Exploiting Big Data and analytics to improve productivity in manufacturing
By applying lean tools...
<p><strong><span><span><span>Lean and continuous improvement methodologies have successfully driven increases in factory performance over the last 30 years. </span></span></span></strong></p>
<p><span><span><span>By applying lean tools and techniques and empowering the shop floor we have seen real improvements in manufacturing performance. However, with the rise of the connected and integrated shop floor we are now seeing a new revolution in factory productivity. Data is at the centre of that improvement, with our work with a range of companies in life sciences, automotive and consumer goods, showing that effective use of data can bring 10-15% improvements in productivity. </span></span></span></p>
<p><span><span><span>A leading life sciences company which runs a batch manufacturing process was experiencing a range of bottlenecks that were affecting productivity and overall equipment efficiency. By extracting and analysing multiple data sources ranging from ERP, machine, environmental and maintenance we could identify where they could find the next level of improvement opportunities. In particular, the data was analysed to identify the characteristics and optimum machine settings of a perfect batch in one of the bottleneck machines. By applying the settings and characteristics of this perfect batch to all batches, the company achieved a 15% improvement in scrap and 2% improvement in uptime. They are now scaling up this approach across other machines on the site and looking at the potential of applying it to their operations worldwide.</span></span></span></p>
<p><span><span><span>Better data can also help the automotive sector address the problem that, when it launches a new car, it can take weeks or even months before quality and warranty problems in the field are identified centrally. This is a major challenge for OEMs and can also lead to costly product recalls (example size of recalls). By collecting and analysing data from vehicles as they are launched and using it immediately to start to assess failures and feed this back to engineering and manufacturing they can deal with these quality issues before they become a big problem. That significantly reduces the cost of quality and warranty in manufacturing and their repair requirements.</span></span></span></p>
<p><span><span><span>These examples show the potential of data to address specific bottle necks and points in the manufacturing process or the supply chain. However, as the technologies and their capabilities improve, the next level of data-driven transformation will come from the ability to analyse the factory eco-system and even the supply chain as a whole. To help them do this companies are already starting to build digital twins of their manufacturing processes and supply chains based on the data and processes they are running. Once these digital twins are in place then machine learning and AI can be used to test and optimise the operation in the virtual world before applying it to the real factory environment. The advantage of using a digital twin is that it can gain an understanding of all of the variables which drive in efficiencies across the whole process and find ways to optimise that process rather than just looking at an individual machine.</span></span></span></p>
<li><span><span><span><span><a href="https://www.manufacturingglobal.com/technology/opinion-piece-industry-4… Piece: Industry 4.0 and the changing job landscape</span></span></a></span></span></span></span></li>
<li><span><span><span><span><a href="https://www.manufacturingglobal.com/technology/collaboration-key-europe… is key for Europe's advanced manufacturing and technology (AMT) companies</span></span></a></span></span></span></span></li>
<li><span><span><span><span><a href="https://www.manufacturingglobal.com/leadership/manufacturers-how-you-be…, this is how you become future-proof</span></span></a></span></span></span></span></li>
<li><span><span><span><span><span><span><span><a href="https://www.manufacturingglobal.com/magazine/manufacturing-global/manuf… the latest issue of Manufacturing Global here</span></a></span></span></span></span></span></span></span></li>
<p><span><span><span>So how can a company start to use data driven manufacturing to change what they do? For us the mantra is “Think big, start small, scale fast.” <strong> </strong>Thinking big is all about developing the vision of what data can do to transform the factory and supply chain, and creating a clear picture to show senior leaders where it will bring value. Then, by starting small, businesses can prove the value of data through initial use cases. That then opens up the opportunity to scale fast by accelerating the delivery of the use case by making sure the processes, technologies and applications are in place to implement them in an effective and agile way, and giving the data scientists the inputs they need to scale up the solutions.</span></span></span></p>
<p><span><span><span>The main challenges in getting these projects off the ground are firstly to ensure that the structures are place to extract the right data to address the challenges, understanding that this is often not the obvious data. For example, factory environmental data, humidity and temperature can have an impact on machine set up and performance, but may not have previously been considered important. So the business will need to build the data collection mechanisms, put sensors in place, and provide the support cloud IT infrastructures and, at the same time, make sure the data is clean and accurate.</span></span></span></p>
<p><span><span><span>Secondly, like any data driven improvement it does not just happen by hiring a great set of data analysts. What is needed is a combination of data analytics capabilities, knowledge and experience of the manufacturing processes and the shopfloor and importantly the ability to change the way the operators and engineers work. That can require a real adjustment in mindset from engineers as they move from being the experts to being part of a process which trusts the data to deliver the results. </span></span></span></p>
<p><span><span><span>Using Big data, analytics and machine learning will bring the next big wave of performance improvement in manufacturing, and will build on the achievements we have already seen from lean. The results and use cases derived from these technologies can be delivered in a matter of weeks, so the time to generate a return on investment is short. However, like any other significant change in operations they will require new skills and changes to existing capabilities and the challenges of putting these in place should not be under estimated.</span></span></span></p>
<p><span><span><span>However, the potential of data is clear. Manufacturers are already seeing tangible benefits and there will be much more to come for those who are willing to embrace the opportunity and put the right processes, culture and investment in place.</span></span></span></p>
<p><span><span><span><u>Credit</u>: <a href="//www.paconsulting.com/manufacturing">PA Consulting </a></span></span></span></p>
First Solar to Invest US$684mn in Indian Energy Sector
First Solar is about to set up a new photovoltaic (PV) thin-film solar manufacturing facility in Tamil Nadu, India. The 3.3GW factory will create 1,000 skilled jobs and is expected to launch its operations in Q3 of 2023. According to the company, India needs 25+ gigawatts of solar energy to be deployed each year for the next nine years. This means that many of First Solar’s Indian clients will jump at the chance to have access to the company’s advanced PV.
Said Mark Widmar, First Solar’s CEO: ‘India is an attractive market for First Solar not simply because our module technology is advantageous in its hot, humid climate. It’s an inherently sustainable market, underpinned by a growing economy and appetite for energy’.
A Bit of Background
First Solar is a leading global provider of photovoltaic systems. It uses advanced technology to generate clear, reliable energy around the world. And even though it’s headquartered in the US, the company has invested in storage facilities around the world. It displaced energy requirements for a desalination plant in Australia, launched a source of reliable energy in the Middle East (Dubai, UAE), and deployed over 4.5GW of energy across Europe with its First Solar modules.
The company is also known for its solar innovation, reporting that it sees gains in efficiency three times faster than multi-crystalline silicon technology. First Solar holds world records in thin-film cell conversion efficiency (22.1%) and module conversion efficiency (18.2%). Finally, it helps its partners develop, finance, design, construct, and operate PV power plants—which is exactly what we’re talking about.
How Will The Tamil Nadu Plant Work?
Tamil Nadu will use the same manufacturing template as First Solar’s new Ohio factory. According to the Times of India, the factory will combine skilled workers, artificial intelligence, machine-to-machine communication, and IoT connectivity. In addition, its operations will adhere to First Solar’s Responsible Sourcing Solar Principles, produce modules with a 2.5x lower carbon footprint, and help India become energy-independent. Said Widmar: ‘Our advanced PV module will be made in India, for India’.
After all, we must mention that part of First Solar’s motivation in Tamil Nadu is to ensure that India doesn’t rely on Chinese solar. ‘India stands apart in the decisiveness of its response to China’s strategy of state-subsidised global dominance of the crystalline silicon supply chain’, Widmar explained. ‘That’s precisely the kind of level playing field needed for non-Chinese solar manufacturers to compete on their own merits’.
According to First Solar, India’s model should be a template for like-minded nations. Widmar added: ‘We’re pleased to support the sustainable energy ambitions of a major US ally in the Asia-Pacific region—with American-designed solar technology’. To sum up: Indian solar power is yet the next development in the China-US trade war. Let the PV manufacturing begin.