May 16, 2020

How companies can reduce time wasted in assembly

assembly line
3 min
How companies can reduce time wasted in assembly
The average worker spends approximately 348 hours per year commuting to work. This is the equivalent of a quarter of the working week spent travelling f...

The average worker spends approximately 348 hours per year commuting to work. This is the equivalent of a quarter of the working week spent travelling from A to B with no direct value to either employee or employer. Similarly, many businesses find that moving products through the supply chain creates a lot of non-productive time. Here, Andy Owen, Managing Director of electric tug specialist MasterMover, explains how businesses can improve efficiency and productivity.

A survey conducted by in 2014 found that 89 per cent of employees waste at least some time at work, 20 per cent more than the previous year. In particular, 94 per cent of engineering, manufacturing and construction professionals admitted to wasting time. The amount ranged from 30 minutes up to several hours.

When you look at these figures against the backdrop of the productivity crisis that many countries are reported to be facing, it paints a worrying picture. As production levels drop and wasted time increases, businesses must do everything they can to improve productivity. This comes as a result of improved efficiency.

However, it's not just intentionally wasted time that's the culprit. A lot of wasted time occurs as part of the warehousing and manufacturing process itself. The first step is to determine where efficiency can be improved. Plant managers must therefore assess the main causes of non-value added time (NVAT) in their supply chain, which is often either the initial assembly or the subsequent transportation of products.

For example, an aerospace manufacturer will be required to move large parts across a plant as part of a staged production process. However, an engineer will need a large industrial load carrier to do so, which may even need to be operated by a specially trained member of staff. This means that engineers must first wait for equipment to be available before the production process can move on.

An engineer might only spend five minutes waiting each time, but over the course of a working week this will accumulate and account for significantly more. The wait may also lead to a production bottleneck, with many parts ready for assembly but only limited capacity for moving them.

Any plant manager observing this would come to the conclusion that the process could be made more efficient and lean by changing it entirely. For example, there would be less wait time if each member of staff could use equipment to move parts to the assembly area without delay.

Businesses could act on this in several ways. On one hand, a business could invest in machinery that automatically moves completed parts to assembly areas on a set track. However, this may pose several logistical issues while also substantially increasing capital expenditure.

The cost-effective alternative is for plant managers to invest in powerful electric tugs for large or heavy-duty loads. These pedestrian operated tugs allow a single engineer to safely transport heavy parts across plants with ease, reducing NVAT by eliminating the need for waiting or specialist assistance.

Electric tugs from MasterMover, for example, can move loads from 50kg up to 60,000kg on castors. This not only makes the tug suitable for bulky parts, but also for moving completed products ready for storage or distribution.

Of course, this is only one way businesses can reduce NVAT. In order to truly maximise efficiency, businesses must adopt a lean manufacturing approach and slowly introduce changes. Each incremental improvement, such as saving five minutes wastage per day in assembly, is one step closer to creating a company culture of efficiency.

While businesses can’t do much to directly change the amount of time it takes employees to commute to work, plant managers can take action to ensure that work time is spent productively. This means more time working and less time waiting.


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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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