When Lean Meets Industry 4.0

For several decades, manufacturers have used lean principles and tools to reduce operational complexity and improve productivity. The lean approach provides the foundation for operational excellence by standardizing processes, instilling a culture of continuous improvement, and empowering workers on the shop floor. However, given the increasing complexity of operations, many companies have found that lean management by itself is not sufficient to address their operational challenges. Recently, a set of advanced digital technologies known as Industry 4.0 has emerged to offer new approaches for dealing with complexity and improving productivity. By deploying the right combination of technologies, manufacturers can boost speed, efficiency, and coordination and even facilitate self-managing factory operations. (See the sidebar “The Basics of Lean Management and Industry 4.0.”)

Manufacturers seeking to optimize their operations need to understand the interplay between traditional lean management and Industry 4.0. Supporting hundreds of operational excellence programs in recent years, we have seen companies generate valuable synergies by implementing lean management and Industry 4.0 holistically, rather than independently or sequentially. Indeed, in most cases, the integrated application of lean management and Industry 4.0—which we call Lean Industry 4.0—is the most effective way to reach the next level of operational excellence.

Manufacturers that have successfully deployed Lean Industry 4.0 can reduce conversion costs by as much as 40% in five to ten years—considerably better than the reductions captured by best-in-class independent deployment of lean or Industry 4.0. The higher cost reductions are, in many cases, achieved with technologies that improve plant processes and structures by, for example, optimizing layouts. (See The Factory of the Future, BCG Focus, December 2016.) However, fewer than 5% of the manufacturing companies that we have observed have reached a high level of maturity in Lean Industry 4.0. (See Exhibit 1.)

To capture the greatest benefits, a manufacturer must tailor the application of Lean Industry 4.0 to address its specific challenges along the supply chain and at the plant level.

Using Integrated Solutions to Achieve Significant Benefits

A recent global survey by The Boston Consulting Group found that leading industrial companies recognize the importance of both lean management and digitalization in their long-term planning. In a survey of more than 750 production managers, 97% of automotive respondents said that lean management would be highly relevant in 2030, compared with 70% who said that it is important today. Among these respondents, 70% said that plant digitalization would be highly relevant in 2030, compared with 13% who said that it is important today. 

Although the need to implement both lean management and Industry 4.0 is clear to many manufacturers, they are uncertain about how to combine the two for maximum benefit. Our experience has shown that it is essential that companies think of Lean Industry 4.0 in terms of use cases—the optimal combination of lean tools and digital technologies. They must then carefully select which use cases to implement in order to most effectively address a specific pain point.

By using integrated Lean Industry 4.0 solutions to address pain points, manufacturers can achieve a variety of benefits. We present a selection of five benefits and highlight an exemplary use case for achieving each one. 

Flexibility: Sensors and Software Facilitate More Efficient Changeovers

Manufacturers want flexible operations that allow them to use one production line to make multiple products. However, the benefits of flexibility are hard to capture because time-consuming changeovers are required to prepare machinery to manufacture different products. By implementing lean tools, such as single-minute exchange of dies, manufacturers can remove non-value-adding activities from the changeover, thus significantly accelerating the process. Industry 4.0 technologies support these efforts. New sensors and software make it possible for machines to automatically identify products and load the appropriate program and tools without manual intervention. Because the changeover is automated, operators can focus on performing value-adding activities. 

For example, a manufacturer implemented a tracking system that uses radio frequency identification tags on individual work pieces to classify each product. Assembly stations use the system to identify the product that will be produced next and to set tools to the right parameters. With no operator intervention, the production line can change over instantly.

Productivity: Predictive Algorithms Improve Automous Maintenance

In many manufacturing industries, equipment breakdowns and failures lead to high inventory levels and low productivity. Companies can use lean methods, such as autonomous or preventive maintenance, to boost overall equipment effectiveness (OEE). To use autonomous maintenance, for example, companies assign responsibility for specific do-it-yourself maintenance activities to their operators, significantly reducing the downtime required to correct minor issues. Leading manufacturers are making the most of these lean methods by using advanced analytics algorithms and machine-learning techniques to analyze the huge amounts of data collected by sensors. The output identifies the potential for breakdowns before they occur. Such predictive insights prepare operators to perform autonomous maintenance at the optimal time, thereby reducing disruptions and minimizing unnecessary downtime and replacement costs. 

An aluminum producer uses mobile devices to provide its maintenance teams with real-time information on equipment performance, including where breakdowns are occurring or about to happen and their underlying causes. The teams use the devices also to access maintenance documents (such as machine plans) and to receive remote guidance on the tools required for making repairs. 

In addition to improving maintenance productivity, the greater transparency afforded by big data and analytics generally adds to the effectiveness of lean tools and promotes continuous improvement. (See the sidebar “A Food Production Plant Uses Big Data to Enhance Lean Levers.”). 

Speed: Real-Time Data Accelerates Production Management

Manufacturers struggle with the complexity of production planning as they seek to increase the number of product variants while reducing batch sizes. Operators use shop floor management and other daily routines—core elements of lean management—to react daily to deviations in production, identify issues, and update employees on required changes to production plans. However, these tools are not effective for planning and controlling production in real time.  

By applying certain algorithms, manufacturers can overcome the challenges of managing production in real time. Two key elements in the effective use of algorithms are a centralized “control tower” that collects data and directs all material movement inside and outside the factory and a horizontally integrated value chain. For example, after first improving the reliability and stability of its production process, a home appliance manufacturer created an algorithm that generates each day’s ideal production plans on the basis of orders, capacity utilization, and inventories. A control tower consolidates the data collected from various sources in an integrated value chain and feeds it into the algorithm. With the output, the company can select plans in real time, using such criteria as efficiency, lead time, and customer priority. 

Real-time data also helps enhance and accelerate continuous-improvement efforts. Line staff and managers can use real-time data to identify the root causes of performance issues and speed the validation of improvement measures, thereby permitting a faster rollout of the measures throughout the plant. 

Companies can use real-time monitoring daily to shorten reaction and response times. A supplier of C-parts (for example, screws, nuts, and washers) has attached camera systems to the parts containers in its customers’ warehouses and production lines. The systems trigger the automatic replenishment of parts when inventory falls to a predetermined minimum, and the manufacturer captures the benefits of just-in-time restocking.

Quality: Data-Driven Quality Control Supports Self-Inspection

Production capacity is wasted if products do not meet specifications. Even worse, if a manufacturer ships poor-quality products to customers, they will incur higher costs and likely lose trust in that supplier. Many lean management tools—such as self-inspection, poka yoke, and jidoka—have been developed to reduce the likelihood of errors and increase the rate and speed of error detection. For example, our analysis shows that self-inspections improve the process of providing feedback to engineers and operators, thereby accelerating error detection and reducing the number of defects by 50% to 70%. However, to achieve zero defects, manufacturers must support self-inspections by using a data-driven analytics approach to identify the root causes of errors. Industry 4.0 technologies allow such support by providing reliable context data and the ability to conduct detailed tracking. The analysis of errors is enhanced through, for example, camera-based visual inspection, correlation models, and real-time monitoring of process parameters. 

An automotive supplier was able to improve its quality control significantly by using an integrated Lean Industry 4.0 approach. First, the supplier implemented a self-inspection process, giving workers responsibility for performing visual quality checks of their output. 

Next, the supplier implemented a camera system that can detect surface defects. The camera, linked to the quality system, automatically creates failure reports and detailed analytics, reducing the visual-inspection and manual-reporting time by 70%. By analyzing the inspection system data in real time, operators can make sure that the output of the production process adheres to high quality standards. 

Safety: Sensors and Training In Virtual Reality Improve Working Conditions

Safety is among the most important production KPIs. To ensure operator safety, one lean approach uses signs to tell operators where they may walk. Another lean approach uses detailed tracking of incidents and near misses to identify areas for improvement. Companies can use low-cost wireless sensors to improve the effectiveness of such efforts. For example, they can fit operators with sensors that will alert them to the presence of dangerous gases or the possibility of a clash with nearby forklifts or trucks. 

Companies can further improve safety by using virtual reality to train workers. Offsite training in a virtual environment is more efficient and effective than training in an actual work environment, and the approach appeals to the younger generation of workers. Seeking to reduce the high accident rate among new hires, a provider of service rigs developed immersive-training sessions in which workers practice often-dangerous tasks in a virtual simulation of the work site.

Quantifying the Improvement Potential

The improvement potential of an integrated approach is significant. We have found that when either approach—lean initiatives or Industry 4.0—is applied alone, it can reduce conversion costs by approximately 15%. However, in our experience, companies that use the integrated Lean Industry 4.0 approach can reduce conversion costs by as much as 40%. (See Exhibit 2.) Companies have also used the integrated approach to reduce costs associated with poor quality by 20% and work-in-process inventory by 30%. 

Because the integrated approach allows lean management and Industry 4.0 to be mutually enabling, its improvement potential is greater than the sum of the improvements achieved by either approach independently. Mutual enablement promotes benefits beyond the typical limits of either of the two approaches. For example, using sensors and data to provide full transparency into bottlenecks allows the company to sharpen the focus of its lean efforts to improve OEE. 

In BCG’s experience, the payback time for use cases that are properly implemented is less than three years. However, we often see investments that exceed required levels. Because the benefits derived from Lean Industry 4.0 correlate with the efficiency of the underlying process, the company must avoid “automating waste” in the form of processes that include non-value-adding or nonstandardized activities. If a company invests in deploying robots before processes have been optimized, the robots will perform non-value-adding activities—such as unnecessary movements—that reduce the deployment’s financial return. 

Getting Started with Lean Industry 4.0

A structured approach is essential to the successful implementation of Lean Industry 4.0 and achieving a higher level of operational excellence. The Lean Industry 4.0 journey comprises three main phases: innovate, pilot, and scale. (See Exhibit 3.)

Innovate. To initiate innovation, the company must gain transparency into its business needs and challenges, the improvement opportunities, and the extent to which the enablers of Lean Industry 4.0 are in place. BCG has developed a comprehensive assessment that clarifies the current state of implementation and identifies the priorities for improvement. (See the sidebar “BCG’s Lean Industry 4.0 Assessment.”)

During this phase, it is critical that operations managers and executives gain firsthand experience so that they understand the state of the art in Lean Industry 4.0. BCG supports these efforts through its Innovation Center for Operations. The ICO provides access to model factories and mobile labs where managers and executives can try out new technologies and gain insights into their company’s pain points and opportunities. 

To deepen their understanding of how Lean Industry 4.0 could help them, executives and managers of an industrial goods company participated in interactive workshops in BCG’s mobile labs. Participating in several rounds of hands-on simulations of a production environment, the company leaders saw how use cases can improve processes and performance. This experience helped make the principles and benefits of Lean Industry 4.0 more accessible and tangible. 

Over the course of the simulation rounds, the BCG team introduced participants to Lean Industry 4.0 tools that address the pain points observed in their processes. Standard production KPIs, such as OEE and production costs, were tracked so that participants could see the ways that implementing each use case improves performance. Between rounds, experts enriched the learning experience by providing detailed discussions of the relevant Lean Industry 4.0 principles, reviewing the simulation results, and introducing participants to selected Lean Industry 4.0 use cases, such as the following:

• Connecting smart devices on the shop floor through Industry 4.0 gateways that make it possible to share data and information
• Combining digital standard operating procedures with cloud technology and augmented-reality technology to improve operators’ productivity
• Using real-time KPI tracking and digital performance boards to accelerate response times
• Integrating 3D printing technology to produce spare parts 

Once a company’s participants have identified the potential use cases, they prioritize them on the basis of their value, quantifying their potential impact and developing a business case for investment so that they use only those that are financially viable. They can then establish a target for the end state and develop a roadmap for achieving it. 

Pilot. To apply the insights gained, the company first tests solutions in a specific part of the supply chain or plant. The objective of each pilot is to develop a minimum viable solution quickly and then improve it through iterations using agile development methods. By implementing an initial set of use cases, the company leaders can validate the approach and showcase the opportunities for value creation. At the same time, the company should deploy all relevant enablers for Lean Industry 4.0. 

Scale. Solutions that have been successfully tested and refined in pilots are ready for launch at scale throughout the supply chain and plant. At this point, the company should conduct the rollout in a logical sequence that allows for integrating solutions effectively when deployed at full scale. Progress toward the target state should be tracked rigorously. 

If they aim to reach the highest level of operational excellence, companies cannot rely solely on either lean management or Industry 4.0. Nor should they implement one technique without the other. Lean tools are essential for unlocking the potential of Industry 4.0 and preventing the automation of waste. New digital technologies are essential for reaching higher levels of impact from lean initiatives. To achieve the biggest payoff, a company must design innovative ways to combine lean tools and Industry 4.0. Manufacturers that master the ability to apply Lean Industry 4.0 will be the operational excellence champions in the years ahead.