Jabil
• Ability to predict and prevent failures of over half of all circuit boards at step two of the 32-step process, with the remainder identified at step six – this was done with an 80% accuracy rate
• Savings in excess of 15% in scrap and rework costs, as well as an additional 10% reduction in energy costs.
Duke Energy
• Transitioned from collecting four readings from a data point per year, to collecting readings every five seconds. This allowed a shift from analysts spending 80% of time collecting data with only 20% of time doing the higher value task of analyzing the data, to analysts spending 80% of their time on data analysis tasks.
• Over 4 years, Duke Energy has avoided costs of 130% of their capital budget spent with ability to avoid higher costs associated with failures.
Siemens
• They enabled a custom, built-to-order process involving more than 1.6 billion components for over 50,000 annual product variations, for which Siemens sources about 10,000 materials from 250 suppliers to make the plant’s 950 different products.
• Reduced time to market by up to 50%
• Cost savings of up to 25%
• Improved reliability and quality with 99.9% accuracy in product quality
• 100% traceability from component suppliers
• Equipment utilization rates average raised to 80% - 95%
For more details about these case studies, and Big Data in the packaging and processing industry, download the FREE white paper here.
Source: PMMI Business Intelligence “How to Utilize Big Data to Enhance Manufacturing Processes”
Jabil
• Ability to predict and prevent failures of over half of all circuit boards at step two of the 32-step process, with the remainder identified at step six – this was done with an 80% accuracy rate
• Savings in excess of 15% in scrap and rework costs, as well as an additional 10% reduction in energy costs.
Duke Energy
• Transitioned from collecting four readings from a data point per year, to collecting readings every five seconds. This allowed a shift from analysts spending 80% of time collecting data with only 20% of time doing the higher value task of analyzing the data, to analysts spending 80% of their time on data analysis tasks.
• Over 4 years, Duke Energy has avoided costs of 130% of their capital budget spent with ability to avoid higher costs associated with failures.
Siemens
• They enabled a custom, built-to-order process involving more than 1.6 billion components for over 50,000 annual product variations, for which Siemens sources about 10,000 materials from 250 suppliers to make the plant’s 950 different products.
• Reduced time to market by up to 50%
• Cost savings of up to 25%
• Improved reliability and quality with 99.9% accuracy in product quality
• 100% traceability from component suppliers
• Equipment utilization rates average raised to 80% - 95%
For more details about these case studies, and Big Data in the packaging and processing industry, download the FREE white paper here.
Source: PMMI Business Intelligence “How to Utilize Big Data to Enhance Manufacturing Processes”
Jabil
• Ability to predict and prevent failures of over half of all circuit boards at step two of the 32-step process, with the remainder identified at step six – this was done with an 80% accuracy rate
• Savings in excess of 15% in scrap and rework costs, as well as an additional 10% reduction in energy costs.
Duke Energy
• Transitioned from collecting four readings from a data point per year, to collecting readings every five seconds. This allowed a shift from analysts spending 80% of time collecting data with only 20% of time doing the higher value task of analyzing the data, to analysts spending 80% of their time on data analysis tasks.
• Over 4 years, Duke Energy has avoided costs of 130% of their capital budget spent with ability to avoid higher costs associated with failures.
Siemens
• They enabled a custom, built-to-order process involving more than 1.6 billion components for over 50,000 annual product variations, for which Siemens sources about 10,000 materials from 250 suppliers to make the plant’s 950 different products.
• Reduced time to market by up to 50%
• Cost savings of up to 25%
• Improved reliability and quality with 99.9% accuracy in product quality
• 100% traceability from component suppliers
• Equipment utilization rates average raised to 80% - 95%
For more details about these case studies, and Big Data in the packaging and processing industry, download the FREE white paper here.
Source: PMMI Business Intelligence “How to Utilize Big Data to Enhance Manufacturing Processes”
Jabil
