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Monday 26th July 2021 09:27 PM

Unlocking Data Value in Manufacturing


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Nearly three-quarters of manufacturing managers worldwide consider data sharing to improve their operations. In a recent study, the World Economic Forum, in collaboration with Boston Consulting Group (BCG), entitled Share to Gain: Unlocking Data Value in Manufacturing, examined the opportunities associated with data sharing in manufacturing and the enablers for building successful data-sharing ecosystems.

Although manufacturers are making strides in applying data-driven technologies, most focus on applications within their companies and have difficulty maximizing the return on investment. By sharing data across companies, manufacturers can create even more value and foster faster innovation. The potential value of data sharing by focusing on manufacturing process optimization alone has been estimated to be more than $100 billion.

"True champions apply shared data to improve existing solutions like predictive maintenance that benefit from larger data sets," says Daniel Küpper, BCG managing director and partner and a report coauthor. "They also successfully implement new solutions where suppliers share a digital product twin with the OEM, enabling autonomous handling of parts."

To demonstrate the value of data sharing in manufacturing, the study examined five main areas where data sharing can help improve operations. For example, in one application area, manufacturers can improve algorithms to optimize asset performance by combining data from similar production machines. In other areas, manufacturers can apply shared data to track products, trace process conditions, and verify provenance along the value chain, thereby eliminating duplicative processes.

The authors propose a five-step approach to help manufacturers start the process of building successful data-sharing ecosystems. Understanding the business challenges to address by data sharing, identifying suitable applications, selecting viable ones, finding the right partners, and selecting the right model for collaboration are essential for getting off to a good start.

In examining successful data-sharing collaborations, the authors identified four important enablers:

  • Selecting the right technologies. Manufacturers have several options today for enabling secure data-sharing collaborations. Risks will always exist, but companies that select the right technology infrastructure and architecture can reduce these risks to an acceptable level. For instance, a data lake on a cloud service might be sufficient for one application, while another might require a blockchain solution. 
  • Using common standards. To overcome interoperability issues, data-sharing arrangements require several layers of standardization. Initiatives are under way to help manufacturers meet the challenges. Some organizations are developing standards that facilitate the communication of manufacturing data, while others are developing common reference architectures to use industry-wide.
  • Building trust. The main barrier to successful data-sharing relationships is building trust between collaborating partners around a clear value proposition. To promote trust, companies need to first regard data as a business asset and consider it in their value proposition. Additionally, companies need to build relational contracts that focus on common goals and mutual benefit.
  • Having legal and regulatory clarity. Governments have an important role in helping manufacturers realize the potential of data sharing. Regulations and policies relating to data can create barriers to data sharing. Localization requirements are an example. Countries use these requirements to make companies store specific data within their boundaries or to place restrictions on the flow of data. Such requirements can make crossborder data sharing in supply chains quite difficult. Indeed, the World Trade Organization has cited data localization laws as digital trade barriers.

Various organizations are pursuing activities to develop these enablers, which the authors find encouraging. "To accelerate innovation, all stakeholders in manufacturing ecosystems must work together to address the key questions related to data sharing and unlock its full potential," says Francisco Betti, head of Shaping the Future of Advanced Manufacturing and Production at the World Economic Forum. "Insights from the report will help kick-start the development of new tools and establish new partnerships to take data-based excellence in manufacturing to the next level."


DATA SHARING IN MANUFACTURING





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Posted on : Monday 26th July 2021 09:27 PM

Unlocking Data Value in Manufacturing


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Posted by  Tronserve admin
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Nearly three-quarters of manufacturing managers worldwide consider data sharing to improve their operations. In a recent study, the World Economic Forum, in collaboration with Boston Consulting Group (BCG), entitled Share to Gain: Unlocking Data Value in Manufacturing, examined the opportunities associated with data sharing in manufacturing and the enablers for building successful data-sharing ecosystems.

Although manufacturers are making strides in applying data-driven technologies, most focus on applications within their companies and have difficulty maximizing the return on investment. By sharing data across companies, manufacturers can create even more value and foster faster innovation. The potential value of data sharing by focusing on manufacturing process optimization alone has been estimated to be more than $100 billion.

"True champions apply shared data to improve existing solutions like predictive maintenance that benefit from larger data sets," says Daniel Küpper, BCG managing director and partner and a report coauthor. "They also successfully implement new solutions where suppliers share a digital product twin with the OEM, enabling autonomous handling of parts."

To demonstrate the value of data sharing in manufacturing, the study examined five main areas where data sharing can help improve operations. For example, in one application area, manufacturers can improve algorithms to optimize asset performance by combining data from similar production machines. In other areas, manufacturers can apply shared data to track products, trace process conditions, and verify provenance along the value chain, thereby eliminating duplicative processes.

The authors propose a five-step approach to help manufacturers start the process of building successful data-sharing ecosystems. Understanding the business challenges to address by data sharing, identifying suitable applications, selecting viable ones, finding the right partners, and selecting the right model for collaboration are essential for getting off to a good start.

In examining successful data-sharing collaborations, the authors identified four important enablers:

  • Selecting the right technologies. Manufacturers have several options today for enabling secure data-sharing collaborations. Risks will always exist, but companies that select the right technology infrastructure and architecture can reduce these risks to an acceptable level. For instance, a data lake on a cloud service might be sufficient for one application, while another might require a blockchain solution. 
  • Using common standards. To overcome interoperability issues, data-sharing arrangements require several layers of standardization. Initiatives are under way to help manufacturers meet the challenges. Some organizations are developing standards that facilitate the communication of manufacturing data, while others are developing common reference architectures to use industry-wide.
  • Building trust. The main barrier to successful data-sharing relationships is building trust between collaborating partners around a clear value proposition. To promote trust, companies need to first regard data as a business asset and consider it in their value proposition. Additionally, companies need to build relational contracts that focus on common goals and mutual benefit.
  • Having legal and regulatory clarity. Governments have an important role in helping manufacturers realize the potential of data sharing. Regulations and policies relating to data can create barriers to data sharing. Localization requirements are an example. Countries use these requirements to make companies store specific data within their boundaries or to place restrictions on the flow of data. Such requirements can make crossborder data sharing in supply chains quite difficult. Indeed, the World Trade Organization has cited data localization laws as digital trade barriers.

Various organizations are pursuing activities to develop these enablers, which the authors find encouraging. "To accelerate innovation, all stakeholders in manufacturing ecosystems must work together to address the key questions related to data sharing and unlock its full potential," says Francisco Betti, head of Shaping the Future of Advanced Manufacturing and Production at the World Economic Forum. "Insights from the report will help kick-start the development of new tools and establish new partnerships to take data-based excellence in manufacturing to the next level."


DATA SHARING IN MANUFACTURING




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data sharing digital product manufacturing ecosystems