The manufacturing sector has played a very important role as an economic engine of countries across the world. Global competition in this sector is fueling a race to convert factories into digital hubs. By Dr. Darshan Desai and Om Desai, Experfy
Despite the differences in the role of the manufacturing sector in advanced nations compared to that of emerging economies, disruptive technologies, big data, and digital transformations have made their mark on companies in this sector around the world, and have opened up tremendous opportunities for these companies. This is especially relevant for the Asia-Pacific region. According to new figures released by Microsoft, if the region’s manufacturing sector embraces these opportunities and unlocks the potential of digital transformation, the GDP of the whole region can increase by US$387 billion by 2021. According to this study, companies in this sector are likely to invest more in big data analytics, cloud, and Internet of Things solutions this year. While these companies are exploring ways to leverage big data and analytics to gain a competitive advantage, it’s a good time to take a closer look at how big data and the Industrial Internet of Things (IIoTs) can have a transformative impact on the manufacturing sector.
Big Data And IIoTs
For centuries, we have used data to inform our decisions. This has been increasingly significant, as today, explosive amounts of data are being created every minute. Every action we take leaves digital footprints. On top of that, the amount of data created by consumer and industrial machines is also growing. Smart home devices can generate data and communicate with each other, while industrial machinery is increasingly able to use sensors to gather and transmit data. The term “Big Data” refers to the collection of all this data, structured or unstructured, human or machine generated, and our ability to use it. This concept is still evolving, and is considered the driving force between many revolutionary waves of digital transformation, like artificial intelligence and the Internet of Things.
The Internet of Things generally refers to a giant network of connected things that includes interactions and interconnections between people and things, people and people, and things and things. The IIoT or Industrial Internet of Things is a subset of the Internet of Things (IoT). Accenture estimates that the Industrial Internet of Things (IIoT) could potentially add an additional US $14.2 trillion to the global economy by 2030.
Potential Impacts And Advantages
Big data analytics and the IIoT can help manufacturers in multiple ways in many different applications. Generally speaking, these disruptive tech innovations are helping manufacturers enhance their security and automation, reduce their financial risk, eliminate production downtime, and increase the quality of processes and products. These benefits of big data are far-reaching and used in all areas of operations, from product quality and stock control, to supply chain optimisation and improved health and safety. The potential impacts and advantages of big data analytics and IIoT in the manufacturing sector can be categorised into four broad categories: predictive maintenance and automation, productivity and efficiency gains, supply chain management, and quality management.
Predictive Maintenance And Automation
With increasing IT investment in the manufacturing sector, a range of off-the shelf software for tapping and integrating data sources, analysing big data, and for closing the loop in optimising processes including manufacturing is now becoming available in the market. The driving force for automation and IT integration is companies’ overall goal to gain actionable data-driven insights to improve products and processes, which can be enabled by IIoT and big data analytics. For example, manufacturers use inexpensive sensors to reduce condition-based monitoring and maintenance in machines. Wireless devices, along with big data processing tools, make it affordable and easier to mine actual performance data and gain actionable insights to maintain equipment health. A recent article published in The Stack magazine provides an excellent example of the tech giant Intel using big data analytics for predictive maintenance, more specifically, for predicting equipment failure in one of their microchips. They were able to gain a 50% reduction in maintenance time, 25% higher yields, and a 20% reduction in the cost of spare parts, which all added up to $3 million saved. Big data is an opportunity for manufacturers to improve process performance, reduce waste, focus on better products, and produce products more efficiently.
Productivity And Efficiency Gains
Data-driven manufacturing is driving efficient and responsive production systems. Manufacturers have been able to boost their productivity by understanding plant performance and measuring the operating data of individual machines, which was made possible by analyzing large data sets. Effective and efficient sales and operations planning processes are very crucial to the productivity of any manufacturing company. These processes can create a factory’s load forecast over a period of time, which can help a company decide which products it needs to manufacture at which plant. These types of plant loading decisions can affect the operational and financial performance of a company. Big data analytics and data points like historical load, industrial record, completed projects, and customer patterns can help optimise plant loading.
Supply Chain Management
Big data analytics and IIoT can provide manufacturers with increased access to real-time supply chain information. When plants are connected to suppliers, all parties in the supply chain can access information and monitor material flow, interdependencies, and product manufacturing cycle times. This type of real- time monitoring of supply chain information can help quickly discover issues, reduce inventory, and as a result minimise capital requirements.
A significant amount of a manufacturer’s annual revenues can be lost through defects in the production process. Many quality issues can be spotted and rectified as soon as they arise by analyzing real-time data from sensors on the production line. Big data use in manufacturing and quality management can reduce product, assembly, and quality management costs for manufacturers. With such large success in cutting cost, many companies are interested in leveraging big data and predictive analytics to increase return on investment.
Concerns And Challenges
A Harvard Business Review article points out a few concerns and potential challenges the companies in the manufacturing sector may face in reaping the fruits of big data analytics IIoTs. According to the article, in addition to the culture and paradigm shift from time-triggered control systems to event triggered control systems, significant challenges can be related to the integration of legacy systems, and creation of unified data. A significant problem is legacy manufacturing equipment that consists of systems that haven’t been upgraded in parallel with the technological innovations. For many organisations, total replacement of expensive legacy systems isn’t always an option. The challenge, then, is to think of innovative ways to integrate existing systems with tools that allow them to communicate with the newer, more streamlined systems that make up the IIoT. The good news in this situation is that it’s possible to leverage the benefits of big data and IIoT without ripping and replacing existing systems.
Despite the benefits and significant potential impact of big data and IIoT, to get beyond the hype, managers need to understand the underlying challenges. In these types of situations, taking an incremental approach can help companies unlock the transformative value of big data and IIoT.