Manufacturers that get smarter about data benefits from factory and supply chain transformations are set to reduce growing risks around global food security.

The Ukraine-Russia conflict, while a tragedy, represents yet more disruption to global supply chains, bringing food security fears to the fore once more – the region being a major source of critical commodities including wheat and fertilisers.

The good news is that smart factory principles can be harnessed to reduce dangers to our food supply – helping manufacturers innovate in areas from packaging and storage to flexible sourcing and product quality.

Anja Strothkaemper, VP of SAP Commodity Management and SAP Agribusiness, notes that business and macroeconomic intelligence on agricultural supply chains across food manufacturing, retail and consumer are increasingly key to investment decisions.

“There has always been relationships between growers and manufacturers, but when you look at the current situation, you have an overlap of several crises (including climate change, the COVID-19 pandemic, supply chain disruption and now the Ukraine war),” she says. “For crops like wheat or sunflowers for example, where Russia and Ukraine are like the breadbasket of Europe, with sanctions and prices that are rising, markets are going up and down.”Your browser does not support the video tag.

Customers are now looking for efficiencies, sustainability and resilience that will take them through tough or unforeseen times, with technologies including SAP software making a “modest contribution” to those goals where possible, either by adapting existing solutions or building industry specific functionality. “There is a dire need for system support. If you go back 50 years, a lot of decisions might have been all experience-based or ‘gut feeling’. That’s no longer what works, because the external factors are changing,” Anja says.

Kevin Worster, Head of Digital Enterprise Solutions and Business Development at systems integrator Yokogawa UK, agrees that food and beverage (F&B) manufacturers can gain from digitisation, including smarter factories with innovative Industrial Internet of Things (IIoT) or manufacturing execution systems (MES) solutions.

“Using sensors, artificial intelligence (AI) and machine learning (ML), crops (or livestock) can be monitored 24/7; everything from the pH of the soil, temperature, humidity and moisture content can be captured so crops are irrigated or other interventions can be made based on single truth data about conditions,” he says.

Kevin sees advantages in utilising cloud-based systems that connect farmers themselves more directly into the F&B industry, allowing transparency of critical data to help drive efficiency, which in turn helps reduce unforeseen overheads and waste.

With a smart factory approach managing and monitoring data across the four key ‘Ms’ of machine, man, material and method, incorporating automation, IIoT connectivity and the like, resources can be applied optimally to concentrate the value-add.

However, he cautions that manufacturers themselves need to develop a clear digital transformation roadmap that is linked to their business key performance indicators (KPIs), to really start seeing the benefits flow through.

“A large proportion of digital transformation strategies fail because of the lack of an overall execution plan and having the correct people and processes in place before any interventions are started.”

According to Elena Moruzzi, VP Automation and Digital, Tetra Pak, by automating processes and embedding data analytics tools, food and drink companies can look at granular details, such as which products have the highest carbon footprint or consume the most energy to manufacture.

Machines at different production stages, such as processing, filling and distribution, can communicate with each other and synchronise themselves.

Data-driven ‘smart factory’ insights used by packaging manufacturers, both today and in the future, will continue to drive innovations in packaging that can improve quality, even extend due dates and use-by dates of essential foodstuffs and beverages.

“For instance, Tetra Pak’s Connected Workforce offers digital tools, devices and services to drive improvements in food safety and quality, as well as reducing operational waste. Ultimately, smart factory technologies give more control, helping to solve problems more quickly,” Elena said.

“With the Tetra Pak Connected Package platform, milk or juice cartons can have a unique code that, once scanned, transforms the package into an interactive information channel, full-scale data carrier and digital tool.”

Digital labelling can offer more detailed information on a product; packaging advances could use IoT sensors in production processes to help F&B better manage and monitor their partner networks and supply chains, with potential benefits including reduced food waste. Perhaps a third of the food produced is wasted every year, according to Tetra Pak.

Even in the UK and Europe, a risk of food insecurity exists, as the relentless inflationary spiral of commodity costs and, in turn, retail prices has made abundantly clear this year.

Even with F&B, the UK’s largest manufacturing sector, with the supply chain accounting for 6.8% of gross value add or around £107bn a year, the UK only produces 60% of domestic food by value, part of which is exported. In 1984, the UK was 78% self-sufficient, according to Agriculture and Horticulture Development Board (ADHB) analysis.

It’s not as simple as being self-sufficient or even just importing what you need; a diverse range of sources is typically needed to ensure a resilient food supply. That means attention not only to production but across manufacturing, wholesale and retail inputs, logistics and systems of inspection. So the scope for smart factory data-driven innovations to ‘tip the scale’ towards greater value is very wide.

Anja says data-based systems increasingly enable manufacturers to work more closely with growers and producers, helping each other make better decisions based on data from smart sensors and equipment deployed ‘in the field’ as well as in the factory and across the supply chain.

“A lot of food companies haven’t really been in the agricultural/agronomy space but, depending on the crop, they may support their growers into being more efficient, sustainable and having better agricultural practices. And because they are typically bigger, they have more funds than the individual farmer,” she says.

Food companies are building up centres of excellence supported by solutions such as SAP Intelligent Agriculture, enabling data from the field to be fed into advanced analytics that could potentially feed into manufacturing processes. Further insights can ultimately be shared back to the grower, helping develop a positive feedback loop.

Smarter end-to-end supply chains mean planting, growing, irrigating, harvesting, transporting and manufacturing can move in sync.

“The tricky thing is that nature isn’t as predictable as a manufacturing plant; the same logic, routines and algorithms don’t apply. So we have a framework to bring in intelligence – machine learning models that in-house data scientists develop or you work with universities or start-ups that bring you the knowledge on how to take data that is available and make predictions,” she says.

“This is like a precision farming type approach; we support manufacturers who have an interest in capturing the data.”

Arrangements can be sufficiently flexible, while offering information and giving manufacturers insights that will help them develop the right services to offer their growers, as well as increase sustainability and resilience, with a view to reducing costs and carbon emissions along the way, she suggests.

With open application programming interfaces (APIs), software solutions can be adapted to communicate and interface with those from other vendors, solving the traditional problem of incompatible technologies.

Business networks and logistics solutions, similarly, increase visibility of supply chains, including different types of tracking and tracing such as materials traceability. Details can also feed into ERP-based solutions such as SAP’s Agricultural Contract Management offering, to model complex agricultural, buying and selling contracts using quality criteria to help manufacturers not only respond to price peaks and troughs but improve batch production.

The overall picture is one of technologies (particularly software and services technologies), converging to enable future F&B enterprises to share more and more data that can then be analysed at faster speeds and with greater accuracy, to the benefit of each link in that chain, even helping develop the levels of trust around provenance and quality that build and sustain brands.

Index, rank and file data for greater insights Philip Dutton, Co-CEO and Co-founder of data software company Solidatus, notes that smart factory deployments have already created a lot of data, well beyond the level of detail they had on customers, for instance, only several years ago. For most, the data isn’t yet being used to the fullest to drive better business decisions, however. “We might have known our customers’ ages, for example, but not their location, and we’d predict buying habits based off those few metadata points that we had,” he explains. When those are combined with transactional data such as detail on who purchased what, the ability to predict future purchasing patterns improves.

Solidatus focuses on data governance and lineage approaches that help customers create “much more valuable” insights. Data lineage software is essentially about the indexing of different datasets to enable the user to go directly to the data he or she requires. It’s an emerging next step in using Big Data in ways that can improve a business.

By joining more datasets together, manufacturers and their partners can learn and understand more, even as the data continues to flow, in a rapidly changing market situation, to make changes in real-time to respond to threats or challenges. The whole is more than the sum of its parts.

“Now we’re going to bring in not just what you bought but the things that you window shop for; we can create far richer targeted messaging to you,” Philip says, noting that better understanding of the metadata and drilling down into data can then flow on to better data collection as well.

Data blueprinting has become critical, especially for increasingly mandatory environmental, social and governance (ESG) initiatives. Scope 3, for instance, means looking all the way back through the supply chain. ESG alone is going to create the “most complex data problem that most organisations have ever faced”, adds Philip.

“Because in the past, all the data we were dealing with was held within our own organisations,” he says. “You don’t just walk into the library with 134 million books to find the book you want, you go to the index. And an ability to do this becomes the accelerator.”

Chris Royles, Field Chief Technology Officer – EMEA, at cloud computing company Cloudera, reiterates that smart factory implementations are about expanding and developing system thinking, networking data together to create “data mesh” arrangements. This is the way to enable “in the moment” analysis and responses that take in considerations and details from across an entire system or several systems linked together.

“Think of kitchens, where you can cook food in a managed, controlled way, with all the health and safety that goes with it. Everyone’s delivering that food using various apps or services, right? So how do you get the material to them, and the goods the last mile, and preserve all that?” Chris says.

“You’ve got a lot of throughput. You’ve got millions of transactions, and you need to analyse every transaction with care. That’s a transition we’re seeing in terms of data and analytics, into being much more event-based, streaming analytics.”

He points to a Hello Fresh customer story as an illustration of the direction of travel for F&B. The online cookbox ordering and delivery service combines cloud and edge computing technologies to manage its business in a much more granular way than in the past, delivering small orders at pace to individual customers scattered across a wide area.

If, as Cloudera believes, larger investments are happening post-COVID in AI/ML, robotics, automation, plant and material handling, and 48% of manufacturing companies are moving toward greater IoT integration that includes better responses to real-time data and feedback and external datasets, it can only be a matter of time.

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