Big Data, Analytics, and Industry 4.0

Stephen DeAngelis

April 21, 2021

Despite years of discussion about the fourth industrial revolution (aka Industry 4.0), the revolution remains in its infancy. Analysts from the Boston Consulting Group (BCG) explain the first three revolutions were driven by steam, electricity, and automation. They write, “Industrial production was transformed by steam power in the nineteenth century, electricity in the early twentieth century, and automation in the 1970s.”[1] The fourth revolution is being driven by data. The BCG analysts explain, “Today, another workforce transformation is on the horizon as manufacturing experiences a fourth wave of technological advancement: the rise of new digital industrial technologies that are collectively known as Industry 4.0.” Joe Lichtenberg (@joelichtenberg), Head of Product and Industry Marketing at InterSystems, explains many manufacturers are still struggling to join the Industry 4.0 revolution. He observes, “For many manufacturing and supply chain organizations, the Covid-19 pandemic highlighted multiple shortcomings in their data management strategies. Despite access to a wealth of data, they were often unable to use it to gain the overarching and accurate view of their business needed to plan for growth, make informed business decisions, and respond to disruptions. This problem is rooted in processes and supporting technologies that have not been designed to work in unison. Systems generate data and produce reports independently, with the result that decisions are made separately, undermining efficiency.”[2] Venkat Eswara (@VenkatEswara9), Vice President of global product marketing at Syncron, is not surprised manufacturers are playing catchup with new technologies. He explains, “Technology always evolves faster than our ability to adapt.”[3] In the Industry 4.0 world, he believes cognitive technologies (often lumped together under the artificial intelligence (AI) rubric) are essential. He writes, “AI is becoming mainstream in the consumer and enterprise sectors. So, it’s not a question of if but when AI will become the norm in the manufacturing sector. In fact, it already is.”

 

Data and Advanced Analytics in Industry 4.0

 

Industry 4.0 begins with connectivity. Venkat Viswanathan (@venkatlv), Founder and Chairman of LatentView Analytics, explains, “There has been a lot of talk about the enormous potential and market opportunity for smart factories. Yet, for all the talk, the reality on the ground for a lot of enterprises in manufacturing is that their machines are not yet connected to any network. Before they can even talk about digitization and predictive maintenance, they need to first get connected.”[4] These networks are often referred to as the Internet of Things (IoT) or the Industrial Internet of Things (IIoT). The IoT is really an ecosystem that includes sensors that generate data, connectivity that transmits the data, and advanced analytics systems that analyze the data. The editorial team at Manufacturing Business Technology (MBT) explains, “The Internet of Things is a collection of sensors and data platforms that feed data into central networks. The IoT networks physical objects — ‘things’ — together with the internet.”[5]

 

The MBT editorial team adds, “Perhaps one of the most prolific industrial uses of IoT is in the supply chain. A report by Supply Chain 247 found that IoT implementation reduced supply chain labor costs by 30 percent whilst increasing the speed and efficiency of the industrial supply chain also by 30 percent. Data can be used to identify and plug supply chain gaps, optimizing increasing factory uptime and reducing supply chain issues. Deliveries can be made on-time and logistical costs can be cut. This frees up resources that can be redirected elsewhere in the business.” Lichtenberg insists you not only have to connect “things” you have to connect the data they generate. He writes, “To gain resilience, agility, and faster more accurate decision-making capabilities with the aim of futureproofing operations, supply chain and manufacturing organizations must implement technologies to link silos in which data and processes sit.” Cognitive computing systems generally have the capability to integrate data from disparate sources; thus, eliminating the silo problem that plagues so many organizations. At the same time, cognitive computing systems with embedded analytics are providing insights that make processes run better and help executives make better decisions. Eswara asserts, “Emerging technologies such as AI, [machine learning], and IoT are providing [original equipment manufacturers (OEMs)] with the necessary building blocks to evolve their business models towards better customer service while focusing on becoming more agile in the face of the unprecedented pressures facing them as a result of the COVID-19 pandemic.” Eswara goes on to list a number of benefits these technologies can provided to manufacturers. They include:

 

Inventory Optimization. Eswara writes, “Inventory management in the supply chain is a key area where OEMs can leverage AI to achieve efficiencies in the supply chain network. Currently, inventory management in the OEM and dealer network is siloed and plagued by inefficiencies that result in a high chance of error that impacts business operations and customer service. … Through AI-enabled inventory management, OEMs can gain better visibility into their inventory operations. This allows them to better forecast needs, understand market conditions, keep customers happy, and eliminate the heavy overhead costs.”

 

Adaptive Pricing. According to Eswara, “Pricing can make or break a business, which is why it’s so important to have advanced pricing tools and strategies in place. The keys to pricing success today are relatively straight forward: Dynamically address underpriced items and capture margin potential, maximize revenue potential for parts with low market share, find opportunities for price differentiation, and adapt to local market conditions.”

 

Service Uptime. “The downtime of equipment has a direct impact on customer experience,” Eswara explains. “Keeping equipment up-and-running — without any disruptions — is key for manufacturing end-users. … Predictive maintenance utilizes a wealth of historical and real-time data of equipment usage, coupled with advanced machine learning models to predict failures well before immediate action has to be taken. It avoids any unplanned situations and maximizes uptime for the end-customer.”

 

Mahendraprabu Sundarraj, an Enterprise Architect at Sunpower, asserts, “The benefits of digital transformation – competitive edge, deduction in operational expenses, and improved product quality – are indisputable.”[6] He also notes, “Digital transformation is never a one-time exercise, but a continuous improvement program.” The good news is that transformation efforts are assisted by leveraging cognitive technologies. Sundarraj cautions, however, that no effort will succeed without a strategy in place. “Lack of strategy, architecture, and tools,” he explains, “not only make the digital transformation program a failure, but also sub-optimize the existing production efficiency.”

 

Concluding Thoughts

 

Nick Davis, Deloitte UK’s Industry 4.0 Leader, reports that, even though the advantages of joining the Industry 4.0 are numerous, many companies remain slow to get on board. “While Industry 4.0 technologies have the potential to disrupt and transform many different areas of business for the better,” he writes, “executives do not appear to be leveraging them as broadly across their organizations as they could. Only 17% of CXOs say making effective Industry 4.0 technology investments is a priority, ranking lowest among 12 investment priorities. And while leaders seem to understand the merits of taking a connected, integrated approach to implementing Industry 4.0 technologies, only 5% indicate significant progress in this area.”[6] At the same time, he notes, the pandemic has forced many companies to reexamine their digital strategies. “While COVID-19 has caused major disruption,” he writes, “many businesses achieved a step-change in their use of new digital technologies born out of necessity. Digitization has had an impact far wider than just production: many companies are now getting better at managing supply volatility and predicting customer demand.” Lichtenberg insists companies that jump on the Industry 4.0 bandwagon will discover that connectivity and cognitive technologies will “provide telling insights that drive up the quality of critical decision-making and provide the firmest possible basis for future resilience and greater overall efficiency.”

 

Footnotes
[1] Markus Lorenz, Michael Rüßmann, Rainer Strack, Knud Lasse Lueth, and Moritz Bolle, “Man and Machine in Industry 4.0,” bcg.perspectives, 28 September 2015.
[2] Joe Lichtenberg, “Smart data: resilience in manufacturing & the supply chain,” Manufacturing Global, 21 March 2021.
[3] Venkat Eswara, “Why Now for AI in Manufacturing?” Dataversity, 25 January 2021.
[4] Venkat Viswanathan, “Are You Wasting Perfectly Good Data?” IndustryWeek, 13 January 2020.
[5] Editorial Team, “What Data Analytics Really Means to the Industrial Sector,” Manufacturing Business Technology, 10 March 2021.
[6] Mahendraprabu Sundarraj, “Industry 4.0: Challenges for Digital Transformation,” Dataversity, 18 March 2020.
[7] Nick Davis, “Fourth Industrial Revolution: DIGITAL TRANSFORMATION,” The Manufacturer, 16 October 2020.