The dangers of labour overspending – is it possible to save without cutting staff?
Recent research by Deloitte shows that labour overspending in manufacturing has reached concerning levels. Jenine Bogrand, Manufacturing Sector Lead for LaborWise, Deloitte, talks us through the findings.
Manufacturing seems like it’s always in a state of flux as new technologies develop and the world changes accordingly. People still need products to be made and companies still need to profit from making them but the way that manufacturing sits within the greater economic landscape has changed immeasurably in the recent past.
Recently, the industry has enjoyed somewhat of a renaissance, particularly in the United States where the Institute of Supply Management Manufacturing Index recently rose to a 13-year high of 60.8%. The report which revealed the rise also relayed that employment saw the biggest bump, jumping 5.5 points.
According to Deloitte though, these numbers are not necessarily indicative of positivity. The consultancy’s own findings show the average employer overspends on payroll by up to $800 per hourly employee each year, resulting in a considerable negative impact on manufacturers’ bottom line.
Chobani CEO and leading entrepreneur Hamdi Ulukaya once remarked: “Unlike the objective of far too many companies, manufacturing is not about a quick 'exit’. It is centered on long-term value creation.”
If this is the case, why does Deloitte’s research indicate that labour overspend in the US is reaching such concerning proportions? To try and find out, Manufacturing Global spoke to Jenine Bogrand, Manufacturing Sector Lead for LaborWise, Deloitte’s workforce analytics tool. She claims that often, when consulting with businesses in the manufacturing sector, savings of between 0.5% and 2% on total hourly spend are uncovered. When talking about companies which are often turning over tens and hundreds of millions of dollars, that is a lot of money.
“I do think there's a drive for more consumer goods out there, and that's driving the manufacturing,” she says. “What I'm passionate about is really making sure that the manufacturing world is being creative and assessing what can they do to really bring out their efficiencies.
“I know efficiency and worker productivity is always top of mind, but there are times that they need to step back and be a little bit more creative in how they're looking at it.”
The power of manufacturing as a base which helps bring together communities is inarguable, and probably more prominent than in most other industries. Bogrand recognises that and reckons the drive to ‘bring manufacturing jobs back’ to more developed countries like the US, so that they can have more control over quality, is one of the driving forces behind the 13-year high seen in the manufacturing index.
“Many of the manufacturing plants are in small towns,” says Bogrand. “They're not in the large towns. They're out in the middle of very rural areas, and they are the bread and butter, and the heart of those areas. When plants close, it affects that community. Obviously the bottom line is always important, but there's also a drive to continue to be part of a community.”
Bogrand recalls a client which considered moving offshore. “When they really did the research about the effects it was going to have on the community and their workers, they worked with the small town to make sure that they weren't taking those jobs away. The client didn't want to go, but there was also the cost of labour that had to be considered. In the end, they kept the plant open in the small town, because, had they left, the town would have had nothing and people would have moved away. It kept the town alive, and it kept their people with jobs, which was really a key piece.
“How do you balance profitability and shareholders’ needs with your community presence and jobs that have been there for years and years and years? It's absolutely a challenge for so many companies today,” she concludes.
Whether or not a company chooses to remain in its traditional surroundings or to move on to a new location is at the discretion of the individuals in charge of the organisation, but an issue for each and every one of these manufacturing companies, regardless of geography, is whether they can be more efficient with regards to labour costs, and how this can be achieved.
“That's the conversation that I think needs to happen within every organisation, because it is the number one cost in making anything,” asserts Bogrand. “That way, as long as you're using it efficiently, and you're looking at all the metrics, and you're being able to cost-effectively produce that product, then maybe that conversation turns a little bit. Instead of looking to, say, get a client to go cheaper, before you do that we need to look and make sure that what we're doing with your current workforce is efficient – that you're not wasting money with inefficiencies, whether it's scheduling, staffing or whatever – and seeing if we can tighten it up first, before we start looking to go to a different staffing model.”
Bogrand gives an example of doing exactly this for one of Deloitte’s clients, which shows just how stark the overspend situation can become if not dealt with early on.
“We saw this really high percentage of overtime, and I started drilling in,” she explains. “We found the major culprit was one plant. The more senior workers were the only ones that had the skillset needed for the overtime. So, the more expensive workers were the only ones that were able to take advantage of overtime and more junior workers, which would have been cheaper, weren't having that opportunity.
“Just by cross-training the workers, there was not only a bigger pool to choose from, but it reduced overtime costs, as long as it wasn't against a union contract. That was hard savings. The firm was able to retrain a group of employees that were lower in pay, and started seeing their overtime costs go down. It also benefitted from a better-skilled workforce that knew multiple skills, instead of how to work just one machine.”
Bogrand is also of the belief that the 5.5-point employment bump that manufacturing has seen in the US, as per the index, was not actually needed and perhaps a result of similar examples to the one cited. She points out that employing people inefficiently now could lead to cuts being made to jobs which are needed in future. “Your labour costs have to be optimised, or you're going to start cutting,” she warns. “That overspend is going to affect your overall financial performance. We always like to see employment increase, but was that due to what we call a labour overspend, or could we have not had that increase and been okay?
“Frankly, the leadership, the shareholders, they want their money to be being well spent. They have to produce a quality product, no matter who they are, but they also need a productive, efficient workforce. You have to have efficient processes out in the plant, or the quality is going to start going down, or labour costs going up. This will result in an increased cost of product, so it's just a boomerang effect. It goes out and comes right back at you, in different ways.”
It all comes back to cost efficiency, as Bogrand concludes: “Making sure that they're efficient is going to provide a better bottom line and financial impact, and will allow them to be a little bit more flexible in their cost-to-build.
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.