Every dairy facility runs on a foundation of standards.

Not the food safety certificates on the wall, but the invisible engineering standards that determine how machines talk to each other, how recipes move from screen to tank, and whether a new production line will fit cleanly into everything that came before it.

Most production managers know these standards exist. Far fewer know how to use them as a competitive tool.

In this episode of the Food Tech Podcast, Lars Linnet, Product Director at Au2mate, makes a case that is both practical and urgent.

The three core automation standards in dairy, ISA-88, S-95, and PackML, are not compliance exercises, but rather a shared language that determines whether your facility can scale, optimise, and integrate new suppliers without rebuilding from scratch every time.

In this conversation, Lars breaks down what these standards actually cover, where facilities typically fall short, and how a pragmatic, project-by-project approach to compliance builds the kind of operational foundation that makes data-driven optimisation possible.

In this Episode

• The three foundational automation standards every dairy production manager should know, and why ISA-88 is the most important starting point
• The real cost of custom, non-standard integration work, including the hidden overhead when new products need to be introduced years later
• Why large producers tend to handle this better than smaller ones, and what the tipping point looks like for growing facilities
• The pragmatic approach: why a full gap analysis is rarely the right first step, and how to build compliance incrementally
• Why supplier agnosticism, the ability to choose between vendors without operational risk, is one of the most underrated benefits of standards compliance

Chapters

00:00 Opening quote and episode framing

01:55 What the three core standards actually cover

04:10 Additional standards: food safety, machine safety, and more

04:44 Which standard a production manager should prioritise first

06:10 A real-world example: cheese line expansion and recipe integration

08:52 How widespread is standards compliance across the industry

10:06 The tipping point: when does non-compliance become a real problem

14:31 From compliance to competitive advantage: using standards proactively

15:49 The link between structured data, naming conventions, and AI readiness

17:10 What it actually looks like when data has no structure

18:33 The business case: what is at stake when optimisation is not possible

20:49 Starting right: building the model from the design phase

22:06 Do standards constrain innovation, or enable it

24:39 Who owns standards compliance in a dairy organisation

26:09 Three immediate actions for a production manager

28:40 How to pitch standards to operators, production managers, and senior leadership

32:28 Supplier agnosticism and the trust argument

About Lars Linnet

Lars Linnet is the Product Director at Au2mate, a specialist automation and MES provider serving the food and beverage industry. He has spent his career at the intersection of IT and OT in dairy production, helping facilities design and implement structured automation environments that meet the demands of modern data-driven operations.

Lars brings a rare combination of hands-on engineering depth and strategic perspective, having worked on projects ranging from greenfield builds to complex multi-supplier integrations. In this episode, he speaks with characteristic directness about where the industry is doing well and where it consistently falls short.

Production

This podcast is brought to you by Au2mate.

This podcast is produced by Montanus.

There is a tempting assumption in modern manufacturing: that better data means better decisions, automatically. Deploy the sensors, build the dashboards, and the factory runs itself.

Henrik Kæmpe disagrees.

Not because data does not matter, but because the path from measurement to decision is far more complicated than the automation playbook suggests.

Henrik is Operations Development Manager at Mammen Mejerierne, one of Denmark's largest dairies, with an annual turnover of around 270 million euros across three production sites. He returns to the FoodTech Podcast for a second conversation to go deeper on one of the most practical questions in food production: how does data actually change the way a factory operates, from the sensor on the shop floor to the board's strategic decisions?

The answer depends heavily on time.

An operator can course-correct within minutes, but a management team making a change to a mature aged cheese may wait a year to see whether it worked.

That gap, from seconds to years, sits at the heart of why data-driven operations in dairy require a different kind of thinking than most industries apply.

And this is what this episode of The Food Tech Podcast is about: yield as a KPI and why decades of craft knowledge cannot simply be replaced by a sensor.

In This Episode

• How a steady flow of trusted data shifts production from reactive to proactive
• How to break a complex KPI like yield into actionable sub-components
• The transition from tactile craft knowledge to sensor-based standardisation in cheese production
• How to set guardrails and limits to protect against false positives in data interpretation

Chapters

02:11 A steady data flow as a shift from looking backwards to looking forward

03:14 Time horizons by role: from operator minutes to management years

05:22 How much production data is used reactively versus proactively

08:15 Which data points matter most and why you need all of them

13:39 Yield at leadership level: what it actually means to look at it

19:03 Craft knowledge and the limits of gut feeling in industrial production

22:31 Industrial consistency, the tasting ritual, and the silent customer

25:21 Guardrails, limits, and handling outliers in data

28:49 Why human expertise will not go out of fashion

In most production facilities, collecting data has never been easier. Sensors measure temperature, flow, pH, and pressure around the clock. And yet, for many dairy producers, the gap between collecting data and acting on it remains wide, because there is a step that often gets skipped: validating that the numbers being collected are actually right.

In this episode of the Food Tech Podcast, we talk with Henrik Kæmpe, Operations Development Manager at Mammen Mejerierne, the second largest dairy in Denmark.

Henrik has spent years building the data infrastructure that allows an organisation to make good decisions at every level, from the operator managing a batch on the floor to the site manager reviewing weekly performance. His perspective is grounded in practice rather than theory.

The conversation works through the full stack of production data, from sensors and batch control through KPI dashboards and sales forecasting, before arriving at one of the more counterintuitive realities of dairy manufacturing.

What makes Henrik's perspective valuable is that he approaches all of this as a people manager first. The goal, in his framing, is not a perfectly instrumented facility. It is an organisation where the right person has the right data, trusts it, and knows what to do when it changes.

In this Episode

• How decision loops in a dairy facility are structured, and why the data that matters at operator level differs from the data that matters at management level
• Why traditional statistical process control fails in batch cheese production, and what that means for optimisation efforts
• Why ownership of a KPI matters more than how it is designed
• The first data points Henrik would introduce at any new cheese facility, and why yield sits at the centre of everything

Chapters

01:43 How data use is structured across organisational levels

07:21 What production problems catch Henrik's attention and prompt new solutions

11:51 When sales forecasts go wrong and how production responds

15:57 How to ensure KPIs stay relevant to the people using them

24:06 Statistical process control and why batch cheese production makes it hard

29:30 Could dairy ever embrace natural variation and sell it as a feature?

36:00 How pH, salt, protein, and texture interact and complicate standardisation

36:32 The first data point Henrik would introduce to a new facility.

About Henrik Kæmpe

Henrik Kæmpe is Operations Development Manager at Mammen Mejerierne, the second largest dairy in Denmark with an annual turnover of approximately 270 million euros across three production sites. He has spent many years working with statistical process control and data infrastructure in cheese production, and his work sits at the intersection of operations, technology, and people management. Henrik's focus is on building the organisational and technical structures that allow teams at every level to make better decisions from the data they already collect.

This podcast is brought to you by Au2mate.

This podcast is produced by Montanus.

It starts the same way in almost every plant. A small team, a good product, growing demand.

Someone builds an Excel sheet to track orders. Then another for recipes. Then one more for raw materials. Before long, the entire operation runs on spreadsheets, manual counts, and tribal knowledge.

In this episode, Erik Søndergaard and Lars Linnet join the FoodTech Podcast to walk through what it actually takes to move a food or beverage production facility from low integration to full digitalization.

From walking the factory floor and reading the posters on the walls, to implementing recipe systems, real-time dashboards, and automated job order management, this conversation covers the full journey step by step.

What makes this episode especially practical is that Erik and Lars bring two different perspectives. Erik represents the management and financial side, focused on cost, compliance, and customer delivery. Lars brings the engineering view, grounded in what operators need and what the control systems must deliver. Together, they show how these two worlds must connect for digitalization to actually work.

00:59 Introduction to the episode and guests Erik Søndergaard and Lars Linnet

02:41 Establishing ground zero and why mid-range companies hit a glass ceiling

05:05 Walking a real Danish food production plant and identifying opportunities

07:30 What management needs from data versus what operations needs to monitor

10:43 Label validation, recipe systems, and where manual processes create real risk

15:53 What belongs in a MES system: batch IDs, traceability, OEE, and warehouse integration

19:32 The levels of integration from PLC and SCADA up to automated job orders

22:23 Dashboards, KPIs, and presenting data without creating overload

24:41 Change management, operator trust, and why involvement decides success

30:46 Quick win examples and why small steps build momentum for larger transformation

Production

This podcast is brought to you by Au2mate,

This podcast is produced by Montanus.

We all know the image. Bottles racing through the line at full speed. Conveyors packed. Motors humming. It looks efficient.

But when you step onto real production floors, the picture is very different. Belts run empty. Pumps throttle instead of slowing down. Motors consume energy even when nothing moves.

In this episode, Gregors Geilager from Danfoss Drives joins the Food Tech Podcast to explain why energy efficiency is no longer a side project, but a core competitiveness issue for food and beverage producers. From frequency converters and pump laws to condition-based monitoring and empty conveyors, this conversation is packed with practical insights you can use immediately.

If you are responsible for production, utilities, or technical decisions in a dairy or beverage plant, this episode will challenge how you look at motion, energy, and data on your lines.

In this episode, you will learn:

1. Why empty conveyors quietly waste more energy than most people think
2. How small speed reductions can cut energy use dramatically
3. What frequency converters really do and why they matter everywhere
4. How pumps, belts, and motors reveal their condition through data
5. Where to start if you want fast payback on energy optimization

Episode Content

00:10 The perfect production image versus reality on the factory floor

00:45 Why timing and balance matter more than raw speed

02:09 Energy prices, volatility, and why efficiency decides competitiveness

03:43 What frequency converters are and why modern plants need thousands

06:45 Why flexibility and frequent changeovers demand speed control

08:36 Why tiny inefficiencies matter at high production volumes

09:44 The affinity laws and why pumps are the biggest low-hanging fruit

10:34 How reducing speed by 20 percent cuts energy by half

13:08 Where frequency converters create value beyond simple speed control

17:28 Predictive maintenance using built-in machine learning

18:25 Cavitation explained and how drives detect it early

21:42 How drive data feeds SCADA and maintenance systems

25:19 Why most plants still miss easy energy savings

32:22 Where production managers should start their efficiency journey

Production

This podcast is brought to you by Au2mate.

This podcast is produced by Montanus.

What happens to your production when the power flickers, a server reboots at the wrong moment or a firewall rule opens a door you did not know existed?

In this episode, Erik Søndergaard joins The Food Tech Podcast to unpack operational resilience in food and beverage production. From power drops and UPS age to backups, segmentation and NIS2, Erik explains how to think about uptime like an insurance policy: decide what an hour of lost production costs, then secure your systems to match that risk.

If you run a dairy, brewery or any process plant, you will hear concrete steps to keep lines running and systems ready to restart safely when something goes wrong.

In this episode, you will learn:

1. What operational resilience really means on the factory floor

2. Why power disturbances and aging UPS units are still the biggest real-world risks

3. How to use redundancy, backups and restore tests to protect critical servers

4. How network segmentation and OT/IT separation limit the blast radius of an attack

5. Why NIS2 is not just paperwork but a catalog of good uptime practices

Episode Content

00:06 What operational resilience means in a digitized production

01:34 Real-world blockers of production and why power is enemy number one

03:38 IT vs OT - why five minutes offline is different in an office than in a cheese vat

05:44 Defining operational resilience as the ability to keep producing and restart safely

09:04 Calculating the cost of downtime and using risk analysis as an insurance model

11:20 Legacy equipment, isolation and why “air gaps” still matter for old systems

13:13 Why security is never “done” and the need for regular hygiene walk-throughs

16:05 The firewall rule that opened everything and what it teaches about everyday shortcuts

20:42 How segmentation limits the blast radius when something does go wrong

22:35 The basics to fix first - UPS age, server redundancy, backups and restore tests

26:23 Thinking in fire doors and zones for OT networks and systems

27:48 Securing vendor remote access without importing new risks

30:53 Clear roles when something breaks and anchoring responsibility at board level

33:31 Treating NIS2 as uptime engineering instead of box-ticking compliance

This podcast is brought to you by Au2mate.

This podcast is produced by Montanus.

AI is everywhere, but turning it into real outcomes in dairy and food processing is where the value is. In this episode, Anna Olsson, co-founder of Intelecy (a no-code platform for industrial AI), cuts through the hype to show what plants can do today: predict failures before they happen, optimize processes in real time, and capture expert know-how so it scales across sites.

If you run operations, engineering, maintenance, or production IT, you will get practical steps to start fast, prove ROI, and avoid pilot purgatory.

In this episode, you’ll discover:

1. The “learn, act, detect” framework for industrial AI

2. Why data quality and coverage beat big promises

3. How to move from pilots to scaled, maintained models

4. Where predictive maintenance ends and process optimization begins

5. How no-code tools bridge the IT–OT gap and protect operator trust

Episode Content

01:57 After ChatGPT – expectations vs industrial reality

03:06 LLMs vs industrial AI and time-series sensor data

04:47 The “learn, act, detect” framework for process optimization

05:26 Predictive maintenance in practice and planning stops instead of reacting

06:40 Predicting future process states and adjusting before quality drifts

10:23 Tacit know-how and “knocking on pumps” vs data-driven models

12:21 Prerequisites for AI: stored sensor data and data quality

15:20 Case: how TINE detects bacterial contamination with AI

17:28 Energy optimization and small savings that add up 24/7

19:37 Why AI projects fail and end up in “pilot purgatory”

21:02 Build vs buy – scaling beyond the first AI model

31:25 Towards Industry 4.0 – closing the loop from prediction to automation

This podcast is brought to you by Au2mate.This podcast is produced by Montanus.

How important is data in modern dairy production, and how do you turn it into real outcomes on the factory floor?

In this episode, we talk with Erik Vedfald, Chief Architect for Production IT at Arla. Erik explains why quality beats quantity in data and what it takes to move from proofs of concept to trusted tools operators actually use. We discuss sensors, governance, UX and the long game of preparing today’s datasets for tomorrow’s analytics and AI.

If you work in operations, engineering or production IT, you will get practical guidance on where to start, how to involve your teams, and of course, how to avoid the common pitfalls.

In this episode, you’ll discover:

1. Why data quality matters more than having lots of data

2. How to pick high-impact use cases that pay back

3. Ways to structure “data zones” and connect them with a digital thread

4. How to earn operator trust and avoid AI “first-try” failures

5. Practical steps medium-sized dairies can take to get started

Episode content

01:18 Why “keeping up to speed” in production is an illusion

03:55 Are dairies slow or simply paced by ROI and business cases

06:12 Why you can’t just drop AI on raw production data

07:36 Sensors and “dumb vs. deep” data that actually matter

09:21 The skeleton and digital thread analogy for connecting data

10:33 Building high-quality “data zones,” starting at milk intake

11:47 Adoption challenges: UX, change management, and operator workflows

14:50 Data governance and the three-year horizon for training models

16:58 Trust is fragile: the high cost of early bad AI answers

26:37 Bottom-up innovation and scaling local wins across sites

Production

This podcast is brought to you by Au2mate.This podcast is produced by Montanus.

How can tweaking milk fat content by just 0.15% unlock huge savings – even for smaller dairies?

This week, we sit down with Michael Sievers from FOSS, who has spent three decades shaping the future of dairy automation. He reveals how FOSS inline sensors now measure around 80% of the world’s milk, and also how that data helps dairies boost yield, cut waste, and put real money back in the business.

Whether you run a small creamery or a mid-sized dairy, you’ll hear how precision and automation can help you save on raw materials, ride out volatile market prices, and stay competitive against the big players.

In this episode, you’ll discover:

1. How inline sensors turn raw milk data into efficiency gains.

2. Why real-time analysis is a game-changer for dairies of every size.

3. How automation drives higher yield and lower costs.

4. The surprising bottom-line impact of tiny fat adjustments.

5. How Industry 4.0 is reshaping the dairy industry.

Episode Content

01:53 Importance of Production Optimization in Dairy

04:33 Real-time Process Monitoring for Medium-sized Dairies

05:18 Practical Examples: Butter and Milk Fat Optimization

10:06 Financial Benefits of Automation for Smaller Dairies

12:30 Supply Chain Dynamics: Fat and Protein Utilization

14:19 Selling Excess Cream to Larger Dairies

17:45 Steps to Implement Inline Automation Systems

22:30 Overcoming Resistance to Technological Change in Dairies

27:18 Future Trends: AI and Inline Measurement Integration

Production

This podcast is brought to you by Au2mate.

This podcast is produced by Montanus.

How can medium-sized dairies extract more value from every liter of raw milk?

In this episode, we talk with Jesper Kjeldal, Sales Director at MMS Nordic and a specialist in membrane filtration. He explains how modern separation technologies not only reduce waste but also open up new opportunities to tailor the composition of milk, including lactose, minerals, and proteins, to specific products and customer demands.

If you work in operations, engineering, or production in the dairy industry, you will find practical insights here you can apply in your daily work.

Tune in for inspiration on how even smaller dairies can take the next technological step.

In this episode, you’ll learn:

1. How membrane filtration maximizes the value of raw milk

2. The key differences between ultra, micro, and nano filtration

3. Emerging whey processing trends and their profit potential

4. How efficient separation techniques improve sustainability

5. What challenges and innovations lie ahead for the dairy industry

Episode Content

01:01 Overview of Separation Techniques in Dairy

04:51 Traditional vs. Modern Filtration Techniques

09:56 Benefits of Early Filtration in Cheese Production

10:31 Types of Membrane Filtration: RO, NF, UF, MF

12:32 Collaboration with Clients: Understanding Needs

13:54 Key Metrics Clients Look for in Filtration

21:11 Medium-Sized Dairy Innovations in Separation Techniques

24:47 Future Trends: Plant-Based and Hybrid Dairy Products

32:26 Closing Thoughts on the Future of Dairy Industry

Production

This podcast is brought to you by Au2mate.This podcast is produced by Montanus.