May 16, 2020

Why contractors must take responsibility for drug testing of employees

construction
industrial
drug testing
alcohol testing
Admin
4 min
Why contractors must take responsibility for drug testing of employees
Many industrial organisations make use of contractors to fulfil certain aspects of their operations. The contractor sends their employees to report to t...

Many industrial organisations make use of contractors to fulfil certain aspects of their operations. The contractor sends their employees to report to the client’s site to fulfil their duties, yet these employees are not part of the industrial organisation. To avoid time wasting and having to deal with contractors whose employees are reporting for work under the influence of alcohol, many companies have now made it the responsibility of the contractor to test their own employees prior to arriving on site and to ensure that they are not intoxicated when clocking in for work at the client company’s premises.

Subcontractors employees who test positive for alcohol and drugs on industrial sites are typically blacklisted from the site for up to five years and after a number of strikes the company themselves may be blacklisted.

The Occupational Health and Safety Act provides that employers should not allow any person who is under the influence, or who appears to be under the influence of alcohol or drugs to enter into the workplace. The Act also says that employers should use reasonably practical means to make sure that they enforce the Act. 

As the site owner is liable for any accidents or incidents that occur on its premises, it is vital for contractors who work with them to conduct alcohol and drug testing on all their own staff.

The compulsory testing should takes place daily using a high speed breathalyser, prior to the client company performing its own tests. Generally, the client may also perform random tests during the work day and post-accident tests to ensure that alcohol was not a factor in causing the accident on the site.

The benefits of contractors taking responsibility for the testing

Performing regular alcohol and drug testing on employees can help a company lower its accident rates and reduce financial losses associated with these accidents.  It also lowers absenteeism rates and reduces alcohol abuse in the workplace, leading employees to perform their jobs more effectively.

For many companies, the mandatory requirement for contractors to test their employees before they go onsite has also dramatically reduced the number of workers who arrive at work under the influence of alcohol. By testing their own employees before arriving at their contracted site they are able to stop intoxicated employees from ever trying to enter the main contractors site, they can deal with the problem internally and avoid being blacklisted from the site. This is also a great benefit for contractors, as they are typically small companies who rely on skilled personnel to fulfil their contract and cannot easily replace their employees should they test positive at the client site.

It also costs time and money for a contractor to prepare new staff members to qualify to enter an industrial site, as they have to go through medical testing and safety training. This makes it even more difficult to replace an intoxicated employee.

Testing for alcohol and drugs also helps the contractor’s employees to become comfortable with the notion that the client company will perform alcohol and drug tests too. This helps to forestall possible objections from subcontractors who may have substance abuse issues. It also makes testing a routine part of the relationship with the client company,

How to choose a quality breathalyser

It is vital for contractors to invest in reliable breathalysers that can test quickly and efficiently so they can test as many people as possible. It is important to purchase business equipment as an investment, rather than buying the cheapest model, as a less costly model may malfunction or break more often, costing time and money in repairs.

Cheaper models may also stop working after around five hundred tests, requiring them to be recalibrated, whereas a quality model may perform at least ten thousand tests before it needs recalibration.

The breathalyser should also be SABS approved and use an electro-chemical fuel cell sensor, as it provides the most reliable readings and is favoured by the Commission for Conciliation, Mediation and Arbitration (CCMA) and the labour courts when there are disputes.

Rhys Evans is MD of ALCO-Safe

 

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 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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