AI & Manufacturing: Productivity, Efficiency & Innovation

Venture Capital Enabling Convergence

The manufacturing industry is experiencing  an extraordinary transformation, driven by the convergence of advanced technologies and the integration of artificial intelligence (AI) into production processes, supercharged by venture capital investments. This article takes a deep dive into the exploding realm of intelligent manufacturing platforms, where cutting-edge technologies and AI capabilities merge to revolutionize the industry. By exploring the fusion of technology, the profound impact of AI, and the seismic shifts brought about by these new platforms, we uncover the potential for unparalleled levels of productivity, efficiency, and innovation in manufacturing.

When the documentary film American Factory was released in 2019, it provided a glimpse into life on a manufacturing production floor in the 21st century. The story chronicles a Chinese company’s takeover of a manufacturing plant in Ohio. Amidst the inevitable culture clash between Chinese and American workers, familiar narratives about global manufacturing emerge. The relentless competitiveness of global markets and the pressure to maximize profits is laid bare. Yet just a few years later, as we sit on the precipice of radical changes in the manufacturing sector, the film’s narrative arc seems to be missing the point. The human characters make for good drama, but will humans continue to occupy the central role in manufacturing as we approach 2030?

From the perspective of venture capital, the answer is a resounding “no.” That is not to say human manufacturing workers will be relegated to the dustbin of history anytime soon. Far from it. But the clear investment trends point to a sustained focus on artificial intelligence (AI) capabilities that will augment (though not replace) human labor. 

Data show that artificial intelligence is converging with other emerging manufacturing technologies like robots and 3-D printing, triggering a new wave of intelligent manufacturing systems. AI serves as a pivotal enabling software that will help usher in a new era where manufacturing becomes faster, more agile, and tailored to the unique needs of customers. The consequences for companies, workers, and the global economy will be far-reaching as new models and production methods disrupt supply chains, redefine product availability, and reshape the very notion of competitive advantage within the industry. The most successful firms will develop proprietary models leveraging high-quality data sets, unlocking operational insights and efficiencies that were previously unimaginable.

In this article, we will explore promising AI manufacturing technologies and their impact on economic trends. Forward-thinking investors and entrepreneurs already understand how AI can be used to transform manufacturing operations – and not just on the factory floor. The power of AI will be harnessed for the plant control center and the corporate boardroom as well. 

Toward Intelligent Manufacturing Platforms

The bedrock principles of manufacturing have remained remarkably consistent over time. Design the best product, produce it with as much efficiency as possible, and ensure it meets the quality specifications of the end user. A Bronze Age artisan and a modern automotive assembly line worker could both agree on the basic formula. But as manufacturing technology developed, humans created new terms to better reflect the increasing complexity of production tasks. Throughput, total quality control, and agile design all had their time in the sun. Now, advances in AI are bringing intelligent manufacturing to life and radically altering the industry landscape.

The traditional manufacturing landscape is undergoing a paradigm shift, propelled by the development of intelligent manufacturing platforms. These platforms bring together cutting-edge technologies and AI capabilities, revolutionizing how products are conceptualized, designed, and produced. By leveraging AI, manufacturers can streamline operations, optimize processes, and achieve unprecedented levels of productivity and efficiency. Intelligent manufacturing platforms offer a holistic approach to manufacturing, transforming every aspect from supply chain management to quality control, and even customer interaction.

Like other forms of narrow AI, intelligent manufacturing models harness vast amounts of data and computing power to automate tasks and increase productivity. AI models are already helping companies glean fresh insights into the full suite of manufacturing operations, including production processes, procurement decisions, and product design. Intelligent manufacturing platforms will take these capabilities a step further. Instead of simply curating data and making recommendations, the manufacturing AI models of the future will be able to execute actions and fine-tune production without human intervention. Let’s look at three specific areas where intelligent manufacturing is disrupting the status quo and making its largest impact.  

For starters, intelligent manufacturing will be able to make much more precise quality control and safety assessments. One of the major challenges in manufacturing today revolves around assessing product quality and monitoring output. Systematic production approaches such as kanban, developed by Toyota to facilitate just-in-time manufacturing, are geared around solving this challenge. The trouble is that these systems largely rely on humans. Computers and remote monitoring systems aid in the process, but lots of human labor is still required. Before improvements in data collection and analysis, humans were the best tool available. AI advancements are changing that.

Consider LandingAI, ($51M Series A 2021) a computer vision AI model that aids manufacturing employees with visual inspection. The software can detect defects that are not visible to the human eye. Similarly, Surveily (£1.4M Seed 2022) is an AI tool that aids humans in monitoring safety on the manufacturing floor. 

Another example is Acerta Analytics ($7.8M Series B 2022), a company that helps turn automotive plant production data into actionable insights. One product, LinePulse, helps manufacturing professionals diagnose the root causes behind production anomalies. As part of their offering, Acerta also provides customers with access to an extensive, proprietary database of automotive products, processes, and failure modes. The firm claims that their domain expertise “gives our data scientists a head start on any data set.” Companies with specific domain expertise and data insights are sure to emerge in other manufacturing verticals as well. 

Second, AI innovations will shorten the time it takes to design and build a product. Current manufacturing operations are marked by high capital expenditures and significant fixed costs. Furthermore, pivoting to produce a different product is costly and time-consuming. By contrast, AI entrepreneurs are working to enable more modular approaches to manufacturing, where product designs and prototyping can be expedited. If these technologies come to fruition, manufacturers will be able to employ modular designs and respond to changing market conditions quickly.  

Imagine how valuable such capabilities could have been during the outbreak of COVID-19 when the manufacturing sector experienced a sharp increase in demand for medical supplies. It took many plants weeks to transition their facilities to start producing hand sanitizer, respiratory masks, and other in-demand products. Machina’s robotic AI craftsmen could likely have executed the transition much quicker.  

Machina’s capabilities enable rapid iteration and production in days, compared to months and even years with conventional processes. Similarly, AI Build’s ($5M Seed 2022) software-as-a-service platform for 3D printing design and production could have played a constructive role. Both Machina and AI Build are focused on delivering rapid increases to the speed of prototyping, designing, and building a range of industrial products. Quicker innovation cycles lead to more rapid improvements and increased responsiveness to customer needs. Most manufacturers today would struggle to pivot production quickly. AI entrepreneurs envision a different world.

Third, AI will help manufacturers optimize their use of inputs. Natural resources required for manufacturing operations are a major driver of financial and environmental costs for firms. AI upstarts are applying advanced analytics techniques to identify and capitalize on potential production efficiencies. 

Carbon RE (£5M Seed 2022)focuses on fuel use and carbon emissions. Carbon RE’s cloud-based service, called Delta Zero, can be applied to production environments in energy-intensive industries. The company hauled in $4.5 million in seed financing to further scale their proprietary diagnostic platform. Meanwhile, Sortera Alloys ($23M 2023) is looking to conquer the massive scrap metal market. Sortera’s AI is trained to identify and sort various types of metal that can then be reclaimed and reused for industrial purposes. Current scrap metal recycling relies on exporting waste to a foreign country, hand sorting, and then re-importing. By sorting scrap metal on the spot using advanced cameras and AI models, Sortera can offer customers both environmental and cost benefits through their technology. 

Finally, the last category of AI start-ups in manufacturing aim to build intelligent manufacturing platforms that integrate disparate capabilities into a comprehensive solution. Think of these players as building something like an airplane auto-pilot system for the factory floor (and inventory warehouse). Instead of training their AI models for one specific task or function, these intelligent manufacturing start-ups are building software that can connect data and variables across functions. They can also integrate external data from markets or customers. Taken together, these service providers are using AI to break down information silos and provide manufacturing firms with incredible insight across a range of variables. 

SmartMore ($200M Series B 2021), BrightMachines ($311M A,B & debt 2022), and BSG Technology (CNY100M Series A 2022)are the emerging leaders in the space. Their software runs the gamut of manufacturing operations, from helping companies predict demand, keep up with plant maintenance, and plan production runs. Real-time data is constantly integrated into the model to facilitate continuous improvement for production decisions. 

One key differentiator of these technologies compared to current plant operations equipment is the ability to easily customize and adjust production volumes. For example, BrightMachine’s “Microfactory” for modular manufacturing allows simple customization, assembly, and scaling of a wide array of products.  

These intelligent manufacturing system providers go far beyond using data analytics to improve individual functions, a type of AI model already widely in use. This AI software is running models with hundreds of data points, while calibrating and learning in near real-time. Furthermore, many of these general providers have the added benefit of drawing on production data across several industry verticals. As a result, their computing platforms generate even more insights and applied learnings.


Technology convergence and AI in manufacturing

The adoption of AI in manufacturing has become a critical driver of the industry’s transformation. As AI algorithms become increasingly sophisticated, they can analyze vast amounts of data in real time, enabling manufacturers to make data-driven decisions and optimize their production processes. AI-powered systems can autonomously monitor equipment performance, predict maintenance needs, optimize energy consumption, and even detect anomalies or quality issues on the production line. These capabilities not only improve operational efficiency but also reduce costs, minimize downtime, and enhance overall product quality.

Furthermore, AI enables manufacturers to leverage predictive analytics and machine learning to optimize supply chain management. By analyzing historical data, AI algorithms can forecast demand, optimize inventory levels, and streamline logistics, leading to more efficient and responsive supply chains. This proactive approach to supply chain management helps manufacturers reduce waste, lower inventory carrying costs, and improve customer satisfaction through timely deliveries.

Also, the combination of niche capabilities, general intelligent manufacturing systems providers, and other advances in manufacturing technology underscores an important aspect of technology development. The real power of AI is not what it can do in isolation; it is what it can do when paired with other advances. The co-development of AI with other emerging manufacturing technologies, like robotics and additive manufacturing, is what will ultimately create a new manufacturing approach that is differentiated from the past. AI capabilities alone could not have the same level of impact. 

Previous revolutions in the manufacturing sector highlight a similar phenomenon. Henry Ford’s implementation of the assembly line in the early 1900s is often cited as launching the era of mass production. But the assembly line concept does not deserve sole credit for the advance in American manufacturing productivity. The introduction of electricity into American factories just a few years before Henry Ford organized his factory floor was pivotal.

Electricity helped power the automated assembly lines that made Ford cars a household name. Furthermore, much like the AI models being built today, the electrification of manufacturing plants freed up resources for other, higher value-added tasks. Workers that were previously devoted to firing the energy source at factories could suddenly devote their time elsewhere. 

The convergence of electricity and the assembly line is what truly unleashed a manufacturing revolution. That system laid the foundation for standardized, mass production. Average product throughput increased exponentially, driving down the cost of manufactured goods and increasing prosperity.

The AI-enabled manufacturing platform, which is converging with advances in robotics and additive manufacturing, is setting the stage for disruption of a similar magnitude. Instead of focusing on standardization, the success of future manufacturing will be built around customization. AI will power much more nimble and adaptable manufacturing approaches. The promise of modular manufacturing is that products can be rapidly tailored to the unique needs of different customers, whether in B2B or B2C markets.

Early evidence of the power of convergent manufacturing technologies can be found in Tesla’s gigafactories. The gigafactory approach is hailed for integrating robots and AI in an innovative fashion. The innovative production method is one of the reasons Tesla found initial success in breaking into the highly competitive personal automobile market. But today’s gigafactory is only scratching the surface of what the convergence in manufacturing will unleash. By the time some of the AI and manufacturing venture bets fully mature, Tesla’s gigafactory could seem like an Atari 2600. 

Tectonic shifts on account of the new platform

The emergence of intelligent manufacturing platforms has triggered tectonic shifts across the manufacturing landscape. Traditional linear production models are giving way to agile, flexible, and highly adaptive manufacturing processes. These platforms enable manufacturers to quickly respond to changing market demands, customize products on demand, and accelerate time-to-market. The seamless integration of AI with other technologies allows for improved connectivity, collaboration, and synchronization across the entire manufacturing ecosystem, from suppliers to customers.

Additionally, intelligent manufacturing platforms are fostering collaboration between humans and machines. While AI technologies automate routine tasks and enhance operational efficiency, human workers can focus on more complex and creative aspects of production. This collaboration leads to a highly efficient and productive workforce, where humans and AI work synergistically to achieve optimal results.

The development of a new manufacturing platform portends major shifts in the global economy. In the United States alone, manufacturing contributes over $2 trillion to the gross domestic product and employs nearly 15 million people. Disruption to an industry of that scale changes more than just economics – it changes politics and society as well. Here are a few trends to look out for.

First, software and data will increasingly become competitive advantages for the manufacturers that can successfully harness AI. Differentiation in manufacturing used to come from capital expenditure or investments in research and product development. In the era of intelligent manufacturing, software and data will be added to the list. Investments in digital technology provide another outlet for manufacturers looking to up their game. Venture capitalist Marc Andreesen’s comment that software is eating the world looks more and more prescient. 

When CapEx and R&D are not the only games in town, a whole new universe of business decision-making opens up for manufacturing executives. Relying on third-party IT professionals and outsourcing digital initiatives may no longer seem wise. Here again, it is worth mentioning Tesla, as the firm was able to navigate pandemic-induced shortages in semiconductors better than industry peers due to in-house expertise and ownership of firm software. Maintaining access to high-quality data that can be used to train AI models will also be essential, since AI models are only as good as the data they are trained on. This reality will add yet another dynamic to the IT needs of manufacturers large and small.  

Second, the full integration of AI and other emerging technologies into manufacturing will change the relationship between firms and their employees. Advances in automation will help firms navigate tight labor markets and reduce the impact of turnover on operations. With AI models and their robotic cousins taking responsibility for more aspects of manufacturing, expectations for employees will also change. Less need for routine monitoring and repetitive tasks will leave more time for creativity, innovation, and strategy development. The employees who set themselves apart will be critical thinkers who ask the right questions, focus on the right metrics, and continuously add value through innovation. 

Manufacturing workforce development is already anticipating these trends. Established industrial players like BASF are transitioning from Science, Technology, Engineering, and Math (STEM) education to STEAM – an acronym that includes Arts. The switch from STEM to STEAM reflects an understanding that the purely technical aspects of manufacturing will largely be outsourced to machines in the future. Humans will add value by focusing on their unique differentiation, which includes the ability to reason broadly and connect disparate domains of knowledge.  

Third, the rise of modular, intelligent manufacturing platforms will reshape global supply chains and distribute capacity more evenly throughout the globe. In the wake of COVID-19, the world’s dependence on China’s manufacturing capacity was made painfully clear. The status quo is no longer palatable for political and business leaders, but unwinding decades of investment is no easy feat. 

Despite large US government subsidies (Chips & Science Act, Inflation Reduction Act) and an overall desire to “de-risk” China, a massive amount of “reshoring” of manufacturing on American soil does not appear to be taking place currently. However, manufacturing firms have demonstrated an eagerness to pivot away from an over-reliance on China. Vietnam, India, and other markets are the current beneficiaries of this change. Over the long term, AI-powered companies will accelerate this trend. 

Deeper integration of AI capabilities into supply chain management will make it even easier for manufacturers to navigate global complexity and set up resilient supply networks. AI start-ups Geomiq and Flowlity attracted venture investments of £8.5 million and $5 million to help manufacturing customers do exactly that. Moreover, modular manufacturing capabilities will reduce reliance on low-cost labor and change incentives for site location. Indeed, BrightMachines makes an explicit link to this trend in their marketing materials, highlighting how their automation solutions enable firms to “create a more distributed – and better balanced – factory network.”

For their part, Chinese economic leaders are investing heavily in AI capabilities to ensure they can remain at the forefront of global manufacturing. Several of the companies profiled in this article hail from Mainland China, as do dozens of start-up AI firms attracting impressive valuations. Chinese manufacturers have every intention of using their market position and deep manufacturing ecosystem to develop the compelling AI value propositions.

Fourth, the rise of intelligent manufacturing platforms will enable the creation of smart factories where machines, equipment, and systems are interconnected and communicate seamlessly. This connectivity, facilitated by the Internet of Things (IoT), allows for real-time monitoring, data collection, and analysis, leading to enhanced efficiency, predictive maintenance, and improved overall productivity. The proliferation of IoT devices in the manufacturing sector will also raise concerns about data security, privacy, and the need for robust cybersecurity measures.

Fifth, the integration of advanced technologies will drastically change the nature of work for human employees. As intelligent machines take on repetitive and mundane tasks, the nature of work for human employees undergoes a fundamental transformation. While some jobs may become obsolete, new roles will emerge, emphasizing skills such as programming, data analysis, and human-machine collaboration. The challenge lies in upskilling and reskilling the workforce to ensure a smooth transition and to capitalize on the opportunities offered by these emerging technologies.

These trends represent just a glimpse of the transformative shifts underway in the manufacturing industry. As intelligent manufacturing platforms continue to evolve and mature, their impact will extend beyond economic boundaries, reshaping politics, society, and the very fabric of how we produce and consume goods. Embracing these trends and proactively adapting to the changing landscape will be vital for manufacturers to thrive in the era of intelligent manufacturing.

Future Forward

The advent of intelligent manufacturing platforms powered by AI represents a significant leap forward for the manufacturing industry. As technology convergence continues to accelerate and AI capabilities evolve, manufacturers can unlock unprecedented levels of productivity, efficiency, and innovation. The integration of AI into manufacturing processes empowers companies to make data-driven decisions, optimize operations, and adapt swiftly to changing market dynamics. By embracing intelligent manufacturing platforms, businesses can position themselves at the forefront of the industry, ready to thrive in an era characterized by seamless connectivity, rapid innovation, and unparalleled customer satisfaction.

The assembly line of today is giving way to a new model of manufacturing. The pieces are not fully in place, but venture capital investment highlights the key contours of how it will take shape. Modular manufacturing and intelligent systems will create a sea change in how and where manufacturing work is done. In addition, it will change the desired skill sets for workers moving forward. The firms that can successfully integrate AI capabilities with other advancements in manufacturing technology will be poised for long-term success. 

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