The Costly Impact of Unplanned Downtime in Milk & Yogurt Production

In the fast-paced world of dairy manufacturing, unplanned downtime can be catastrophic. Every minute of unexpected production stoppage translates into significant financial losses. From lost product and wasted raw materials to missed delivery deadlines and unfulfilled customer orders, the consequences can quickly escalate.

The milk & yogurt production process is a delicate balance of precise temperature controls, intricate equipment synchronisation, and stringent food safety regulations. Any disruption in this finely tuned system can lead to costly product spoilage, contamination risks, and potential regulatory violations.

To top all this up, many dairy manufacturers operate a 24/5 or even 24/7 production schedule, which means that optimising maintenance operations becomes even more crucial in mitigating these risks. 

Unsurprisingly, our customers in this space continue to tell us that reducing unplanned downtime and getting ahead of headaches with the right data on the right assets is a top priority. 

In this article, we wanted to offer more context about where predictive maintenance can be helpful to dairy, and more specifically yogurt & milk producers. We’ll go over some of the key assets online condition monitoring can help with, and address the common situation where a site might be doing some manual vibration inspections, and is debating whether or not implementing an effective predictive maintenance software could be valuable. 

This article will be particularly helpful for producers such as the likes of Danone, Yoplait, Chobani, Müller, Nestlé, Lactalis, Fage, and Fonterra, where unplanned downtime can have significant impacts on their global supply chains and their brand as well! 

Critical Assets in Yogurt Manufacturing

Filling Machines

Filling machines represent a critical point in the production line where reliability is paramount. Many of the components powering the machine will require consistent vibration condition monitoring to prevent unexpected failures that could halt production, A recent study published by IEEE found that embedding a vibration sensor in filling machines was able to pick up failures ahead of time with a very impressive accuracy of 96% (Kühnel et al., 2022, "Real-Time Condition Monitoring of Filling Machines with Vibration Analysis and Edge AI”, p. 173-178). 

Conveyors

This isn’t one you would have thought about at first. In fact you’re probably raising your eyebrows now. Here’s the thing; the small motors driving conveyors are particularly susceptible to being missed if you have very short maintenance windows. Add to this that some of these conveyors will be overhead and therefore hard to access, and you are prone to missing the classic belt wear and bearing issues on them. 

For this reason, leveraging consistent vibration and temperature condition monitoring will do a great job here to prevent unexpected failures that could halt production. Here, you will be able to detect early signs of bearing degradation and belt wear, allowing maintenance teams to schedule repairs during planned downtime.

Some sites will have multiple dozens of these motors, and with some experiencing a MTBF that’s quite low, these can form a great part of a pilot program to prove the technology will work well at your site. We wrote more about how specifically we have a history of preventing failures on them in this article : From Preventive to Predictive Conveyor Maintenance in the food and beverage industry.

Fermentation Tanks

Fermentation tanks, and more generally other large tanks will likely all have their crucial agitator systems which are essential to product quality and consistency. The mechanical components in these agitators systems, particularly bearings and seals, can experience wear that leads to costly failures. Often these are very large tanks, and these agitators are sitting on top of the tanks, making them particularly hard to access. Hard to access and crucial make them perfect candidates for machine health monitoring through advanced condition monitoring software. Once again, here we can identify early warning signs of agitator issues before they result in tank failure or product contamination.

Homogenisers

Homogenisers represent another critical asset in yogurt production, operating under extreme pressures to ensure consistent product texture and quality. These systems face multiple wear points that benefit from continuous monitoring: high-pressure pumps and valves experience constant stress, homogenising heads suffer gradual abrasion from continuous product flow, and seals and gaskets undergo significant pressure and temperature stress. Vibration condition monitoring proves particularly effective for these assets, as the technology can detect subtle changes in pump vibration patterns, pressure variations in homogenising heads, and early signs of seal deterioration. By implementing comprehensive machine health monitoring on homogenisers, maintenance teams can identify developing issues before they lead to costly failures or product quality problems, ensuring optimal performance of these essential systems.

Smart Valves

In modern yogurt factories, thousands of "smart valves" are employed to precisely control the flow of liquids and semi-solids throughout the manufacturing process. These intelligent valves use frequency-based actuation rather than traditional voltage or amperage, enabling highly accurate and responsive flow control. Valves can be challenging to monitor easily given how small they are, and the sheer number of them that exist on sites. You can include them as part of your predictive maintenance strategy through an integration with your historian, your SCADA, or another system in which you keep track of the key operating parameters that these valves operate under. 

Given there tends to be more complexity with integrating them, we recommend that you don’t start with valves immediately, but rather integrate them in future once you’ve gotten some wins and traction with your predictive maintenance efforts. 

Challenges of Maintenance in a 24/7 Production Environment

A number of yogurt production facilities we’ve spoken with will operate around the clock to meet consumer demand for their popular products. This 24/7 manufacturing schedule presents unique challenges when it comes to equipment maintenance. With limited downtime available, maintenance teams have extremely tight windows to inspect, repair, or replace critical assets.

Any unplanned failure that occurs during active production can be catastrophic. Yogurt is a viscous, highly perishable food product, so equipment malfunctions can quickly lead to costly spills, contamination risks, and production shutdowns. As one reliability manager noted, the costs of such incidents go far beyond maintenance - unplanned downtime is considered a "marketing cost" that impacts the entire business.

For such sites, continuous, online condition monitoring is especially helpful for maintenance planning. If you have planned maintenance work orders on all of your conveyor motors, but your limited time window won't allow you to cover all of them, then having critical asset data on these will be the best way to help you coordinate your efforts. 

The Power of Condition Monitoring

As we’ve suggested above when going over some key pieces of equipment, we’re seeing that implementing comprehensive vibration condition monitoring across critical assets has proven particularly effective for preventing unexpected failures in yogurt production. As a quick overview to dive deeper into some common failure modes, for filling machine motors, vibration analysis can detect:

• Early-stage bearing wear
• Belt misalignment
• Mounting looseness
• Shaft imbalance

In fermentation tank agitators, condition monitoring software can identify:

• Seal degradation patterns
• Bearing temperature anomalies
• Unusual vibration signatures
• Shaft alignment issues

Using AI to Predict Equipment Failures

The key to preventing costly failures lies in the ability to detect subtle anomalies that precede such events. By harnessing the power of artificial intelligence (AI) and machine learning, modern predictive maintenance software can analyse vast amounts of data from equipment sensors to pinpoint patterns indicative of impending failures.

At the core of this AI-driven solution is a sophisticated algorithm that continuously monitors data streams from critical components. This data encompasses a wide range of parameters, including temperature, pressure, vibration, and electrical signals. The AI model is trained to recognise normal operating patterns and identify deviations that may signal potential issues.

Why is AI better than thresholds? 

Here’s another common question; Why is AI better than thresholds that are traditionally used in SCADA systems? 

AI-powered predictive maintenance models offer significant advantages over traditional threshold-based monitoring systems. While conventional systems rely on pre-set thresholds based on generic motor classifications, AI models learn from your specific equipment's actual operating patterns. This contextual learning is crucial because each production facility operates its assets differently, making standardised thresholds less effective. This is especially true for production related equipment; if you run multiple different products on the same lines (a common occurrence for brands that market many different flavours, and with marketing departments that like to create punchy temporary products), than thresholds will fire off lots of false positives when a new product that is more demanding on your equipment starts to get produced. 

For equipment that operate with a more consistent operational profile, AI's more sophisticated pattern recognition capabilities can detect subtle changes in equipment behaviour long before they would trigger traditional thresholds. This enhanced sensitivity provides maintenance teams with greater lead time to plan and execute repairs, shifting from reactive maintenance to truly predictive interventions. The AI approach also eliminates the complicated and often imprecise process of manually setting and adjusting threshold values, resulting in more accurate and site-specific early warning detection.

For Sites Already doing Manual Inspections: What’s The Value of Online Condition Monitoring? 

While manual vibration inspections have traditionally been a cornerstone of maintenance programs, online condition monitoring offers significant advantages for yogurt producers. Manual inspections, typically performed monthly or quarterly, can miss critical developments that occur between measurements. Here's why continuous online monitoring represents a crucial upgrade for modern production facilities:

Catching Rapid Deterioration

Some equipment failures can develop rapidly, especially in high-speed production environments. A bearing might show normal readings during a monthly inspection but fail catastrophically just days later. Online condition monitoring software provides continuous oversight, catching sudden changes in equipment health that manual inspections might miss.

Comprehensive Data Analysis

Manual inspections provide only periodic snapshots of machine health, making it difficult to identify subtle trends or patterns. Online monitoring captures continuous data streams, enabling advanced analysis of:

• Long-term deterioration patterns
• Correlation between operating conditions and equipment stress
• Impact of process changes on machine health
• Early warning signs that might be invisible in monthly readings

Real-World Production Context

Online monitoring systems can correlate equipment behaviour with actual production conditions. This context is invaluable for understanding how different product types, speeds, and operating parameters affect machine health - insights that are impossible to gather from periodic manual measurements.

Cost-Effective Resource Allocation

While manual inspections require significant time and labour investment, online condition monitoring software can monitor hundreds of assets simultaneously. This allows maintenance teams to:

• Focus their efforts on genuinely problematic equipment
• Eliminate unnecessary routine inspections
• Reduce travel time between sites
• Deploy specialist resources more effectively

24/7 Protection

In round-the-clock production environments, equipment failures don't wait for scheduled inspection times. Online monitoring provides constant vigilance, ensuring that potential issues are flagged immediately, regardless of the time of day or day of the week.

Enhanced Predictive Capabilities

By combining continuous monitoring with machine learning algorithms, modern predictive maintenance software can:

• Learn normal behaviour patterns for specific equipment
• Detect subtle deviations that might indicate emerging problems
• Predict potential failures weeks or months in advance
• Provide increasingly accurate remaining useful life estimates

Integration with Production Systems

Online monitoring systems can integrate with broader production management software, enabling:

• Automatic production adjustments based on equipment health
• Coordination of maintenance activities with production schedules
• Real-time alerts to operators and maintenance teams
• Comprehensive reporting and trend analysis

Conclusion : Getting Started with Predictive Maintenance

Taking the first step towards implementing an AI-powered predictive maintenance program can seem daunting. However, with the right approach and a trusted partner, you can unlock significant cost savings, increase asset reliability, and optimise your maintenance operations.

Key considerations include:

• #1 - Conducting an initial asset survey : Begin with a structured assessment of your critical equipment. Our proven 8-step evaluation matrix helps identify the assets that will provide the highest return on your predictive maintenance investment. This systematic approach ensures your program starts with clear objectives and measurable outcomes.
• #2 - Establishing data integration protocols : Prioritise your integration needs by distinguishing between essential integrations and nice to have. This helps avoid scope creep while ensuring your core maintenance objectives are met effectively and efficiently.
• #3 - Implementing comprehensive condition monitoring software vs. running pilot programs : Starting with predictive maintenance doesn't require a full-scale deployment. Many successful programs begin with targeted pilot projects focused on critical assets combined with other assets with a lower MTBF. We can connect you with customers who have successfully scaled from modest pilots to comprehensive programs, demonstrating the value of starting small and growing strategically. 
• #4 - Training maintenance teams : So much of transitioning to predictive maintenance is about behaviour and culture change. Anytime a workflow we’ve used for years becomes different, we all need a hand to get enabled, and support along the way. 
• #5 - Continuously optimising and expanding the program : The majority of our successful customers have adopted a layered approach to their predictive maintenance journey. Start small, build on the wins, and continue to improve. 

By following these steps and partnering with an experienced predictive maintenance provider, you can unlock the full potential of AI-driven maintenance, reducing downtime, minimising product loss, and maximising the lifespan of your critical assets.

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