Servitisation: A new lease of life for manufacturers
At the very beginning of the year, the prognosis for the UK manufacturing sector was looking optimistic, with reports predicting the greatest period of productivity since the financial crisis. Big strides towards technology spending was at the heart of this trend, with many manufacturers adapting their business models to embrace Industry 4.0 technologies across factory floors.
As we now enter a phase—hopefully a short-lived one—where the latest figures show a slightly tougher operating environment for many UK manufacturers, we are likely to see that those who have invested well, will be best positioned to take advantage of new technologies and business models in order to gain a competitive edge, maintain market share and increase revenue.
Manufacturers differentiate themselves in different ways. Some rely on their technical expertise to keep them at the leading edge of innovation. The London Electric Vehicle Company (LEVC) is one such example–an automotive engineering company, LEVC’s latest engines incorporate innovative technology that sets new standards for zero emission capable vehicles.
Others do everything they can to provide a higher quality product—like Boers, which focuses on delivering fine mechanical parts. Others compete on cost. However, there is another method, called servitisation, and it could offer substantial competitive and growth benefits for manufacturing firms—not just in the UK, but worldwide.
For manufacturers, servitisation is the process of developing capabilities to provide services and solutions that supplement traditional product offerings, and provide additional revenue streams. The idea of manufacturers providing services is not new. At a basic level, manufacturers have been supporting their product offering with spare parts for generations. The next step in the servitization model is to offer intermediate services such as a helpdesk, periodic maintenance, repair and overhaul.
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Again, many of these are standard fare, and have been for a long time, but even so these intermediate services present an opportunity for businesses to strengthen relationships with their customers, and provide ways to generate additional revenue streams. Yet, by offering advanced services on top of this, the opportunity for business growth is even greater.
With an advanced service offering, the customer receives an outcome, or capability, rather than simply purchasing a product. For example, an office manager might sign up for the provision of ‘document management solutions’ rather than buying a photocopier. Similarly, an airline might enter into an agreement for a number of flying hours rather than ordering a jet engine. In northern Europe, consumers have already been offered a 'pay per wash' option as part of a trial run by their domestic washing machine manufacturer.
The advantages of these advanced services benefit both sides of the transaction. The customer benefits from a ‘pay-per-use’ model rather than spending a large amount of cash up front, a guaranteed service level, as well as commitments regarding product development and enhancements over time. In return, the customer agrees to a longer-term contract over several years. A stronger partnership between the two is formed, which improves long-term cash flow and customer lifetime value.
In order to create additional revenue streams, manufacturers could provide service and product combinations that are tailored to individual customer requirements. If executed correctly, this servitisation model could allow for a new lease of life for manufacturers, and help set them on course for future business growth.
For success with servitisation to be achieved, technology needs to be at the foundation of its implementation. For example, enterprise resource planning (ERP) is critical for this journey, from initial set-up to long-term growth. As the complexity and amount of a company’s operations increases, it will become equally as important for business processes to become automated. Here, ERP can help boost efficiency and productivity, and provide real-time data insights—which will help support these new business offerings, designed to support the company’s growth strategy.
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.