Digital transformation offers tremendous opportunities for machinery and equipment manufacturers to weather setbacks and disruptions while still innovating and improving operations. The idea of implementing organization-wide digitalization may seem daunting. But available cloud-based solutions provide organizations of all sizes with the customizable services, expertise, and connectivity they need to digitalize at scale. Large cloud-service providers—known as hyperscalers—have already built ecosystems that companies can leverage to greatly expand their capabilities and options. Hyperscalers’ solutions seamlessly integrate with their partners’ solutions in these ecosystems, giving companies the support of system integrators and independent software vendors during implementation.
These benefits are why leading manufacturing companies use cloud-supported digitalization to address the same challenges the machinery sector faces: inflation; chronic talent shortages; and higher energy, materials, and labor costs. Digital technology is also helping companies advance their net-zero objectives.
In this article, we illustrate how machinery and equipment manufacturers could realize considerable competitive, operational, and sustainability gains with a holistic digital transformation enabled by cloud-based services. We highlight examples of leading-edge organizations that optimized their operations and strengthened their resilience with digitally enhanced supply chains, production, product development, aftersales, and more.
Digital transformation: Fully realizing the benefits of Industry 4.0
As promising and potentially beneficial as it is, the expansive, evolving array of data, connectivity, analytics, human–machine interaction, and robotics solutions available in the machinery sector can feel overwhelming. Manufacturers are often tempted to dip their toes into the Industry 4.0 pool rather than dive into the deep end with an organization-wide digital transformation. But a piecemeal approach to digitalization can put machinery and equipment manufacturers at risk of falling into the “pilot purgatory” trap that afflicts many companies’ Industry 4.0 endeavors: companies see promising results from early trials of new digital approaches but cannot scale their successes due to technical, organizational, or capability constraints.
Digital transformation is no longer optional for machinery sector companies seeking to differentiate themselves and gain an edge on their competition. According to a 2020 survey by VDMA and McKinsey on customer-centric digital platforms in mechanical engineering, machine availability and machine performance are the top priorities of both end users and machinery companies.1 And while applying digital approaches at sufficient scale to achieve a significant impact on EBIT is not a trivial task, holistic digitalization is precisely what is needed to realize this impact. The gains in insight, efficiency, productivity, communication, and customer satisfaction achieved by fully digitalized companies are symbiotic, not independent. Digitalization enables the analysis that provides the insights, which inform the decisions and innovations that lead to great efficiency, higher production, and so on.
Implementing digital use cases at scale requires a scope that encompasses the whole company, committed senior-management leadership, and a relentless focus on rapid and meaningful business impact. Successful transformations emphasize the creation of an environment capable of supporting and sustaining digital initiatives—for example, with a robust technology infrastructure, the systematic acquisition and development of digital skills, and the creation of a digital-first organizational culture and mindset.
Indeed, drawing upon McKinsey’s experience with at-scale digital transformations across sectors, implementing organization-wide digitalization with cloud applications and a portfolio of digital tools could improve a machinery manufacturer’s EBIT by as much as five to eight percentage points (Exhibit 1). Automations, improvements in data analysis, and more can increase efficiencies and free up employees to focus on higher-level improvements that lead to further savings and product innovations. These efficiencies could yield cost savings of up to 20 percent in some functional areas. General and administrative expenses could be reduced by 10 to 20 percent by implementing, for example, a digital spend control tower (SCT) or robotic process automation (RPA) for enterprise resource planning (ERP).
Machine learning and AI-powered processes can enable improvements in production as well as product quality, both of which can lead to increased revenue. New revenue streams could also be created by using cloud-assisted digital systems to provide predictive maintenance as a service for manufactured machinery and equipment. And the time is ripe for machinery and equipment sector players to explore the service-centered offerings that digital transformation makes possible. For many companies, automated systems will account for 25 percent of capital spending over the next five years, as shown in the 2022 McKinsey Global Industrial Robotics Survey.2
Digital transformations can take different forms, but they tend to afford common benefits across three broad dimensions: business focus, digital capabilities, and cross-functional scalability.
Improved business focus of digital solutions
Companies that scale digitalization successfully view digitalizing as an investment in their future growth and profitability rather than as an IT project. Such organizations apply a broad portfolio of use cases driven by their respective business, following a careful prioritization based on business impact, feasibility, and ease of implementation. For example, digitally enabled automation can yield higher productivity that can offset higher material costs and economic volatility. Automating repetitive, monotonous tasks can help retain talent in manufacturing by reducing employee stress; it can also attract new talent with opportunities that appeal to digital natives.
Enhanced digital capabilities of the organization
Digital transformation requires systematic changes across an organization. Successfully digitalized companies approach their transformations as multiyear journeys. Leaders drive the transformations, but they are supported by a central transformation engine that guides change efforts for individual units, monitors progress, and ensures best practices are adopted universally. Companies invest in digital talent by hiring new employees with specific skills and by running large-scale capability-building programs to rapidly upskill existing staff. The resulting organizations are more efficient, productive, and able to engage in proactive efforts to innovate and improve and reactive efforts to adapt to external factors and course correct when needed.
Greater scalability across functions
Because digital transformation requires an organization-wide approach to technology, companies that scale digitalization successfully develop dozens of different digital-tool use cases and build the most effective applications on a common infrastructure that is scalable, affordable, and secure. To implement, sustain, and develop this infrastructure, companies typically forge strategic partnerships with key technology players. These partnerships complement and augment internal resources, creating additional capacity and opportunities to scale.
Once core elements are in place, machinery and equipment manufacturers can accelerate their digital efforts, identifying and capturing multiple opportunities to boost their performance. Mastery of the digital toolbox could unlock sources of future growth. Effective remote-monitoring and diagnostic capabilities, for example, can enable a shift to new machinery-as-a-service business models in which companies own, operate, and maintain equipment on their customers’ behalf.
Cloud applications can streamline and optimize digital transformation
Cloud-based solution platforms can make it much easier for organizations engaging in digital transformation to prioritize efforts that help differentiate them from competitors (Exhibit 2). Using a cloud solution to manage a supply chain, for example, can make it more efficient. Or, rather than resourcing the creation of a new algorithm for a machine-learning platform, a manufacturer could apply the cloud solution provider’s algorithm to address its challenges. And rather than adapting to working with an ecosystem of software-as-a-service (SaaS) providers or integrating it into their systems, a manufacturer could engage a cloud hyperscaler with a technology platform that has been integrated at scale with several organizations.
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.
As companies intensify their efforts to hit increasingly aggressive Scope 3 emission-reduction targets, they put pressure on their machinery and equipment providers to demonstrate a clear roadmap for net-zero operations. Cloud capabilities can help machinery sector companies contribute to these objectives by allowing them to measure and support sustainability across their value chain (see sidebar, “Cloud-powered sustainability solutions”).
Digital supply chains improve transparency and minimize disruptions
Digital supply chains revolutionize business operations by providing unparalleled advantages in today’s interconnected landscape. Enabled by technologies such as cloud computing, AI, and blockchain, digital supply chains offer real-time visibility from sourcing to delivery. This transparency allows organizations to identify bottlenecks, optimize inventory, and make data-driven decisions. Moreover, digital supply chains enable a swift response to disruptions, whether caused by natural disasters, supplier issues, or market fluctuations. Savings on materials costs can, for example, be realized with AI-based supplier benchmarking that compares suppliers’ performance on metrics most important to a given manufacturer or by using AI to predict a need for spare parts.
One global electronics manufacturer, for example, used digital-supply-chain risk-management tools to minimize supply disruption during the COVID-19 pandemic. The manufacturer had a global manufacturing network of around a dozen plants. It created a digital model of its entire supply chain—and a large multitiered supply base with more than 5,000 suppliers—and used the model to assess the supply chain’s relative vulnerability across multiple factors such as lead time, supplier concentration, COVID-19-specific country risk, and financial resilience. This effort identified more than a hundred priority high-risk suppliers, of which around 10 percent were either financially distressed or previously unknown tier-two suppliers. Detailed review of those suppliers allowed the manufacturer to implement a mitigation plan that reduced its spending at high-risk suppliers by more than 40 percent.
Cloud computing 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.
Faster, cheaper, and more flexible production with Industry 4.0
AI has made it possible for organizations to compile robust data sets on all aspects of production. And digital twins can replicate an entire factory site, supplying critical insights such as equipment downtimes and product assembly times. This information can then be used to customize machines and to enable AI-powered predictive maintenance, which machinery and equipment manufacturers can offer as value-added services to their customers.
A large industrial company that operated a wide variety of equipment and shared inventory storage between production lines boosted its production throughput using an advanced production-scheduling optimization tool. To improve its planning and scheduling processes, the company developed a mathematical model of its entire production process. The model included all relevant constraints and cost drivers—45 nodes, 80 flows, and 50 technical constraints—and was used to build an optimization tool that helped the company achieve a 25 percent increase in throughput. The cloud made it possible to break down data silos and provide a single, uniform source of the data needed for the model, as well as the computing power needed to run it.
Smarter product development with AI
Digitalized product development enables organizations to accelerate product and service innovation. Cloud-based platforms enhance digital improvements by providing cross-system data transparency and access to unrestricted computing power for simulations. Companies can take advantage of digital twins, remote computer-aided design (CAD) and desktops, and high-performance computing on demand; use life cycle data to optimize products; and optimize product designs via an AI- and machine learning–based design-to-value (DtV) process. Companies can also benefit from AI-based product-complexity management: for example, one power tool manufacturer realized a 20 percent reduction in processing time, an improvement of more than $20 million in operating margin, and a more than 40 percent reduction in its finished goods’ SKU count.
A company in the power generation equipment sector is using AI to optimize the design of large turbines for hydroelectric plants. In a pilot project, the company’s engineering team partnered with external specialists to create a deep-learning model (enabled by robust cloud-based computing) that simulated the performance of the four major components in its turbines. The model was designed to accept the desired operating point as an input and considers different constraints—from the acceptable mass and strength of each part to fluid flow problems such as cavitation or pressure pulsation. Using this approach, the company reduced the engineering hours required to create a new turbine design by 50 percent and cut the end-to-end design process by 25 percent. Better still, the approach generated turbines that were up to 0.4 percentage points more efficient than conventional designs, an improvement that could be worth millions of dollars to the operator over the lifetime of a power plant.
Greater cost transparency and pricing capabilities
Digital marketing and sales help companies foster connections to their customers at all stages of their purchasing journey. A 360-degree view of customer data provided in a comprehensive, centralized source affords companies opportunities to analyze, track, predict, and personalize customer services and interactions. And AI-based offers and pricing, asset and quote management, and optimized sales-and-support operations are just some digital use cases that improve decision making and increase efficiency and transparency.
Digital transformation that taps into cloud-based platforms considerably expands efficacy and agility in pricing strategies. More than €400 million in sales was recovered by a tier-one automotive supplier that implemented analytics-based claims management to help reprice its finished products profitably following a significant increase in raw-materials costs. Similarly, an industrial manufacturer facing across-the-board cost increases from suppliers deployed a cloud-based transparency tool to document costs in granular detail. The resulting data helped to better understand the exact cost drivers of each product or service and improve the manufacturer’s internal cost models. Sales teams use these models in negotiations to ensure that the prices they offer reflect current production costs.
Other organizations have also used a data-driven approach powered by AI and cloud-based platform capabilities to address the impact of component shortages. For example, dynamically setting lead times for different product configurations according to the availability of the component parts helps sales teams set appropriate customer expectations or steer purchasing decisions toward standard products that are quicker and cheaper to produce.
Expanded opportunities and aftersales services
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. And because OEMs that provide these services have firsthand, real-time access to information on any defects and other needs of the product, they could realize gains in sales of spare parts and repairs. Other potential sales could also be identified via OEMs’ expanded connections and insight into customers’ typical machine use, breakdowns, and more.
A global manufacturer of mechanical components has partnered with a major IT services company to offer cloud-based remote-monitoring and analytics services. The technology collects vibration, temperature, and other operational data from customers’ equipment and uses that data to identify and diagnose potential reliability problems. By moving condition monitoring from on premises to the cloud and by automating much of the necessary data analysis using AI tools, the company has been able to scale up its service offering, providing monitoring to a wider range of customers and using the data it collects to rapidly improve the diagnostic and predictive capabilities of its systems.
At-scale digital transformation is an imperative for machinery sector players. The operational and financial advantages are essential to remain competitive amid ongoing supply disruptions and manufacturing market fluctuations. And digitalizing an organization of any size at scale is more common and less costly and complex than ever before due the to the availability of cloud-based platforms and services. Whether taking advantage of cloud platforms’ array of ready-made tools, software, hardware, networks, and services or networking and sharing data across facilities, machinery players can enjoy the same advantages realized by companies in many other manufacturing sectors. Gains in sustainability, increased profitability, and new revenue streams from the as-a-service model are just some features of the promising digitalized, cloud-enriched landscape in machinery and equipment manufacturing.
