A key driver for Industry 4.0 in the manufacturing sector, Michael Nelson explores the way in which data can facilitate advanced manufacturing ideas, and the challenges that this presents for businesses.
Data is at the heart of the digital transformation in manufacturing. From enabling advanced manufacturing ideas with control data, to preventative maintenance and the ‘Internet of Things’, data is helping to reduce downtime, increase uptime and throughput, and raise quality.
A modern manufacturing environment can have anything from a few thousand to hundreds of millions of sensors across its operations, each one recording and sending data about the operational status of devices and machines.
With research from data analysis software developer KX showing that 74 per cent of manufacturers are considered data visionaries who value real-time data as being very important in making smarter business decisions, more work is needed to make data more accessible and easier to process.
Storing, retrieving, and understanding data
Manufacturers are managing new data streams, data coming from different directions, and data getting stored in different places. The major challenge of this digital transformation is choosing the data that is needed and finding where it is located.
Siloed data can hold back an organisation from being able to perform analysis and gain insight to improve efficiency, quality, and processes. Meanwhile, further challenges arise when data is not captured and made available quickly enough to inform decision making. For example, although tools are available in the cloud for analytics, they do not translate to running near the source of data where action must take place – on the equipment and factory floor.
Przemek Tomczak, SVP of IoT and utilities at KX, says that the worlds of smart manufacturing and IoT are grappling with the enormous challenge of fast data and automation required for today’s changing environment.
“Data is information and from information you gain insights, insights that can drive actionable outcomes right across a manufacturing environment from improved operational processes to delivering competitive advantage,” explains Tomczak.
“Streaming analytics is the technology that enables such processes, allowing manufacturers to collect and analyse data in real time at the edge of their network as well as at the data centre while comparing it to historical records and context.”
That data can be used for a multitude of use cases, from analysing temperature and vibration data, identifying potentially faulty machinery, and improving R&D testing and manufacturing processes. This high volume, high velocity, and high variety data can become signals, insights, and actions that improve operational and commercial performance actions.
Innovation in data application is continuing at a rapid pace. Cloud computing, for example, is proving to be a transformational technology in manufacturing, providing significantly cheaper storage and processing capabilities while also giving rise to a vast marketplace of software and services that manufacturers can use to further enhance their processes and productivity.
Despite concerns over security, reliability, and latency, Tomczak expects data management architecture to contain a hybrid of edge computing and centralised data management, either on-premises or on the cloud, as the preferred manufacturing use case. Together, they have the potential to bridge the worlds of big data and fast data, allowing manufacturers to manage the massive volumes of data being created, while providing a means to bring historic and real-time data together for deeper levels of insight and understanding.
Distrust in the digital transformation
Many organisations, however, are failing to exploit the benefits that real-time data analytics can bring. According to Jaco Vermuelen, CTO at digital transformations experts BML Digital, industrial sectors have a heavy dependence on expert resources, and have rarely progressed to systemise them.
“There is comfort in the status quo and legacy ways of working that centre on humans manually handling, processing and responding to information; using skills and knowledge that stands ‘versus’ the difficult need to codify this in models, algorithms, business rules, all whist extracting this information in a coherent manner.
“These industries in general also have a ‘distrust in the machines’ mind-set and see putting trust and responsibility into digital solutions as a high hurdle to overcome when the business leaders in these organisations were always hands-on and directed people,” adds Vermuelen.
Many larger organisations in the sector, however, are starting to realise that their projects and estates are too complex to continue to manage in a manual fashion and remain too dependent on key resources for knowledge and skills.
Like any industry, there are leaders in adopting new methods and modernising how they work, and this becomes evident in the proposition they offer and the ever-increasing profitability of the outcomes they deliver. This puts pressure on those that lag to try to digitally transform – usually taking a technology only approach instead of looking at how they operate and where and how the use of data and automation can enable them effectively. The early adopters had the benefit of experimentation and refinement, and not needing to try and address all shortcomings in a condensed time, and this has allowed for incremental improvement and digitisation.
“Boardrooms are typically filled with executives that only know an analogue and manual world, and therefore struggle to grasp how digitalisation enables the business to be more efficient and effective,” explains Vermuelen.
“There is a general feeling that machines cannot perform at the level required, despite the fact that it has been proven that humans are more error prone and inefficient compared to systemised operations and digital models which represent the real world or physical environment, which can highlight risks and opportunities.”
Vermuelen argues that the board should not craft a digital strategy but have a business strategy that effectively utilises digital capabilities to enable them to deliver services.
Some services in the contemporary world are becoming purely digital, such as data access, visualisations and insights, or collaborative design and development through information sharing. It is not essential for companies to move to digital business models, but businesses need to be able to operate in a digital world; their customers and suppliers are expecting seamlessly integrated, consistent, accurate, and real-time transactions and interactions.
The supply chain, the product and service value chain, and the business operating model needs to go through a rethink to ensure all parties effectively work together, concludes Vermuelen. The entire ecosystem needs digitalisation.
“The main barrier is that many organisations cling to legacy ways of working, with manual and paper-based processes coupled with a dependency on people. This creates a disconnect in the supply chain where others have already ‘evolved’, resulting in those not modernising to be left behind and potentially falling out of the marketplace.
“A further barrier is the standards of data required by various parties, as well as authentication and data integration, since many do their own thing instead of looking at how the ecosystem operates.”
Ultimately, the transformation starts with a company realising the value of data and automation, and then finding the suppliers that fit the modernised model. Companies should help existing suppliers to become digital if they value them or are dependent on them, but suppliers are quickly realising that they need to catch up, or risk being left behind and excluded in further business.