Industrial applications of artificial intelligence

Artificial intelligence (AI) adoption is crucial for the success of companies. AI solutions, in particular, are becoming increasingly significant in industrial sectors as they help to optimise manufacturing processes, detect machinery faults, and offer more efficient smart services. Industry can also embrace big data and smart ICT to boost productivity and performance while paving the path for innovation.

Background

AI is increasingly a priority not only for businesses, but also for governments, academic research institutes, and the general public. AI approaches are projected to assist governments, citizens, and enterprises (including in the fight against Covid-19), by enabling adaptability and improving green, sustained development, among other things. Simultaneously, AI has the ability to disrupt and potentially displace corporate paradigms, as well as influence how individuals live and work.

The digital enterprise is now a reality as the industry becomes increasingly digitised. Data is generated, processed, and evaluated in real time. Data volumes in manufacturing contexts serve as the foundation for creating digital representations of entire plants and systems. For some time, these digital advancements have been used to structure the planning and design of products and machines – as well as production operations – and do so more flexibly and efficiently while producing high-quality, customised items faster and at a lower cost. But what if machines and processes could acquire insights from massive amounts of data on their own and optimise their processes in real time? The potential is huge. The good news is that this is already possible, thanks to AI.

For more than 30 years, artificial intelligence has been the subject of research. During this time, significant breakthroughs in technology have been made, including more powerful hardware and software, as well as improved computational power and data transfer. Using artificial intelligence opens up totally new possibilities for flexible, efficient production, especially for complicated and increasingly personalised items produced in small batch runs. According to a Roland Berger analysis, the repercussions will be significant: Intelligent, digitally networked systems and process chains might contribute an additional €420 billion in growth in Western Europe alone by 2035. According to a PwC report, artificial intelligence (AI) might contribute up to $15.7 trillion to the global economy by 2030.

While AI’s incremental GDP impact is initially small (up to 1.8 percent of additional cumulative GDP growth by 2025), the study contends that there is tremendous long-term potential (up to 13.5 percent of cumulative GDP growth by 2030), with variances between regions and industries. However, the full promise of AI will be realised if European businesses are adequately supported in their AI transformation and recognize the competitive advantages it may give.

According to the European Commission’s  Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs, AI is expected to have the greatest economic impact on:

• Manufacturing and the Industrial Internet of Things – IIoT, with a total AI impact potential in Europe of up to €200 billion by 2030.
• Mobility, with a €300 billion AI impact. 
• Potential smart health, with a €105 billion AI impact potential.

The effects of AI and automation technologies on the labour market are important in at least four categories:

1. Labour cost substitution is projected to substitute some workforce 
2. investment in AI and AI-enabled products and services. Direct jobs may be created as a result of innovation.
3. Wealth creation may have a positive economic spillover impact.
4. AI may enable greater participation in global processes (data and trade), resulting in the creation of new jobs.

AI and Industry 4.0

Big data and AI provide a significant boost to Industry 4.0. Intelligent software products can use a factory’s massive amounts of data to detect trends or patterns that can subsequently be used to improve manufacturing processes and minimise energy consumption. This is how plants constantly adapt to new conditions and optimise themselves without the need for operator intervention. As the amount of networking develops, AI software can train to “read between the lines,” leading to the discovery of numerous complex linkages in systems that aren’t yet or are no longer visible to the human eye.

Intelligent software with advanced analytical technology is currently available. However, whether processing of data is done on the cloud or utilising Edge computing depends on the user’s needs. Data on an Edge platform is available faster and at a greater resolution, whereas significant computational power is available in the cloud. In many circumstances, mixing edge and cloud computing is necessary to reap the benefits of both worlds.

What are the main benefits of artificial intelligence in manufacturing?

When used correctly, artificial intelligence has various advantages for the manufacturing business. We’ve chosen three of the most important benefits for the industrial industry today:

1. Error reduction: after being educated, intelligent algorithms may accomplish jobs that are prone to errors in human-executed procedures very well. Because algorithms are not vulnerable to external circumstances, they should be spared the effects of these causes. Investments in the AI can reap several rewards from predictive maintenance, including decreased downtime, fewer productivity delays, and cost-saving advantages.
2. Cost savings: several e-commerce businesses and banks use robots to initiate client assistance. The human attendant is only summoned if the problem is more complicated. Companies can cut staff costs by designating people to other responsibilities in more critical areas that can boost profit and focus on their business using this technique. 
3. Revenue Growth: because there are less errors and staff are focused on more vital  operations, decision-makers will have more time to think about the core company and leave other AI duties to others.

Big data sharing and access between businesses

Despite the enormous economic possibilities, data sharing between corporations has not taken hold on a large enough scale. Although in Europe there are already initiatives to amend this situation, as indicated in the February 2020 Communication, “The European data strategy” the Commission wanted to identify and resolve any unjustified barriers to data exchange and the use of privately owned data by other enterprises.

Again, specifically in Europe, and related to business-to-business (B2B) data sharing, two big data pilot projects are being launched to investigate the innovation potential and novel business models generated by data sharing between data-producing/controlling entities and third-party enterprises. These pilot initiatives are being carried out in two strategic value chains: smart health (with the goal of using diabetes data from healthcare providers) and automotive (where sharing in-vehicle data produced by connected vehicles will be examined). Both projects are part of the 2019-2021 ‘Big data and B2B platforms: the new frontier for Europe’s industry and enterprises’ study.

Industry 4.0 is now a reality for many businesses throughout the world. Still, it is worth noting that all of the changes required to fully participate in this industrial revolution and reap its rewards will not occur suddenly.

This, like Digital Transformation and Automation, is a continual process that is constantly being improved. As we can see, artificial intelligence is a critical component of this transformation.