Data Excellence: Transforming manufacturing and supply systems

The report argues that manufacturing is moving toward hyperconnected value networks where data and analytics can reduce conversion costs by up to 20%. It identifies three primary value areas: increased productivity, enhanced customer experience, and positive societal and environmental impacts. To capture this value, the authors propose six priorities: defining a data-to-value strategy, incentivizing partners, building digital capabilities, implementing open platforms, enabling high-bandwidth connectivity, and ensuring data security. The World Economic Forum is also developing a Manufacturing Data Excellence Framework to help companies assess their maturity across these dimensions.

• Data-and-analytics applications in manufacturing have the potential to reduce conversion costs by up to 20%, depending on the industrial sector.
• While many applications can be managed with internal data, sophisticated use cases—such as training AI algorithms or ensuring full supply chain transparency—require the exchange of data across corporate boundaries.
• A Boston Consulting Group survey of over 1,300 manufacturing executives revealed that 72% believe advanced analytics are more important now than three years ago, yet 62% of manufacturers have not scaled these use cases beyond a single plant's value stream.
• The primary challenges hindering the scaling of data solutions include insufficient skills and capabilities (26%), data-security risks (24%), lack of effective processing applications (22%), complex internal governance (22%), and undefined roles and responsibilities (20%).
• To unlock value, decision-makers should focus on three organizational priorities: defining a data-to-value strategy and roadmap, incentivizing internal and external ecosystem partners, and building capabilities to capture and use data.
• Technological priorities for data excellence include implementing open platforms to break data silos, enabling low-latency and high-bandwidth connectivity (such as 5G or WiFi 6), and ensuring data security and privacy.
• Data and analytics can drive positive environmental impact by enabling ethical production, increasing resource efficiency through machine learning, and providing transparency on carbon footprints, which are often 5.5 times greater in supply chains than in internal operations.
• The World Economic Forum is developing a Manufacturing Data Excellence Framework consisting of 20 dimensions across three pillars: application areas, organizational enablers, and technological enablers, to serve as a self-assessment tool for company maturity.

APA

World Economic Forum (2021). Data Excellence: Transforming manufacturing and supply systems. http://www3.weforum.org/docs/WEF_Data_Excellence_Transforming_manufacturing_2021.pdf

Chicago

World Economic Forum. Data Excellence: Transforming manufacturing and supply systems. 2021. http://www3.weforum.org/docs/WEF_Data_Excellence_Transforming_manufacturing_2021.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Data Excellence: Transforming manufacturing and supply systems |date=January 2021 |url=http://www3.weforum.org/docs/WEF_Data_Excellence_Transforming_manufacturing_2021.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2021data, author = {{World Economic Forum}}, title = {{Data Excellence: Transforming manufacturing and supply systems}}, institution = {World Economic Forum}, year = {2021}, month = jan, url = {http://www3.weforum.org/docs/WEF_Data_Excellence_Transforming_manufacturing_2021.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }