Staying innovative in manufacturing
Manufacturing and consumer goods companies are operating in a world where pressure on costs is relentless; where new competitors are disrupting business as usual; and customers are more demanding and less loyal. In this environment, companies’ ability to grow and thrive depends more than ever on their capacity to innovate.
As a result, innovation is at the top of the agenda for leaders in the sector. Yet PA’s recent survey of 100 senior leaders in consumer goods and manufacturing businesses found that many of their companies are struggling to innovate effectively. Too many good ideas are going to waste, opportunities are being missed and many are finding it difficult to manage the risks of innovation projects efficiently.
These challenges were reflected in the survey responses, which showed that most companies play it safe when it comes to innovation. Only 40 percent said that they often back high-potential but risky innovation. Even when they do invest, they do not put enough resources in to making it happen. That is understandable when the costs for creating new product lines can run into tens of millions of dollars. For big companies with huge global brands, doing something innovative can be hugely costly and the risks so high, that they tend to shy away from it. However, the evidence is clear that those who play it safe, tend to slow down innovation and miss out on opportunities and growth further down the line.
One way of managing those risks is to leave innovation to start-up companies and then buy them out, once they have got the proposition to a point where there is confidence in its technical and commercial viability. However, to do this successfully requires entrepreneurial thinking and we found that only 44 percent of survey respondents felt that this kind of thinking was a strength for them. This was considerably lower than the 76 percent in IT and technology, and 69 percent in life sciences. This underlines that the sector faces a real challenge both in identifying potential acquisitions and then in ensuring their decision making is nimble and rapid enough to secure those opportunities.
Another obstacle getting in the way of innovation is a lack of co-ordination between R&D, production, marketing and sales. This can reflect a lack of active management support and mismatched incentives. So there can be conflict between chief supply chain, technology and marketing officers over which innovation to back because their success is measured in different ways. This lack of an overall strategic vision to support innovation is clearly holding companies back.
Yet there are companies that are making innovation happen and we identified a number of key characteristics of these innovation leaders. The first of these is that they understand that a commitment to innovation inevitably means some ideas will fail but if people feel they have the freedom to test new ideas, they will be more innovative. What is important is that they know how to fail frugally, to pilot new products in inexpensive ways.
The second feature of successful innovators is that they know their customers better than they know themselves. They look for insights beyond those provided by traditional focus groups. Instead they borrow techniques from social sciences such as anthropology and psychology to develop a real understanding of their consumers and how they can tailor products to meet their needs.
They then take an entrepreneurial approach to acquiring innovation, moving quickly to collaborate with or buy start-ups and ensuring that the combined venture is placed within a culture that understands and actively nurtures innovation. Without this culture, the host organisation can squash the newly acquired capability and waste its investment. This applies not just to acquisitions but also to the need to actively seek out opportunities to operate in new ways, such as collaborating with external partners or pursuing joint ventures.
Digital is another key area which drives innovation and where the sector is leading the way. Three-quarters of our survey respondents said digital is helping them become faster and more efficient. However, there is still some way to go before consumer and manufacturing businesses fully embrace digital’s potential to disrupt or lead their sector. This includes focusing on securing the benefits of developments such as the internet of things and 3D printing that are driving both product development and creating new types of business from associated services.
Another critical feature of successful innovators is their openness to new ideas. Many companies still tend to think that innovation comes from the R&D department but we found that 38% of respondents in the sector had seen some of their best ideas originate from rank-and-file employees. By enabling employees to contribute, companies create greater enthusiasm for innovation and generate more ideas. The innovation leaders understand that developing new ideas needs a different mind-set, different talent and skill sets, and few companies will have all these available internally so they are comfortable with looking outside.
By taking these steps, companies can create an innovative culture, supported by strong leadership that is confident with risk-taking and learns quickly from failure. It is vital they do so because it is clear that the consumer products and manufacturing companies that are committed to innovation will be the ones that deliver the best returns in today’s competitive world.
Tim Lawrence is head of manufacturing at PA Consulting Group.
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.