The challenges encountered by manufacturing companies when it comes to handling data are well reported, but what can they do to ensure that data is an asset rather than a problem?
Data has long been treated in the manufacturing industry as the orphan nephew living in the cupboard under the stairs. While operational and service industries have leapt on the benefits of data as the catalyst of business growth and efficiency gains, the manufacturing sector has been slow to adopt the culture of becoming a data-driven business. According to Accenture, only 13 per cent of manufacturing companies have seen through a digital transformation of their processes.
“In many ways the core approach to manufacturing has remained unchanged for the past 50 years despite the industry experimenting with offshoring and integrated manufacturing in mega factories,” Tim Hall, VP products, InfluxData, says. “While these have resulted in short-term costs savings, the real cost of labour having caught up in the offshore location, it has not fuelled a growth in labour productivity. The long-term trend has been a diminution in productivity growth that does not bode well for future generations.”
Although coined as a phrase back in 2012, the buzzword of Industry 4.0 has gained increased currency in recent years with its promise to use the power of data to revolutionise manufacturing. Industry 4.0 represents the fourth revolution that has occurred in manufacturing. From the first industrial revolution, mechanisation through water and steam power, to the mass production and assembly lines using electricity in the second, the fourth industrial revolution will take what was started in the third with the adoption of computers and automation and enhance it with smart and autonomous systems fuelled by data and machine learning.
“The big difference between Industry 4.0 versus Industry 3.0 is that while in the former computers were introduced to enhance existing processes, the latter seeks to reinvent the entire process around the power of data,” Hall adds. But outside of the buzzwords, what does this mean in practice?
Transitioning to Industry 4.0
According to Hall the road to establishing a data-driven manufacturing business encompasses three key steps:
- The inclusion of advanced manufacturing technology such as robotics, simulation, and augmented reality
- The reinvention of processes, products and services based on big data science and causal analytics
- Operational efficiencies derived from IoT networks and enhanced system control
“In practice this means a soup to nuts reassessment of the entire manufacturing process so automation in the form of cyber physical systems and data exchange through IoT is in the core of the architecture of the manufacturing plant rather than an afterthought and bolt-on,” he explains. “To underpin this reinvention of the manufacturing process necessitates a data architecture that can ingest the huge volumes of real-time data generated by the IoT sensors and other devices and enable the nano-second control of the entire environment. The critical issue here is time – and how all elements in the production process must adhere to and be controlled by the central management system.”
The role of data in Industry 4.0
The foundation stone of an advanced manufacturing plant is the central control system. Since time is the critical element in its operation, the time-series database offers by far the best route to providing this required precision. “The implementation of an Industry 4.0 manufacturing plant requires an adherence to data standards that can ensure that in operation, the process applications running on the time-series database deliver two critical services: keeping the production line running efficiently and minimising downtime,” Hall adds. “Although these may sound like one and the same thing, they are vastly different in practice.
“The efficiency of the production line comes down to the control and sequencing of events in the manufacturing process. This control requires the ingestion of huge amounts of sensor data so that real-time instructions can be delivered to the cyber physical systems and other aspects of the line.
The minimisation of the production line downtime is ensured through the analytics of the data to predict problems and equipment failures before they occur. Through this predictive failure analysis, the problems can be forestalled, and actions taken to eliminate the risk of an unscheduled stoppage.”
He believes that the role of the time-series database is to deliver three aspects. First the precision monitoring of events to the nano-second and second, the monitoring across multiple data sources. Finally providing a context on the data so that, for example, the huge volumes of high precision data might be kept for short periods while low precision data would be kept for longer or indefinitely
Another key aspect is the handling of the manufacturing data and the need for scalability and open exchange.
“The data generated from a manufacturing environment can be highly variable and unpredictable in its volume,” he continues. “The core time-series database needs to be able to both ingest the high throughput of data and sustain the real-time querying. If either of these fails, then the operational integrity of the production line could be compromised.
The future of data-driven manufacturing
The inevitability is that data will now be the cornerstone strategy for any manufacturer considering a new plant or redeveloping an existing one. Before location, supply chain and floor plans are considered, the data architecture will be the central consideration of how the plant will operate to maximise throughput and maintain optimum efficiency.
“Through this approach, manufacturers will reap the rewards of more flexible manufacturing, process optimisation, rapid scalability and predictive failure analysis to maintain near 100 per cent uptime,” Hall concludes. “Data in manufacturing has travelled far from its days under the stairs and is set to be the wizard that shakes up industry and ushers in a new era of productivity growth.”