May 16, 2020

The problem of Brexit harassment

Brexit
manufacturing industry
immigration
the problem of Brexit harassment
Admin
3 min
The problem of Brexit harassment
Since Brexit we have seen a worrying increase in reports of race-related harassment occurring. It may be that this is a by-product of more conversations...

Since Brexit we have seen a worrying increase in reports of race-related harassment occurring. It may be that this is a by-product of more conversations about Brexit or immigration in factories and workplaces. Whatever the cause, employers need to be aware of the risks arising from such harassment and, where possible, should try to address the risks.

 

Race-related comments made which are deliberately intended to offend are likely to be unlawful harassment. Comments such as innocently intended observations or shop floor 'banter', which weren’t intended to offend but still do so, are also harassment. Unlawful harassment requires that someone must be offended or feel their dignity at work is adversely effected, however many claims we see arise from the person who overhears something that was not intended for them to hear.

What the Equality Act says is that unlawful racial harassment occurs where one employee engages in unwanted conduct related to race, nationality or national origin, and the conduct has the purpose or effect of violating another employee’s dignity or creating an intimidating, hostile, degrading, humiliating or offensive environment. 

Any conversation between employees which is immigration related carries a degree of risk for any employer. Comments about 'immigrants' can be race related and certainly have the ability to offend. What carries a greater risk are comments directed at an employee precisely because of their race, nationality or national origin. So referendum-related observations, such as “you won’t be able to work here soon”, directed at an employee born in Eastern Europe (for example) are race-related and it is entirely possible that it will cause offence.

The context in which comments are made can be important. Whether it is reasonable for the conduct to have that effect, is part of the legal test.  An academic discussion in the factory about the merits of Brexit may be unlikely to reasonably offend, whereas badly expressed comments on immigrants or 'jokes' directed at an employee because of their nationality, will be unlawful harassment. 

This is a serious issue. For unlawful racial harassment occurring at work, the employer is liable for an employee’s conduct and the victim has a valid claim against the company. Damages are also uncapped (albeit still mainly based on loss).

So what can you do about it?

We have seen reports of some employers banning Brexit-related conversations at work altogether. This does seem an extreme over-reaction and may carry its own legal risk if enforced (albeit at least the message is clear). In most manufacturing environments the appropriate approach will be to take the opportunity at a toolbox talk or other briefing, to remind employees about their responsibilities. Think before you speak is a good message. It is worth emphasising the Brexit connection and that race discrimination covers European nationality, as well as nationalities/ethnic origins which may more traditionally be considered to give rise to a discriminatory risk. At its strongest a robust programme of diversity training can equip your employees with the knowledge to avert incidents occurring and can also give you a legal defence even if such comments are made.

The most important thing to do is to address any potential issues swiftly and effectively. Doing so will ensure a site where workers feel included and supported. How exactly any such incidents are best addressed will depend upon what is said and to whom, and often what the 'victim' wants will be key. Organisations certainly do not need to dismiss everyone who expresses a view on immigration whilst working. However do not just ignore potentially offensive comments, deal with them and stop them re-occurring.

Phil Allen is a Partner in the Employment, Pensions and Immigration team at national law firm Weightmans LLP

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

SmartManufacturing
ArtificialIntelligence
bigdata
Technology
Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of 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?

PwC research 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. 

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