How to build AI-driven Manufacturing - and where to start
Manufacturing is the backbone of our economy. If we dive deep into the workflows we see the potential to impact not only this industry but also everything else we are doing.
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Manufacturing, at its core, is about turning "stuff" into "better stuff." It's a part of our economy that's hard to miss, underpinning most other sectors and keeping the wheels of commerce steadily turning. Even the slightest shift in manufacturing has significant ripple effects on the global economy.
Imagine manufacturing as a giant puzzle, where each piece represents a different process – from sourcing raw materials, running production lines, to managing logistics and distribution. Each piece is crucial and getting them all to fit perfectly is no small feat.
In this context, the role of AI is akin to a master puzzle solver. It's not about reinventing the puzzle or introducing flashy, brand-new pieces. Instead, AI's magic lies in making each existing piece smarter, more efficient, and more interconnected. It's about ensuring that the supply chain is more responsive, the production lines more intelligent, and the distribution networks more agile.
AI in manufacturing is like a series of small yet powerful interventions that collectively transform the entire landscape, from enhancing precision in production lines, predicting maintenance needs before they become problems, or optimizing supply chains in real time. Each of these advancements might seem incremental in isolation, but together they redefine what's possible.
“As AI continues to make its mark, we're witnessing a quiet yet profound revolution, not by turning the big wheel, but by transforming all the many different cogs until the whole system is unrecognizably better.” Silviu Homoceanu told me. He, and Max Fischer together founded Deltia, a venture that is using Computer Vision and Edge AI to optimize assembly line processes. They both helped me deepen my understanding of manufacturing and how AI can make a difference.
Supply chain management
Let's talk about supply chain management, a crucial piece in the manufacturing puzzle. Picture this: you've got a product you want to bring to life. The first step is figuring out how to gather all the necessary components – the "stuff" to build your product. This is where the supply chain comes into play, and it's a bit like walking a tightrope in the business world.
If your supply chain is weighed down with unnecessary costs, when you scale up your business, those costs scale up as well, and that's not good for anyone. Stability is another key factor. If your supply chain wobbles and you have to slow down or, worse, halt production, you're looking at some serious losses. That's a scenario you want to avoid at all costs.
So how do we keep things lean and stable? Globalization has been the name of the game for supply chains in recent decades. We're talking about sourcing and procuring materials on a worldwide scale. To keep a handle on all this, we use Enterprise Resource Planning (ERP) tools. These tools help manage everything from orders to supplier relationships, ensuring we're as efficient as possible.
Optimization is our constant goal. We aim for Just-In-Time production – it's all about having just enough inventory to meet demand without overdoing it. And when it comes to predicting that demand, accuracy is king. A small increase in forecast accuracy can mean saving a fortune.
Now let's bring AI into the mix. Given that supply chain management revolves around forecasting and optimization, it's a perfect match. By integrating AI with our ERP systems, we unlock deeper insights. We can achieve a more nuanced understanding of supplier performance, risk assessment, and compliance. This isn't just about data crunching; it's about making smarter decisions that reduce costs and bolster the stability of our supply chains.
Through machine learning, we can sharpen our demand forecasting. We can dissect and understand supplier performance and production schedules in ways we never could before. It's like giving our supply chain management a super-powered telescope to see further and more clearly into our operations.
In essence, supply chain management in manufacturing isn't just about moving things from point A to point B. It's a complex balancing act, requiring precision, foresight, and a fair bit of agility.
Manufacturing Operations
Once our supply chain and inventory are set up, the real action begins in the world of manufacturing operations. It's the phase where we turn our plans and schedules into tangible products
Our first task is to meticulously plan and manage our production capacity. This means striking a balance between the available resources - manpower, machinery, and materials - and the ever-fluctuating market demands. We're constantly fine-tuning this balance to ensure efficiency. The key here is optimization: the better we optimize our production workflows, the more cost-effectively we can produce.
If possible, we use automated machinery and robotics for consistent and efficient production. Alongside automation, we emphasize standardized processes, especially in mass production. These processes are crafted with multiple goals in mind: safeguarding our workers, minimizing waste, and achieving cost and time efficiency.
Quality control is a critical checkpoint in our operations. Traditionally, this has been a domain led by human oversight. However, there's a growing space for innovation and improvement. For instance, companies like Deltia are transforming assembly workflows through advanced camera systems. These systems collect vision data, offering insights that help refine processes, enhance worker safety, and reduce costs. Even minor improvements in the assembly line can translate to significant benefits for the entire organization.
There are many more use cases of AI within manufacturing operations.. A big pain point, for example, is the maintenance of equipment. Since every failure of equipment results in downtime for the manufacturing process, predictive maintenance is extremely important to ideally fix problems before they even arise.
Logistics and Distribution
After the final product is assembled in our manufacturing setup, we enter the critical phase of logistics and distribution. This process is vital in ensuring that products reach the end customer efficiently and reliably.
To ensure efficient route planning, minimizing delays and costs, we utilize logistics management systems. Manufacturers often have established networks of distribution centers and retail outlets. These networks are strategically located to ensure wide-reaching and timely distribution. The primary focus here is on reducing delivery times and costs, which is crucial in maintaining customer satisfaction and reducing overheads.
One of the most important parameters to optimize in this context, similar as in supply chain management, is warehousing. Here, Warehouse Management Systems (WMS) are used to track inventory levels, orders, and deliveries. This software is essential for maintaining visibility and control over stock.The processes in the warehouse, including storage, picking, and packing, are predominantly standardized. This ensures consistency and efficiency in handling goods. Operations are mostly manual or semi-automated, with the use of forklifts, conveyors, and manual labor for moving items around. Space optimization is achieved through static shelving and rack systems, and manual planning is used for organizing warehouse layouts.
In the logistics and distribution sector, AI is about enhancing efficiency and accuracy. AI-driven systems are capable of optimizing delivery routes in real-time, taking into account factors like traffic and weather conditions. This not only speeds up delivery times but also improves overall logistical efficiency.
AI-enriched Warehouse Management Systems (WMS) elevate operations to a new level. One could forecast inventory needs, automate replenishment orders, and integrate seamlessly with IoT devices for real-time tracking. Moreover, the deployment of Automated Guided Vehicles (AGVs) and robotics for tasks such as material handling and packing significantly reduces manual labor while boosting efficiency. AI also plays a crucial role in dynamic space optimization, enabling warehouses to adapt their layouts in response to fluctuating inventory and order profiles. This results in more efficient use of space and faster retrieval times, transforming the traditional warehousing model into a more responsive and agile operation.
Summary
Based on my conversation with industry experts, such as Silviu and Max, I believe that there is great potential for AI in manufacturing. Here is a summary of three major use cases from the categories I mentioned above::
Warehousing
Access inventory is very costly for manufacturers who face potentially long lead times. Many processes here are still manual.
AI solution: Improved forecasting enables lower inventory. Many currently manual tasks can be automated. Computer Vision can supercharge warehouse workers, logging, counting and monitoring inventory.
Examples: Powerhouse AI: Provides automated inventory services. (Backed by Entrepreneur First and Y Combinator). Deskera: Warehousing company, using AI to reduce inventory. Series A $180M (Backed by Naver and Susquehanna)
Maintenance
Processes can fall apart due to faults anywhere in the process, causing huge costs. Process remains reactive instead of proactive.
AI solution: Predictive Maintenance enables early interventions and a proactive approach. Avoidance of failures reduces repair costs and prevents unplanned downtime
Examples: Tractian: Cloud based predictive maintenance company. Series B $73.3M (Backed by Y Combinator and General Catalyst). Sparkcognition: Startup tackling Predictive Maintenance, among other issues. Series D $286.6M (Backed by TEMASEK)
Assembly
Assembly processes are still surprisingly manual. Machines are unable to changing situations.
AI solution: AI can supercharge factory workers by finding snags in production. Measures such as Edge Computing and anonymisation can address trust issues among workers towards AI.
Examples: Deltia: Using Computer Vision and AI analytics to provide insights into manual assembly processes. Pre-Seed EUR 1.65 M (Backed by Merantix). Bright Machines: integrating software and ML-based intelligent capabilities to achieve assembly automation. Series B $311M (Backed by Eclipse and LUX)
Startups in Manufacturing
As you can see, there are ample opportunities to innovate in manufacturing. Startups play a special role in the transformation of this industry. Often entrepreneurs experience significant pushback if they try to innovate in a conservative industry like manufacturing. According to Silviu, gaining acceptance among established players is possible if you have a deep understanding of the industry and empathy for the people who work there.
Despite manufacturing being a traditional industry, most people within the sector welcome AI innovations. The growing popularity and awareness of AI technologies has made the integration of AI more acceptable in the manufacturing space. However, there is some fear among workers, who may worry that AI technologies could replace them or invade their privacy. To address these concerns, companies must be transparent about their intentions and communicate that AI will be a copilot or assistant for workers rather than a replacement. In order to increase transparency and alleviate fears, one approach that Deltia is taking is to provide workers access to the system.
There is ample room for more entrepreneurs to enter the AI-manufacturing scene, as manufacturing significantly contributes to the global GDP. SMEs, especially, could benefit from increased support to democratize access to technology. By helping SMEs to become more efficient, entrepreneurs can contribute to making products more affordable. As the foundations of AI evolve, more innovations can be expected in the manufacturing space. However, entrepreneurs should demonstrate benefits for SMEs from day one without expecting them to have highly skilled personnel in data analytics or AI.
For AI entrepreneurs to succeed in the manufacturing industry, they must be customer-centric and adapt their offerings to the sector's realities. In addition, they need to understand that SMEs may not have the resources or expertise to handle complex data sets or advanced IT infrastructure. By bridging the gap between the two worlds of AI and traditional manufacturing, entrepreneurs can unlock significant value and drive innovation in this crucial industry.
Thanks for reading, and please let me know what you think, what further opportunities in the market you see or what technological trends on the horizon you believe will make an impact. A special thanks goes out to Eduard Hübner, who was my great co-author for this piece.
- Rasmus
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