Infor M3 User Community
Help
News of countries boosting their domestic industrial capacities and manufacturing capabilities are today’s top stories. Some latest developments include the CHIPS and Science Act in the US that allocated $57.2 billion for semiconductor manufacturing; the European Green Deal that aims to drive the EU’s sustainable transition; and a 500 billion yuan fund in China which focuses on strengthening SMEs in the technology sector.
While the global manufacturing sector expands and continues to attract investments, the need to prioritize green technologies also grows. In 2023, manufacturing contributed 23% of greenhouse gas emissions. Technology developments like artificial intelligence, machine learning, etc. are integral to innovation, efficiency, quality control of products and services, and achieving sustainability commitments in the manufacturing sector.
Yet we also need to understand how the manufacturing sector will address labor shortages, upskill its workforce, work around supply chain disruptions, and become more sustainable. And, given the recent events, how can this sector stay competitive in volatile times?
This blog discusses the impacts of five key technologies, their various applications, and how you too can adopt these technologies in your operations.
Cloud migration in manufacturing empowers core issues such as scalability, data accessibility, security, flexibility, and collaboration between teams and various factories.
The Cloud also enables manufacturers to:
A food manufacturer’s Australia and New Zealand-based operations experienced production inefficiencies and higher operational costs due to system integration issues. Fortude worked with the manufacturer to implement a custom cloud solution that improved their production processes through comprehensive supply chain visibility, efficient inventory management, and manual process automation.
Shifting towards more sustainable manufacturing processes depends largely on data-driven insights into production. This means more accurate demand planning, insights into customer preferences, historical sales patterns, etc. Enterprise Resource Planning (ERP) software plays a key role here as it enables data centralization to give you a complete view of all your data and trends to support forecasting. Manufacturers can alter production capacity as needed with real-time data on inventory levels, order numbers, and storage operations.
Case Study: Fortude worked with a Southeast Asia-based animal feed manufacturer with 22 production facilities and global operations to implement an ERP system that addresses productivity, efficiency, cost, and waste management. Now, the ERP system gives them a centralized portal to manage global operations and scale future growth, improved access to financial data to support decision-making, and streamlined reporting and analytics capabilities.
Predictive analytics also play an important role in machine maintenance. Factory owners have knowledge of which machines are functional, and they can prevent unnecessary downtime by temporarily retiring faulty machines for maintenance prior to breakdown. A Ford factory in Valencia, Spain, deployed a data analytics system that sends real-time updates to engineers about which components are not functioning optimally via a mobile app. This system resulted in significant cost savings and on-time production.
A recent Financial Times article anticipates that over 80% of manufacturing facilities could use AI in some capacities in the coming decade.
Sensors in manufacturing machines generate data, all of which are collected and used by Artificial Intelligence (AI) and Machine Learning (ML) applications for a range of improvements. AI and ML have helped factories with machine maintenance while sensors and industrial IoT software enable close monitoring of machines and their lifecycles. The same also applies for manufacturing processes, where AI is used to improve production efficiency and to release products to market faster. Pepsi is one well-known example of AI-powered manufacturing, addressing issues ranging from predictive machine maintenance to improving the production line.
Product design, too, benefits from AI and ML applications, since product designers and engineers can take a futuristic approach to design, as in the case of Hyundai. The auto manufacturer’s planned Ultimate Mobility Vehicle leverages AI-powered software to design parts for prototypes of two types of vehicles.
While Robotic Process Automation (RPA) tools are popular, there’s a growing interest in no-code and low-code platforms. The reasons behind this trend are greater flexibility, convenience, and accessibility, as enterprise owners can create applications without in-depth coding knowledge—a highly appealing prospect. Both RPA and no-code/low-code platforms help manufacturers automate mundane tasks, reduce human error, tackle labor shortages, and shorten production cycles. There are cost benefits too, as automating repetitive tasks allows your team to focus on more productive tasks, and enterprises can function with smaller teams if needed. Minimizing human errors means fewer instances of product recalls, saving money in the process as well.
Take, for example, the success Ardent Mills achieved with a low-code tool. Before automating tasks, the team dealt with manual data entry tasks, which caused hindrances to their quality control processes. They also lacked real-time reporting capabilities. By using the low-code tool, Ardent Mills shortened manual data entry time, gained real-time data on operations, and created a community of citizen developers to foster future innovation.
Low-code tools were also used for finance automation by Komatsu Australia. At the organization, a small team found themselves processing large volumes of invoices. The sheer volume increased the risk of human error and delays. A low-code tool enabled them to automate 1,200 invoices annually, enabled 24/7 invoice processing, and reduced errors associated with manual data entry.
The industrial metaverse combines virtual and physical worlds in industrial environments. This means that manufacturers can immerse themselves in virtual supply chains, warehouses, showrooms, and production lines. The industrial metaverse is creating new possibilities for transforming manufacturing capabilities. Unsurprisingly, a World Economic Forum blueprint expects the global industrial metaverse to become a $100 billion market by 2030. Virtual twin technology, or digital twins, visualizes physical products and processes.
Virtual twin technology can be used to:
Research and development benefits from virtual twin technology:
Training and skill development receive a boost from the metaverse too:
A Unilever factory in Indaiatuba, Brazil, that manufactures laundry detergent uses virtual twin technology to predict its production processes to save costs and become more efficient. It has reduced energy costs and emissions as a result. The factory also introduced a digital program to upskill its team. Siemens’ digital native factory in Nanjing, China, was designed virtually before it was constructed in the real world. Virtual twin technology enabled them to create processes that increased manufacturing volume flexibility by 30%.
Manufacturing is undergoing rapid transformations propelled by national and regional policies and technology. On the technology front, manufacturers worldwide are migrating to the Cloud to scale operations and store their data securely; using real-time data analytics to understand their processes and inventory levels to become more sustainable. AI and ML/Language Learning Models (LLM) models are being used to monitor their machines and production lines and even redefine product design. Furthermore, automation, either using RPA or low-code/no-code options, is helping improve factory functions and output; and merging the virtual and physical worlds to enhance products, processes, and team collaboration.
Find out how you can implement your custom solution, whether it’s single or multi-site, and balance costs, improve time to market, and stay innovative. Get in touch with us to get started.
End of Messages
