{"id":65228,"date":"2024-10-02T14:40:29","date_gmt":"2024-10-02T18:40:29","guid":{"rendered":"https:\/\/www.apriori.com\/?post_type=glossary&p=65228"},"modified":"2024-10-02T16:29:05","modified_gmt":"2024-10-02T20:29:05","slug":"manufacturing-analytics","status":"publish","type":"glossary","link":"https:\/\/www.apriori.com\/glossary\/manufacturing-analytics\/","title":{"rendered":"Manufacturing Analytics"},"content":{"rendered":"

What Are Manufacturing Analytics?<\/h3>\n

Manufacturing analytics refers to the use of data analysis tools and techniques, such as machine learning and predictive and statistical analysis, to improve and optimize manufacturing processes and lower costs. Data is derived from Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP) systems as well as human input to generate and provide actionable insights that improve decision-making.<\/p>\n

Manufacturing analytics provide critical feedback across product development that can identify design and cost outliers, improve fact-based supplier negotiations<\/a>, streamline operational efficiencies and production, and accelerate time to market.<\/p>\n

Examples of Manufacturing Analytics Use Cases and Their Benefits<\/h3>\n

Manufacturing analytics can be leveraged in various ways. aPriori\u2019s AI-driven analytics help manufacturers lower costs and optimize production processes. Here are examples of manufacturing analytics use cases:<\/p>\n

    \n
  1. Cost Reduction and Design Optimization<\/strong>: Manufacturing analytics are most critical in the early design stage. Identifying manufacturability and cost issues in this phase can save money and time<\/a> and reduce late-stage engineering change orders (ECOs) as well as reduce material and energy waste.<\/li>\n
  2. Supplier Negotiations<\/strong>: Manufacturing analytics like aPriori\u2019s AI-powered sourcing insights provide organizations with invaluable data to collaborate and negotiate with suppliers more cost-effectively<\/a>, and also reduce quoting times to get to market faster and more efficiently.<\/li>\n
  3. New Product Introduction (NPI)<\/strong>: Time is money. Manufacturers need to innovate new products faster while maintaining profit margins and safeguarding quality assurance. Analytics improve operational efficiencies and enable manufacturers to meet these objectives. Those who use manufacturing analytics to streamline their NPI design and production get to market faster and recognize substantial cost savings.<\/a><\/li>\n<\/ol>\n

    Data derived from manufacturing analytics tools can drive faster decision-making, cost-effectiveness, and a competitive advantage for manufacturers.<\/p>\n

    How Do Manufacturing Analytics Work?<\/h3>\n

    Manufacturing analytics\u00a0leverage data-driven insights to optimize production processes and improve overall performance. aPriori\u2019s Manufacturing Insights platform automates and simulates the entire production process for digital twin CAD models<\/a>. Here\u2019s an overview of their capabilities:<\/p>\n

      \n
    1. Digital Factories<\/a> generate extensive manufacturing data.\u00a0This data helps product development teams make more informed design, cost, sourcing, and production decisions.<\/li>\n
    2. The Manufacturing Insights Platform allows users to upload their CAD model and select production inputs. The platform<\/a> then simulates the production process, allowing users to receive real-time feedback on cost and manufacturability.<\/li>\n
    3. This feedback includes tools such as 3D visualization to identify, analyze, and\/or remediate issues. Heat map visualizations clearly and automatically highlight high cost drivers<\/a>.<\/li>\n
    4. Manufacturing insights provide automatic warnings for manufacturability issues or those that require costly specialized tooling<\/a>. It also provides recommendations for optimizing product design for manufacturability (DFM).<\/li>\n
    5. The platform can be connected to Product Lifecycle Management (PLM) systems<\/a>.\u00a0When a new CAD model is checked in, it triggers a workflow to evaluate DFM issues.<\/li>\n<\/ol>\n

      Manufacturing analytics empowers organizations to harness data in a user-friendly manner for smarter, more effective production management and continuous improvement.<\/p>\n

      \u00a0<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

      Manufacturing analytics refers to the use of data analysis tools and techniques, such as machine learning and predictive and statistical analysis, to improve and optimize manufacturing processes and lower costs. Data is derived from Product Lifecycle Management (PLM) and Enterprise Resource Planning (ERP) systems as well as human input to generate and provide actionable insights that improve decision-making.<\/p>\n","protected":false},"template":"","class_list":["post-65228","glossary","type-glossary","status-publish","hentry","no-post-thumbnail"],"acf":[],"yoast_head":"\nWhat Are Manufacturing Analytics?<\/title>\n<meta name=\"description\" content=\"Get a description of manufacturing analytics. 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