Digital Transformation in Manufacturing: A Complete Guide

Manufacturers’ spending on digital technology for operational transformation is projected to grow at an average annual rate of 17% to 24% over the next several years based on research reports—but the reports all agree that spending will top $1 trillion by 2031. Despite economic uncertainty and a difficult business climate, the potential to improve efficiency, optimize costs, and develop new products faster is driving manufacturing forms to make this investment.

But to achieve digital transformation, manufacturing business leaders must grapple with thorny change and process management challenges. A major obstacle is the need to replace legacy technology that often has been in use for decades, especially if removing it takes work away from long-tenured employees. As a result, the search for the right approach to digital transformation focuses on streamlining tasks ripe for automation—typically repetitive, prone to human error, or even unsafe tasks—and aggregating data that can drive decision-making that better supports business goals.

What Is Digital Transformation in Manufacturing?

Digital transformation in manufacturing refers to the integration of digital technologies, such as cloud computing, automation, artificial intelligence, Internet of Things (IoT), and data analytics, into all aspects of production and back-office processes. This level of integration fundamentally changes how manufacturers operate and deliver value, as well as the way they embrace efficiency, innovation, and competitiveness.

In addition to automating manual processes that are labor- and time-intensive, if not potentially dangerous, digital transformation has made it possible for manufacturers to predict when equipment will break down, standardize inspection and service, and improve visibility into the broader manufacturing process. Replacing outdated systems and processes with connected technology is also referred to as smart manufacturing.

Key Takeaways

• In manufacturing, digital transformation applies emerging technology to both production workflows and data analysis.
• Key digital transformation technologies include artificial intelligence, virtual reality, automation, and cloud computing.
• Insights that come from digitizing an entire production process can help manufacturers cut costs, improve efficiency, and create a safer working environment.
• Resistance to change, legacy infrastructure, and the up-front cost of investment are common barriers to digitalization.
• To build momentum for digital transformation, manufacturing firms can focus on achieving small wins, such as automated production scheduling and better forecasting.

Digital Transformation in Manufacturing Explained

The term “digital transformation” tends to be overmarketed. After all, manufacturers have been steadily adopting digital technologies over the course of decades to improve operations. But the combined effects of COVID-19’s shock to supply chains, the drift away from on-site labor, and the maturation of AI—especially generative AI—coalesced immediately after the pandemic’s peak to make manufacturing primed for digital transformation. The pandemic forced manufacturers to virtualize processes almost overnight, and the marriage of AI with IoT sensors, cloud computing, and digital-twin platforms is equipping them to reshape core value streams (design, quality, maintenance), rather than merely automating individual tasks.

Thus, leading manufacturers are embracing manufacturing innovations to enhance productivity, improve product quality, identify new business opportunities, create jobs, and maintain competitive advantages amid ongoing market pressures.

As part of their digital transformation efforts, many manufacturing companies are transitioning to smart factories. In the past, individual business lines tended to adopt a few pertinent technologies, such as networking, process automation, sensors, and analytics. However, because these systems (and the data they collected) were isolated, decision-makers had to manually extract data to view and analyze it. This made forecasting difficult and real-time, data-driven decision-making all but impossible. In the smart factory, by contrast, systems are integrated throughout the organization, and data is stored in a central repository. This makes it possible to apply advanced analytics, gain insights, and intervene before problems like low inventory or broken equipment can slow down processes.

Technologies Facilitating Digital Transformation in Manufacturing

Because manufacturers see digital transformation as a business imperative, the technology they put in place touches all aspects of their organizations. Much of the focus is on simplifying front-line manufacturing processes without compromising safety or quality. At the same time, though, expanded adoption of technology for data aggregation and analysis helps management teams make better-informed decisions to drive business growth and respond to changing market conditions.

Technologies that are facilitating this transformation include:

• IoT: Gathering data manually in manufacturing is time-consuming and sometimes dangerous. But sensors connecting industrial devices to computer networks make it possible for manufacturers to closely monitor equipment and production processes from afar. The data gathered from devices can be shared with applications for business intelligence, predictive analytics, inventory oversight, supply chain management, and environmental controls.
• Automation: Humans get fatigued doing manual work, whether from assembling products or approving work orders, and that fatigue can lead to errors. Process automation can increase efficiency and reduce errors for tasks that run the gamut from programming to quality control to packaging. Robots, meanwhile, can assist with picking inventory, transporting it across a warehouse, and assembling products on a factory floor. These examples show how automation removes workers from tedious and potentially dangerous processes, especially if items are heavy, sharp, or corrosive.
• Artificial Intelligence (AI): Real-time data analysis and process automation gets a big assist from AI models that recognize patterns in data and can make predictions. Within the smart factory, AI systems provide recommendations to drive production process improvement, expedite quality control, and help robotic systems manipulate objects on an assembly line. Business units can also use AI models to help track inventory levels, forecast product demand, and predict when equipment will wear out—features that help increase efficiency and improve decision-making.
• Predictive maintenance: When equipment needs to be repaired, manufacturing production screeches to a halt. Research has shown that annual losses from unplanned downtime for the world’s largest manufacturers are equivalent to 11% of revenue. Predictive maintenance uses a combination of sensors and wireless communication to collect information about equipment condition, analyze that input, and provide alerts and maintenance recommendations to minimize unplanned downtime.
• Augmented reality (AR) and virtual reality (VR): With so many literal moving parts in a manufacturing facility, workers may struggle to locate items they need or to put them together properly. By using specialized glasses, VR applications can display hard-to-find items on a shelf in front of a worker’s eyes or demonstrate how parts fit together. AR displays digital information superimposed over a real image on a smartphone or tablet, which is helpful in scenarios when workers cannot wear a headset.
• Modern software solutions: Some software, such as enterprise resource planning (ERP), can help manufacturers gather data from disparate systems, aggregate it in a central database, and make it available for different business units to leverage for decision-making. As part of their digital transformation efforts, many manufacturers turn to cloud-based ERP and other business applications. The cloud offers greater flexibility in deployment, often requires fewer resources to manage, and allows for faster deployment of new applications than legacy on-premises infrastructure.

Advantages of Digital Transformation for Manufacturers

Digitizing helps manufacturers in several important ways. Production processes become safer, more efficient, less wasteful, and more responsive to supply chain disruptions or changing market conditions. This often drives the quality of goods up while reducing the number of defects. All told, manufacturers benefit from both immediate and long-term cost savings that help them remain competitive in an increasingly challenging global market.

Improves Safety

Robots can automate industrial processes that involve heavy lifting, corrosive chemicals, or dangerous equipment to reduce the likelihood of workplace injuries or accidents. In addition, by practicing predictive maintenance and automatic monitoring of equipment for signs of wear and tear, manufacturers can reduce the risk of machines breaking or malfunctioning in a way that can cause harm. Monitoring technology can also watch for evidence of unsafe conditions and automatically alert management. Finally, incorporating VR and AR technology into onboarding and training processes can help employees become acquainted with complex processes, helping them understand how to perform tasks safely before they set foot on the factory floor.

Generates Long-Term Cost Savings

In addition to immediate efficiency gains, digital transformation can help manufacturers lower costs over time. For example, predictive maintenance can help minimize the cost of unplanned downtime. Data from sensors along the production line can help managers trim scrap, energy consumption, and cycle times. The same data can shed light on tasks that could be targeted for automation, further reducing labor cost per unit. When these capabilities are linked to demand-driven planning and digital twins, manufacturers carry less inventory, reduce logistics miles, and spot design flaws before they reach production—all of which can mean greater material, labor, and capital-efficiency savings year after year.

Improves Productivity

Digital transformation boosts manufacturing efficiency by automating repetitive manual tasks throughout the production process. One example is automating operational processes, such as work-order prioritization and maintenance servicing. Automation supported by AI can limit the preventable human errors that result in unplanned production delays. From an organizational perspective, automation provides two more key benefits: One is freeing employees’ time to devote to higher-order tasks that cannot be easily automated, such as strategic planning; the other is speeding up the product development life cycle, which helps companies pivot quickly in response to changing market conditions.

Strengthens Operational Resilience

Aggregating data across all aspects of production—from shop-floor IoT streams to ERP systems’ inventory data to external market signals—and analyzing it in a unified process gives manufacturers early-warning radar for both equipment failures and supply-chain shocks. Furthermore, AI capabilities can use that data to improve demand forecasts, which can help manufacturers cut raw-materials safety stock. Digital-twin simulations can help manufacturers stress-test various “what-if” scenarios—what if a supplier fails, what if there is a sudden energy price spike—so managers can turn to preapproved playbooks rather than scrambling when the event occurs. Predictive maintenance models can suggest scheduling service just before wear turns into failure, slashing unplanned downtime and lengthening equipment lifespan. In all, these digital transformation capabilities let plants absorb disruption, pivot quickly, and restore output with minimal cost or customer impact—all hallmarks of true operational resilience.

Increases Quality

Leveraging technology to keep close tabs on the production process lets manufacturers quickly identify errors that could contribute to product defects, such as misconfigured equipment. This vigilance can help scale back the number of products that need to be reworked before they’re shipped; it also can cut down on the number of products that malfunction prior to the expiration of their warranty and must be replaced at the manufacturers’ expense. Companies can also deploy sensors and cameras to conduct imaging-based inspections to find defects or production inconsistencies that are difficult to detect with the naked eye. Data and insights gathered from the plant floor can also help management determine which steps in the manufacturing process may be susceptible to human error and thus may benefit from automation.

Reduces Waste

Because a well-monitored and automated manufacturing process increases efficiency and diminishes bottlenecks, firms can consume less energy, use fewer raw materials in production, and spend less time reworking improperly built products. Along with contributing to cost savings, reducing waste can be a competitive advantage, as companies seeking to lower the carbon footprint of their supply chain will look to manufacturers that have taken steps to improve sustainability. The ability to programmatically update or fix robotic systems also means service calls can be completed remotely, which helps curtail emissions from maintenance vehicles.

Overcoming Common Challenges in Manufacturing Digital Transformation

The most common hurdles that manufacturers face in their pursuit of digital transformation initiatives are resistance to change, the cost of investing in modern technology, and the challenge of layering new tools atop legacy systems or replacing them entirely. Organizations may also struggle with shoring up data and infrastructure security and finding the right personnel to use and manage new digital systems.

The following are deeper dives into each of these common impediments:

Initial Investment Cost

Digital transformation can require significant up-front investments. Manufacturers may need to acquire robotic systems and IoT sensors, upgrade employees’ computing devices, update technology infrastructure for data storage and processing, and make certain their network can handle increased data traffic. The price tag for these investments can be daunting, particularly if firms haven’t yet quantified anticipated long-term savings from digital transformation.

Given the increased appetite digital transformation has for data ingestion, processing, and analysis, manufacturers should consider using cloud-based software and infrastructure. A transition to the cloud helps manufacturers reduce their spending on maintaining onsite infrastructure and reallocate those resources to mission-critical purposes. Cloud-based services also offer firms greater scalability and flexibility than on-premises architecture.

Personnel Limitations

Front-line workers are well-versed in their organizations’ traditional manufacturing workflows and proprietary technology systems. Unfortunately, this system knowledge and skill set seldom apply well to modern digital systems. And hiring new workers with the right skills is easier said than done. One survey showed 61% of manufacturers cannot recruit enough talent to fill critical roles. Another projected nearly 2 million unfilled manufacturing jobs by 2033.

In response, many manufacturers have increased focus on training that aligns with gaps in workers’ skill sets and their level of interest in using different types of emerging technologies. Along with formal training, worker upskilling strategies include mentorship and rotational programs, which exposes workers to multiple roles as opposed to requiring them to specialize in a single task.

Organizational Resistance to Change

Large-scale business process changes are bound to encounter some degree of resistance, and this is especially true when it comes to digital transformation. Automation, remote monitoring, and predictive analytics might be unfamiliar—maybe even scary—to teams operating on the front lines of production. And, if the benefits of these systems aren’t properly communicated, workers may believe they’re being replaced by machines.

Change management best practices are designed to make the process easier on the people who must make the shift. Two hallmark practices are effective communications and taking into account employees’ needs, wants, and human reactions. Business leaders must make certain that everyone understands the rationale for change, its impact on the organization and each individual, its progress, and its benefits. Ultimately, an organization’s people make the change happen—or not.

Security and Privacy Concerns

Digitizing formerly analog processes increases the number of potential entry points into a manufacturer’s corporate network. Every new sensor, robot, or cloud API added to a smart factory widens the cyberattack surface. Legacy programmable logic controllers that once sat on air-gapped operational technology (OT) networks now share data with ERP and other business systems, yet still speak in protocols that lack built-in authentication. In fact, the convergence of IT and OT has made manufacturing the top ransomware target, with attackers using compromised vendor credentials or unpatched human-machine interfaces to pivot from IT to plant-floor controllers—and shut down entire production lines.

Data-privacy risks also escalate: Vision systems record employee biometrics, IoT gateways may stream European Union (EU) customer telemetry to non-EU regions, and engineers sometimes paste proprietary CAD models into public generative AI chatbots, all of which spells potential data leakage.

Mitigation should start with a formal security and privacy impact assessment, but shouldn’t stop there. Manufacturers should implement zero-trust network segmentation between IT and OT, require signed firmware and software bills of materials from suppliers, apply rigorous patch windows for critical controllers, encrypt data in transit and at rest, and store immutable, offline backups to recover rapidly from ransomware. Privacy-by-design policies—data minimization, role-based access, regional residency controls—can also help ensure that your organization’s digital-transformation gains are secured rather than sacrificed—that is, wiped out by inadequate safeguards.

Infrastructure Barriers

Manufacturing firms can be reluctant to sunset legacy systems and processes. Part of the hesitation stems from the sunk cost of time and money to develop these systems, as well as the challenge of transferring data from legacy systems managed by individual business units to a shared, enterprisewide platform. Along the same lines, organizations may be wary of the disruptive potential of digital transformation on manufacturing workflows.

As noted above, developing a comprehensive change management strategy can help identify the most common concerns about the transition away from legacy systems. If leadership can anticipate and address challenges in advance, then the entire organization will have a clearer path to follow.

Examples of Manufacturing Digital Transformation

Digital transformation promises manufacturers a future in which fully integrated processes leverage a multitude of digital technologies to learn from previous batches so they can continuously increase precision and quality, while always paring down errors and shrinking the cost of production. But digital transformation is already translating into measurable results on real factory floors. The snapshots below highlight different digital levers—IoT, AI, digital twins, and advanced analytics—and the operational wins they’re producing even now.

• Rolls-Royce: In its aero-engine factories, IoT-driven digital twins predict component wear and schedule service, extending time-on-wing by more than 70 % and avoiding unplanned removals.
• Schneider Electric: At its Lexington, KY, smart factory, Schneider Electric uses edge-based energy-management analytics to reduce facility emissions by roughly one-third.
• Ford: Ford uses IoT sensors plus AI models for predictive maintenance cut equipment downtime by about 25%.
• Siemens: The company’s Amberg Electronics Factory uses real-time quality analytics—fed by thousands of line-side sensors—to keep first-pass yield at 99.99885%, virtually eliminating scrap.
• GE: “Brilliant Factory” sites use plantwide IoT data lakes to optimize throughput and energy, reducing unplanned downtime by 10% to 20% and lowering power consumption.
• Henkel: AI-generated “virtual adhesives” and digital-twin simulations speed electric vehicle battery adhesive design, slashing the cost of physical prototypes and development time.

A Forward-Thinking ERP Suite for Growing Manufacturers

For digital transformation to succeed in manufacturing, companies need end-to-end visibility into operations, as well as the capability to manage procurement, production, and manufacturing in one platform. NetSuite ERP for manufacturing enables companies to aggregate data from the factory floor and make informed decisions centered on process improvement, inventory control, quality assurance, and more. Using an ERP tool purpose-built for manufacturing—including subsectors, such as medical devices, building materials, and packaged goods—companies can harness insights and make the most of their investments in digital transformation.

The manufacturing industry is increasingly leveraging technology to gather data, automate processes, and generate real-time insights. Its emphasis on digital transformation is positioning the industry to improve efficiency, safety, and product quality while reducing waste and lowering costs. That said, manufacturers need to overcome hurdles associated with managing change and transitioning from legacy technology systems. A pragmatic strategy for addressing pressing needs, such as optimizing production schedules or automating forecasting, can provide quick wins that demonstrate the value of digital transformation and help make the case for broader efforts throughout the organization.

Digital Transformation in Manufacturing FAQs

What is the primary goal of digital transformation

Manufacturing firms pursue digital transformation as a business strategy to use technology for production modernization. This enables firms to improve productivity, cut costs, and improve product quality—all of which support the primary goal of increasing competitiveness in a complex and increasingly digital industry.

What are the 5 Ps of digital transformation?

The five keys to successful digital transformation are purpose, people, process, platform, and project. These five Ps address what an organization is changing and why (purpose), who will make the change happen (people), what workflows will change (process), what tools will support the change (platform), and how the change will be executed (project).

How is smart technology being used in manufacturing?

The manufacturing industry has embraced smart technology, including connected devices, cloud computing, robotics, and big data, as a way to increase competitiveness, efficiency, and resilience. The benefits of increased process automation driven by smart technology include higher revenue, reduced cost, and improved product quality.