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It’ s no secret that the manufacturing industry is in the midst of a significant transformation, grappling with technological shifts, increased customer expectations, and supply chain vulnerabilities. The effects are starting to show. Our latest Global Business Optimism Insight Report reveals that confidence in supply chain resilience is still slipping. After a sharp 9.7 percent drop in Q3, the index dipped again in Q4, pointing to ongoing uncertainty.
In a landscape this complex, data is a manufacturer’ s critical ally, allowing teams to stay agile, anticipate disruption, and make smarter decisions. Yet, in my daily conversations with manufacturing leaders, one thing has become increasingly clear: despite having more data than ever at their fingertips, many still aren’ t using it to inform critical business decisions. There’ s a clear confidence gap when it comes to actually turning data into action. So why has this happened and how do we fix it?
The root causes of the data confidence gap
1. Poor data quality and manual processes Clean, structured, compliant, and up-todate data is the foundation of effective decision-making, but many manufacturers still struggle to achieve it. The reliance on manual data collection, a practice still widespread, introduces errors and delays that erode trust in the data from the start. When data is collected manually, it’ s susceptible to human error and outdated information, making it unreliable for analysis. These issues often cause leaders to rely on intuition and experience rather than a datadriven approach. While intuition will always have its place, it can’ t keep pace with the complexities of modern manufacturing, such as volatile supply chains and technological change. Inaccurate supplier data can lead to sourcing from high-risk partners, or failing
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