How to Ensure Your Manufacturing Plant is Ready for Growth
At the beginning of the year, the UK manufacturing industry saw its ninth consecutive month of growth – positive news for the sector. Now, according to the trade group EEF, more positive news has surfaced, revealing that the growth in Britain’s manufacturing will increase this year by three per cent, nearly half a per cent greater than previously expected.
EEF, which surveyed 275 companies, also revealed that it was not just the automotive and aviation industries that are forecasted for growth, but all sectors of manufacturing. This research is welcome and timely based on improved orders and confidence in British firms, resulting in the creation of jobs and enthusiasm that a sustainable economy will support. But by creating a buoyant market place, there will inevitably be an increase in competition. Businesses have been static for a long time, coiled tight like a spring waiting for market confidence to surface and ready to launch.
If this is the case, should manufacturers be upping their game to exploit the opportunities of a growing market place? The requirement of investment follows closely behind growth. Sales and marketing strategies created in a flat market place are usually in the subject of revision and refocus for an economy that has greater opportunities, so businesses need to take a step back and assess their people, processes and technology. To ensure its success during a positive economic period, an organisation needs to ensure that it has the optimal infrastructure and competence to attract and retain customers.
Whether its new customers, or those customers that they have retained, organisations need to be aware the customer they spoke with yesterday, last month or last year will now have changed their expectations of the experience that a supplier can and should provide.
This difference will not only be influenced by the changes in the industry, but also by the technologically advanced world we live in. Be it either a business or retail customer, expectations of the experience journey through an organisation is constantly being influenced. And it’s not just the influence of every point of interaction, but also the experience that customers have received during and following the interaction. Influencers could be from the radio station they listen to, to social media feeds read on a smart phone, to the car they drive, through to experiences from other brands and even their colleagues at work. The list goes on with every moment of the day.
Now think about the next time you interact with a specific brand, will your expectations have changed? If not, or your expectation is reduced – then either you are an opportunity for a competitor to offer you betterment, or you’re already, even subliminally, planning to defect to a competitor. If you’re expecting an increase, however small – how realistic is it that your expectations are going to be fulfilled?
Are you setting yourself up for a fall when the brand cannot provide that increment?
This desire for an increase in experience comes from the world we live in and the daily bombardment from multiple brands providing different levels of interaction experiences. The change in expectations for customers and can be encapsulated by the formula:
E > (CX + Brand Promise) / 2 + (Mean – CX)
E = Customer expectations at their next interaction with your brand.
CX = Customer’s last interaction experience with your brand.
Brand = Customer’s perception of the brand (created promise from market place influences e.g. adverts, friends, social, previous experiences).
Mean = Average of the NPS experiences that received from other brands.
In essence, existing and potential customers have a level of expectation for experience from a brand or organisation and if they can’t supply it, then there is an issue that results in customer disappointment, dissatisfaction and ultimately defection. Organisations also need to ask how loyal are its customers, or how difficult is it to pitch against a competitor that offers a similar product, at a similar price, but with a proven superior customer journey than your organisation can provide?
The solution would be to act rapidly and assess if your organisation is fit to maximise opportunities in an improving market. Now is the time to review your people, processes and technology is focused on the customer and to ensure that your business is capable of pursuing and achieving the revised objectives and strategies.
The market conditions are now right for growth, but failing to learn from the difficult trading conditions of the past could lead to a failure to capitalise on the opportunities. By focusing efforts on improving the customer journey could give you the vital edge needed to succeed over your competitors.
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.