Rethinking resilience in the manufacturing industry amid disruption

While technology is advancing at an unprecedented pace, manufacturers must confront the stark reality that substantial foundational work is still required to unlock its full potential. For decades, manufacturers have relied on AI, but the focus has now shifted to groundbreaking innovations such as generative AI (GenAI), which generates text and images based on conversational prompts, and agentic AI, which is still in its infancy but is designed to autonomously execute complex processes with minimal human oversight. These advancements serve as a wake-up call: Many manufacturers are unprepared to capitalize on these new AI capabilities. To truly harness the power of AI, to enable the future of manufacturing, enterprises should first tackle critical foundational issues and embed these efforts within a comprehensive strategy aimed at optimizing structural costs and driving superior quality.

Standardized work is central to this mission: consistently performing tasks to eliminate variability. Like AI, standardized work is not new; it’s emerged in waves over the decades, gaining and losing prominence. Today many manufacturers produce the same products in different ways across various sites, especially after acquisitions. This inconsistency highlights the need for a unified approach, and consequently, standardized work is regaining importance, often linked to digital transformations and new production management systems.

Standardized work offers numerous benefits, including driving consistency, minimizing product defects, and aligning manufacturing sites around clear, repeatable processes that reduce reliance on tribal knowledge. Additionally, institutionalized work generates data sets with low variability, which are essential to AI, analytics and other technology investments. Standardization lays the groundwork for optimization, enabling processes that previously varied to become effective and ultimately more efficient.

Integrated systems as tools for unifying processes

Standardized work manifests in various forms across the manufacturing industry, with the primary goals of streamlining operations, enhancing communication, reducing rework and waste, improving employee engagement and satisfaction, and enabling real-time data analysis to identify bottlenecks and enhance performance. Importantly, a standardized approach does not imply a static method; rather, it incorporates mechanisms that promote the continuous evolution of capabilities, tools, methods and metrics to drive improvement throughout the manufacturing process.

One methodology that embodies these principles is Procter & Gamble’s integrated work system (IWS). The IWS fosters an “ownership mentality” and a zero-loss mindset among all employees, empowering them — not just front-line supervisors — to identify, highlight and resolve work process and equipment losses. This approach aims to significantly reduce stops, quality defects and safety incidents. Leaders play a crucial role in this system by supporting their teams in removing barriers and building the capabilities necessary to identify and eliminate losses. They focus on addressing inefficiencies at the interfaces among departments, suppliers and customers. These practices are then shared throughout the organization to sustain improvements and continuously elevate performance standards.

A great deal has been written about the impact of AI and GenAI on white-collar or “knowledge sector” jobs. However, these technologies also have the potential to fundamentally transform operations on manufacturing shop floors, unlocking new capabilities that were previously unimaginable — even for leaders who have been utilizing AI for the past decade. To maximize the impact of AI, manufacturers need to look at their data. Data derived from standardized work is crucial to enhancing AI capabilities, as demonstrated in various use cases, such as:

• Predictive maintenance: Manufacturers can equip machinery with internet-connected sensors to collect real-time data on performance, temperature, vibration and other critical parameters. AI algorithms then aggregate and analyze this data to identify patterns and trends that may signal potential failures, enabling manufacturers to schedule maintenance proactively and minimize downtime. Additionally, GenAI can be used to by workers to query and explore how various factors may contribute to wear and tear, as well as generate maintenance documentation, reports, and training materials based on these insights.
• Quality control: AI systems utilizing computer vision technology can automatically inspect products on the production line. These systems employ high-resolution cameras and advanced algorithms to detect defects, measure dimensions and ensure that products meet quality standards. AI can also analyze historical data to identify patterns and trends that may lead to defects, considering factors such as raw material quality, machine settings and environmental conditions, thus enabling proactive quality management. Furthermore, GenAI can help simulate different manufacturing scenarios to assess how changes in processes or materials impact product quality, providing valuable insights for decision-making.
• Supply chain optimization: AI can help enhance supply chain management from end to end. For instance, in demand planning, algorithms can analyze historical sales data, market trends and external factors (such as economic indicators) to improve accuracy and optimize inventory levels by predicting when stock will run low and automating reordering processes. AI can also evaluate supplier performance based on metrics such as delivery time and cost, recommending the best suppliers for materials or components, and performing similar evaluations for logistics. Additionally, GenAI can assist in product design by generating multiple alternatives based on specified parameters, helping to refine products for easier and more cost-effective manufacturing.

Building on layers: further integrating advanced technologies

AI driven automation can be understood in two tiers: The first involves assisted decision-making, while the second encompasses fully automated processes. Currently, AI tools guide operators on how to adjust production or quality parameters to improve processes. While this level of AI-driven automation is driving significant productivity and quality improvements, we’re moving toward a future where AI can make optimization decisions autonomously, leading to true closed-loop control. For instance, if the technology detects that the viscosity of a product is increasing, it can automatically respond by slowing down the fill rate during bottling or increasing pressure during mixing operations. We have already begun to see early implementations of such operations and expect to see increasing deployment of this technology as companies work to address cost and workforce pressures.

These advancements for the future of manufacturing are essential — the ultimate data-driven shop floor that harnesses today’s leading practices and technologies. In this highly automated, interconnected and flexible production environment, machines, systems and processes communicate and collaborate in real time. In addition to the technologies mentioned, physical automation (i.e., robotics) plays a crucial role by automating repetitive and labor-intensive tasks such as assembly, packaging, and material handling, all while maintaining high precision and consistency. Modern robotic systems can be reprogrammed or reconfigured, allowing manufacturers to adapt operations to changing production needs.

Furthermore, data from standardized work and Internet of Things (IoT) sensors on equipment enable the creation of digital twins — virtual replicas of a specific asset, your shop floor, a complete plant, an entire supply chain, or any other data-driven system or process. These digital twins simulate the real world, providing valuable insights into system behavior and performance, allowing for scenario planning and systemic process optimization. Ultimately, digital twins can enhance the ability to monitor, control and innovate in manufacturing environments, driving productivity and reducing operational risks.

As companies adopt new tools and technology platforms, they must consider the evolving role of the workforce, approaching it with humans at the center. In the manufacturing industry, labor is sometimes viewed as a cost to be managed. This mindset leads to change management being treated as an afterthought, even when significant transformations are planned.

The success of future multimillion-dollar digital transformations in manufacturing relies on having effective work processes, technology and, crucially, people. Advanced technologies and improved workflows will fall short of expectations if the workforce is not effectively engaged and aligned with these changes. Manufacturers that successfully recruit and retain high-performing employees who embrace change will gain a competitive advantage.

On a typical shop floor, employees often expend considerable effort just to understand what is happening and what tasks need to be completed. They frequently walk around to communicate with multiple colleagues and sift through extensive data and spreadsheets. This creates operational hurdles and results in wasted effort, making the process time consuming, inefficient and frustrating.

Workers deserve training and tools that allow them to access information quickly and easily, enabling them to turn data into meaningful actions that solve problems and enhance manufacturing performance. Manufacturers that create a frictionless experience for their production, quality control and maintenance personnel can gain a competitive advantage as the demand for skilled manufacturing workers continues to accelerate. Here are proven strategies to empower your workforce, allowing them to become differentiators for your organization:

• Modernize your tools: Automating data flows across processes and providing greater visibility simplifies the identification and elimination of losses while reducing repetitive tasks, leading to improved worker satisfaction. With advanced analytics and machine learning, employees gain more autonomy and capability in troubleshooting complex issues, uncovering root causes that might otherwise go unnoticed.
• Re-engineer your work processes: New technologies necessitate updated processes. For example, one EY client is transitioning from paper batch records to electronic documentation, eliminating 12 of 20 steps in the process. In some cases, technology is used to enhance and extend the value of robust existing processes.
• Upskill your employees: Many manufacturers still rely on traditional training methods, such as passive presentations or conference room sessions. A more effective approach is to implement guided workflows. For instance, a maintenance worker fixing a pump can watch a video of the process on a tablet, augmented with GenAI captions, rather than searching through a manual or tapping on a tablet to diagnose the issue. They can also receive real-time guidance from a more experienced technician remotely.
• Create meaningful career paths: Skilled employees who take pride in their work, consistently meet high quality targets, and embrace new tools and responsibilities are likely to seek opportunities elsewhere. This shift moves away from the old model of that rely on a stable of seasoned veterans in the same roles year after year. To retain top talent, organizations should build clear career paths that provide opportunities for all workers to develop capabilities, demonstrate capabilities and progress their careers in the company.

This final chapter dives into the heart of the matter in the manufacturing industry, offering bold strategies that can transform uncertainty into resilience. Imagine a manufacturing ecosystem where agility reigns supreme — where production networks flex seamlessly between onshore, offshore and nearshore operations in response to market demands. Picture real-time data systems that illuminate every corner of the shop floor, empowering teams to make informed decisions on the fly. Envision a future of manufacturing where decision intelligence not only anticipates disruptions but actively drives proactive solutions.

• Activate real-time data integration: Equip your operations with cutting-edge IoT sensors and advanced analytics that deliver instant insights into every facet of your production process. This technology empowers you to identify bottlenecks before they escalate into significant issues; optimize maintenance schedules using predictive tools; and make informed decisions swiftly when disruptions occur. Real-time data is not just an advantage; it’s your lifeline to operational excellence, enabling you to enhance productivity, reduce downtime and maintain a competitive edge in a fast-paced manufacturing environment.
• Revamp your network operating models: Transform your production networks into agile powerhouses that can adapt on the fly to changing circumstances. Develop a comprehensive plan that allows for seamless transitions between onshore, offshore and nearshore operations and between in-house production and contract manufacturing, enabling you to effectively tackle geopolitical disruptions and fluctuations in demand. Flexibility should be your competitive edge; this means proactively understanding the impacts of tariffs before they are fully implemented and ensuring that your site-level models align with this dynamic approach.
• Implement a comprehensive workforce empowerment strategy: A humans-at-the-center approach should include modernizing tools, re-engineering work processes, upskilling employees and creating meaningful career paths. Start by automating data flows and enhancing visibility to streamline operations and reduce repetitive tasks. Transition from traditional training methods to guided workflows that provide real-time hands-on support. Finally, establish clear career progression paths to retain your top talent and foster a culture of continuous improvement and engagement among your workforce.

Summary

In a world where uncertainty is the new norm, the manufacturing industry stands at a pivotal moment that demands bold action and innovative thinking. The challenges posed by geopolitical shifts, technological advancements and evolving workforce dynamics require a proactive approach to resilience. By reimagining operating models, implementing real-time data systems and leveraging decision intelligence, manufacturers can transform disruption into opportunity. Embracing these strategies not only equips the manufacturing industry to navigate the complexities of today’s landscape but also positions it for sustained success. The time to act is now — will manufacturers rise to the challenge?