Within the current climate, manufacturing autonomy is also a timely idea, offering a chance to democratise both manufacturing and innovation.
By creating autonomous and automated manufacturing solutions, it is possible to reduce substantially the labour cost element in manufacturing, allowing higher labour cost regions to bring manufacturing home. This is extremely opportune, given the desire of most nations to use manufacturing as part of their post-pandemic recovery strategy.
The future will witness a broadening symbiotic relationship between human and machine, with data-driven tools and advanced wireless networks, enhancing production employees’ capabilities and extending manufacturing beyond the traditional factory set-up.
Socio-political drivers of autonomy
The harsh realities of operating during the COVID-19 pandemic and in the ensuing years is just one of a myriad of factors driving the autonomous journey. The much sought-after talent in the labour market increasingly values remote work and life models that have historically been at odds with manufacturing work. Workers with high levels of skill and experience are leaving the workforce and the younger workers replacing them have different levels of experience and comfort with technology.
Another crucial driver for autonomous operations is to improve efficiency in order to reverse the trend for offshoring and develop greater localisation of manufacturing, by allowing profitability operation in the face of higher labour costs and restrictive regulation. This desire for local manufacturing is heightened by political turmoil that increases the risk of volatility in both supply chains and labour markets. Increased unpredictability means more system disturbances and a greater imperative to optimise decision making around them.
Levels of autonomy
Autonomy is not an ‘all or nothing’ concept, there are stages on the destination to full autonomy, if indeed that is the desired end game. A good comparison is the move to autonomous vehicles. Although most of the technology is available now for autonomous driving, automotive manufacturers are taking different strategies, due to regulations, public sentiment, and existing portfolios.
There is a well-understood scale for autonomous vehicles, which moves from Level 0, in which there are no driver assistance systems, through to Level 5, where the system autonomously controls the vehicle under all conditions. This scale can easily be translated into action for the autonomous factory.
• Level 0 – No operator assistance systems. The operator is fully responsible and carries out all tasks to run manufacturing equipment.
• Level 1 – The system can perform one equipment-operating task. The operator can delegate an individual task to the system.
• Level 2 – The system can perform several equipment-operating tasks. The operator can delegate multiple tasks but must permanently monitor the system.
• Level 3 – The system can run autonomously on certain defined routines. The operator can turn attention away from the equipment but must always be ready to take full control.
• Level 4 – The system can perform all equipment-operating tasks. The operator can transfer complete control to the system but can take control at any time if desired.
• Level 5 – The system autonomously controls the equipment under all conditions – no operator needed.
Developing a digital thread
There are two bedrocks on the road to autonomy – the digital thread and existing automation. Taking first the digital thread that creates a closed-loop between physical and digital worlds, transforming how products are designed, engineered, manufactured, and serviced. They seek to create simple universal access to data by following a single set of related data as it traverses the various business processes and functions.
The world of product innovation and manufacturing can be broadly divided into four processes: design a product, make or buy the parts, assemble those parts, and then serve your clients. This includes digitalising product research and development to make better products that run efficiently and improve speed to market, as well as receiving feedback from the clients on how the products are being used as an input into research, development and design. Digitalising the technical transfer from product concept to development into manufacturing operations and feeding manufacturing data back into the product development process. Then, product development and manufacturing data are collected, analysed and distributed throughout the network, to support insight and decision making.
When we look at the quality of a product or production system, there are several that influence that. It all begins with how you design the product, based on recipes, CAD drawings and product specification data you define how the product should behave and be used. Then, moving from the design phase into the manufacturing environment where lines and machines can be simulated and emulated with the real behaviour even before the machines arrive.
Training the workforce and identifying optimisation – as well as giving clear instruction for the workers and securing the highest throughput and quality results by optimising safety and sustainability of your products – are key elements in all companies’ agenda. You then capture production data because of how you produce the product, including process information and machine parameters along with data from laboratory tests.
Combined, this creates a massive amount of data and if they are not integrated, optimisation along this value chain is not possible. When it comes to quality, it is clear how important this open-loop feedback is. This is only possible once you have an integrated path from a data management perspective and physical equipment to the virtual shop floor, with all this coming together in a good model.
The road to autonomy starts with areas currently automated
Establishing the self-driving digital ecosystem will require clients, the customers, and supply chain partners to have established well run automated manufacturing (physical) and business (process) systems. Only by doing this can a company move towards a state where the integrated ecosystem can begin to run autonomously.
The skill sets to effectively work within this new system will only be developing over the next decade. In the manual world, it is driven by PLC, PLM, ERP, and Cloud Computing. We are a year or so away from a true automation age that requires robotic process automation (RPA), IoT, and augmented intelligence. By the end of the decade, we will have augmentation with the addition of machine learning, Blockchain, digital twin and responsible artificial intelligence. Thereafter, full autonomy can be delivered through the addition of human augmentation with AI.
Manufacturing control tower
In our model, this is all driven by the Manufacturing Control Tower concept. This uses three levels of manufacturing operations information visibility to improve decision making and harmonise global processes – the machine/operator level, plant line/order level and, finally, globally across all locations, processes, and production assets.
What you want to have in an autonomous factory are partners and an ecosystem around you, where you can include ‘factory as a service’, which might not be your own factory. The way you run this and the way you start that up is defining the area of your control towers. You take a snapshot of the manufacturing environment and decide where you want to establish a control tower and then automate that part of the process and make this information available.
Traditionally, that availability would come from a central control room, but with technology today, this could be a flexible environment. There might be experts who access this information on their tablet or mobile phone. This information needs to be available from a machine perspective, a plant line perspective and from an audit perspective, and this should be globally available across all the sites you want to focus on in the future.
Once you have established a control tower strategy, you can run an open loop. In this scenario, you still have people being responsible on the site, but the control tower is now supervising and providing remote support in this environment and being able to guide operators and give them additional information. This allows them visibility into any problems or bottlenecks, with information on how they can be optimised by utilising an algorithm in closed-loop control. Autonomous manufacturing is the next goal to reach.
With Kalypso, a Rockwell Automation business, the company will expand its connected enterprise consulting expertise
Rockwell Automation supports customers in their digital transformation journey, to create a connected enterprise. This impacts the entire value chain, from components to systems and from suppliers to customers. This transformation is the key to hidden value and can make a significant contribution to the productivity, quality, compliance, and profitability of your enterprise.
Digital transformation can accelerate business growth, maximise productivity, and optimise operations. The most successful digital transformation initiatives result in better products that can improve people’s lives and expand human possibility.
Rockwell Automation focuses on the digital transformation of the value chain, from the product to the plant to the end user. This means leveraging digital technologies and capabilities to change fundamentally the way companies discover, create, make, and sell new products. Rockwell helps its clients accelerate digital transformation with the digital thread.
The foundation of digital transformation is a connected enterprise that unites and integrates information technology (IT) and operational technology (OT). The result is a digital thread of information that spans the entire value chain – a seamless flow of data that delivers top-line growth, improves operational excellence, and enables risk mitigation. Companies can accelerate digital transformation with the digital thread.
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