Simon Niesler looks at how food companies are taking Industry 4.0 to the next level

In the same way that food and drink has a shelf life, data must be consumed quickly to be relevant and useful. Yet, food and beverage manufacturers often struggle to process and apply data insights with appropriate urgency. Multiple factors get in the way, with data overload often being the most debilitating. Industry 4.0 has gone some way to addressing this shortfall but there is still some way to go.

A closer look at the challenges
Industry 4.0, in conjunction with digitalisation, is transforming the way we work, collaborate, create products, and go-to-market. Data is the driving force behind success, helping to track everything from the identification of plant assets and associated maintenance requirements to traceability and customer buying trends. But, as more and more organisations roll out their own digital initiatives, there are some common stumbling points.

Volume is a key challenge. The very nature of Industry 4.0 and the Internet of Things (IoT) means that they generate mass volumes of data. It’s easy to become bogged down in the never-ceasing flow of data points. Organisations are often ill equipped to aggregate, prioritise, and draw conclusions from the data. Even storage of the volumes of data can be a challenge, forcing companies to turn to cloud computing with elastic flexibility. This is partly because of a lack of expertise. For example, AI-driven analytics that can forecast likely outcomes and prescribe responses are technologies available that typically require IT professionals with data science skills. A shortage of skilled professionals makes recruiting and retaining data scientists very hard – especially for plants located outside of technology hubs. Companies have often been forced to turn to third-parties which can become costly.

In turn this makes it challenging to scale true Industry 4.0 initiatives as AI-powered analytics often take years to build and deploy. While massive projects offer promising opportunities, they tend to require advanced data science principles and specialised skills, including report-writing expertise. A more practical approach is needed for the every-day decisions that keep operations running effectively.

A better approach
A new breed of AI tools puts powerful predictive analytics in the hands of front-line users, helping them address day-to-day needs with greater insight. It’s no longer necessary to turn to code-writing developers to create use-case-specific applications. Now, with functionality assured through solid back-end code and APIs, users can delve into the data they care about with an unprecedented array of automated analytic tools. The toolset is easy to use on the front-end, and powered by sophisticated technology on the back-end, which makes it easy for users to gather, deploy, integrate and consume information at a level scarcely imagined a decade ago.

AI can be used for more than automating some simple processes. The true potential of AI technology comes from applying machine leaning and predictive analytics to a variety of practical and personalised use cases, whether it’s the farm that wants to project optimal field yield or the brewery that needs to estimate the amount of barley and hops to procure by month. AI has the potential to provide advice, discover performance patterns, analyse multiple influencing factors, and draw complex conclusions about a specific question — including questions that require a window into the future.

The maximum potential is reached when AI can emulate and enhance human performance, offering advice that is reliable and intelligent. This predictive insight helps organisations anticipate, understand and prepare for future trends and outcomes. This is especially important in food manufacturing as manufacturers race to develop new products based on changing customer preferences for non-GMO and additive-free foods. As ‘classic’ products fade into history and new buying habits are established, companies can turn to AI-driven analytics to monitor costs, optimise margins, and refine supply chain decisions.

Putting AI in the hands of every day users
Thanks to modern AI tools, the hands-on user can be highly engaged in defining objectives, assigning priorities, and setting parameters and conditions.

For line-of-business managers and plant operational teams to make good use of AI, the technology has to offer an interface that is highly intuitive, automating decisions about what algorithms to apply and how to incorporate relevant contextual information, like weather variables, crew shifts or time of day. The system should automate the machine-learning process, continually refining the 20 or 30 different factors that might go into the algorithm.

Behind the curtain, the AI solution will autonomously analyse data, generating reports that that the business user will be able to apply in daily tasks. The data patterns can be used to spot issues, opportunities or unknowns that simply would never be visible through traditional spreadsheets and reports.

Unleashing the true potential of Industry 4.0
Rather than being overwhelmed by mass amount of data, forward-thinking food manufacturers must invest in tools to help them turn numbers into insights. New AI-driven analytics allow users to consume the data and formulate meaningful, practical applications. This helps business users at all levels in the organisation to gain the benefits of data science – without requiring customer report-writing or help of code-writing IT experts. Democratising data analysis empowers individuals. It leads to a well-thought-out AI strategy that can accelerate the flow of information across the enterprise. To succeed in the modern world, food manufacturers need smart tools to help them make fact-based decisions and unleash the true potential of Industry 4.0.

Simon Niesler is Senior VP and General Manager, Western Europe at Infor. Infor is a global leader in business cloud software specialised by industry. With 17,300 employees and over 68,000 customers in more than 170 countries, Infor software is designed for progress.