August 18, 2020
Machine learning (ML) and artificial intelligence (AI) rely heavily on data. The completeness, accuracy, and scope of the data directly influence the benefits of ML technologies. To increase the capabilities of AI systems, many companies have begun actively sharing their data with other companies. Data sharing provides an efficient way to increase the quantity and quality of available data.
Sharing data benefits everyone from manufacturers to suppliers. With greater data, AI systems are better equipped for improving smart manufacturing processes and reducing waste. They can also improve the logistics of the supply chain. As more companies practice data sharing, those that refuse to share are likely to be left behind. Data sharing may even become a requirement to prevent competitive disadvantages.
Removing Data Localisation Promotes Data Sharing
The Support Centre for Data Sharing (SCDS) recently released its analytical report and recommendations for legislation related to data sharing. The SCDS found that one of the biggest barriers to data sharing is data localisation.
To address these barriers, the European Parliament released the framework for the free flow of non-personal data in the EU (Free Flow Regulation). The framework became applicable in May 2019 and helps eliminate data localisation restrictions.
By removing data localisation restrictions, manufacturers and suppliers have greater freedom for sharing data between countries and regions. For example, a manufacturer in the UK may need to share data with suppliers in Asia or the Pacific. Cloud service providers can now facilitate this sharing, thanks to the removal of data localisation restrictions.
Data Sharing May Help EU Companies Compete Globally
Without data sharing, companies in the EU and the UK may struggle to compete on a global scale. Companies from larger initial markets have greater access to big data, which enhances the capabilities of AI and ML technologies.
The European Commission recently released a white paper on AI in the EU. The paper notes that a small number of big tech companies hold a large part of the world’s data. Most of these companies are based in the US and China, which gives the EU a disadvantage in the race for AI superiority.
The EU aims to create a single European data space where companies in various sectors can pool data. These steps may help create a European data market and improve data sharing in manufacturing, mobility, health, and other key sectors.
Companies in the UK and the EU serve smaller markets, which limits their ability to obtain data. This means that smaller manufacturers and suppliers in Europe have less data compared to the major manufacturers in the US and Asia that serve hundreds of millions of customers.
For example, a small manufacturer in the UK may currently use factory automation technology in their manufacturing processes. Due to the limited market size, the company’s ML software receives limited data, which reduces its potential benefits. By pooling data from multiple EU member states and the UK, data science in manufacturing may become more accessible to smaller manufacturers.
Data Sharing Is Already Creating a Competitive Advantage
Data sharing is already helping to strengthen the efforts of some of the largest corporations, especially in the banking sector. A 2016 investigation in the UK found that the free flow of data can create a greater level of transparency and encourage more competition in the banking sector. After the UK adopted open banking policies, Australia followed suit with the Consumer Data Right Act (CDR).
What does all this mean for the world of manufacturing? AI and optimised production technology help reduce waste, improve efficiency, and aid quality control. However, quality control tools and optimisation technologies depend on data.
Smaller manufacturers benefit from data sharing by supplying their AI systems with more information, which enhances the advantages of ML software. Creating an open environment to facilitate the sharing of data allows manufacturers to develop more efficient processes. Here are a few of the ways that data sharing in manufacturing creates real value:
1. Enhance asset optimisation
2. Trace processes in the value chain
3. Track products in the supply chain
Manufacturers and suppliers are already exploring these benefits. A recent report found that 72% of managers in the manufacturing sector plan on using data sharing in the coming year to improve their operations.
Using Data to Enhance Asset Optimisation
Sharing data may allow manufacturers to optimise their machinery. When multiple companies use the same machinery and equipment, pooling data creates a larger sample size.
By supplying the ML software with additional data, the software can make more accurate predictions. This may lead to improvements in predictive maintenance, machine uptime, product quality, and waste reduction.
Tracing Processes Throughout the Value Chain
Openly sharing data allows suppliers to create digital records of the regulatory processes that they follow. Manufacturers can then use the digital record to ensure that their products meet all necessary industry and regional standards.
Tracking Products Throughout the Supply Chain
While suppliers and manufacturers already track products, sharing additional data provides greater end-to-end visibility. Manufacturers can react more quickly to unexpected issues and adjust their inventory or production output as needed.
Along with tracking products throughout every stage of the supply chain, manufacturers and suppliers can synchronise their processes. For example, suppliers may easily share data on the shape and composition of a component. Manufacturers can then instantly synchronise their production processes to accommodate the component based on the digital twin.
How Can Companies Safely Share Data?
One of the potential risks of openly sharing data is the exchange of personal data and sensitive company data. Companies still need to protect their interests and the privacy of their customers. Third-party intermediaries may help address the privacy concerns of data sharing. New data-sharing platforms are emerging to aid the exchange of data between multiple parties.
Another solution is a peer-to-peer (P2P) network for data ecosystems. This allows companies that cannot afford to build their own data platforms to freely share data.
In the coming years, data sharing may become an integral part of smart manufacturing. Companies that fail to take part are likely to get left behind.
Feel free to also check out our other posts:
Looking Behind Big Data in Manufacturing – 8 steps to ML readiness
Experience-Based vs. Fact-Based Decisions: How AI/ML Is Changing the Way Engineers Work
Machine Learning Projects in Manufacturing: Expectations vs. Reality
