STAEDTLER: the value of automation in manufacturing
1. What are the current trends in automation for manufacturing?
The manufacturing sector is subject to structural change. The manufacturing area of the 70th till the end of the 20th century was characterized by automation through electronics and Information technologies (often referred as Industry 3.0). Inventions like the Modicon 084, the first programmable logic controller, allowed for massive gains in efficiency through the application of automated machines together with robots equipped with vision and other sensing systems. Due to economies of scale the prices for robotics and hence the application of robotics is steadily decreasing worldwide (see IFR figures). Currently the manufacturing area is evolving towards the application of cyber physical systems, which means to connect the physical with the virtual world, to connect men, machines and products to strive for more automated self-controlled value streams (incorporating Engineering, Supply Chain, Maintenance processes in the manufacturing operation). This evolution is born out of need, a consequence arisen from the individualization trend as an answer to allow for the mass customization of products.
2. What value does automation provide the manufacturing industry?
Today’s manufacturing (and company!) environment is characterized by a fast-paced changing environment, mostly born out of globalization, individualization, and digitization trends. Conditions change quickly and comprehensively. To be capable of adapting it is already a prerequisite to have a high degree of flexible processes. Automation can greatly enhance flexibility. Additive manufacturing can reduce lead time through much faster prototyping, production, and repair times. Simulation techniques (e.g. virtual twin) greatly impact performance, effectiveness, and quality of (manufacturing) assets, which results in less lead time. Flexible automation (equipment design, robotics) reduces change over times, which results in less lead time.
Increased Workplace safety, increased productivity and enhanced product quality allow for reduced manufacturing costs beside the lead time reduction effects. In this way manufacturing supports or even adds a new level of competitiveness.
3. What are the challenges of adopting automation?
Challenges for adopting automation technologies are versatile. In general, more automation results in the need for fewer workers with different (higher skilled) capabilities (programming, co-working with cobots,…). This changing labor force will need an open-minded organization for adopting new technologies; problem solving, creativity, data analytics, taking risks capabilities will be more important personal characteristics. This also will impact decisions for manufacturing locations where relocation to low cost countries becomes less priority. An increased level of automated processes also means an increased risk for cyber security. Changing economics of the value chain will result in a blurring line between traditional manufacturers, retailers, and service companies. Those are only few examples for a changing environment which companies need to address in an agile cross functional way to stay competitive.
4. How can organisations unlock the value of automation?
To unlock the full value, to transform to get tangible business effects, organizations need to understand the opportunity and need to move early on. It is imperative to understand the opportunity cross functional, value stream based where employees need to be involved from the early beginning by defining high impact use cases. This allows for organizational Learning. Those use cases need to happen based on quick wins and need to be part of a broader vision, grounded on a Technology Monitoring since most effects result from the technology adaptation moving through rapid technology changes. To capture the full organizational value, impacted jobs need to be redesigned and organizational change (end-to-end-processes) need to be managed. This also means to integrate technology enhancements into the core business functions, to enable a continuous improvement culture based on collecting and analyzing data to drive informed decisions.
5. How has automation been impacted by COVID-19?
Manufacturing has faced huge disruptions with vast changes in the consumer behavior, which will lead undoubtedly to more automation/ virtualization in the future (to better enable a non-remote workforce, allowing critical functions to be monitored remotely, Customer, supplier, and manufacturing partner visits happening remotely via the use of data glasses …). On the other hand, the majority of investments and spending for Research and Development (R&D) has been stopped or at least delayed caused by a volatile environment. Volatile is the watchword of the current time, entailing for companies’ different business models and characteristics! Industry 4.0 (and automation as one pillar) was already reshaping Manufacturing before the pandemic. Certainly, it will not stop to accelerate with even better speed to market and service effectiveness ratios through manufacturing efficiency improvements, product customization and new business model creation.
6. What does the future look like?
The future of automation in Manufacturing is heavily progressing on the newly evolving manufacturing technologies and the rapid technology changes. Especially the opportunities arisen by the IIoT will drive new economic rules also impacting ecological and social concerns. Technology is serving as the primary driver for innovation. More in detail control systems trends will drive the future of automation allowing for adoption of new systems and networking architectures and looking toward interoperability of devices and systems. Integration and connectivity will become easier and intelligences will become advanced.
Christian Haupt is the Head of Group Business Development Technology and Global Director Continuous Improvement at STAEDTLER
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.