Increasing employee engagement in manufacturing
Over the past decade, the manufacturing industry has faced increasing global competition and seen the movement of assembly jobs overseas, meaning the continuation of innovation and retention of highly skilled employees has become even more critical to long-term success. In the UK, the vote for Brexit has meant that British manufacturing businesses will need to work even harder if they want to maintain or grow their share of the market. A lot of businesses involved in manufacturing in England and Wales are small and medium-sized operations of under 100 employees and in today’s challenging business environment, these small to midsized organisations need to get the most from every available resource — especially their employees — to stay competitive and fuel growth.
However, as manufacturers strive to retain highly skilled labour in today’s tough economic climate, increasing employee pay is not always an option. A more feasible yet highly effective retention strategy instead focuses on increasing employee engagement. Taking steps to boost employee engagement not only helps in retaining valued employees, but it also increases an organisation’s level of performance. According to a recent study, engaged employees have productivity rates that are 70 percent higher than those of non-engaged workers. They also enjoy a 78 percent higher safety record, 70 percent lower employee turnover, 86 percent greater customer satisfaction, and 44 percent greater profitability.
So what can be done to ensure employee engagement within the manufacturing industry?
Making sure your employees feel valued is more important than remuneration when it comes to employee engagement. Recent research by Kronos and the Workforce Institute revealed that remuneration ranked a lowly 10th out of 11 as a reason for an employee resigning, whereas not feeling valued topped the list with 60 percent citing this as the key factor when considering resignation. Especially in a 24/7 production environment, taking the time to listen to your employee ideas and feedback and giving your employees more ownership over their own job can help make them feel valued and as a result more engaged with the business, after all they are the ones who know your production operation in most detail. Where possible, taking into account employee preferred working hours and activity when creating work schedules can positively impact engagement. Scheduling is a careful balancing act. Managers need to assign employees with certain skill sets and certifications to each shift to keep production on track and stay compliant. Generating schedules that maximise productivity and employee satisfaction is critical in the smooth running of a business, none more so than manufacturing, and this is where workforce management systems can really help.
Fostering career development and professional growth is also a key way to engage employees As most manufacturers continue to seek fractional reductions in cost, especially post Brexit, the ongoing development of employee skills — one of the most significant drivers of improved business performance and profit margin — is too often overlooked. After all, many of today’s manufacturers are facing a potential skills shortage due to the large number of employees nearing retirement age. Properly managed training programmes can help ensure that manufacturing organisations develop workers with the necessary skills, keep employees challenged and motivated, and nurture future leaders. At the same time, ongoing professional development has been proven effective in retaining top talent, maintaining quality levels, and achieving competitive advantage.
When manufacturers truly engaged employees, the long-term benefits translate to the bottom line. Research continues to show that a well substantiated relationship exists between employee engagement and business results and workforce management technology can help manufacturing organisations increase their employee engagement. By providing employee self-service applications and automating processes such as time and attendance tracking, scheduling, HR, and labour analytics, manufacturers can empower employees to play a more active role in HR and scheduling activities, take advantage of training and professional development opportunities, and get the continuous feedback on performance required to motivate and encourage innovation. This not only fosters more engaged employees, but also frees up time for HR professionals and production managers to focus more on driving additional business benefits.
For manufacturers looking to control costs post Brexit, while at the same time increasing productivity, focusing on employee engagement through the effective use of workforce management technology is one part of the answer.
Neil Pickering is Marketing & Industry Insights Manager at Kronos
Follow @ManufacturingGL and @NellWalkerMG
5 Minutes With PwC on 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?
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.