McKinsey & Company (McKinsey)
Canvas Category Consultancy : Company : Strategy
We help organizations across the private, public, and social sectors create the Change that Matters most to them. From the C-suite to the front line, we partner with our clients to transform their organizations, embed technology into everything they do, and build enduring capabilities. With exceptional people in 65 countries, we combine global expertise and local insight to help you turn your ambitious goals into reality.
Assembly Line
🦾 Boosting machinery sector profitability via cloud-aided digitalization
Cloud capabilities open up an important potential source of revenue in the machine and equipment sector: the equipment-as-a-service (EaaS) model. This business model gives OEMs a way to satisfy customers’ increasing preference to operationalize their equipment expenses and reduce their capital expenditures. The EaaS model benefits customers by supplying them with rented equipment as part of a service that includes software updates, spare-part replacements, and predictive maintenance. And OEMs and manufacturers benefit from increased access to machine data and customers, which can lead to additional revenue.
Cloud computing also offers businesses scalability and enhanced system interoperability—for example, with supplier systems. And by taking advantage of cloud service providers’ existing networks and technology resources, machine and equipment manufacturers can more easily integrate new supply chain–enhancing capabilities such as data lakes or pretrained machine-learning models.
Cloud-based platforms allow OEMs to establish permanent connections to the digital components of the products they sell. Offering services such as predictive maintenance, steering via a product app, and remote problem solving directly to their customers can help OEMs create additional revenue streams.
Success factors to deliver competitive Gigafactories on time
Battery demand will grow to ~5.8 TWh in 2030, with >30% growth across all regions.
Unlocking the industrial potential of robotics and automation
Some aspects of productive activity are more amenable to automation than others are, with routine tasks at the head of the line. Activities such as picking, packing, sorting, movement from point to point, and quality assurance are already automated to some extent, and these will continue to see heavy investment over the coming years. Conversely, activities such as assembly, stamping, surface treatment, and welding, all of which require high levels of human input, are less likely to be automated in the short to medium terms.
A standout message from the survey is that automation is not easy. Participants report that the primary challenges to adoption include the capital cost of robots and a company’s general lack of experience with automation, cited by 71 percent and 61 percent of respondents, respectively. Some say that business confidence in technology is low, leading to challenges around conviction and funding. Moreover, respondents’ expectations of production and reliability gains through automation are offset by the belief that such gains will eliminate jobs and may affect existing contracts. In fact, that is not the case since automation typically leads to changes in workplace roles rather than the creation of redundancies.
Deep learning in product design
Digitization has also allowed engineers to give computers a more active role in the engineering process. Generative design and related optimization approaches work by programming a computer to run hundreds or thousands of simulations, tweaking the design between each run until it finds the best solution it can. The resulting geometries can outperform the work of the most experienced human designers.
At its outset, a deep learning surrogates (DLS) process looks a lot like other digital design optimization approaches. The engineering team defines the constraints and desired performance characteristics of the product, and the computer runs multiple conventional simulations on different design options. That’s where the approaches diverge, however.
As those initial simulations are run, they are used to train a neural network, which is set up to take the same inputs and attempts to replicate the outputs of the simulation system. When training is complete, this deep learning model will work just like the conventional simulation, but much, much faster. In real-world projects, deep learning simulation models can run orders of magnitude more quickly than their conventional counterparts.
The future is now: Unlocking the promise of AI in industrials
Many executives remain unsure where to apply AI solutions to capture real bottom-line impact. The result has been slow rates of adoption, with many companies taking a wait-and-see approach rather than diving in.
Rather than endlessly contemplate possible applications, executives should set an overall direction and road map and then narrow their focus to areas in which AI can solve specific business problems and create tangible value. As a first step, industrial leaders could gain a better understanding of AI technology and how it can be used to solve specific business problems. They will then be better positioned to begin experimenting with new applications.
The Titanium Economy: Emerging stronger in the face of disruption
Companies in the Titanium Economy have taken steps to build agility in response to changing preferences and global supply chain challenges. They have innovated to meet their customers’ evolving needs by consolidating their value chains, maintaining regional sourcing and production, and minimizing the distance between engineering and manufacturing and their supply chains.
In the face of disruptive macroeconomic trends, the Titanium Economy has demonstrated resilience by adopting a playbook of calculated, through-cycle steps that companies across all sectors can learn from and implement. In our next article, we will explore the specific actions of this playbook and how they can help American industrials lay the foundation for the country’s future position on the world stage.
Building sustainability into operations
The path discrete manufacturing companies have taken to make their operations carbon neutral has important lessons for any business pursuing the dual mission of profitability and sustainability. Today, manufacturers of physical products find themselves on the front lines of sustainability. In part, that’s because their customers demand cleaner, lower-carbon products right now. In the high-tech sector, for example, Apple’s targets for reducing Scope 1 and Scope 2 emissions far exceed the minimum requirements of the Science Based Target Initiative’s (SBTi) 1.5° pathway, and the company is committed to achieving Scope 3 carbon neutrality by 2030. The electric carmaker Polestar has established a “striving for net zero” mission that aims to create a truly climate-neutral car by 2030 through intense collaboration with suppliers, entrepreneurs, and innovators.
Discrete manufacturing organizations are also well positioned to understand, manage, and mitigate their environmental impact, thanks to their progress in digitizing operations and supply chains over the past decade. To recognize manufacturing enterprises that embrace both sustainability and the Fourth Industrial Revolution (4IR) at scale, last year the World Economic Forum (WEF) announced a new category in its Global Lighthouse Network program: the Sustainability Lighthouse. These businesses are applying 4IR technologies to reduce their environmental footprint significantly.
Delivering the US manufacturing renaissance
A strong manufacturing economy unlocks important employment and advancement opportunities—a factor set to grow in significance if current job market pressures ease. Manufacturing is the main economic engine and primary employer in around 500 US counties today, and in those communities, the industry employs a broader-than-average swath of the overall population and does so more inclusively. In most cases, employees don’t need four-year degrees, and they can earn twice as much as those holding equivalent service-sector jobs, as employers invest in upskilling and reskilling their current workers by offering expanded learning opportunities. Our analysis suggests that reviving manufacturing could add up to 1.5 million jobs, particularly among middle-skill workers, which would help recalibrate the US labor market and bolster the middle class.
Any reinvigoration of US manufacturing will also require reinvention. Around the world, companies are taking a fresh look at the paradigms that have dominated the industry’s evolution for decades, with the aim of making manufacturing more sustainable, more digital, more skilled, and more resilient.
How mining companies reach the operational excellence gold standard
While the ten largest companies in the manufacturing and business services industries have seen their productivity index grow by around 15 percent and 25 percent respectively over the past 25 years, the ten largest companies in the mining industry have seen only marginal growth of around 1 percent over the same period.
The mining industry also has several unique features that may help explain why a culture of operational excellence has not yet been widely adopted. Productivity in the sector is often constrained by physical factors, such as ore quality. The industry also has a heavy focus on technical elements and capital levers rather than organizational culture and processes, while its dispersed and fragmented nature creates barriers to sharing best practices.
A dual approach to decarbonization in aerospace
Commercial aviation accounted for roughly 3 percent of global CO2 emissions in 2019. When all related factors are included, such as the impact of NOx, contrails, and water vapor, the share could be double that or more. Airlines have already committed to achieving net-zero emissions by 2050, but companies within the aerospace industry—airframe OEMs, propulsion specialists, and other suppliers—also have an opportunity to make the greener products. These companies cannot only support their airline customers in decarbonizing flight operations; they can also decarbonize their own operations—the part of the process they truly own.
For a typical narrowbody aircraft, our analysis shows that about 99 percent of the lifetime CO2 emissions come from fuel, including its sourcing and combustion. About 1 percent is attributed to aircraft manufacturing, assembly, and maintenance, or to the materials used in these processes.1 That is significantly different from the lifetime emissions of a typical passenger car, which has a higher share of emissions from manufacturing, assembly, and materials (Exhibit). A large driver for that difference is that cars typically have a shorter operational life than commercial aircraft and get used less each day.
What are Industry 4.0, the Fourth Industrial Revolution, and 4IR?
To get there, six core enablers can boost the odds of success for your company’s 4IR transformation:
- An agile approach that incorporates quick iterations, fast fails, and continuous learning, with teams transforming bundled use cases in waves to drive innovation and ongoing refinements.
- Agile digital studios can help people collaborate effectively, providing designated space where team members from different functions are in proximity for co-creation.
- The IIoT stack allows for seamless integration of IIoT infrastructure (both legacy and new) to build a stable, flexible tech backbone. Costs can by limited by leveraging existing systems with efficient investment in a new technology stack.
- An IIoT academy uses adult-learning best practices to upskill the workforce, offering customized learning programs based on the unique individual needs.
- Tech ecosystems partner with vendors, suppliers, customers, and related industries to source the latest capabilities, offering access to extensive data sets and creating opportunities for innovating together.
- Transformation offices can form a governance hub to support the launch and scale-up of a lighthouse, making progress and priorities transparent, ensuring value continues to be captured, and accelerating change.
Product sustainability: Back to the drawing board
Up to four-fifths of a product’s lifetime emissions are determined by decisions made at the design stage. By building on proven cost-optimization techniques, companies can get those choices right.
Two factors are pushing design up the sustainability agenda. The first is technological: an ongoing shift of lifetime emissions from product operation to product production. The shift is partly thanks to user demand for extra features and capabilities that require additional materials to deliver. But it’s also because technical changes designed to promote efficient operation tend to involve additional product complexity. For example, domestic heat pumps require more materials than the gas or oil boilers they replace. Compared with their energy-hungry predecessors, high-efficiency electric motors may contain additional carbon-intensive materials, including extra copper and rare-earth magnets. The variable-frequency drives that are used to optimize the control of these advanced motors need their own circuitry and semiconductor components.
Toward smart production: Machine intelligence in business operations
Our research looked at five different ways that companies are using data and analytics to improve the speed, agility, and performance of operational decision making. This evolution of digital maturity begins with simple tools, such as dashboards to aid human decision making, and ends with true MI, machines that can adjust their own performance autonomously based on historical and real-time data.
Global Lighthouse Network: Unlocking Sustainability through Fourth Industrial Revolution Technologies
The Global Lighthouse Network is a community of production sites and other facilities that are world leaders in the adoption and integration of the cutting-edge technologies of the Fourth Industrial Revolution (4IR). Lighthouses apply 4IR technologies such as artificial intelligence, 3D-printing and big data analytics to maximize efficiency and competitiveness at scale, transform business models and drive economic growth, while augmenting the workforce, protecting the environment and contributing to a learning journey for all-sized manufacturers across all geographies and industries.
Scaling AI in the sector that enables it: Lessons for semiconductor-device makers
Because of their high capital requirements, semiconductor companies operate in a winner-takes-most or winner-takes-all environment. Consequently, they have persistently attempted to shorten product life cycles and aggressively pursue innovation to introduce products more quickly and stay competitive. But the stakes are getting increasingly high. With each new technology node, expenses rise because research and design investments, as well as capital expenditures for production equipment, increase drastically as structures get smaller. For example, research and design costs for the development of a chip increased from about $28 million at the 65 nanometer (nm) node to about $540 million at the leading-edge 5 nm node (Exhibit 1). Meanwhile, fab construction costs for the same nodes increased from $400 million to $5.4 billion.
As companies attempt to increase productivity within research, chip design, and manufacturing while simultaneously accelerating time to market, AI/ML is becoming an increasingly important tool along the whole value chain.
Transforming quality and warranty through advanced analytics
For companies seeking to improve financial performance and customer satisfaction, the quickest route to success is often a product-quality transformation that focuses on reducing warranty costs. Quality problems can be found across all industries, and even the best companies can have weak spots in their quality systems. These problems can lead to accidents, failures, or product recalls that harm the company’s reputation. They also create the need for prevention measures that increase the total cost of quality. The ultimate outcomes are often poor customer satisfaction that decreases top-line growth, and additional costs that damage bottom-line profitability.
To transform quality and warranty, leading industrial companies are combining traditional tools with the latest in artificial-intelligence (AI) and machine-learning (ML) techniques. The combined approach allows these manufacturers to reduce the total cost of quality, ensure that their products perform, and improve customer expectations. The impact of a well-designed and rigorously executed transformation thus extends beyond cost reduction to include higher profits and revenues as well.
Smart quality in advanced industries
Technological advancements have enabled a fundamentally new way of delivering quality. Under this approach, companies view the quality function as a partner and coach that delivers value, not just a business cost. This perspective helps them integrate quality and compliance into regular operations while enabling speed and effectiveness.
A manufacturer's guide to scaling Industrial IoT
Despite tailwinds from declining compute power costs and improvements in IIoT integration, connectivity, and platform usability and management, few manufacturers have successfully scaled up their IIoT-enabled use cases in a way that achieves significant operational or financial benefits.
To understand the key enablers behind IIoT-based value capture at scale, we drew on our field work and extensive research of those companies successfully scaling IIoT to offer manufacturers ready-to-use guidance on strategically orienting their business, organization, and technology toward IIoT success.
Industry 4.0: Reimagining manufacturing operations after COVID-19
Industry 4.0 technologies were already transforming manufacturers’ operations before the pandemic. Now adoption is diverging between technology haves and have-nots.