May 16, 2020

How Apple Manufacturer Foxconn Plans to Go Green

Foxconn
Apple
Green Manufacturing
Chinese Manufacturing
Glen White
3 min
A town in Guizhou where Foxconn plans to open a new manufacturing facility.
Guizhou is currently one of Chinas poorest and least developed nations. But it also boasts an environment so pristine that President Xi Jinping recently...

Guizhou is currently one of China’s poorest and least developed nations. But it also boasts an environment so pristine that President Xi Jinping recently joked that the region’s air should be bottled.

As a result, Taiwan’s Foxconn Technology Group, the world’s largest consumer electronics producer that employs more than a million workers in 30 industrial parks across China, has set its sight on the gorgeous expanse of Guizhou.

Foxconn, the maker of Apple’s iPad and iPhone and Hewlett-Packard servers, is building an industrial park in China’s southwest, a far cry from its massive Shenzen manufacturing base.  The company aims for the park to be state of the art in energy efficiency and environmental friendliness.  Set among karst hills on the outskirts of provincial capital Guiyang, the 500-acre park will keep roughly 70 percent of the natural vegetation undisturbed.

The park will produce smartphones and big-screen TV’s as well as house a 130,000 square meter research and development center and a 2,160 square meter big data center, all while remaining eco-friendly.  By March of 2015, the park will employ 12,000 workers and have an annual output of 35 billion yuan ($5.6 billion) according to the company’s projections.  In 2018, it plans to more than triple in acreage to employ 50,000 workers and reach 50 billion yuan in sales.

The gem in its green cost-savings model is the use of a north-south wind tunnel, already bored through one of the area’s hills.  The tunnel will cool the 12 containers of servers in Foxconn’s big data project.  Generally, 35 percent of the energy consumed to run servers goes towards air conditioners for cooling.  Foxconn aims to reduce that portion to a mere ten percent by using wind to blow over its servers and dissipate the heat they generate.

Speaking at a recent forum on environmental protection in Guiyang, Foxconn founder and CEO Terry Gou commented on the plans by saying, “We have succeeded in leveraging technology to enhance all aspects of manufacturing, and we are focusing our investments in areas that link technology with sustainable economic growth in a way that also protects the environment.”

Construction will include close to 100 percent recycled steel in the zone’s buildings.  In order to reduce energy use while operating, the company will install its own patented heat-reflective laminated glass for all windows.  For outdoor lighting, it will install “intelligent” street lamps of its own design, which rely on solar panels for energy and are equipped with sensors that are able to determine traffic load and weather conditions and can adjust the strength of the light to eliminate unnecessary usage.

The company has also taken steps to change how its smartphones will be manufactured in the region, with a focus on cutting energy and water use as well as reducing the paint and chemicals needed in production.  Foxconn will use a special mold process that ensures that less than one percent of the paint is wasted, significantly reducing the amount of chemical fumes released into the air.  Additionally, they will replace the traditional film used in smartphone screens with a new carbon nanotube film that requires 80 percent less energy to produce and cuts water use to near zero.

Like many manufacturers, Foxconn is facing pressure from its customers to ensure that its production process has fewer environmental costs.  At a press conference held earlier this month in Guiyang, Gou stated, “In the past, people thought being green would increase costs or lower efficiency. That is not the case. This industrial park represents a new model for growth: green and responsible.”

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

SmartManufacturing
ArtificialIntelligence
bigdata
Technology
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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