The future of data analytics in manufacturing

As we venture further into the digital age, it's clear that data analytics will hold an ever-increasing influence over the manufacturing sector. The terms ‘Big Data’ and ‘Industry 4.0’ have been around for some time now, but many companies have yet to fully embrace them. New technologies such as AI also offer a tantalising potential big leap forward in our ability to harness the myriad of data that we now collect on a daily basis. So, what are our trends and predictions, and how can you take advantage of them?

Data analytics explained

Firstly, what is Data Analytics? It involves the examination, cleaning, transforming, and modelling of data with the aim of extracting useful information, identifying hidden patterns and trends, with a view to formulating conclusions. Data analytics allows manufacturers to proactively unearth trends, measure performance, and make data-driven decisions that fuel efficiency and innovation. 

You need a platform for your data

Before we discuss the future, let’s think about the past and present. To be able to perform analysis on data you’ll need a common platform to store it. This is where ERP comes in. It becomes a single-source repository for all your business data, covering sales, purchasing, stock control, bills of materials, routings, and works orders, right through to despatch and invoicing. When you start to build a history of data across all these areas, only then can you start to analyse it and make intelligent business decisions, using systems such as 123bi.

Predictions and trends

There are a number of areas that are likely to see significant growth in 2024 and beyond.

Real-Time Analytics: 

Embedded sensors and IoT devices are becoming increasingly widespread on the shop floor, enabling real-time analytics to become the norm. As a result, companies will gain immediate insights into their operations, optimising productivity levels and reducing the time it takes to identify and address issues. An immediate benefit of collecting this data is that quotes will become far more accurate, as data such as consumable consumption (gas, electric, water, etc.) can be more accurately tracked, along with hourly machining rates, will be based on real data rather than educated guesses.

Hyper-Personalisation of Products: 

Data analytics will advance the trend towards customised products tailored to individual customers or specific groups. Manufacturers who effectively harness data insights will be able to adapt designs and production levels to cater to this growing market demand. For example, furniture manufacturers can use parametrics design and programming to have a standard product range, where the customer can then provide specifics (such as dimensions, or custom design parameters). CAD systems can be used during the quotation process to parametrically build custom products, generating the manufacturing data required to pass back to ERP for quoting and ordering.

Supply Chain Resilience: 

Recent supply chain issues have been a painful reminder for many that events such as the war in Ukraine, the semiconductor shortage or even the Suez Canal blockage can play havoc with your lead times. Data analytics will be instrumental in fostering supply chain resilience. Companies will utilise analytics tools to proactively monitor their supply chains, anticipating disruptions and swiftly developing backup strategies, thereby enhancing overall robustness in the face of uncertainty.

Sustainability and Resource Optimisation: 

The broad embrace of data analytics can aid sustainability and resource optimisation as manufacturers gain insights into production processes. By closely monitoring energy consumption, emissions, and waste, companies can develop greener manufacturing practices that allow them to meet both financial and environmental targets.

The Rise of AI and Machine Learning: 

Artificial intelligence (AI) and machine learning algorithms will play a crucial role in the predictive and prescriptive analytics models of the near future. Many people visualise AI as products like ChatGPT or Google Bard, seeing them as little more than alternatives to a Google search, but AI’s ability to crunch through reams of data and quickly provide meaningful analysis will also make its way onto the shop floor. For example, manufacturers will be able to forecast equipment failures long before any operators can identify them. Rather than using a ‘mean time between failure’ (MTBF) figure of expected wear and tear, it will be based on the hardware manufacturers themselves using AI to spot early signs of failure – the first you will hear about it will be when the equipment manufacturer contacts you to arrange a service call, perhaps based on data uploaded from your equipment and analysed remotely.

It's important to note that while AI is certainly the current buzzword, it’s still embryonic and often prone to mistakes. AI is only as good as the dataset that it’s trained on – if there are mistakes in the data, then the AI will further those mistakes. An AI trained to identify faults in manufacturing may not be trained on a wide enough range of defects or, worse still, may inadvertently have included items with defects, which it now subsequently thinks are not. A another example is autonomous vehicles that have failed to properly interpret the environment around them or respond to unexpected situations. Therefore, while AI may appear to magically hold all the answers, remember that its conclusions can be based on flawed data, and should always be second-checked before making any significant decisions. In Hitchhiker’s Guide to the Galaxy, the computer Deep Thought took 7.5 million years to calculate the answer to the ultimate question of ‘What is the meaning of life, the universe and everything’, coming up with ‘42’. While the answer appears vague, it could be attributed to the quality and clarity of the question!

Summary: Taking advantage of data analytics

Before you can start to embrace any of these predictions, you need to get your house in order, in terms of collecting and being able to understand and analyse the data that flows through your business. Many companies still muddle through on Excel spreadsheets, paper-based systems, and home-grown databases, but it will become increasingly difficult for them to compete effectively. By identifying key areas where you currently lack data, or the data is split across disparate, manual systems, you can focus on implementing systems that will allow you to start to track it effectively.