May 16, 2020

The Toyota Way: How the automotive giant manages health and safety

Health and safety
People Management
Glen White
3 min
Health and safety at Toyota.
Toyota is the largest automotive manufacturing company in the world, producing in excess of 10 million vehicles each year. Its factory floors are busy...

Toyota is the largest automotive manufacturing company in the world, producing in excess of 10 million vehicles each year. Its factory floors are busy, bustling places full of state-of-the-art manufacturing robots and equipment, moving vehicles and thousands of workers, so health and safety is top of the agenda.

Manufacturing Global goes behind the scenes at Toyota to understand a little bit more about its comprehensive health and safety strategy.

Toyota’s health and safety strategy is very much governed by the company’s world-class principles, known collectively as the Toyota Production System.

The company’s health and safety strategy is known as K-HYP - short for Kaizen and How’s Your Process. Toyota management teams at each facility meet every morning to discuss safety and wellness as well as quality, efficiency and other metrics you would expect at a manufacturing plant. During the meetings, managers pass around a clipboard to each team member, asking them to for input on any safety issue, injury or improvement suggestions they may have. Underpinning the meeting is the question: How is your process?

After the meeting, those managers meet with each worker individually to discuss their issues and develop a solution, whether it's for a sore elbow or a slip risk. “We are looking for ways to improve safety, quality and productivity all at once,” said Dixon Churchill, manager of environmental health and safety at Toyota Industrial Equipment Manufacturing, talking to EHS Today, “So we merged both sides of it into one daily discussion."

The effect of this approach is two-fold, he explains. First, the system empowers the individual workers to take an active role in developing a safer, better system for their daily production responsibilities. But it also empowers managers across the floor to take a more active role in the EHS conversation.

SEE MORE: Toyota named top automotive manufacturing company, followed by Volkswagen and GM

“While these leaders are certainly responsible for the overall objective of meeting their production numbers and quality numbers on a daily basis, this system puts safety numbers very much on the forefront of that team,” he explained.

For a company the size of Toyota, continuous improvement when it comes to EHS is critical to success and long-term sustainability.

Training: Key to continuous health and safety success

As well as having daily management meetings, Toyota is also dedicated to developing a group-wide culture of health and safety from the grassroots up. To this end, the company runs a six-day training for every new hire – permanent or temp – as standard. Every single employee on the production floor must pass through the training program before working on a station.

SEE MORE: Highly trained takumi employees give Toyota the edge

Churchill explains the outline of the program: “Our orientation process includes three full days of classroom training,” he said. “We have 11 different classes as part of the orientation process, including environmental classes and we supplement those with side-line dojos where they can observe the specifics about the tools and processes they are going to be using.”

In the center of each plant is an enormous cell dedicated solely to safety training, referred to as the ‘safety dojo’. The dojo is lined with PPE, instructions, charts and training gear and gives new recruits the opportunity to work with the systems first hand, before being let lose on the production floor.

Safety is taught from day one, even before employees have a chance to get to grips with the actual manufacturing process. It’s ingrained in the minds of every worker on the production line. That being said, safety is not a one-time experience for Toyota employees, but an ongoing process. Every year, Toyota employees revisit their dojo training to reinforce that early education and correct lapses or bad habits picked up over time.

Combined with the K-HYP system, this not only expands the reach of EHS through all levels of the organization, but it ensures that everyone it reaches is, in some way, an EHS expert. 

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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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