May 18, 2021
In the age of big data, the need for data-sharing solutions has become increasingly apparent. Artificial intelligence and machine learning technologies provide endless potential for the manufacturing industry. However, these technologies are limited by the amount of data that they can access.
Manufacturing processes already frequently involve several stakeholders working together to meet tighter timelines and rising quality requirements. Each of these stakeholders has separate pools of big data, yet the data remains isolated.
Bringing together more data can help remove previously hidden bottlenecks and increase manufacturing output. Of course, data sharing also requires trust and honesty between stakeholders. Breaking through these barriers can lead to innovative new business models.
Discover how data sharing is the key to vertical innovation in manufacturing.
What Is Data Sharing?
Data sharing refers to the practice of sharing data between multiple users. It is a primary component of database management systems (DBMS). However, it is also increasingly important for companies to share data with other companies.
Data sharing policies permit organisations to share data resources, typically to improve operational processes. The data is shared between computer systems and often used by artificial intelligence (AI) and machine learning (ML) technologies. In the manufacturing industry, shareable data may include information related to the performance of machines or the characteristics of parts. It does not involve the sharing of sensitive information.
What Are the Applications for Data Sharing in Manufacturing?
According to a recent study completed by the World Economic Forum, difficulty measuring the value of data is the main reason why manufacturers choose not to share data. However, about 72% of manufacturers have stated that they would consider sharing data to improve operations.
Data sharing is already growing in other industries, including construction. For example, construction companies and contractors have discovered the value of Integrated Project Delivery (IPD). IPD integrates with business structures to harness big data from multiple organisations. The IPD system helps optimise projects and processes for all participants.
Illustrating how data sharing can improve the quality of products or optimise existing processes may convince stakeholders. Here are five of the biggest examples of how data sharing can lead to greater vertical innovation in manufacturing:
1. Exchange Product Characteristics
Manufacturers frequently rely on other manufacturers for additional components. Data sharing allows the systems used in manufacturing processes to exchange information on the characteristics of products and individual parts.
For example, a supplier and an OEM can create a digital product twin of a part to eliminate the need for additional incoming quality control inspections or measurements. This may streamline one stage of the production process.
2. Improve Asset Optimisation
Manufacturers often use predictive maintenance to minimise downtime associated with the upkeep of machinery and equipment. AI systems can improve the effectiveness of predictive maintenance by analysing the performance of each machine and the past maintenance schedule.
AI systems can make more accurate predictions when given more data. By sharing data, manufacturers can supply their systems with more information to analyse.
3. Track Products Along the Value Chain
Manufacturers track products along the entire supply chain. However, they need to collaborate and share data to track the product throughout every stage.
Better tracking allows manufacturers to react more quickly to unexpected changes in the supply chain that may lead to bottlenecks. Manufacturers can quickly adapt to these changes when they have access to more data.
4. Track Process Conditions
Along with tracking products, manufacturers can use data sharing solutions to track processes. Manufacturers can use big data to verify that suppliers comply with the terms of the contract and all industry and government regulations.
The pharmaceutical and food industries have already implemented data sharing for improved process tracking. The data provides a record as a product moves through the production processes and enters the supply chain. This may also assist with the handling of warranties, as manufacturers gain proof of the manufacturing conditions that may impact the warranty.
5. Enhance Product Quality
The previous benefits lead to enhanced product quality. Sharing digital characteristics and optimising the maintenance of assets allows manufacturers to reduce defects and downtime. They can produce a higher quality of product faster and with less waste, which increases the operating margin.
Enhancing the quality of a product without adding new processes drives vertical innovation. However, implementing a data sharing policy requires the approval of stakeholders from multiple companies.
How to Convince Stakeholders to Share Big Data
Stakeholders hesitate to share big data because they do not see the value that it can bring to their company. Decision-makers are concerned with the bottom line. They do not want to pursue a new endeavour without knowing the potential return. You need to illustrate the ROI of data sharing.
Trust is another concern for stakeholders. Sharing company data goes against the typical practices of a company. Stakeholders may be concerned about the competition using the data to further their own interests instead of using it for mutual interests.
Before presenting the idea of data sharing to stakeholders, identify potential partners that are more likely to share mutual interests. This may include existing partners that are not direct competitors. For example, you may source the production of specific parts to another manufacturer. Sharing data allows you to synchronise and optimise your production processes.
Stakeholders also need to understand that your company does not need to share all data. Data sharing practices typically focus on the sharing of relevant information that impacts the operational processes of all parties.
Conclusion
Companies need to remain competitive to continue innovating. However, collaboration and data sharing can lead to greater outcomes for all parties. The main challenge is getting stakeholders to look past their competitive nature and understand the value of pooling big data. Sharing is not a common practice in manufacturing but does occur in specific cases.
Make sure that stakeholders comprehend that manufacturing data is a valuable business asset, especially when combined with data from other manufacturing facilities. Working with a third party can help facilitate these goals. Consider partnering with a company that specialises in AI-driven production management to explore data-sharing solutions that stakeholders can get behind.
