem in a browser How to split the job so the platform stays dependable and only the optimisation is vibe coded Where the point of diminishing returns sits, and why some decisions should stay with a human The four-week proof of concept to run before you sign anything Chapters 00:40 Who this episode is for 02:17 Where production planning sits after MRP 04:00 The gap between what is possible and what happens 04:53 Two options, configure everything or give up and use Excel 07:22 Casting, furnaces and a 20-step workshop 10:37 Why the business case is easy to find 11:42 Why not just vibe code it 13:41 From impressive demo to production reality 15:20 Knowing when to stop automating 17:19 Keep the platform, vibe code only the optimisation 21:09 What the platform gives the building block 22:29 The planner, the dashboard and the embedded AI 26:08 Prove it with a four-week POC 27:09 Wrap up 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 supply chain planning and execution, we are here to help. Production This podcast is brought to you by Roima. (https://www.roimaint.com/da) This podcast is produced by Montanus (https://montanus.co).

2. 

   2 Sept 2026

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

   "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. (https://www.roimaint.com/da) This podcast is produced by Montanus (https://montanus.co).

3. 

   5 Aug 2026

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

   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 Chapters03:18 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. (https://roimaint.com) Production This podcast is brought to you by Roima. (https://www.roimaint.com/da) This podcast is produced by Montanus (https://montanus.co).

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YouTube views

852,005

Score snapshot 15 Sept 2025

Apple Podcasts (US)

5.0 / 5

1 ratings

Podcast Authority Score: 57 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

65

Social presence

0

YouTube

93

Engagement

30

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