How to mitigate risk in manufacturing with the Brexit clock ticking
With just six months to go until Britain leaves the European Union, the only thing that is certain is uncertainty. Currency values rise and fall with each announcement, negotiation update and policy document released. For manufacturers, the biggest problem they face is that there is still no clear indication of whether the UK will remain in the European Economic Area (EEA). This affects the reliability of goods arriving on time and the ability to move them across borders, as well as profit margins for each layer of the supply chain given potential tariff implications on imports and exports.
Right now, manufacturers are being told to prepare for the worst to ensure their supply chains are unaffected, but they are no closer to knowing what to prepare for. Airbus has warned that Brexit “chaos” is endangering UK operations with the lack of clarity. The government has issued advice on how organisations can act in a no deal scenario, however, delivery firm ParcelHero compared this advice to the infamous campaign recommending that citizens “hide under a table” during a nuclear strike.
So, what can actually be done while manufacturers wait for the situation to unfold? Simply put, it’s time for manufacturers to get out of the “Brexit bunker” and start building agile infrastructure that will help them to roll with the punches and adapt quickly to the uncertain landscape which is likely to be reality for many years to come.
Adapting for Brexit and beyond
While it may seem counter-intuitive, the first step to preparation is to stop trying to predict the final outcome of Brexit. While there will be major changes if the UK doesn’t remain in the EEA, manufacturers can’t afford to gamble on one outcome and find themselves wholly unprepared if Brexit goes another way entirely. As such, the manufacturers who prosper after Brexit need to be able to act fast and adapt not only when the final decision is announced, but for how the market will evolve throughout the process as new deals are struck and new tariffs and regulations are enforced.
Manufacturers need to ensure they have the capability to view their supply chain in almost real-time to identify potential risks as soon as they emerge. This holistic view must give total visibility and help to identify areas in which manufacturers need to diversify and have multiple options for supplying certain goods, in the event of disruption.
To gather this information, manufacturers must collate supplier data into a single platform, allowing them to make more informed decisions and spot upcoming risks to their supply chain. Technology investments in such platforms must ensure 360 degree supplier visibility, pulling information from suppliers, internal stakeholders and 3rd parties (ex. relevant news, predictive risk scores…). The type of information that can be gathered must be flexible. For example, in the case of Brexit it is important to survey suppliers in a scalable manner on the sources of their supply and ability to shift based on tariff policy.
Such a comprehensive and agile approach will empower manufacturers to ensure that upcoming regulations are spotted, suppliers which aren’t complying are identified and changes to tariffs and their impact are flagged. This information will give manufacturers the insights they need to mitigate any potential disruption to the supply of essential goods and services.
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In other words, organisations will be able to make informed decisions about Brexit at every turn, even after the exit date. These decisions mean manufactures can continually review supply risk and ensure the critical manufacturing of goods can continue uninterrupted – even during times of change.
Collaboration not retaliation
Manufacturers can’t afford to sit on their hands and wait for the next Brexit news cycle to propose a new scenario or possible outcome. With a system in place to identify supplier risks, manufacturers must communicate effectively to ensure suppliers are prepared too. The best way to guarantee all parties act fast in the face of change is to build partner relationships and communicate regularly to ensure suppliers can meet their goals and any potential risks can be discussed. Suppliers are facing the exact same uncertainty and challenges and the most agile supply chains will generally be those that are most collaborative, working together to deal with each new reality.
This kind of collaboration is vital in times of flux, allowing manufacturers to keep on top of changing regulations throughout the Brexit process, while giving them the opportunity to see how suppliers are faring if changes in tariffs and trade routes are disrupted.
By taking a holistic approach to supplier management, organisations will be able to react quickly when the supply of goods is disrupted, or supply routes are suddenly changed.
Agility over instability
In an increasingly uncertain landscape, manufacturers need flexibility, insight and the ability to collaborate to effectively manage supplier risk and keep the production line going – but this won’t happen overnight.
Now is not the time to bunker down and hide under the table, waiting for the inevitable Brexit strike. Instead – regardless of the outcome – manufacturers need to assess their supply chain, establish where slowdowns of goods and component might occur and understand how the supply chain can adapt to these changes to avoid shortages. This is especially important, as a frictionless border and tariff free movement of goods may no longer be a reality pending the final deal.
Smart procurement can help organisations to make decisions quickly to reduce the impact of supply chain disruption, allowing manufacturers to effectively evaluate risk and make smarter sourcing decisions in a world where the landscape will literally change overnight.
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.