May 16, 2020

Automation and Engagement: The Eight Personalities of the Modern Employee

employee behaviour
people & skills
people mana
4 min
Mastering the manual labour-automation balance
Automation projects aim to increase efficiency and productivity, yet can strike fear into a workforce that is sceptical, fearful for their jobs and relu...

Automation projects aim to increase efficiency and productivity, yet can strike fear into a workforce that is sceptical, fearful for their jobs and reluctant to take on the extra project responsibility. 

On the other hand, automation projects can inspire and galvanise teams to achieve more and work together to bring energy and efficiency across an organisation. 

The difference lies in the level of engagement with a project and, in large part can also be about personality and a person’s natural disposition to change.

Some people are more pre-disposed to scepticism and pessimism than optimism and enthusiasm, yet that does not mean the pessimist cannot become engaged with a project, they may take a bit longer to get there, but with the right communications and involvement in the planning and implementation of a project, they can become more proactively engaged.

It helps to be able to identify the various levels of engagement and have a number of tactics to create understanding and shared vision, building a desire to be part of a team that makes a difference. As a starting point, it is useful to understand that everyone may be on a different engagement scale and to identify where they are on their journey from negativity, passivity, positivity and action.

Festo Training and Consulting has worked to identify eight personality types, based upon an employee’s level of engagement and ability to become more closely aligned to a project.

Highly Engaged - Champions & Ambassadors

Champions and Ambassadors will be fully engaged in any automation project. Their principle difference lies in their drive and proactivity.

Champions are highly aligned to the organisation, love their role and have immense drive. They will typically endorse a project and will work hard to bring people round to their point of view. It can be very useful to have someone from the shop floor as a Champion with peer to peer endorsement powerful in bringing about change.

Ambassadors are engaged, but less proactive. Although they will be positive about any strategy change and are highly aligned, they will not proactively try to change people’s opinions.

Not Engaged – Challengers & Sceptics 

Challengers are satisfied in their role, however they will question the validity of any change. Lower in the alignment scale, they can initially be negative about an automation project. Challengers will seek facts, figures and a reasoned and robust argument.  Allow the time to provide this and Challengers can soon see the need for change and become more engaged with a project. 

Sceptics are less aligned to the organisation. They will have a lower sense of personal satisfaction in their role, often overtly opposing the strategy. Due to their questioning and their lack of engagement, as well as low drive, other team players can find them difficult and draining to work with.

Not Engaged – Prisoners & Passengers

While well aligned and probably with a lot of knowledge within the organisation, the Prisoner is lower on the personal satisfaction scale. They will frequently air their concerns vocally and this can demotivate others.  Working with Champions and Ambassadors may help here and finding reasons to recognise their achievements and expertise can help to break down many barriers to engagement. 

A Passenger on the other hand is really just along for a ride. They may be highly unsatisfied with their career and although they might understand the reasons for change, they will not be proactive in the process.  They can be responsible for stalling a project through inactivity and unwillingness to treat it as a priority.

Actively Disengaged – Saboteurs & Thieves

Saboteurs are usually dissatisfied with their role and responsibilities and may harbour a great deal of unresolved conflict. However, they are proactive with high energy and this energy can be used to good effect if they are moved towards becoming a Challenger, rather than actively hindering a project. 

Thieves on the other hand, are the most disengaged and highly dissatisfied on the engagement scale. The term ‘thief’ implies their covert nature and they will have little regard for anything other than their own agenda. 

This agenda might be to take whatever they can from the company and sometimes other employees as well,  including time, information, training, material goods, money, data, software and anything that they think will benefit them now or in the future.

It has been known for a Thief to deliberately sabotage a project to prove their theory that it won’t work.  Hopefully an automation project will not have a Thief. If there is one, the best advice is to eject them from the team as soon as possible.

The key to a successful automation project is to move people closer towards the aligned and highly engaged end of the scale.

In many respects, level of engagement is a measurement of success, rather than a target. It is the way that people are involved in the project, consulted and recognised that provides a favourable engagement outcome.

Unfortunately, disengagement is often only analysed when a project hasn’t worked well and this is far too late and at a time when attitudes and behaviours have become more deeply entrenched.  

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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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