May 16, 2020

The great holiday pay and overtime debate - the implications for manufacturers?

People Management
4 min
The big overtime and holiday pay debate.
The Employment Appeal Tribunal (EAT) has,last week,handed down judgment in the closely watched appeals in the holiday pay cases Bear Scotland v Fulton a...

The Employment Appeal Tribunal (EAT) has, last week, handed down judgment in the closely watched appeals in the holiday pay cases Bear Scotland v Fulton and Baxter, Hertel (UK) Ltd v Wood and others; and Amec Group Ltd v Law and others. DLA Piper acted for Bear Scotland. Given the  reliance which many manufacturers place upon overtime and other forms of shift supplement in order to achieve workforce flexibility, the decision of the EAT is likely to be of particular concern to them. It is also likely to lead to higher wage bills and an additional administrative burden for manufacturers at a time when future prospects for industry as a whole are uncertain.  

SEE MORE: Can US manufacturers cope with healthcare costs and remain globaly competitive?

These claims arose due to an apparent conflict between UK and European law as to how holiday pay should be calculated and in particular whether overtime must be included. Although employees have had a statutory right to paid annual leave under the Working Time Regulations since 1998, the rate at which this was paid has typically only been at their normal basic rate of pay, excluding additional payments such as overtime and bonuses.  There have already been a number of challenges to this settled view, with cases having been referred to the European Court of Justice on bonuses and other types of pay supplement.  In these cases, the European Court of Justice has held that such payments should form part of the calculation of holiday pay under the Working Time Directive.

The EAT decision in Fulton confirms that overtime payments, which are currently excluded from the holiday pay of many workers, must also now be included in future calculations, leading to increased holiday pay liability for manufacturers. However, any claims in respect of underpaid holiday pay in the past are only possible to the extent that no more than three months has elapsed between the date on which each of the underpayments occurred - in practice this is likely to mean that employees can only claim in respect of one leave year rather than, as had been a possibility, in respect of all underpaid leave as far back as 1998. Although not as significant as originally feared, the impact of the decision will still hit manufacturers hard, as many rely on overtime as opposed to other methods of dealing with fluctuating labour demands.  This is often due to the fact that many workers are in skilled roles which can't easily be covered by bank or agency staff.

As the judgment was only concerned with the 4 weeks' leave payable under the Working Time Directive, the immediate effect of the decision is that this leave and the additional 1.6 weeks' leave provided by the Working Time Regulations are payable paid at different rates. This will cause administrative headaches for manufacturers and in the long run the Government may seek to remove the distinction between the two; however, this is unlikely to be a legislative priority before the election. Manufacturers will also need to decide in the short term whether to pay holiday at different rates or to pay all leave at the same rate.

SEE MORE: The Toyota Way: How the automotive manufacturer handles health and safety

Manufacturers must also consider what specific elements of their own remuneration packages need to be included in the calculation of holiday pay and will need to decide how to deal with any existing claims - Unions have already filed a substantial number of claims for backdated holiday pay in anticipation of this judgment. Many manufacturers operate in a unionised environment, meaning that there may be an increased risk of claims as the litigation is heavily Union-driven.

The decision of the EAT may, however, provide an incentive to settle claims, as the potential for back pay is now limited. In the longer term, manufacturers will need to look at how they structure working arrangements in order to minimise the increased liability for holiday pay. Options might include using bank or agency staff to cover periods of increased demand rather than offering permanent staff overtime, revising commission plans to schedule payments at a time which impacts less on Directive leave and preventing leave from being taken at certain times of the year.

The government have set up a taskforce to urgently review the implications of the judgment. Although the government are unable to overrule the findings of the court without an appeal or a change in legislation, they can still offer employers much needed practical guidance on what this decision means and how best to respond to it. This should go some way to providing manufacturers with a clear path through the holiday pay minefield. 

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

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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