May 16, 2020

Call of Duty: protecting your workforce

Health and safety
HSE
worker protection
regulations
Admin
5 min
Call of Duty: protecting your workforce
The need for businesses to protect the health and safety of their workers is enshrined in UK legislation. But although theres no doubt that companies en...

The need for businesses to protect the health and safety of their workers is enshrined in UK legislation. But although there’s no doubt that companies endeavour to comply with HSE regulations, a nuanced question remains; employers know they have a duty of care, but do they care about their duty? In the case of protecting lone workers, the answer, ironically, is enough to set alarm bells ringing. A surprising number of companies do the bare minimum to safeguard those who work alone, adopting approaches that – whilst compliant – leave their workers highly vulnerable. The potential consequences – financial, reputational and human – are significant. But they can be avoided. With expert advice, robust risk assessment and the appropriate application of inexpensive technology, organisations can quickly enhance their lone worker strategies. For many, it’s time they did, before the accident that’s waiting to happen leaves their business, and their staff, in a precarious position.

Single player

Protecting the workforce is not a choice, it’s a Call of Duty. But unlike the video game of the same name, failure to protect your troops on the front line can have real-life repercussions. With the penalties for failure severe, a lone worker accident could mean ‘game over’ for many businesses. And the victims don’t get a second life.

The most dangerous locations for lone workers are obvious; wind turbines, oil/gas refineries, manufacturing plants and distilleries are well-known hazardous environments. But conventional workplaces also present risks. The most high-profile recent HSE fine was issued to a high street bookmaker. The odds of it happening to you may be shorter than you think.

Yet despite increasing regulatory scrutiny, a high number of UK organisations admit their ability to identify and respond to an emergency is inadequate. A 2016 survey of UK organisations1 revealed that a quarter of companies that deploy lone workers would take more than 30 minutes to discover if one had been rendered unconscious. A worrying 15 percent would take longer than an hour. Similarly, 25 percent of companies would take more than ten minutes to locate an unconscious worker, with 12 percent taking over half an hour. These are troubling revelations. In the game of life, every second counts.

Manual play

Perhaps the results shouldn’t surprise us. After all, the survey also shows that 60 percent of companies with staff who work alone don’t issue them with lone worker devices. Many rely on manual processes where lone workers use their mobile phones to check-in with site-based operators at regular intervals. Conversely, some companies require lone workers to dial an emergency number in the event of an accident. This approach is not only contingent on a mobile signal, it’s futile in the event of serious injury. In either situation, the process is dependent on busy operators being available to take the call and escalate a response. If they’re not, the vulnerable lone worker is required to try again. And all the while, the clock is ticking. That many companies fail to document this activity to create an accessible audit trail is just the icing on the cake.

The study does little to dispel fears that some organisations treat lone worker protection as a tick-box exercise. Sometimes, even the companies that have recognised the need to adopt lone worker devices do so without conducting the necessary risk assessments or giving due diligence to the procurement process. Purchasing decisions are often based on price rather than business needs, and they commonly result in the acquisition of solutions that are inappropriate, ineffective or, worst of all, unused. Fittingly, such decisions are made in isolation, without insight or buy-in from the individuals they’re designed to protect.

These traits, however inadvertent, are characteristic of organisations that recognise their duty of care, but don’t do enough to show they care about their duty. It’s unintentional – but the ramifications are unforgiving.

Intelligent strategy

Thankfully, the risks associated with the vast majority of lone worker emergencies can be mitigated if companies adopt the right approach. In a legislative environment where failure to safeguard employees is, at its worst extreme, an imprisonable offence, organisations can do more to prevent being exposed to avoidable human tragedy.

The EU Directive around Best Available Technology Not Entailing Excessive Cost (BATNEEC) encourages businesses to make optimal use of cost-effective innovation that can mitigate risk. In the area of lone worker protection, such innovation not only exists, it commonly takes the form of technology we use every day. The best tech can help automate processes, accelerate alerts and escalate response. Furthermore, with many companies still operating expensive buddy-buddy systems as the centrepiece of their lone worker strategies, automation can help maximise productivity, reduce costs and increase efficiencies.

Multi-player

However, on its own, the tech is not enough. Its application requires customisation and design that can only come through a comprehensive appraisal of business requirements, environmental contexts and existing systems and processes. That’s why the best lone worker strategies are developed in partnership with telecoms experts whose familiarity with the variable demands of remote and hazardous environments can help tailor the most appropriate solutions. In addition, with health and safety a collective responsibility, developing the right roadmap requires cross-functional engagement with stakeholders across the enterprise – guided by a trusted partner.

Paradoxically, lone worker solutions do not sit in silos – they integrate into the fabric of an organisation. As such, the procurement of a lone worker system should be a holistic consideration. For example, the introduction of a GSM-based solution not only offers effective lone worker protection, it can also provide a platform for mobile communications, where additional value-added services can be overlaid to futureproof and transform a business. The breadth of opportunities that come from looking at the bigger picture only highlights the myopia of treating lone worker protection as a narrow, commodity-based decision. And it’s just another reason why businesses with sub-optimal lone worker provision should step up and do their duty.

Call of Duty

In an era where increasing regulatory scrutiny is matched by rapid advances in disruptive innovation, there can be no excuse for companies who fail to take advantage of the best available technology. To optimise it, it makes sense to partner with a trusted expert, evaluate your exposure and plan for a safer future.

Fundamentally, from the board room to the coalface, workplace health and safety is not a game, it’s a Call of Duty. It’s time to dodge the bullet. The smartest companies will be those that arm themselves now – before they run out of lives.

Klaus Allion is Managing Director at ANT Telecom

 

Follow @ManufacturingGL and @NellWalkerMG

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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 PwC on the application of artificial intelligence (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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