By the end of the 20th century, lean-management techniques had migrated from their birthplace in Japan to remake the operations of manufacturing and process industries across the world. In heavy industries, such as mining, pulp and paper, and oil and gas, today’s leaders can point to decades of operational improvement thanks to lean practices and principles. But a lot has changed since lean was conceived. The next phase of operational excellence has arrived: Industry 4.0 technologies such as advanced analytics, AI, and connected equipment help heavy-industry leaders to increase the productivity, flexibility, resilience, and sustainability of their operations.
The trouble for many organizations, however, is that so far results from these technologies have been mixed. While many companies have run successful pilots or local deployments, only a few managed to scale up their initiatives to take full advantage of the new technologies across the enterprise. The experiences of those few point the way to a new kind of technology-informed operational excellence.
The next step
A handful of heavy-industry companies have found a new way to operate that is revolutionizing the way they create value in operations. The combination of lean and technology is allowing these companies to create new augmented production systems that capture the full value of digital at scale in a sustainable manner. The benefits include year-on-year improvements in productivity, environmental protection, safety, employee engagement, and customer satisfaction (Exhibit 1).
Such companies have adapted some of the core principles underlying lean production to suit an environment where advanced technologies are applied in everyday operations. Those principles have guided the redesign of processes, management systems, and organizations to make truly effective use of technology.
Technology is generating real operational value
Scientific thinking is the principle that inspires lean companies to use hard evidence and experimentation to continually improve their operations. Lean has always relied on rigorous, fact-based decision making. But the analytical tools available to most lean practitioners force them to focus their attention on just a few variables at a time. For that reason, processes in traditional lean companies tend to improve incrementally.
Digitization helps companies take scientific thinking to a new level of sophistication. Connected, digitized value chains give teams access to much richer data on the performance of processes. Advanced analytical models allow those teams to gain deeper insights from the data by revealing subtle cause-and-effect relationships hidden among thousands of data points. With these tools and the right capabilities, companies can apply scientific, data-driven problem solving to a wider range of challenges—and get better answers faster than previously possible.
Digitization also transforms systemic thinking—another core lean principle. Because technology can bring together data from across the value chain, companies can take a holistic, end-to-end approach to optimizing processes. In many heavy-industry sectors, for example, processes encounter high levels of variability caused by differences in input materials, process conditions, and demand. Operators in traditional production systems can only react to that variability, by altering parameters on the fly to keep processes under control. Digital and advanced-analytics technologies help companies move to a proactive approach. By using upstream data and detailed process models to adjust parameters before variability hits, these companies can dramatically improve the stability of their production systems.
Because technology can bring together data from across the value chain, companies can take a holistic, end-to-end approach to optimizing processes.
At one copper mine, for instance, operators now use an end-to-end model to change their processes as the characteristics of ore change over time. Data on the quality of ore is collected at the mine and transmitted to the processing plant several hours before the ore arrives. The processing plant’s AI model identifies changes in the characteristics of the ore and makes recommendations—timed to coincide with the precise moment it enters the mill—for adjustments to water pressures, grinder settings, and other variables. If the characteristics of the ore require it, similar recommendations are passed on to the teams operating downstream process steps (Exhibit 2).
Changing people’s work, not just their tech
When this new approach was introduced, the copper mine’s organization also changed: a permanent continuous-improvement squad is now responsible for copper production from the mine to the plant. The KPIs that operators monitor now reflect this end-to-end thinking. All operators have visibility into the ways their actions affect upstream and downstream variables, for example. Since the introduction of the new approach, the mine’s output has increased by 3 to 7 percent.
Another company, this time in the pulp and paper sector, has adopted a similar augmented approach to maximize yields and optimize resource consumption in its pulp production process. To trace the characteristics of pulp fiber, the company uses a set of models beginning at the forest floor and ending at the point of dispatch to the customer. These models, used to adjust parameters several times per shift, change the mix of wood chips, the chemicals used, and the process settings to ensure that customer requirements are met in the most efficient way.
How to add tech to lean for operational excellence
Why isn’t every company working in this way? Our observations of digital initiatives in a wide range of organizations, along with our conversations with their senior leaders, reveal a handful of reasons. New technologies don’t always deliver what they promise. Companies may focus their efforts on the technical aspects of new digital use cases, for example, without considering the wider principles and practices of operational excellence. Or they may lack the internal capabilities to develop, deploy, and operate sophisticated digital tools. Finally, executives at many organizations fail to take their teams along on the digital journey. Operational experts, such as team leaders, process control engineers, and metallurgists, tend to ignore tools and processes they don’t understand or trust.
Digitization can dramatically enhance the power of traditional approaches to operational excellence but doesn’t replace the need for strong organizational foundations. Today’s leading players overcome these barriers by adopting a holistic approach to operational excellence and ongoing improvement. That approach addresses all the technological, organizational, and human dimensions required for sustained success.
As with earlier approaches to organizational excellence, leading companies recognize that digitally enabled operational excellence is a journey. The benefits increase as the organization develops the ability to use more sophisticated tools and to apply them across more of its operations. The most successful initiatives tend to share a handful of characteristics.
1. Ownership through robust understanding and conviction
Across all levels of the organization, leaders and frontline operators ought to understand and fully support the use of technology in their day-to-day decision-making processes. This goal can be achieved in different ways. The leaders of one large mining company, for example, built understanding and conviction through a bottom-up process. They organized technical workshops to define a shared vision for the future functioning of their operation, so that the entire organization could see how the effort would improve its work. Another mining company generated excitement by having employees visit operations at similar companies, where the successful deployment of technology built on a previous commitment to operational excellence—and made improvement cycles faster, more agile, and more effective.
2. Updated organization and management systems for optimal decision making
High-performing organizations structure teams in working cells linked to value streams (such as end-to-end operations, reliability, or planning) rather than the value chain’s individual process components (mine operations, plant operations, technical services, and the like). This structure empowers managers to make decisions across the entire value chain and therefore aligns objectives and performance metrics with bottom-line business results. At another mining company, a value-stream product owner with full visibility over the whole process makes final operational decisions at an integrated operations center. That manager directs a multidisciplinary team of processing and technology experts who assist in decision making.
3. Evolved roles and capabilities to empower and engage the front line
At companies that have succeeded in using technology to achieve operational excellence, roles and capabilities are adapted to suit the new way of working required to sustain and continuously improve technology solutions. Product owners are empowered and accountable to ensure that their teams meet the objectives and key results defined for each value stream. Data scientists and engineers use data collected throughout the process to build optimization models that assist in decision making. “Translators” serve as a communication bridge between processing experts and the digital team. Finally, frontline leaders with access to more data are supported by everyday insights from AI tools. They also have more scope to improve operations, pilot new ideas, and collaborate with their colleagues upstream and downstream.
In another copper-mining operation, for example, the product owner of the multidisciplinary operations cell now runs day-to-day operations. That manager, who understands insights from prescriptive analytical models, has a profile rather different from those of traditional mine or plant managers. Their roles too have evolved, to focus on ensuring the optimal condition of assets, so that the production cell can operate at its maximum potential.
Combining new digital approaches with the proven principles and practices of operational excellence opens a world of possibilities for heavy industries. Early adopters have already reaped substantial benefits: a step change in performance and employee engagement.
Yet the journey to digitally enabled operations isn’t trivial. It requires energy, vision, and persistence by senior leaders, who can ensure that their organizations stay on the right path by continually revisiting three fundamental questions. First, are we applying the core principles of operational excellence in everything we do? Second, are we using the best available technologies to support our operations? Finally, are we using those technologies in the most effective, sustainable way?