September 7, 2021
From AI-powered manufacturing equipment to machine learning algorithms, the future of manufacturing depends heavily on technology. Modern technology gives manufacturers the power to improve almost every aspect of their business operations, including small-batch manufacturing.
Small-batch manufacturing typically involves production runs with less than 500 units. Focusing on smaller production runs offers greater flexibility. Yet, producing small batches can also come with higher costs, delays, and logistical challenges.
Discover how technology can improve small-batch manufacturing through automation, increased efficiency, and other key advantages.
Why Use Small-Batch Manufacturing?
Small-batch manufacturing is a production run with a limited number of units. Producing fewer units decreases lead times, allowing manufacturers to manufacture products on demand.
Small-batch manufacturing may also be used to develop multiple variants of a product. Each variation can be manufactured on the same production line, reducing costs for producing more products.
A smaller production run also tends to result in increased quality control. Manufacturers can test and inspect products more thoroughly, as the production run includes fewer units. However, smaller batches may lead to increased costs and other difficulties. The latest technologies offer a way to address these concerns and make small-batch manufacturing more profitable.
Speed up Product Design
Artificial intelligence (AI) and machine learning (ML) offer solutions for accelerating the speed of the product design stage. Designing a new product involves many steps, including market research, product forecasting, and testing. Here are a few examples of how technology is helping manufacturers bring products to market faster:
● Product forecasting with data analytics
● Digital twin prototyping
● Generative design
The typical product design process starts with defining the product vision. This includes the objectives of releasing the product, such as capturing a specific share of the market or addressing the needs of customers in a new way.
Manufacturers traditionally rely on a team of engineers and marketing experts to brainstorm ideas and develop creative solutions. Yet, digital tools have become the key for exploring ideas and solutions at a faster pace and with greater accuracy. Here is a closer look.
Analytics for Better Product Forecasting
About 45% of manufacturers are already using analytical tools for product forecasting to foresee the potential demand for new products. Another 43% of manufacturers plan on implementing demand forecasting within the next two years.
Product forecasting is an important part of small-batch manufacturing. Understanding the demand for products allows an organisation to better plan for a production run. Analytics can assist with every aspect of market research and demand forecasting, ensuring that you are prepared for upcoming small-batch manufacturing projects.
Digital Twin Prototyping
Software is also being used to digitise products and manufacturing processes. Engineers can create a 360-degree view of a product and analyse its entire lifecycle. Machine learning can also be employed to create models based on historical data.
The digital models created by the software allow manufacturers to test the performance of a product in various virtual environments. For example, manufacturers can test the durability and function of the product in different conditions. The insight provided by the digital simulations lets engineers design and test products and gain real-time data without the need for expensive and time-consuming prototyping.
Explore Alternatives with Generative Design
The latest AI technology can even develop new components without input from engineers. AI-designed mechanical parts and computer chips are now a reality thanks to generative design.
Generative design relies on machine learning to generate multiple outputs based on specific criteria. Engineers can input the parameters for the product, such as materials, costs, and manufacturing processes. The software can then generate every potential configuration and design alternative.
These benefits lead to a shorter time-to-market for new products. Manufacturers spend less time and resources developing quality products while also reducing the need for revisions.
Automate Manufacturing Processes
Along with helping with product design, digital technologies assist with manufacturing processes for small batch production. A common example is the automation of manufacturing processes, which can be used to automate tasks that were previously completed by human workers. Automation enhances the profitability of a product in several ways:
● Reduces labour
● Limits errors and defects
● Increases efficiency
Excess labour, defects, and inefficient manufacturing processes reduce the profitability of new products. Small batch manufacturing leaves less room for delays and unforeseen costs. Manufacturers work with a tighter timeframe and profit margin.
Replacing human labour with automated technology increases the efficiency of small-batch manufacturing by optimising quality control, maintenance scheduling, supply chain management, and the physical production of new components.
Research shows that poor maintenance can reduce a plant’s production capacity by up to 20%. The use of robotic equipment, Industrial Internet of Things (IIoT) sensors, and AI software can automate predictive maintenance, which reduces downtime.
AI can also automate quality control, as computers are better equipped to detect defects compared to humans. Machine learning technology is also used for defect detection. The ML software is given a baseline of what a normal product looks like. It can then quickly compare each manufactured component to the baseline to find defects in real time. By improving quality control and predictive maintenance, manufacturers dramatically increase the efficiency of their operations.
Summary
Small-batch manufacturing allows you to develop and produce new products quickly. You can test new products on a smaller scale, which helps drive innovation. However, small-batch manufacturing can also result in higher costs, lower profit margins, and various logistical challenges.
Digital tools and process digitisation address many of the difficulties of small production runs. The latest technologies can improve the speed of product design through the creation of digital twins, rapid prototyping, and generative design. AI and ML also assist with automation, allowing manufacturers to streamline production, quality control, and maintenance.
Small-batch manufacturing makes it easier to reach the market quickly, which decreases the risk of missing out on new opportunities. Yet, releasing products faster may not be enough in today’s manufacturing sector. Implementing the right digital toolset may be the key to remaining competitive in the coming years.
