From Apps to Infrastructure
Rethinking Manufacturing SaaS
The potential for AI in manufacturing is undeniable, evident by the explosion of SaaS startups entering the space. Over 350 companies have now progressed to at least Series A funding, each aiming to disrupt incumbents such as Dassault Systèmes and Siemens, as well as relative new kids Atlassian (hello JIRA 😊).
Recently, I started to question the proliferation of these platforms, starting with how much does all this cost?
It feels like manufacturers are being offered a plethora of discrete solutions ranging from AI-powered CAD and PLM tools to MES platforms, EHS compliance, and beyond. Often leaving the customer to work out how to integrate everything together.
It’s the equivalent of me subscribing to a Notion, Todoist, ChatGPT and twenty more productivity apps, each with a different promise, with a vague hope they’ll somehow hang together to lift every part of my life.
🛠️ Finding Manufacturing Customers is Hard
Selling to manufacturing is a very different game compared to design, finance, or tech firms. Operationally, manufacturers question every cent, and SaaS subscriptions need to at least ‘wash their face’ demonstrating a tangible benefit to their customer, ideally in terms of dollars saved per part.
Manufacturers are like frugal consumers, scrutinizing every line of their energy bill, in contrast to the tech sector, eager to blow their budget to get their hands on every shiny new release. This is especially true in contract manufacturing or among tier-one and tier-two suppliers, where margins are razor thin and volume is king.
Nowhere is this truer than in China, where competition is fierce and major customers like BYD and Huawei have clear visibility into material and labor costs, enabling them to negotiate aggressive terms.
Contract manufacturers, acutely aware of this cost vs. value equation, build their own software platforms tailored to their needs, skipping the license fees altogether, protecting their razor thin margins.
If SaaS providers want to win in manufacturing, they must think beyond selling isolated features that can be developed in-house. Instead, they must embed themselves deeply into workflows, capital projects, and evolving the way manufacturers design, build, and scale their operations.
So, what are the key considerations that manufacturing SaaS firms need to cognizant of when finding their value proposition?
📐 Integrate Early
The upstream phases of product development are where designers and engineers focus on embedding value into the product. Here, companies are more open to adopting specialized tools, if the value proposition is clear.
Key manufacturing decisions around tolerances, materials, and process flows are made in these upstream phases of product development.
If SaaS providers can help define the process parameters for a manufacturing digital twin, one that carries seamlessly into production with minimal on-cost, they can dramatically lower the barrier to adoption.
Another strategy is to work with equipment vendors, the companies that ‘make the machines that make the machine’, to embed digital tools as part of the production line's initial capex purchase, rather than trying to retrofit software after production begins.
🧠 Help Customers be AI-Ready
Many SaaS firms are leveraging the benefits of AI for tasks like defect detection, optimizing asset utilization, or generate process instructions. But even when these tools are integrated early via simulations, they often diverge from reality once production begins.
To provide manufacturers with the opportunity to course correct quickly, data infrastructure must scale in line with production. This will allow information from closed loop feedback and AI to be leveraged, updating simulation assumptions and improving process parameters.
Yet many firms experience a data 'cold start' problem where they haven’t defined sensor placements for in-process measurements, data standards, or storage systems needed to support real-time learning.
Without forethought during the design-for-manufacturing phase, critical systems like traceability, in-line metrology, and communication protocols are often left undefined or poorly aligned with actual decision-making needs.
To overcome this, SaaS providers must help firms treat data system architecture as a design deliverable, not an afterthought. Traceability, in-process metrics, and communication protocols must be defined early and refined through prototyping and early build phases.
📊 Build Full Workflows that Capture and Amplify Learning
Working with manufacturers early-on to define data collection, reporting metrics and in-process closed loop control requirements, will help SaaS firms to build complete workflows rather than discrete apps.
To optimize the entire production line, it is important to consider manufacturing problems are rarely isolated. In complex products, contributors to a defect can be spaced hours or even days apart in the process.
Specialized apps can silo this data, preventing the system from learning as a complete entity. Just like my productivity apps, my sleep schedule may be optimized without consideration of my work or exercise regime.
Being able to capture all the learning from the subtle adjustments that are made to solve issues and continually improve the product, will allow manufacturing knowledge to accumulate and strengthen over time.
The genesis of these micro-innovations often begins through offline analysis, Slack threads, and production line trials. But in so doing, create a shadow knowledge that is never fully integrated into the manufacturing data architecture.
Being able to capture this data to build large language models (LLMs) trained on real operational history, will help teams query past fixes and avoid solving the same problem twice.
If we think of the future of manufacturing, a few highly skilled individuals will supervise intelligent systems. Giving them tools, such as LLMs, to interact with those systems through natural language to troubleshoot fluctuations and refine operations will be a critical enabler of a smarter platform.
🤖 Build for a Robotic Future
Production lines today are already highly automated, with robots executing precise sequences of welding, bolting, and riveting, adjusting dynamically to sensor feedback.
The introduction of humanoid robots on the shop floor will open-up the number of operations that can be automated, such as assembly operations with limited access, or using flimsy parts.
Initially, they’ll behave like today’s robots, trained to do repeated task, using integrated vision sensors to adjust for process variability, to ensure the operation is completed right first time.
But how will they interact with humans? Being AI driven, they will use natural language rather than being programmed through a terminal.
Also, in time will they be isolated performing one operation?
The inevitable push will be for these robots to be connected to the factory floor, taking in data from upstream and downstream operations. Correlating what they are sensing in their own process to communicate issues and fixes up and down the production line.
Eventually, they may be flexible enough to move out of station, to investigate problems for themselves acquiring more data by performing multiple different operations to seek out the cause for anomalies.
I have presented my vision of the future production line. Manufacturing SaaS companies will have to gaze into the future to understand themselves what it will look like and adapt their software tools to integrate with this robotic future.
🌐 Conclusion: From Tools to Infrastructure
The future of manufacturing will be powered by intelligence embedded into the infrastructure of how we design, build, and iterate. For SaaS firms, this means moving beyond individual apps and dashboards and toward platforms that help manufacturers learn, adapt, and improve, continuously and autonomously.
This is creating a new type of manufacturing intelligence, one where knowledge compounds over time and naturally flows into production optimization, design iteration and the specification of future products.
Manufacturing data will continue to evolve towards a robotic future, where a few skilled individuals collaborate with AI-driven automation through natural language. Guiding and reconfiguring flexible production lines in real time as part of an intelligent, adaptive team.
Can't agree more : Selling into CM and Tier 1 and Tier 2 is a different game than selling into digital first infrastructure.