The last decade has seen companies operating under increasing levels of disruption. Quickly changing customer preferences, as well as demand uncertainty and disruptions, are challenging planning systems to unprecedented degrees. National security interests, trade barriers, and logistics disruptions are pushing businesses to find alternatives to globalized supply chains. Major swings in demand are calling for drastic operational and capital cost reduction in some areas and rapid growth in others. Physical distancing and remote work are forcing manufacturers to reconfigure manufacturing flows and management. Meanwhile, increased global concern for the environmental impact of human activities has forced companies to rethink manufacturing strategies.
To address these disruptions, successful advanced industry (AI) companies are leveraging Industry 4.0 to achieve faster, more sustainable change, shown most dramatically at “lighthouse” manufacturers that have led the way in Industry 4.0 implementation. Through the Global Lighthouse Network (GLN), a research collaboration between the World Economic Forum and McKinsey on the future of production and the Fourth Industrial Revolution, 103 sites around the world have been identified as lighthouses, having successfully transformed their factories through Industry 4.0. These companies are leveraging digital technology to build more agile and customer-focused organizations. This approach lets manufacturers look beyond productivity in order to focus on improving their sustainability, agility, speed to market, customization, and customer satisfaction: a total of five areas of impact.
Challenges to overcome
The lighthouse sites are exceptional in their ability to overcome the obstacles that stand between many companies and digital transformation. A 2020 Industry 4.0 survey by McKinsey of more than 800 businesses globally revealed three major challenge areas: financial hurdles, organizational problems, and technology roadblocks.
Financial hurdles typically include the high costs associated with scaling digital deployments that don’t provide short-term benefits to the organization, and therefore don’t provide a strong incentive for investment. Use cases that don’t provide a clear, quantifiable value to the organization can also yield an unclear road map to digital success.
Organizational problems often involve low buy-in and a lack of concentration from leadership as a business attempts to see a digital transformation through. That hampers the effort’s potential success and long-term viability. Inadequate knowledge of digital capabilities and a lack of organizational talent can prevent broader buy-in and properly scaled transformative efforts.
Technology roadblocks commonly include low support from partners in scaling deployment while facing multiple platform choices, which hinders an organization’s ability to move quickly into new territory. The transformation’s starting point can also stall when leaders aren’t convinced of their ability to increase the size and scope of the digital architecture they choose for implementation.
AI companies have tried many approaches to overcome these barriers and realize improved performance through digital manufacturing transformations. An examination of advanced manufacturing lighthouses reveals two critical reasons that their transformations succeeded: first, they chose the right use cases; second, they looked for ways that those use cases could reinforce one another.
Increasing Industry 4.0 adoption and impact among advanced industry companies
Beginning with 16 sites in 2018, the GLN has grown to 103 sites as of 2022. Nearly 60 percent of the current lighthouses are companies in AI (Exhibit 1).
Industry 4.0 has helped these companies make strides across KPIs that drive growth. These KPIs span all five areas of impact and include sustainability KPIs, such as greenhouse-gas emissions; productivity KPIs, such as factory output; agility KPIs, such as lead-time reduction; other speed-to-market KPIs; and customization (Exhibit 2).
Companies that successfully created lighthouses focused on the use cases that had material impact on these five areas, allowing them to succeed where previous efforts failed. Although there are many possible Industry 4.0 use cases, four are significantly more popular in AI companies than in non-AI companies. We see examples of these use cases in three automobile manufacturers (a luxury-vehicle manufacturer in Europe and two automobile manufacturers, one in Europe and one in Asia), and in a European white-goods company.
Flexible automation. Companies use intelligent robotics to precisely automate previously manual jobs. For instance, the European automobile manufacturer connected robots to efficiently manage process flow and collect the data necessary to monitor the process, optimize production flow, and reduce losses. The luxury-vehicle manufacturer used robust automation with collaboration between people and machines to improve efficiency, quality, and ergonomics.
Digital performance management. Firms use data to monitor and improve performance by driving operational decision making. The European automotive company uses real-time, accurate data to power decision making, resulting in better reaction speed and direction plus stronger competitiveness: the firm’s cost per unit shrank by 3.5 percent. The luxury-automobile manufacturer uses smart data analytics to enable predictive maintenance, reducing a critical asset’s unplanned downtime by 25 percent.
The white-goods factory increased overall equipment effectiveness (OEE) by 11 percent through machine alarm aggregation, prioritization, and analytics-enabled problem solving. The company puts specific machine and custom alarms on operators’ and managers’ smart watches and other interactive displays. The same company uses digital dashboards to monitor production resources and collect real-time production data, including reasons for stoppages.
The Asian automotive company reduced its die manufacturing time by 47 percent by using a real-time production monitoring and scheduling system, with integrated workflows and tablets replacing paper-based processes for operators. It also increased production output by 6 percent by connecting all its production machinery to a single manufacturing information system that tracks performance metrics and automatically detects bottlenecks.
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Quality analytics. Companies use advanced analytics to identify and remediate the root causes of quality defects. The European automobile maker installed a touch device with apps at each workstation to guide real-time problem solving, automatic identification and steering for parts and vehicles, and unit traceability. The result: a 40 percent increase in accomplishing tasks correctly on the first pass.
The luxury-automobile company is using smart maintenance and assistance, employing wireless-sensor technology to let maintenance employees constantly monitor production lines. Rework has dropped by 5 percent.
AI-based inspections. Firms use vision systems to inspect products and leverage artificial intelligence/machine learning to identify defects. The white-goods company uses cost modeling to help it decide what to make and what to buy. It also monitors its grid, power, and asset status in real time to control and adjust consumption.
The same company also reduces its time to market with rapid design prototyping through three-dimensional additive manufacturing. It makes mock-ups and functional prototypes of new products in smart materials.
Between 13 and 19 percent more AI lighthouses cited one of four use cases as one of their highest-impact use cases, compared with non-AI lighthouses (Exhibit 3). Flexible automation, digital performance management, and quality analytics were universally popular among AI and non-AI companies: as many as 69 percent of companies cited one as the use case with the greatest effect. Similarly, AI inspection was also much more prevalent in AI companies than in their non-AI peers.
Synergistic use cases yield bigger impacts
The automobile manufacturers and white-goods company employed multiple use cases in their businesses. Though the use cases are individually effective, the synergies between them in combination yield still better results. These synergies allowed the companies to take advantage of their one-time investment in a technology stack to pursue multiple use cases instead of just one or two, resulting in even greater impact that can build over time (see sidebar, “Track the progress of digital transformation”).
For example, consider the European automotive factory, which is now one of the best-performing commercial-vehicle plants in Europe. This plant implemented a digitally enabled right-first-time tool, a touch device with apps at each workstation to ensure better performance through real-time support to fix problems, automatic identification to guide parts and vehicles, and unit traceability to ensure a quality process.
At the same time, the factory connected people to drive performance by enabling digital daily-management solutions. These solutions are based on accurate information that’s provided in real time and contributes to decision making, reactivity, and competitiveness. The company used digital track and trace, a flexible logistic flow developed to ensure that parts and vehicles are traceable and conform to standards. The system includes radio-frequency-identification (RFID) tags on parts and packaging, blockchain part traceability from end to end, packaging task management automation for forklift operators, and synchronization of kitting parts with the assembly line.
That wasn’t all. The factory also optimized robot cycle time through data analytics on programmable logic controllers (PLCs). It connected robots to efficiently manage process flow and collect data necessary to monitor processes, optimize production flow, and reduce losses. The firm implemented digital solutions that improve sustainability by driving energy consumption. For example, it uses a digital platform and a drone equipped with a thermal camera to find and control temperature leaks in the roof.
Future-proofing the supply chain
Taken together, those changes helped the plant reduce warranty incidents by 50 percent, increase its flexibility to deal with its many vehicle configurations, and reduce manufacturing costs by more than 10 percent.
The luxury-automobile factory also saw substantial benefits from deploying multiple use cases. It used smart data analytics to control predictive maintenance and collect data that let machines self-service within minutes, pairing that with reliable material supply and autonomous transportation systems that increased efficiency.
The firm then added a smart maintenance and assistance program that has maintenance employees constantly monitoring the production line at every place, using wireless-sensor technology in the quality-assurance process. It combined robust automation with worker–machine collaboration to increase efficiency, quality, and ergonomics.
This mix of digital manufacturing and lean, strategy-oriented processes yielded improved quality, lower costs, and higher productivity. It reduced a critical asset’s unplanned down time by 25 percent.
Embarking on the Industry 4.0 journey
AI organizations looking to embark on their Industry 4.0 journeys can use lessons from these successful lighthouse sites. By leveraging digital technology across the most effective use cases, more and more companies can drive outsize impact across several different KPIs. Companies that can deploy Industry 4.0 at scale are transforming their organizations to not only address the toughest disruptions of today but also prepare themselves for the new disruptions of tomorrow.
