List of IBM Predictive Maintenance and Quality (PMQ) Customers
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Since 2010, our global team of researchers has been studying IBM Predictive Maintenance and Quality (PMQ) customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased IBM Predictive Maintenance and Quality (PMQ) for Quality Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using IBM Predictive Maintenance and Quality (PMQ) for Quality Management include: NVIDIA, a United States based Manufacturing organisation with 36000 employees and revenues of $130.50 billion and many others.
Contact us if you need a completed and verified list of companies using IBM Predictive Maintenance and Quality (PMQ), including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The IBM Predictive Maintenance and Quality (PMQ) customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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NVIDIA | Manufacturing | 36000 | $130.5B | United States | IBM | IBM Predictive Maintenance and Quality (PMQ) | Quality Management | 2018 | n/a | In 2018, NVIDIA implemented IBM Predictive Maintenance and Quality to support Quality Management in its chip manufacturing operations. The engagement delivered a highly available and scalable enterprise AI and machine learning platform for chip fabrication, with multiple models deployed to production, and an explicit estimate of annual cost reduction of $8M while maintaining manufacturing quality. The implementation included onboarding new products into IBM Predictive Maintenance and Quality, working closely with engineering to build classification models, execute testing and validation cycles, and perform production deployment of those models. Parallel work with enterprise PTC Windchill PLM applied application upgrades and configurations, and produced deliverables such as BOM comparison tools, Design History Files, Automated Revision Sync up, a PLM Data Mart, PLM reports, and external contract manufacturer BOM packages to improve engineering efficiency. Integrations were executed across the manufacturing stack, including PLM bill of materials synchronization with SAP ERP and ERP integration services that exchanged contract manufacturer planning, commitment, and inventory data to improve demand and supply planning and build execution collaboration. The program also supported Product Information Delivery systems for customer download of drivers and content, and incorporated enterprise security by integrating IT systems into an Enterprise Password Vault and applying security best practices. Business intelligence and operational visibility were enabled through BI reports built with Tableau, SQL, Splunk, and Excel. Governance and operationalization emphasized close collaboration with engineering for requirements gathering, design, implementation, and validation of customizations, and established automated revision synchronization and PLM data governance patterns to control configuration and lifecycle data flows. IBM Predictive Maintenance and Quality was positioned as the core Quality Management application for predictive classification and operational monitoring, with production model rollouts coordinated alongside PLM and ERP integrations to align quality analytics with manufacturing and supply chain processes. |
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