How using data effectively can add value to your manufacturing business

In any manufacturing project, data is fundamentally important to be able to deliver on targets. When we consider any kind of digital technology – for example, AI, augmented reality, digital twins – at the heart of it we’re talking about using data effectively to derive value or a level of impact for an organisation.

Looking at the manufacturing industry today, many of the big challenges centre around data: e.g., data quality, data completeness, and data maturity. All these aspects drive the ability to predict how future operations and future resources will evolve in an organisation.

Creating a data strategy

It’s important to have a very detailed, long-term data strategy, and to think about how data can add value to your manufacturing business. Data should be considered as an asset of your organisation – the more benefit you can derive from that asset, the more value you can create, and the more competitive advantage that you’re going to have.

Increasingly, organisations are recognising the importance of data. They are thinking about how they can improve the quality of their data and how they can evaluate the data maturity that they have. When we work with companies here at Cranfield, they are looking at how they can standardise their processes and improve their systems. Digital technologies can be used in a variety of ways to achieve this.

The impact of digital technologies

There’s a very clear connection between the impact that digital technologies can make on an organisation and the data that’s available or possible to use. Moving forward into the future, it’s likely that organisations will plan much more strategically as to how data can add value to their business, and there’ll be much more design-related thinking as to what type of data should be collected and what type of data should be managed.

This change of mindset will also expand into the wider supply network – whether it’s the customer, the manufacturing processes, the in-service phase, or the design processes – all these different data sets will be integrated across the lifecycle. Essentially, there’s a common theme emerging, whereby organisations want to get value by integrating data across the lifecycle.

For example, sectors that rely on complex engineered assets – planes, ships, trains, etc. – have long lifecycles, whereby an asset could be in use for 30, 40, maybe even 50 years. This is a huge challenge in terms of how data gets to be stored, shared, and accessed, and we need to be able plan for the future with a comprehensive data set that looks at different phases of the lifecycle.

Digital technologies are making it much more feasible to connect data, access data and improve how it is used, in order to add value internally and externally. As such, digital technologies are contributing to a shift in the way people look at data and in parallel business models are changing, because we have more data, or we have better data, and we can process this data much better. This is creating real change in the manufacturing industry, as it is possible to manage risk and uncertainty much better with clearer commercial insights visible.

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