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IoT and Digital Engineering
Bringing Together IoT & Digital Engineering to Create Rich Experiences
Highlights
- The factory of the future is an integrated and connected one, with a responsive system of sensors, feelers, deciders, responders, and analytical actors.
- To get there, manufacturers would need to embrace widespread transformation that would also open powerful new business growth opportunities.
- We explore the possibilities with three steps for businesses to get started on creating greater value with a 'neural fabric'.
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Connected vision
The promise of the digital factory—an interconnected, resilient, and hyper-responsive manufacturing center—has tantalized many industrial organizations.
It has driven companies worldwide to invest heavily in AI, cloud, robotics, haptics, the internet of things (IoT), and digitalization. Such technologies could constitute a neural fabric that guides managers from design through production and even into service-based models of finished goods. But achieving this vision has proven elusive. While digital investments have had positive impacts, they have not produced the transformation many had hoped for or expected.
Even after investing in advanced technologies, manufacturers routinely struggle to respond to supply chain crises or rapid shifts in consumer demand. Organizations have heterogeneous and often mismatched legacy information technology and manufacturing systems that they have built or acquired over the years. Many digital investments remain siloed in innovation-focused teams or stuck in what’s known as ’pilot purgatory’—lacking the scale and integration necessary to deliver meaningful or systemic results. And even when technologies show promise, the nature of corporate governance and decision rights in manufacturing organizations means that individual factories can—and do—resist the full adoption of these technologies.
Losses due to unplanned downtime, assembly changeovers, and supply chain shortages continue to hinder organizations. There is still very little visibility across the entire value chain of a manufacturing organization. This leaves suppliers, customers, field services, and others unable to exchange critical information that could drive better productivity, more responsive production, and more profitable outcomes.
The above problems can be addressed by a digitalization strategy that embraces what we call ‘neural manufacturing’—where the manufacturing function resembles a highly integrated, connected, sensitive, and responsive system of sensors, feelers, deciders, responders, actuators, and analytical actors. This is not merely about automation or more investments. It’s about visioning a fundamentally new approach to reimagine digital integration in manufacturing and drive an autonomous self-aware ecosystem.
Neural manufacturing isn’t merely automation—it’s a fundamentally new approach to driving a self-aware ecosystem.
But this end state is not easy to achieve. It requires a fresh approach to driving strategy, key milestones and project timelines, determining decision rights and governance, and finding the right external partners. In short, the current approach to building the interconnected network of digital factories of the future will require its own type of transformation.
Context
Within manufacturing, IT leaders struggle to have a strong voice at the strategy table, leading to IT-led transformation priorities being overlooked.
Innovation teams have often struggled to gain prominence within manufacturing organizations, with some exceptions. The result? Good ideas linger due to a lack of scalability or a practical plan for wider adoption. But technology could play a far more robust role in addressing strategic challenges. A neural fabric properly built, connected, and scaled could transform existing manufacturers, and even open up new business growth opportunities.
For example, a manufacturer, by embedding IoT, could create a service-based model for its products so they produce revenue over their lifespans, rather than only at the point of sale. This is facilitated by connected world of ‘as designed’ to ‘as manufactured’ and ‘as in use’ further enhanced by services defined by ‘feature on demand’ capabilities. Better data visibility and connected digital assets could help manufacturers produce reliable insights into supply chains, thus helping meet customers’ environmental, social, and governance (ESG) related goals. Haptics, extended reality, and assistive robotics could help manufacturers retrain their newest workers to be as productive as the ones about to retire. These outcomes should alert a broad array of stakeholders to the opportunities on the table and move them forward in three major stages:
Figure 1 - An infographic depicting the three horizons manufacturers need to achieve to realize a digital factory.
A Microsoft study showed that roughly two-thirds of companies are still in the early stages of this journey. Less than 30% of the customers have scaled pilots in use. Most companies, if they want to achieve the vision of a digital factory, will need to build a value-based roadmap, informed by a full ecosystem of partners and stakeholders.
Value-based roadmap
Building a roadmap for futuristic digitalization need not involve wholesale scrapping of existing technology-based investments or abandoning prior technology priorities.
Many existing technologies exist in pockets, often at different maturity levels and with varying degrees of integration in an organization. For example, while many manufacturers have implemented automation and robotics, other foundational digital investments remain undercapitalized. Therefore, the first step is to create what we call a value-based roadmap, where the goal is to create a foundation of connected operational technology assets, enterprise systems, and processes with the future in mind. Only then are use cases for a neural fabric possible.
For example, with IoT, a digital factory would be able to foresee supply constraints and disruptions before they affect production, and monitor production quality after products exit the factory floor. Another example: A mature data system will help the enterprise create a digital twin for its operations, allowing it to test improvements and enhancements before moving to full deployment. This process is conducted in three critical and sequential steps:
Figure 2 - An infographic presenting a value-based roadmap to build the factory of the future.
1. Conduct rigorous business value discovery and execution.
The organization should know its current technological state and future business needs. This requires consensus from a broad range of players, all of whom are aware of potential hurdles. For example, some organizations find that technologies deployed for one purpose in one factory cannot, for technical reasons or internal resistance, be adapted to all factories or for future technologies. This kind of outcome should be avoided.
Organizations need to focus on developing what we call a strong digital spine for future neural manufacturing applications. The goal should be a manufacturing enterprise that can feel, think, and act far earlier than is currently customary. To give a common example, manufacturers should aim to replace or at least augment current KPIs such as overall equipment effectiveness, quality, and wastage with measures that reflect new information from a neural fabric. With real-time, low latency connected systems and cognitive capabilities enabled by cloud, data, and AI, organizations could see problems as they emerge, and sometimes before, allowing them to solve the problems faster or prevent them altogether. Aligning the right use cases and defining its success criteria early is critical.
Also vital at this point is the establishment of clear roles and responsibilities among executive and operational leaders. With strong executive sponsorship, active involvement of operational leaders, and inputs from internal and external technology advocates, the organization can readily implement an integration plan and make necessary investments and course corrections along the way.
2. Demonstrate early success and replicate for value optimization.
Ideally, organizations should choose the right use cases to showcase early successes with minimum viable product (MVP), gain support from stakeholders, and address bottlenecks in processes, people, culture, and systems. These can build confidence and lead to further support within an organization, for instance, in scaling these technology wins to solve multiple problems. They should scale out to multiple sites and expand their capabilities across the enterprise.
The key is to build towards bigger wins. Discrete, straightforward challenges may be easily met, but they also yield small solutions with limited applications. Our recommendation: Don’t settle for solving small or narrow problems. Aim to solve multiple problems, with increasing complexity and scale.
3. Maximize value with the right partner ecosystem.
No one can weave a neural fabric on their own. Manufacturers will need to partner with providers and advisors who can deliver and scale up the technologies essential to neural manufacturing. Additionally, manufacturers should seek out providers and advisors who have a deep understanding of the complexities and differences of various manufacturing sub-categories and customer types.
Ultimately, enterprises should select a partner who can not only solve a specific technological challenge, but also has the vision to deploy technology in an entirely new and transformational way.
Real results
Here's a look at how some of the world's leading companies are creating greater business value with neural fabric.
Tata Chemicals Limited (TCL), the world’s third-largest producer of soda ash and the sixth-largest producer of sodium bicarbonate, built and deployed a neural fabric. It comprised 32,000 sensors implanted in TCL's plants and allowed for the creation of a digital twin of its operations. We worked with TCL to implement various efficiencies and reduce operational costs that mitigated pricing pressure felt by the chemical major.
Johnson & Johnson, one of the world’s biggest healthcare companies, created a digital twin of three of its autoclave systems to help it predict the need for maintenance. Autoclaves are an essential part of biotechnology production systems in the treatment of various diseases. A failure in one of their many parts and any related downtime to repair constitute a major risk. The digital twin, using several years of data on autoclave operations, subsequently allowed the company to predict maintenance needs and set schedules efficiently. This resulted in major improvements in capacity.
Mercedes-Benz, one of the world’s largest automakers, has steadily turned to digitalization to create a far more seamless and high-quality production cycle. The company has moved to multiple interactive digital platforms for design, production, and ongoing relationships with customers. These efforts constitute a neural fabric that can inform production at every stage and lead to significant efficiencies. The company was able to design, develop, and build the concept IAA (intelligent aerodynamic automobile) entirely digitally.
Need for agility
Agile factories that can adapt quickly to current trends are essential for achieving neural manufacturing.
Integral to such factories are highly interconnected and sensing technologies that allow them to achieve higher production, lower costs, increased workforce productivity, and market leadership positions. To make agile factories a reality, innovative solutions are needed to improve visibility across ecosystems, make autonomous decisions, and enhance enterprise efficiency through cognitive and AI-powered operations.
Enterprises that embrace technological changes will stay ahead in this fast-changing world. By following a value-driven approach, securing executive buy-in, building the right partner ecosystem, and implementing a scalable platform, enterprises can achieve their digital factory goals. It's time to think big, start small, and learn fast to stay relevant.
