The best approach may be to let the data dictate where to focus your innovation efforts. Or, if there’s a lack of useful data, then that itself may be the answer.

In this whitepaper, Novotek UK and Ireland explains how utilities operators can get to grips with data management to create an effective data-driven approach to innovation. Covering how to consolidate and modernise assets for data collection, how to make sense of utilities data and which method to use to get the most long-term value from data, the whitepaper is an invaluable resource for utilities operations managers and engineers.

Complete the form below to receive a copy of the whitepaper.

Complete the form below to receive a copy of the whitepaper.

Data is vital in modern industrial operations. However, many businesses often deploy data collection and analytics systems in a way that doesn’t allow them to derive the most value from their data sets. The key to avoiding this is to consider industrial data as an asset in its own right: one that can be optimised to make it more efficient and add greater value.

Applying the three Rs to automation data allows companies to make the most of data. Our infographic below outlines what this means — click the image to view in full quality (opens in new tab):

In the dairy industry as in many others, margins are everything. Business leaders always want their operations to be as efficient and effective as possible, with the highest possible uptime and the lowest possible operational expenses. This means low product wastage, high energy efficiency and lean processes.

However, dairy manufacturers and processing plants face an additional pressure. Raw dairy produce has a very limited lifespan, so it’s vital that it is treated in a timely fashion to prevent potential risks to public health. Unsurprisingly, regulation is very stringent on factors such as hygiene and the correct temperature for milk to be stored at pre-treatment. Each of these are defined in the UK’s Dairy Products (Hygiene) Regulations 1995.

This is one of the key distinctions between dairy processing and many other industrial segments. Improved process speed and operational efficacy isn’t simply sought after to increase throughput and profitability. It becomes an objective because it directly correlates with the safety of products. If raw milk spends too long at a temperature above 6 degrees Celsius without being properly treated, it becomes at risk of harmful bacteria growing.

Whether a dairy manufacturer is setting out to reduce their product wastage or lower energy usage, one thing remains a constant. No matter how many shiny new machines, automated systems or wirelessly connected widgets a business invests in, maintenance will be the key to getting the most out of hardware investments. In spite of this, it’s often the part of the process that many engineers begrudge, due either to it being time-consuming, labour-intensive or highly complex to maintain certain systems.

Digitalising maintenance

Fortunately, advancements in industrial automation systems over the past decade have gone some way to addressing the challenges conventionally associated with maintenance. As more industrial assets — whether that is the pump in a milk pasteuriser or the SCADA system controlling a packaging line — are able to connect with manufacturing execution systems (MESs) and enterprise resource planning (ERP) systems, dairy plant managers are presented with an increasingly reliable and scalable means of determining maintenance schedules.

This eliminates some of the guesswork from maintenance schedule, which means that plant managers can devise maintenance schedules that are more strategic. For example, a SCADA system might pick up a dwindling performance in a motor. If the SCADA system is connected to an MES, the plant manager can easily access this information remotely and make maintenance of that motor a priority. For perfectly healthy systems, they can be maintained only when their performance data indicates they need it.

The only downside to this is that if a plant contains thousands of connected data sources, it can simply be too much data for a plant manager to reasonably analyse to determine the ideal maintenance schedule. More advanced MESs resolve this problem, a good example being GE Digital’s Predix, which incorporates machine learning artificial intelligence (AI) into the system.

This AI can identify correlations and trends in data sets, allowing it to alert managers when maintenance should be conducted on a system. In effect, the AI learns the indicators of poor equipment health and can facilitate a shift to a predictive maintenance model, reducing unnecessary labour and time usage.

Although this makes life easier for plant managers, it does little to simplify the maintenance process itself for engineers. This is where AR comes in, by using the data within the MES and a purpose-built industrial internet of things (IIoT) platform to reimagine maintenance.

Bringing AR to dairy

A contributing factor to the complexity of maintaining some systems is a matter of design. Engineers need to know the most efficient and easiest way of accessing the components that need attending to, and this is not always an easy task — not least because it requires prior knowledge of the specific parts that are under-performing.

Take a milk pasteuriser for example. If the problem is that the flow rate of milk is lower than it should be, it could be a problem with the centrifugal pump, the valve or even the flowmeter measuring flow. With enough performance data from each of these parts, maintenance engineers can easily know which to inspect. And what better way to access this data than a digital overlay showing real-time performance data of each part?

This becomes possible with an industrial AR application like those available through PTC’s ThingWorx 8 IIoT platform, offered by Novotek UK and Ireland. This platform allows dairy engineers to build apps that are specifically designed for their plant and applications, ensuring that the app is suitable for any set up. Of course, the key to achieving this effectively is to work with an industrial AR expert like Novotek to ensure everything goes smoothly.

With that in place, maintenance technicians and engineers can either use AR headsets or their mobile phones to access the application. Simply by holding their phone up to a pasteuriser, engineers could see real-time performance data and could zoom in deeper to see specific parts. With a virtual representation of the pasteuriser’s centrifugal pump on the screen, engineers can inspect and identify if it is the part causing problems. If it is, the AR app can show the easiest way to access and maintain it.

And if a problem is particularly puzzling and the maintenance engineer isn’t sure how to address it, AR applications make it possible for specialist technicians to remotely view and advise on the issue. This encourages the sharing of specialist knowledge and improves the effectiveness of overall plant maintenance.

The value offered by AR is applicable to almost every connected system in a dairy plant. Let’s say that a dairy plant’s manufacturing execution system highlights that a rotary evaporator, used to standardise the dry matter of milk in the early production stages, requires maintenance. As the evaporator consists of several components, a maintenance engineer could use AR to see a virtual representation of the components in the evaporator and identify which needs attending to.

By using a purpose-built AR application, the engineer can view real time system data from the ThingWorx IIoT platform and see which components are performing inefficiently. In this case, it could be that the evaporator’s compressor requires lubrication. The engineer can then resolve this in the least disruptive way possible, minimising the impact that necessary maintenance has on production. Crucially, this technology maximises uptime and improves overall equipment effectiveness in the most efficient, effective and easy way possible. If a dairy manufacturer is looking to make their operations as lean and efficient as possible, AR seems like the ideal tool to help achieve precisely that.

Currently, many food and beverage companies solely rely on manual paper checks for their traceability procedures. Data is collected manually by the workers, which is then collated. The files are then collected and stored in offices, which must be sorted through in the event of a regulatory inspection or a recall.

Manually collecting vital information like this can have an impact on the accuracy of the information and the speed at which it can be collected. However, European companies in the food and beverage industry are bound to laws that mean their traceability procedures must be extremely tight to avoid any prosecution or fines.

All countries in the EU are bound by the General Food Law of 2002 to ensure that food safety is maintained for all European consumers. The law defines traceability as the ability to trace and follow food, feed and ingredients through all stages of production, processing and distribution.

When food or feed is found to be unsafe, companies are under obligation to withdraw or recall the product. They must also notify the national authorities who can then monitor where the product may have spread to and whether further action should be taken. Otherwise, a contaminated feed could have wide reaching consequences across the European food supply chain.

To comply with this regulation, companies must have traceability systems in place that allow them to identify where their products have come from and where they are going to. They must do this quickly to reduce the spread of contaminated product and the amount of product recalled, affecting the supply chain.

It is almost inevitable that at some point in the food chain, mistakes will be made, and contaminated products may enter production. However, to minimise the disruption to their business, food manufacturers can invest in automated traceability systems. Then, if a contaminated product is found, the batches that it has been in contact with are traceable.

To create an effective web of traceability records, data should be collected from the three different stages of food production. Manufacturers can use PLCs and HMIs to collect the data. Emerson Automation’s PACSystems range of control systems are easily integrated into existing systems, meaning that the benefits of improved traceability are not outweighed by a huge cost to integrate the new products.

In the first stage, all raw materials should be easily identified by the batch and date code, meaning they are tracked as soon as they come into the factory. Then, as the raw material is processed, manufacturers should note the number of units produced and the amount of any waste product. Finally, the third stage, where the product is sent to customers, is one of the easiest to keep traceability records of, as most businesses keep detailed records of orders and their recipients.

By feeding the data collected along the production line into a MES system, the plant manager can view all of this traceability information at a glance. In the event of a recall, the plant manager can then share this information with the relevant authorities quickly, without having to track down missing data, minimising the disruption to the plant.

Not only does improving traceability systems help the manufacturer in the unfortunate event of a recall, it also helps the businesses’ transparency. Novotek has previously worked with a Dutch milk producer where GE Digital’s Historian application allowed the company to improve its transparency to its customers. As the brand promoted itself in organic retailer stores, proving the origin of the milk was important to the success of the brand, meaning the producer had to invest in an improved traceability system. Food scandals don’t look likely to disappear anytime soon, but by investing in better control systems to improve traceability, food manufacturers can reduce the effect of the scandals on their business. Just as professional gardeners wouldn’t weed their gardens with inadequate tools, food manufacturers must not rely on out-dated manual collection methods. By carrying out traceability procedures using connected, automated systems, food manufacturers can protect themselves as much as possible against scandals.