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.

In the past 12 months, the food industry has been forced to re-evaluate and re-assess its operational priorities. For years, many manufacturers focussed on flexible production to enable diverse product lines and mass customisation, in line with shifting consumer demands. In 2020, this was forced to change, and production efficiency and operational adaptability became the focus. Once more, automation and digital technologies came to the forefront of food manufacturing priorities.

Digitalisation is a word that has been banded around a lot in industrial markets for the past few years, serving as a catch-all phrase encompassing everything that generates, records and communicates data. Unfortunately, as with most amorphous phrases, this leads to confusion among managers about how to introduce these technologies, which causes costly errors in implementation, such as overlapping data collection systems or introduction of technologies that do not serve a strategic purpose.

For food manufacturers at the beginning of their digitalisation journey, the first step is to define an agreed and important goal, which the company can reverse engineer a solution from. Whether looking to deliver on a continuous improvement object that has been identified as part of a formal process, or just illustrating the value of an engineering team unleashed with time to think, it’s key to let the desired improvement dictate what kind of digitalisation will be needed.

For example, if material costs are too high and the agreed goal is to reduce them, a digitalisation project should establish systems that identify the factors influencing this. Understanding the root causes for yield problems could require a combination of machine data, ambient condition data, quality or lab data and information about material quality provided by suppliers. Thinking through where data is readily available, versus where it’s trapped in paper, spreadsheets or isolated automation, will ensure the plan can deliver on the purpose.

Planning at the outset of investing in digitalisation, but some food manufacturers may will have undoubtedly already rushed into digitalisation in years past. For businesses with some digital or automation technologies in place, one of the most valuable things to do is review the lay of the existing digital landscape. The easiest approach to doing this is to apply the ‘three Rs’ to your existing data: reduce systems overlap, reuse data and recycle data.

Reducing data collection system overlap not only makes it easier for managers to identify the source of a specific data set, it also streamlines costs. Why have a downtime system collecting machine event data, a yield analysis system collecting overlapping data and a work in progress tracking system that is separate to both of those? Having three systems collecting fundamentally the same data means duplicated configuration and deployment costs, as well as possible conflict over which one holds the ‘truth’.

An effective data and digitalisation strategy should also aim to use collected data in various calculations to produce several insights. For example, the downtime event data collected for OEE calculations may be part of what’s needed to solve a quality problem. The energy and water data collected for sustainability reporting may hold the key to real savings opportunities. Wherever there is a connection to a data source, managers should think of ways to make sure that a data point only needs to be collected once in order to be used many times.

Finally, offline analysis tools and some of the new analytics packages on the market could mean that old data offers recurring value as a firm chases finer and finer points of improvement. So, it’s important to set up a data management approach and data management platforms that can give you the option of making repeated use of data.

Digitalisation projects can lead to more innovative and effective ways of working for food manufacturers, but they rely on careful planning and strategic implementation. By giving full consideration to the goals to be reached or how data is used within a site, food businesses can ensure their systems are always effectively aligned with their goals.

According to a report by Gartner, the worldwide manufacturing industry will spend $561 billion on IT in 2018. Emphasis on digital transformation is driving this spend. However, businesses shouldn’t blindly invest in IT without aligning it to their business goals. Here, George Walker, managing director of industrial automation specialist Novotek,  explains.

Business goals and values are important for decision making. For instance, if a company’s business objectives focus on reducing waste, maintaining sustainability or reducing overheads, its manufacturing processes — and subsequent investment — should be defined with these specific goals in mind.

Consider American brewing company Anheuser-Busch as an example. In its 2025 US sustainability goals document, the business outlines a company-wide commitment to maintaining sustainability in its manufacturing processes. Among several other sustainability schemes, the brewer recycles its spent grain into bioreactors to be broken down by bacteria and turned into fuel. By doing this, the company saves money, reduces its carbon emissions and, vitally, remains in line with its company values and objectives.

But, how can manufacturers use new IT investments to help them achieve business goals? Efforts to reduce waste in production provides a good example of this. Let’s say a manufacturer hopes to increase its profits by reducing avoidable wastages in production. A sensible investment would be a data collection software that would allow the manufacturer to identify the six big losses in its facility — a term used to describe common reasons for productivity losses in manufacturing.

The best way to identify these losses is by using an IoT platform to collate and analyse data from processes across the factory floor. A perfect example is GE Digital’s IoT platform. When companies install an IoT platform to monitor their production, they could make some shocking discoveries. Unexpected wastes, such as breakdowns, faulty setups, idling, misalignment, defects in processes and start-up losses become apparent.

With Novotek, after installing an IoT platform, we carry out an analysis based on the six big losses. This allows you to measure and track all your wastages and understand their causes and effects. We would then be able to help recommend suitable fixes.

In the case of a quality defect, the IoT platform would identify if there were any problems, such as micro stops or changeover. Then, using six-sigma-based approach methods to rework processes to reduce waste could be identified. From this analysis, a detailed long-term IT solution can be formulated based on your company values and goals. There are indications from Gartner that manufacturing spend on IT will grow by a CAGR of three per cent through to 2022. It will be more important than ever to make sure IT investments carry forward fixes inline with your business goals and values. As the emphasis on IT focused fixes continues to grow, it will become vital to form cohesive IT strategies.

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):

Digital transformation has become a goal for many businesses in the industrial sector, as automation technologies develop and the fields of information technology (IT) and operational technology (OT) converge. However, digital transformation is also an ambiguous concept to strive towards without a clear strategy. Here, we speak with Richard Kenedi, senior vice president of manufacturing at industrial software provider GE Digital, and George Walker, managing director of industrial automation expert Novotek UK, about the ideal path to industrial digital transformation.

GE was one of the first to make serious investments in digital technology and was arguably the first digital industrial provider. What are the key lessons learned from the GE that you would share with other industrial companies?

Richard Kenedi (RK): “The first is focus. You have to know your customers and how you can best help them. It’s not about the technology, it’s about solving the problem. I think secondly, it’s about attitude. We’ve learned that customers aren’t looking for a hero to save them, they want a guide to help them solve a problem today and see around the corners for tomorrow.

“Thirdly, it’s critical to have deep domain expertise when it comes to the people, processes and technologies of each industry we serve.

“In every industry, there are organisations that are implementing at the edge of technology and others that are slower in adoption. Each organisation is at its own individual point of digital transformation maturity. Issues range from culture to investment to workforce. Digital transformation is critical but planning and implementation can’t slow ongoing production. Each company needs guidance to approach digital transformation in a way that meets their holistic situation and requirements.”

For Novotek, as a GE partner in the UK and Northern Europe, you’ve been helping businesses digitalise and increase automation in their operations for some time. What are some of the common challenges when starting that process?

George Walker (GW): “As with any early stage technology, a lot of people initially struggle to separate the hype from the practical reality. Digital transformation, Industry 4.0 and digitalisation have becoming industry buzzwords in the past few years, with a lot of companies promising the Earth if a company installs its latest widget. Often we find companies want to digitally transform, but don’t have a clear picture of why or what they’re looking to achieve.

“The reality is all these concepts are a means to an end — they shouldn’t be the goal in themselves. The end goal might be improved productivity, higher throughput, reduced downtime or lower operating expenditure. Digital transformation is the vehicle that gets industrial businesses to that destination, and the systems that are required as part of that depend on the company’s specific outcomes, focusses and current operations.”

Often, IT providers claim their offerings can digitalise the manufacturing space. What is the difference between an IT and OT setting and why do you see a need for purpose-made software in the manufacturing space?

RK: “IT and OT have come a long way in convergence. In many cases, we’ve evolved from the boundaries and barriers of the past to greater collaboration. IT has gained respect for the real-time needs and process domain expertise of OT. OT has gained an appreciation for IT’s capabilities such as security and mobility. The people and the systems have to work together.

“Purpose-made software has been important for decades. Build-your-own solution is costly, and now more than ever, with our aging workforce retiring, the knowledgebase that continued to drive and support do-it-yourself solutions is becoming unavailable. Most organisations understand the risk that they’re taking on by considering do-it-yourself today. 

“In contrast, purpose-made, out-of-the-box software provides a long-term solution with on-going innovation, maintenance, and support. Companies benefit from focussed development and best practices. For example, these out-of-the-box solutions help industrial companies support the reality of an operator’s job on the ground all the way to the plant manager, and even for global operations directors. You need to understand the failure modes and the success modes for their processes, and you need to understand how that translates into different KPIs across the organisation – for example, how OEE impacts revenue, or even new technologies.”

GW: “From our side, the biggest disadvantage of IT providers trying to fit corporate software into industrial settings is that although IT and OT are closely interlinked and complementary, they are still fundamentally different. These clear distinctions, between the environments in which they are used, mean that companies with comprehensive experience of serving industrial markets will always be better equipped to meet the needs of manufacturing software.

“GE, for example, is uniquely positioned due to its extensive history in the industrial space, as well as in digital technologies and software. Similarly, Novotek has worked closely with industry for many years and have developed modules and systems in response to industry issues and opportunities. This means we can both ensure that our software is built around the needs, wants and requirements of industrial environments, rather than being retroactively reshaped to meet a market brief.”

How does GE see itself differentiating itself from its competitors?

RK: “We are more focused on our target markets and industries, because these are the places where we believe we can best help our customers win. In those areas, it’s really about simplicity, speed and scale. Simplicity – because we’re investing across all our product lines to make it easier than ever for customers to adopt and adapt our technologies for their needs. That goes for user experience in the field, through to no-code rapid application development visualisation tools like Proficy Operations Hub.

“Speed – because really customers want return on investment as quickly as possible, and they want responsiveness if they have a problem, and scale – because with our technologies, teams and our partners we are able to bring solutions to customers not only for one line or factory but for an entire enterprise globally. When we bring simplicity, speed and scale together for customers, that’s when you see truly transformative results.”

What are the ideal first steps an industrial business can take towards digitally transforming their operations?

GW: “The first step should always be to plan and identify what you want to achieve. For example, if your focus is on innovating to develop better products and find new ways of operating, this might lead you towards systems such as a modern manufacturing execution system (MES). The MES, alongside SCADA systems on the plant floor, allows for automated feeds of data through each level of an industrial business and insight into all processes.

“Likewise, a business who wants to reduce the frequency of maintenance or downtime might opt for  a Historian software that simplifies the collection, aggregation and analysis of data from equipment and operations. A specialist can advise the best systems to ensure the right results, if the business knows what its objectives are.

“This isn’t a decision to be made by one group within a company either. It’s important everyone, from the maintenance engineers to C-suite personnel, are involved in determining what the focus is to ensure buy in at every level. That way, you increase the likelihood that systems are introduced and set up to provide the insight each level of user needs, in the most effective way.”

Innovation has been a buzzword uttered by many a water provider in recent years. Since the UK’s water services regulation authority, Ofwat, published its price review 2019 (PR19) paper in 2017 and made innovation a focal point, most water providers have considered the use of new technologies to bolster productivity, reduce costs and strengthen continuity of service. This has made data acquisition, aggregation and analysis technologies more appealing to water operators — however, introducing new systems is understandably complicated in such complex networks.

The water industry’s need to innovate is not going to pass any time soon. In December 2020, Ofwat opened a consultation on what the industry should look like by 2040 and how the regulator, the sector and stakeholders can meet the challenges of the intervening years.

At the launch, Ofwat commented:

It’s the first of these suggestions, smart networks, that initially appears to be relatively easy for water operators to develop. A smart water network consists of various analytical, measurement and sensing devices and systems that offer insights into everything from water quality and pipe pressure to pump speeds and ambient temperatures in reserves. This field data can provide various insights to stakeholders.

However, a physical network as expansive and complex as water infrastructure presents ample opportunity for devices and systems to be deployed in an isolated and inefficient way. This means that its very likely certain raw data will be collected numerous times, stored in multiple disparate systems and siloed from certain groups of stakeholders. In our view, this isn’t a smart network; it’s a pseudo-intelligent network. If the data is being collected and used to inform strategic decisions, then it stands to reason that the systems collecting and housing that data should be deployed strategically as well.

Water operators at the start of their deployment journey can avoid a wide range of headaches by considering the three Rs of data:

• Reduce overlap of data collection
  
• Reuse collected data for multiple purposes
  
• Repackage data for multiple stakeholders

Reducing overlap in data collection means ensuring that raw data points are collected only once by a single sensor or system, and stored in a clearly defined system, such as a Historian software. This avoids the expense of investing in multiple systems to collect the same data several times for different purposes, as well as the cost of setting up those systems.

Reusing collected data for multiple purposes builds on this. The insights a technician will need to draw from machine data will differ significantly to that of an operations manager or area manager. However, the raw data can be fed into various reports and calculations to offer different kinds of insights. For example, the energy requirement data of water treatment equipment is relevant not only for energy usage reports, but also for sustainability reporting. Making the data readily available for multiple reporting purposes enhances business flexibility.

These values are especially important in the water sector, where context is key and the interplay between different parts of the network can have a tangible impact on overall operations.

One of the costliest oversights that many businesses encounter when focussing on data collection is that technicians often embrace a silo mentality, where they understand the value that data offers to their immediate machine or area of operation. For example, it might be that pump pressure monitoring is perceived as being valid only insofar as it informs maintenance schedules for that system. This mentality might make it appear to be a good idea to invest, separately, in sensor devices to measure water flow rates in the local area, which monitor water flow and pipe pressure.

Instead, repackaging the pump pressure data to support analysis of water flow would reduce overlap between systems, and in turn reduce the time and cost expenditure of configuring another system to also collect pressure data.

This same principle of the three Rs can be applied to data collection and analysis of all kinds throughout the entire network. In effect, data should be treated similarly to the water network itself; collected from a single source and transferred into a defined system, from which it can be supplied to where it is needed, for what it is needed for. If water operators are truly to improve productivity and reduce operational costs with innovation and new technologies, the key is to find ways of strategically collecting data once and using it in multiple ways. Doing so enables the company to be more flexible, adaptable and prepared for changing market conditions.

Click here to find out more about the 3 Rs of automation data with our infographic guide.

Industrial processes generate thousands of terabytes of data every day, most of which can be calculated and analysed to offer insights on how to improve or optimise operations. More users that are experienced with data collection and analysis often eventually encounter the problem of system scalability: as more data is generated, the idea of using the cloud to store and process data might seem appealing.

It is this challenge that leads operators to mentally pit local data parsing and aggregation in Historian systems against storage and analysis in the cloud. The belief is that the large storage sizes boasted by cloud providers, alongside the appealing concept of off-site data processing without the computational requirements, is better suited for higher volumes of data produced by the evolving edge. The process could therefore be logically streamlined to connect edge devices and systems in such a way that they communicate data directly to the cloud, where it can be stored, processed into usable KPIs and accessed by staff.

Because directly connecting edge systems to the cloud means that raw data is pushed directly to the cloud without significant pre-processing, it means that hundreds of messages are sent every hour. It doesn’t appear to be a problem at first — until the finance department receives the monthly invoice.

Processing raw data in cloud systems is also relatively expensive. Most database technology for time series data in cloud is not optimised for read and write functionality. This means that overall performance suffers as a consequence, with seemingly the only way to alleviate these issues being to increase the capacity of the cloud service.

Both of these issues make the use of Historian systems apparent. This is not to say cloud is bad. It is good for scalability and freeing up local resources from data storage. But as with all things, it must be deployed and set up intelligently.

If Historian systems can aggregate edge data and perform simple calculations to refine that information, it reduces the volume of data sent to the cloud. It still provides engineers on the factory floor the option of viewing granular data, while letting managers and executives access the refined data via the cloud — reducing the cloud collection and processing burden.

50 years ago, the thought that a plant manager could stay home and be able to have meaningful oversight of operations, while collaborating with other remote colleagues on the details, was unbelievable. If COVID-19 had struck at that time, most factories would have simply closed entirely.

Today, instead, with the right industrial IT solutions, plant management — along with team supervisors, quality leaders, engineers and continuous improvement managers — can work as a team as if they were together, regardless of where they are. A combination of developments in IT and OT have come together to make this possible.

There are now ways to securely deliver existing automation software applications such as SCADA via the web. Likewise, plant data repositories, or Historian software, have had the speed and power of their collection and storage capabilities supplemented with modern, web-based tools for exploring data. This includes ways to quickly add context and description to otherwise technical data points, so there can now be one source of raw truth that is accessible from anywhere, comprehensible by anyone.

Full-fledged production tracking systems or MESs have similarly had rich web-based front ends built, so that the detailed flow of events and activities can be tapped into from anywhere, regardless of how those systems may have had to be tied to on-site automation and sensors

The driving force behind the evolution of plant tech, though, was to enable greater productivity. With information from core operations readily at hand, alongside information from the broader enterprise, leading firms began to accelerate their continuous improvement efforts, undertake deeper collaboration with suppliers and other industrial partners and develop better insights into how to refine products and processes. The fact that their modern systems lent themselves to remote work and collaboration would come to be seen as a bonus aspect to these capabilities.

Despite the ready availability of modern plant IT and automation, and the numerous documented cases of manufacturers realising the benefits of modern systems, many factories remain wedded to paper, spreadsheets and ad-hoc/as-able machine data analysis efforts (often based on manual extraction and collation of data from individual assets).  The implications of this go beyond it being comparatively inconvenient to deal with remote working.

Firms that have incorporated more modern plant solutions already enjoy significant advantages in their cost of production, their operational flexibility and their predictability in relation to meeting demand. The question is whether such current advantages will be further entrenched, or whether we will see a surge of investment from others to take on these capabilities. There is also a question of whether the firms catching up will look to go beyond simply sustaining their operations and towards fine-tuning or even re-shaping them.

Lessons from leading organisations

The next wave of technology adopters can benefit from observing how organisational structures and behaviours have been changed as modernisation has unfolded. New tech has certainly changed the way line-side operators stage, execute and manage production. However, the freer flow of data to different stakeholders has also seen improvement in surrounding business processes such as supply chain coordination and product design.

One of the cultural changes common in leading firms is broad recognition that detailed operational data supports the work of many stakeholders traditionally seen as removed from the production process. This has prompted the formation of cross-functional teams responsible for ongoing learning about the continuing evolution of automation and software.

Tasked with spotting developments that could yield outsize impact, not just sustain incremental gains in capability, cross-functional teams embody the recognition that technology is not only a critical tool to enable existing strategies, but potentially the key to new ones. That behavioural change also means that tech adoption is no longer intimidating or mysterious. With IT, operations, product design, engineering and quality leaders learning together, each group’s perspective and knowledge becomes part of a common understanding of how to understand the next technology wave in the context of the firm’s challenges and opportunities.

If the COVID outbreak showed how rapidly our steady work routines and supply networks can be disrupted, this is the time to see how technology can provide UK plc with increased resilience and a renewed operational vigour. It’s vital that manufacturers adopt the tools that support better insight and collaboration for the impact they can have on productivity, flexibility and even innovation. Modern plant systems should be seen as critical to success all the time, not just as a convenience during a pandemic.

Recently, I came across the concept of hyper-automation. While it initially sounds like a buzzword akin to the industrial internet of things (IIoT) and smart manufacturing, it actually means quite the opposite. It’s a term for operating environments that are bloated with automated, ‘smart’ systems making production excessively complicated.

This is particularly poignant because it speaks to a situation we have seen time and time again while serving businesses as Novotek UK and Ireland. Plants are increasingly becoming over-automated, with numerous systems installed to perform individual parts of a single process when a single system would accomplish the same thing. This doesn’t often happen with physical automated systems, but it’s a growing problem with industrial automation software and platforms.

Unfortunately, this problem doesn’t seem like it will be going away any time soon. It’s often not due to a lack of communication in an industrial business – although this is unquestionably a factor in some cases – as much as it is the result of the wrong approach to the latest industrial revolution.

For many businesses that Novotek UK and Ireland works with, the focus seems to be on obtaining IIoT-enabled widgets to achieve greater results, whether that be increased throughput, higher production rates or better energy efficiency. But because these systems are evaluated individually for payback and for technology choices, they form a patchwork network of equipment and systems that is expensive in its excessive complexity. Customers lose the chance to understand how they could use a common approach to defining key data requirements and to defining uses for data that cross functional boundaries – and this missed chance leads to overlap of systems and duplication of IT and OT spend.

Many successful adopters of digital technology typically work to become digitally-ready first. The process of becoming ready for digitalisation is generally about setting business objectives and working backwards to the tech that will enable them, while fostering a culture of innovation and collaboration so many stakeholders see how each other’s needs are really related. But in industrial environments, it’s often expressed as if digital readiness correlates to the amount of sensors, control systems and IIoT-enabled devices are installed.

For a leading industrial business to become digitally ready, the first step is identifying what you want to achieve. What is the end goal for the operational transformation? This could be a specified reduction in energy usage across a factory, or it could be an increased rate of production. With these goals in mind, leadership must consider what is currently stopping them from achieving this, whether it’s a lack of insight into key industrial processes or a skills shortfall.

Only once this is established can a business truly look at what systems can help. Fortunately, establishing these areas of limitation involves extensive communication with different aspects of the business, which means leadership can identify overlap between departments. This makes it easier to avoid investing in multiple systems that achieve the same thing.

Illustrating the challenge: Because Novotek UK and Ireland is an industrial automation specialist, we’re often called into businesses where we find there is overlap between the field service monitoring software and plant SCADA systems. These systems provide fundamentally similar performance insights from equipment, but neither the field technicians nor plant managers were aware of the other’s system.

The result of this is bloated networks and expensive, complex automation systems. This can be avoided simply by defining business goals first and working backwards from there, making technology an enabler rather than an emphasis. Working with specialist automation consultants, such as Novotek UK and Ireland, helps ensure that an industrial company’s vision is first achievable and then, ultimately, achieved.

The fourth industrial revolution and the IIoT are industry-changing concepts, but they shouldn’t change a company’s focus. If you treat them as new opportunities to achieve core business objectives, then you’ll find that they’re more tech-enabled than tech-driven.

There are two types of food plant managers. There is the innovator, who is looking for new ways to achieve goals and improve systems, and the cost-conscious individual, who is looking for leaner and cost-effective solutions. Depending on your goal and the category you fall into, your systems and software should be specified and procured with that objective in mind.

Aligning your systems with your goals and aims for the business is the first step in this process. Being honest and realistic with these aims is vital, as without a clear view of what you want to achieve in your food plant, there is going to be disappointment, costly mistakes and confusion along the way.

There is no one-size-fits-all approach to industrial automation, so systems will vary depending on the automation profile of the business. But what systems will benefit each type of food plant manager?

The Innovator

When thinking about innovation, one of the key components is data. With data comes the opportunity to view where improvements, changes and adaptations can be made. Data sources such as control systems and sensors are at the heart of the plant, and will grant access to the information that will change the game altogether.

Implementing these systems is relatively simple but, of course, you must have the necessary equipment to collect and record this data. To start with, you should at least have a SCADA system in place, connected to show an overview of system performance.

To facilitate greater innovation, you can look at installing an MES system, which gives more access and accountability to the humans in control of the systems. From a plant-wide overview through to granular production activity, the system collects data from sensors and industrial control systems and displays it in a ready to view state.

Insight is the key to innovation and that is what these systems offer. So, if you’re looking to change the world of food plants, there’s some food for thought to get you started.

The cost-conscious

When cost is top of the agenda, the systems and software you will look to are different to that of the innovator.

Instead of the wild and wonderful additions of new technologies, the key here is making the most of what is already on site. Optimising asset performance is relatively easy if you look towards an Internet of Things connected platform, such as GE Digital’s Predix platform. This kind of system can predict problems and outages before they occur and connect the factory to the wider logistics chain.

This software uses machine learning to detect normal behaviour and data sets. Automation can detect issues far quicker than a human worker, and can cross reference historical data to see if it is a recurring issue. Employing this kind of intelligence facilitates predictive maintenance to prevent future issues.

Considering your business goals is crucial when looking to improve systems and infrastructure. With so many different solutions to achieve different goals, it is impossible to think that there is one solution that could be rolled out across multiple plants.