Understanding the value of automation in manufacturing
In the wake of COVID-19, industry 4.0 capabilities have become even more critical to manufacturing operations. Industry leaders harnessing digital solutions are better equipping themselves to weather potential crises in the future.
Coupling the new challenges of COVID-19 with the continuous drive for greater throughput and cost reductions, manufacturers are looking to Industry 4.0 solutions - in particular automation - to increase efficiencies.
The value of automation in manufacturing
Speaking with Gavin Mee, Managing Director UK and Ireland at UiPath, on the value of automation for manufacturers he explains that “with time-consuming back-office tasks such as inventory management placed in the hands of software robots, employees can focus on value added activities that require human ingenuity and skill, such as customer communication and innovation. Automation can therefore improve efficiency and employee satisfaction simultaneously.
“Crucially, these robots can work 24/7 without fatigue or error, twice to five-times faster than their human colleagues. Therefore, back-office processes can be performed around the clock, allowing for real-time monitoring of customer demand, production capacity and inventory levels. This all leads to leaner, more efficient operations across the back office and allows employees to have the information they need, when they need it.”
For organisations to be able to unlock the value of Robotic Process Automation (RPA) in manufacturing, “employees must be brought along for the ride. Businesses must provide the training necessary for employees to understand automation and even know how to create and deploy their own robots,” explains Mee.
“This serves two purposes. Firstly, people are more likely to embrace technology when they understand how it will affect and improve their daily working life. By providing this crucial training, employees are more likely to get behind the possibilities automation offers. Secondly, IT teams lack the fundamental inside knowledge of processes required to understand individual needs. Therefore, for the full potential of automation to be unlocked, employees across the organisation need to be involved when adopting automation technologies in order to provide the necessary holistic view of processes.”
Use cases of automation in manufacturing
“Many will have heard of the physical industry robots used to assemble, test and package products,” reflects Mee. “But automation can also be deployed in the back-office to help streamline operational processes. RPA is software that can work just like a human – but virtually. RPA software robots can take control of a screen, mouse and keyboard and operate a computer just as a human would. In other words, they are digital assistants on hand to help with rule-based and data-heavy processes.”
With manufacturers constantly handling time-consuming, data-focused tasks such as procurement, order management, inventory management and payment processing, “the rule-based nature of these tasks makes them perfect for RPA,” adds Mee. “Therefore, many manufacturing companies are turning to RPA to improve agility and streamline operations across the value chain.”
What are the different types of automation?
Christian Haupt, Head of Group Business Development Technology and Global Director Continuous Improvement STAEDTLER explains that automation can be classified in mainly two different ways:
First, there can be a differentiation in three basic types regarding the production volume and product variety: fixed automation, programmable automation, and flexible automation.
- Fixed automation is the process sequence fixed by the equipment configuration. The individual processes are rather simple; complexity is generated through the integration and coordination of several such simple operations.
- In programmable automation the equipment is designed with a capability to change the sequence of operations through a PLC as required by different products.
- Flexible automation is often seen as an extension of programmable automation; the advantage is the flexibility used to have no changeover between different products, the system can produce various products instead of producing batches.
A second differentiation can be more process oriented and technology based, regarding the appliance of different automation technologies. In this view it can be broadly distinguished between robotics (considering all three previous types), advanced materials and additive manufacturing, simulation and modeling software and more generally all the new emerging technologies, often referred to as Industry 4.0 applications (Big Data, Artificial Intelligence, RPA).
Current trends and challenges in manufacturing automation
Tuning into RPA and the possibilities the technology has offered in recent years, “many manufacturers were already exploring the technology in response to changing regulations and compliance measures prior to COVID-19. However, since the advent of the pandemic, the adoption of automation has accelerated and many are looking to RPA to save time and money all while keeping employees safe.”
Whilst adopting RPA “may seem daunting at first, in reality, you do not need a deep understanding of coding nor an entire ecosystem of software robots to get started. The main challenge is to identify where to start.”
Though almost every process has a repetitive nature to it, “many may seem like viable candidates for automation. Process mining, task mining and task capture are a great way to understand which processes are most suitable for automation and which will offer the best return on investment.”
The future of automation in manufacturing
Looking to the future, Mee contemplates that “the future is bound to bring even more automation to the industry. Many firms begin introducing automation to the enterprise by picking the low hanging fruit, namely those processes that offer high potential for automation and low complexity. These can quickly provide a high ROI and therefore are often key in firming up stakeholder and business user buy-in.”
However after these are automated, “many start to ask themselves, ‘How can we make the best of automation?’ The answer should be end-to-end, enterprise-wide automation, where a business uses RPA, AI and other supporting technologies to their fullest potential.”
To achieve this potential Mee continues to explain that “a company can achieve full automation by joining all automation projects into an enterprise-wide programme, spanning multiple functions and disciplines, where processes get automated end-to-end and provide support from build to shipment. In other words, in a fully automated enterprise, all tasks that can be automated, will be automated. As a result, employees will be able to focus on value added front-office processes, whilst the business benefits from incredible levels of productivity and speed.”
While the technology is still in the early days for this to be fully realised, “it is certainly the future and many are already on their way to achieving a fully automated enterprise,” concludes Mee.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.