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  • Manufacturing Solutions: Connected Smart Factory & Products

    The Spotfire platform is used by manufacturing companies across the globe for a broad range of solutions to better understand equipment, processes, products, operations, customers and sales; and then to act on the insights gained.  These solutions are widely used in in the following industries:  semiconductor, electronics and medical devices; automotive & aviation; equipment manufacturing, pharmaceuticals; chemicals, metals and mining and consumer packaged goods.

    Introduction and Overviews

    Introduction to the Connected Smart Factory

    Industry 4.0 is transforming manufacturing. It is powered by new capabilities for processing sensor data, along with big data, machine learning and artificial intelligence, cloud, streaming, and edge technologies.  These capabilities are facilitating an evolution from reactive problem solving towards increasingly proactive, predictive, and adaptive management of equipment, processes, product, and factories.  The Connected Intelligence Platform can help you speed up and automate problem identification, diagnosis, solution and prevention.   


    The Spotfire platform is used by manufacturing companies across the globe for a broad range of solutions to better understand the equipment, processes, products, operations, customers, and sales; and then to act on the insights gained.  These solutions are widely used in the following industries:  semiconductor, electronics, and medical devices; automotive & aviation; equipment manufacturing, pharmaceuticals; chemicals, metals, and mining, and consumer packaged goods.  

    This image summarizes some of the key solutions our manufacturing customers use Spotfire for:


    This solution brief summarizes the use cases and solutions customers implement with the Spotfire platform:  Manufacturing Intelligence in the Age of Industry 4.0 and the IoT. For an overview of how Spotfire provides value to manufacturing customers, visit this Spotfire corporate webpage:  Manufacturing Intelligence for the Connected Factory


    Analytics for Manufacturing

    Hyper-converged Analytics in Manufacturing

    Putting the Connected Digital Factory into Practice - Move from reactive to proactive management of products, processes, and machines by making the connected, real-time digital factory a reality. In the presentation, you will see a number of manufacturing hyperconverged analytics solutions that bring together Spotfire's capabilities in data virtualization, analytics, data science, streaming, and edge. See what is happening now, predict what will happen, and act to optimize results. Automatically detect and classify failure patterns. Continuously relate failure patterns directly to machine sensor data. Monitor and alert on process and product parameters. Deploy machine predictive maintenance and anomaly detection. Create a 360-degree real-time map of overall equipment effectiveness metrics with drill-down to equipment, sensor metrics, and anomalies. 



    • Download the slides below:  hyperconverged_analytics_in_manufacturing_v3_distn.pdf
    • Watch the Presentation video


    Digital Twins in Manufacturing and Industry

    Digital Twins are real things interacting with virtual, high-definition models of themselves. Fueled by the convergence of connected sensors & IoT, AI & big data technology, digital twins expand ordinary human vision and insight.  They allow us to immerse ourselves in micro, macro, and remote environments, access Internal conditions that can’t be directly measured, predict the future, and extract meaning from blizzards of data and complex systems ... in real-time.  They provide the level of agility needed to quickly adapt to changing conditions.  This webinar provides an introduction to the topic, and covers technology requirements for digital twins and examples of their use in manufacturing for improved designs, predictive maintenance, proactive yield management, advanced process control, remote monitoring and control ... and more.
     User-added image   

    The many types of digital twins in manufacturing are shown above.  View video below for some examples from our practice.

    Anomaly Detection in Manufacturing

    Anomaly detection is a step towards resilience that will serve any manufacturer well. Manufacturers with mature anomaly detection capabilities achieve substantial operational cost reductions from reduced defect and scrap rates, improved quality and reliability, prevention of unplanned equipment downtime, and even optimized energy consumption. Typically, most data streams simply confirm normal operations and provide no new actionable information. However, when data shows an anomaly, it can indicate something has changed or is behaving abnormally?leading to actionable insights about how to correct any issues before they become widespread or time-consuming.  This whitepaper covers the basics of anomaly detection for manufacturing, relevant use cases from our practice, and key techniques you can use in your business.

    Read our whitepaper on How to Detect Manufacturing Anomalies - Industry solutions, applications, and machine learning models.  Download the slide deck:  anomaly-detection-for-manufacturing-final.pdf


    Analytics for Semiconductor Manufacturing

    • Watch the presentation 


    Unify for Manufacturing - Data Management & Virtualization

    Unlock the Power of Any Data for Manufacturing - Part 1

    Watch the Webinar

    Every manufacturer understands the importance of data. But getting the right data to the right people at the right time is one of the biggest challenges for manufacturers today.   While your existing data infrastructure can continue to perform its core function, a data virtualization layer uses data from transactions, machines, and the field to give you a holistic view of your manufacturing operations.  By unifying your data silos with virtualization, it's possible to combine real-time production data with historical data, apply advanced analytics, and measure your production process end-to-end to understand where improvements can be made. Better data management increases overall organizational trust. So, whether you?re an industrial, high-tech, or consumer manufacturer, watch this webinar to learn how Spotfire's Intelligent Manufacturing solutions can help you overcome challenges with data management. Learn:

    • The main challenges and value drivers and how you can stay ahead of the competition 
    • Why digitization is so important for the manufacturing industry
    • How to provision data without moving or replicating it across your data infrastructure
    • How a higher data quality minimizes costs and maximizes production
    • How to create data trust in your organization and secure your data
    • How to improve your data and reduce overall costs 

    The webinar also highlights customer case studies from our clients who have successfully implemented Spotfire's Intelligent Manufacturing models.


    Watch the Webinar


    Unlock the Power of Any Data for Manufacturing - Part 2: Materials Management & Supply Chain

    This is part II of a multipart series on unlocking the power of data for manufacturing. In part 1, we covered why getting the right data to the right people at the right time is one of the biggest challenges for manufacturers today. 

    In this session, we focus on data generated by materials management and the supply chain, and critical areas that manufacturing organizations must focus on if they wish to become more effective. Watch as Stephen Archut and Conrad Chuang describe the data quality and governance challenges manufacturers face and examine these data challenges through the lens of the eight kinds of waste (DOWNTIME). Mr. Dutta provides a full demonstration of TIBCO's master data management and data quality capabilities applied to two key areas:

    • Cleaning materials data 
    • Authoring new materials data

    Better data management increases overall organizational trust. So, whether you're an industrial, high-tech, or consumer manufacturer, watch this webinar to learn how Spotfire's Intelligent Manufacturing solutions can help you overcome challenges with data management. 

    Watch the webinar


    Master Data Management for Pharma Manufacturing

    Using a phased approach, the European Medicines Agency (EMA) is the first regional health organisation to mandate compliance of the International Organization for Standardization's (ISO) identification of medicinal products (IDMP) regulation. Master data management (MDM) will allow pharmaceutical companies to comply with these upcoming IDMP regulations and transform their operations.


    Connect for Manufacturing - Data Access & Integration, Cloud, Blockchain

    TIBCO's Future of the Connected Digital Factory - Part 1.

    Watch the on-demand webinar

    As MES systems, automation tools, and connected sensors have evolved, today's manufacturers have more data on the manufacturing process than ever before.  Some of the benefits are:

    • MES systems integrated directly with the manufacturing devices eliminate the need to manually input instructions into devices. 
    • Online process instructions are available to everyone on the assembly floor. 
    • Sensors collect and transmit data related to the environment, product quality, and manufacturing equipment.
    • The ability to predict process results in-flight and take automatic corrective action that greatly reduces waste and increases product quality.

    The challenge to most manufacturers is how to take advantage of all these capabilities.  At times the sheer amount of data can be overwhelming.  Even if it is collected and reported in a dashboard, how can you properly interpret the information and act on it?  Integration of the manufacturing floor involves more than just the ability to connect systems. During this webinar, you will learn how to take advantage of an integrated manufacturing environment by leveraging:

    • The transformation of sensor data into common data models that can be more easily understood and acted on.
    • Real-time correlation of sensor event data, coupled with a rule base to detect and react to potential quality issues.
    • Predictive maintenance models improve the lifespan of manufacturing devices and reduce downtime.
    • Machine learning models are applied in real-time to streams of sensor events to detect anomalies and predict results.
    • Automation of corrective actions and adjustments to manufacturing devices based on sensor readings and data correlation.
    • Delivery of alerts and recommendations to appropriate personnel.


    Watch the on-demand webinar


    TIBCO's Future of the Connected Digital Factory Deep Dive - Part 2.

    Watch the on-demand webinar

    This deep dive session will provide a demonstration of real-time integration to the manufacturing floor.  The demo showcases the following:

    • Integrating real-time sensor data 
    • Converting raw sensor data into meaningful data formats for use with analytics and data science 
    • Applying processing rule functions to data in motion.
    • Correlating sensor events.
    • Alerting, recommending corrective action, advising maintenance activities, and automating corrective actions.

    User-added image

    Watch the on-demand webinar


    The Digital Factory: Gain the agility to integrate your manufacturing systems in the cloud

    Modern manufacturers face many challenges. How is it possible to increase agility, reduce time to market when integrating new services, improve resiliency during times of great volatility, and act on data-driven decisions? In answer to these questions, moving to the cloud is no longer an option, it's imperative.  This whitepaper provides the key information you need to start making the move to cloud computing.

    Read the whitepaper


    Five Core Principles for the Connected Factory of the Future

    Watch the Webinar

    Digital transformation is ramping up in manufacturing, but for many companies, IT is still a bottleneck.  In this webinar, you will learn about the five core principles for a connected factory. We will also show you how to achieve more agility in manufacturing with digital transformation and how to avoid being outpaced by competitors.


    Watch the Webinar


    Solutions by Use Case Domain

    Products:  Quality and Reliability

    Gain insight into quality and reliability issues.  Spotfire helps manufacturers to identify, understand and minimize problems due to process variability, incoming supplies, test, or design.  An intensified interest in product quality and reliability analysis is being driven by a number of market forces. Products today are more complex, have shorter lifecycles, and are increasingly connected.  Reliability failures are more visible and sometimes more costly than ever before.  Meanwhile, the forces of technology, globalization, and regulation make our quality and reliability calculations more complex and urgent.  Many of the world’s leading manufacturers are turning to Spotfire to identify issues earlier, respond more rapidly and effectively and then build better products.


    Equipment Commonality Analysis - Effect of Machine on Paper Towel Product Quality


    Fail Pattern and Defect Classification

    Pattern Classification of Wafermap Images with Human-driven Machine Learning - Identifying patterns of interest in big data is complex, but critical to high-value manufacturing use cases.  Explore Spotfire's novel approach to big data pattern recognition as applied to semiconductor wafer maps. Using a combination of machine learning techniques, you'll see how business users can identify patterns quickly and accurately from a large amount of data. Using these patterns, Spotfire users can train and deploy a model to classify new wafers in real-time.

    For more information:


    Big Data Product Digital Twins

    Spotfire has recently been working with manufacturing customers to make a new, high-value capability available: digital twins for yield. It's about real-time, continuous analysis of manufacturing equipment sensors and process data at very large scales - up to millions of predictor columns - to understand and address the causes of product yield loss.  Digital twins are virtual representations (models) of physical systems.  The current interest in them is fueled by recent breakthroughs in IoT, machine learning and big data.  These technologies are now being directed at the growing volumes of data available from sensors on process equipment and physical measurements from metrology tools.  As process complexity increases, these digital twins are becoming a requirement for efficient operations and high product yields.  They are an important element of the evolution towards increasingly data-driven problem-solving and real-time operational control.    


    For more information about our work on this use case:

    • View a 15-minute demo that shows how the results can be visualized in Spotfire and how a Spotfire Data Science big data workflow is used to generate the data.
    • Watch this 30-minute webinar that includes the demo, adds context to the use case, and presents performance results for the solution. 
    • Read this Whitepaper
    • Download the slide deck below - "Manufacturing Solutions Use Cases Slides"
    • Read this overview blog that features the system architecture
    • Read this technical blog that details the Spotfire Data Science visual workflow powering this work and how it is integrated with Spotfire via a data function. 
    • Watch this AWS re:Invent 2019 presentation on Hot Paths to Anomaly Detection, which features this use case.  


    Machine Learning for Root Cause Analysis

    Machine Learning is a powerful advanced analytics technology that can help uncover the causes of complex manufacturing problems and make accurate predictions about when and how to improve maintenance and operations.  The following assets provide an overview of relevant machine learning techniques and how they are being applied to yield and quality improvements, predictive and condition-based maintenance, micro-segmentation of markets, and resource optimization.  Watch VideoRead Whitepaper.


    Machine Learning - Effect of process measurements on product yield - shows nonlinearities & interactions

    To learn more:

    • Learn how Spotfire AutoML empowers citizen data scientists to quickly create meaningful machine learning applications
    • Download the "Manufacturing Use Cases and Slides" and open this Whitepaper file: bigdatablog4puttingitalltogether_1.pdf which features the use of a machine learning algorithm to understand a manufacturing big data product quality problem
    • Review the Machine Learning article
    • Download machine learning solutions from the Community Exchange


    Six Sigma Connected Production Platform from Genware/DataShack

    Hyper-Converged Analytic Process Performance in Manufacturing - The Connected Production Platform provides manufacturers and producers with Spotfire Hyper-Converged Analytics required for a Next Generation Intelligent Digital Plant, along with a Blueprint that processes source data, applies predefined machine learning models, and includes advanced analytics, all in support of predicting. Learn how this platform provides access to data to monitor live performance across the entire plant value stream, utilizing the Six Sigma Methodology.  

    Visit the Connected Production Platform web page


    Reliability and Warranty Claims

    See how Spotfire can help you monitor and predict claim rates. analyze root causes of reliability failures and analyze warranty repair and call center activity.


    Warranty analysis for all components of an automobile model

    Learn more about Warranty and Reliability solutions 
    Download the Weibull Reliability and Optimal Maintenance solution 


    Product Traceability

    See how Spotfire Data Science can help understand the effects of processing on product characteristics using the Product Traceability add-on.


    Stability and Shelf Life Analysis

    Stability analysis is the study of how drug product potency degrades over time. The primary statistical quantity of interest is the expiration date or shelf life. Typically, a drug product is manufactured in batches. When estimating the shelf life of a medication, it is necessary to evaluate how the batches differ with respect to the potency degradation of the drug product over time. 

    You can find an overview of the solution here and more details here.  


    Design of Experiments

    Design of Experiments is an important tool for experimentally identifying the most important factors and finding their optimum settings in order to improve processes and products. Spotfire Data Science has comprehensive capabilities for the design and analysis of fractional factorial, Box-Behnken, Central Composite, Optimal, Mixture, Taguchi, and a number of other design types.  It also features a prediction profiler for simultaneous optimization of multiple responses. 


    Connected Products in the Field

    Innovation at the Speed of Formula 1 #SPOTFIREFAST - Optimizing Automobile Performance - Formula 1 racing sits at the apex of motorsports. Behind the world's most talented drivers lies an incredible analytics capability that forms the foundation of nearly every decision made throughout a season. From mirroring cars with digital twins, optimizing car configurations, modeling thousands of laps around the world, finding ideal spots to accelerate and overtake, to real-time mid-race decision-making, data is the real fuel of F1. In this fireside chat, Michael O?Connell will share how Spotfire powers the seven-time F1 world champion, Mercedes AMG Petronas racing. 



    Processes: Process Control and Anomaly Detection



    Univariate Process Control

    Control charts are widely used in Manufacturing, Energy, Telco, Technology, and many other sectors. They are the foundation of early warning systems that monitor key metrics, detect deviations from the baseline, and generate automated alerts. Spotfire supports many types of Shewhart (univariate) and multivariate charts; integrated limits generation, storage, and deployment; selection of rules to detect out-of-control points; tagging and annotation; management and operations dashboards; periodic or real-time alerts; process capability studies and root cause drill-downs



    Process Control Summary with drill-down to Control Chart

    Our downloadable solution for Process Control, Monitoring, and Alerting in Operations applies statistical methods to monitor and reduce the variability of measured processes. It is an easily configurable quality control solution built with Spotfire and Spotfire Data Science software that can monitor large numbers of parameters and produce automated alerts when rules are violated. Data is visualized in linked Spotfire dashboards, and Spotfire Data Science software is the calculation engine supporting rules, alarms, and alerts.

    • Read more about the solution here.  
    • Watch a short demo of what the solution does here
    • Watch a more complete demo of the solution and its architecture here
    • Try a live interactive demo on the Spotfire Demo Gallery
    • Download this solution from the Exchange


    Multivariate + Comprehensive Process Control Solutions 

    Spotfire Data Science (Statistica) has comprehensive out-of-the-box Process Control capabilities including Quality Control Charts, and the Multivariate Statistical Process Control for automated monitoring of large numbers of charts. The capabilities are tightly integrated with Spotfire, via the Statistica-in-Spotfire data function, to enable calculations in Spotfire Data Science with data visualization in Spotfire.  View a comprehensive Process Control Monitoring and Alerting Solution here.   

    Watch the Monitoring and Alerting Server Video

    Watch a demo of Multivariate Statistical Process Control Capabilities

    Using AI to detect complex anomalies in time series data

    The Spotfire Data Science team is actively engaged in developing applications of Deep Learning Autoencoders for Anomaly Detection in Manufacturing.  In a dynamic manufacturing environment, it may not be adequate to only look for known process problems, but also important to uncover and react to new, previously unseen patterns and problems as they emerge.  Univariate and linear multivariate Statistical Process Control methods have traditionally been used in manufacturing to detect anomalies.  With increasing equipment, process, and product complexity, multivariate anomalies that also involve significant interactions and nonlinearities may be missed by these more traditional methods.  This is a method for identifying complex anomalies using a deep learning autoencoder.  Once the anomalies are detected, their fingerprints are generated so they can be classified and clustered, enabling investigation of the causes of the clusters.  As new data streams in, it can be scored in real-time to identify new anomalies, assign them to clusters and respond to mitigate potential problems.  These tools are no longer the exclusive province of data scientists.  After an initial configuration, the method shown can be routinely employed by engineers who do not have deep expertise in data science. 



    Real-time Monitoring

    The High Tech Manufacturing Accelerator provides a framework for real-time monitoring of univariate and multivariate control charts.  (See section on Factory Monitoring and OEE.)  Watch a demo of the Autoencoder deployed to the Hi Tech Manufacturing Accelerator for real-time monitoring.  


    Advanced Process Control

    Advanced Process Control is an application of digital twin technology that involves the use of sensor & metrology data to implement real-time tuning and control of processes.  This facilitates greater control of process variability than is achieved with the Basic Process Control techniques above.  Techniques include feed forward, feed backward, virtual metrology and predictive process modeling.  


    Comprehensive Industrial Statistics and Six Sigma

    See a complete list and description of all Spotfire Data Science Industrial Statistics and Six Sigma Solutions 


    Machines: Predictive Maintenance & Anomaly Detection

    The expansion of connected sensor data creates new business opportunities for monitoring machine performance and failures in the field and on the factory floor.  Service organizations have up-sell opportunities to offer options to their customers for maximizing the value of their assets. Manufacturers can increase uptime, minimize costs, and optimize processes for expensive equipment on the factory floor. Spotfire Spotfire® helps organizations optimize maintenance schedules by monitoring and responding to key signals in sensor data. In general, fixed assets, vehicles, plants, machinery, communication devices and computers, and even buildings are becoming smarter. But they are also becoming more complex and more costly to repair. Spotfire can help you understand these machines more fully, monitor them in real-time, and react faster to impending issues. Spotfire supports the following maintenance use cases: Predictive maintenance with automatic notification of impending failures Minimizing scheduled maintenance costs, Root cause analysis of equipment failures.

    Here is a demo on using pump sensor data to predict and prevent failures:

    Machine sensor anomaly detection and text mining of the maintenance log:  This Spotfire anomaly detection solution includes Microsoft Cognitive Services container deployment with anomaly detection, text mining, and root cause analysis. In addition, these containers can be used at the edge, for example, to identify anomalies for asset management in remote locations

    Watch the demo video:  

    Read the blog about this solution


    Monitoring Machine Sensor data with Spotfire Streaming Analytics - Spotfire accelerators jump-start building end-to-end analytics solutions. See what's new and watch a demo of the Spotfire Intelligent Equipment Accelerator. You'll learn how to capture and analyze IoT sensor data in real-time and integrate using industry-standard protocols like OPC UA, OSI PI, MQTT, and Web Services, or build your own. In addition, how to apply custom validations, cleansing policies, rules, and feature statistics to data feed to identify trends and gain insight, and how to use real-time model execution for anomaly detection and classification.


    To learn more:


    Factory: Monitoring, Maps, Anomaly Detection & OEE

    Modern factories are populated with complex, expensive equipment.  Manufacturers want to extract the greatest value from their factory equipment by maximizing equipment uptime, product throughput and quality and minimizing cycle times. Identifying bottlenecks in processing, taking proactive action in response to developing situations, and increasing operational system awareness are all key themes in sensor-driven manufacturing monitoring.

    Overall Equipment Effectiveness or OEE is a high-level measure of equipment productivity.  The OEE model combines measures of equipment availability, performance and quality. 

    • Availability is the percentage of time that the equipment is available to operate ... or Uptime.  Scheduled downtime, unscheduled downtime and non-scheduled downtime (holidays or training) all contribute to availability losses.
    • Performance is the speed at which the Work Center produces product as a percentage of its designed speed.  Performance losses are categorized as either due to Rate or Operational inefficiencies.  Rate losses are caused by equipment running slower than theoretical speed.  Operational Losses may be further broken down into Engineering and Standby Losses.  Engineering losses occur when production turns equipment over to engineering, often to perform tests or experiments.  Standby losses occur when the equipment is available but there is no product or operator to run it.
    • Quality is Good Units produced as a percentage of the Total Units Started. Sometimes referred to as First Pass Yield. Rework and scrap contribute to Quality losses
    • OEE is calculated by multiplying Availability, Performance and Quality percentages together.

    The High Tech Manufacturing Accelerator contains components to allow monitoring of production line performance against established metrics using Overall Equipment Effectiveness (OEE). It captures data feeds from sensors on production equipment, validates the feeds, and evaluates the data against configurable business rules. It includes components to visualize all these activities in a custom web dashboard, allowing operators to take corrective action when production issues are identified.


    Download the Hi Tech Manufacturing Accelerator from the Exchange

    Watch a video of the  High-Tech Manufacturing Accelerator in action


    Supply chain: Demand and Transportation Logistics

    The Supply Chain Nervous System

    Recent AI, automation, and data management breakthroughs help supply chains of all types sense and respond to real-time conditions, like your body's nervous system. They can sense demand, operations, and volatile conditions to respond to what's happening now.  Read this whitepaper to explore:

    • The supply chain landscape in Manufacturing, Logistics, Retail, Transportation, and more
    • Six capabilities that power an unfair supply chain advantage
    • The three phases of supply chain system innovation

    Read the whitepaper 


    Continuous Supply Chain - Continuous Inventory Tracking

    Building a resilient supply chain requires connecting all your data wherever it is, unifying it to achieve consistency, and applying AI to develop deep insights for decision-making and automating manual processes. This accelerator features real-time inventory tracking, with real-time stock alerts, store deliveries, distribution center alerts, and transport logistics optimization.

    Download the Continuous Supply Chain Accelerator from the Spotfire Exchange


    6 Ways to Maximize Your Smart Supply Chain Data

    Watch the Webinar

    Retailers, manufacturers, and logistics operators have been disrupted by not being able to quickly modify and optimize their supply chains.  They have discovered weaknesses are often due to technology gaps. Traditional approaches to managing the distribution, production, and planning of components and items aren?t working anymore.  The scarcity of incoming materials, shipment delays, and a reduced workforce, along with the need to produce and ship large quantities instead of smaller batches, have put pressure on the entire system.  In this webinar, we explore six ways retailers, manufacturers, and logistics operators can redefine operational excellence and take their supply chain ecosystems to the next level.  

    1. Reliable, alternative suppliers
    2. Realistic customer demand
    3. Efficient material requirements planning (MRP)
    4. Optimization and reduction of production waste
    5. Better risk assessment
    6. Real-time AI and end-to-end tracking based on IoT

    Customer Success Stories

    Customer Presentations

    Western Digital:  Ahmer Srivistava from Western Digital shares how the use of Spotfire analytics has transformed high-tech component manufacturing to meet growing demand.  [Ahmer's keynote segment starts at 30:00 in the video]

    Western Digital: Spotfire at Western Digital's Wafer Factories - Over the last 8 years, Spotfire analytics has become the standard platform used by Western Digital engineering and operations to view and analyze manufacturing operational data. At wafer factories in Silicon Valley, the use of Spotfire software as the data analytics and visualization standard for factory operations has grown considerably. We will look at this integration in terms of architecture, data infrastructure, user groups, and business processes over three time periods and showcase solutions that made it possible to significantly increase efficiencies in the way we work. These use cases cover yield analytics, metrology, sensor data, and operational metrics described from the perspective of purpose, implementation, benefits, and differences with and without Spotfire software. 

    Slides:  Download the "Manufacturing Use Cases and Slides" for this file: tibco_taf2021_western_digital_presentation.pdf 


    Pfizer: Digital Manufacturing Intelligence enabling the "Factory of the Future vision at Pfizer" - In this session you will learn about Pfizer's pathbreaking efforts to become an AI-driven organization, utilizing an innovative Manufacturing Intelligence Workbench concept, designed with the goal of driving IT/OT convergence across the interconnected network of manufacturing sites, providing real-time access to data from all sources, and enabling scalable and accelerated AI/ML deployments in support of manufacturing operations. The MI Workbench enables Pfizer manufacturing sites to achieve ?Digital Plant Maturity? and the ?Factory of the Future? vision by becoming more predictive and adaptive and empowering the shop floor. This cloud-based platform provides high-performance environments to develop and deploy a myriad of analytics capabilities, including advanced web-based dashboards and reporting tools, real-time multivariate monitoring and control, golden batch analysis, digital twins and soft sensors, and AI/ML-based predictive models. 

    Hemlock Semiconductor: Care & Feeding for a Successful Analytics Culture - Successful analytics deployment requires focusing not just on data and tools, but also on the people that use them. Leveraging Hemlock Semiconductor's successful deployment of Spotfire and TIBCO Data Virtualization software, this session discusses strategies for building an analytics culture and overcoming challenges along the way. Topics include centralized vs. decentralized approaches, leveraging early adopters, benefits of unpolished data, adapting skill development to meet users where they are, and modifying your approach as the enterprise matures.

    Slides: Download the "Manufacturing Use Cases and Slides" for this file: hsc_-_care_feeding_for_a_successful_analytics_culture_annotated.pdf  

    Texas Instruments: Actionable Insights Using Spotfire at Texas Instruments - Texas Instruments uses Spotfire software for advanced analytics across the company. From manufacturing to sales. Enabling stakeholders to aggregate and analyze vast quantities of data. This session will highlight two examples of the creative delivery of advanced and actionable insights to TI's sales and pricing organizations. We will dive into the tool architecture and how the combination of scripts, data modeling, procedures, and tagging of a visualization drives specific actions across TI.

    Slides:  Download the "Manufacturing Use Cases and Slides" for this file:actionable_insights_using_tibco_spotfire_at_texas_instruments_final.pdf

    Keysight Technologies: Spotfire Data Loading: Hybrid Strategies for Optimal UX - Designing the flow of data from complex datasets into actionable information in the form of fast-loading Spotfire analysis requires a combination of techniques. Through a series of case studies, this session will highlight some successful strategies involving Python scripting, Spotfire Automation Services, advanced information link design, web caching, and data virtualization.

    Slides:  Download the "Manufacturing Use Cases and Slides" for this file: taf_keysight_data_loading.pdf

    Semiconductor Customer & Partner Success Stories

    More Manufacturing Customer Success Stories

    Learn More

    Spotfire Sites

    Download Manufacturing Industry templates, data functions, and accelerators from the Spotfire Exchange.    

    Visit the Visualization, Analytics and Data Science Community Overview Home Page:  This is the starting point for an extensive, constantly expanding collection of linked article pages covering Spotfire Analytics capabilities.  Links to comprehensive, current information on these Main Topics landing pages: Getting Started, Data Access and Wrangling, Visualizations, Maps, Advanced Analytics, Applications, and Vertical Solutions, Extending Spotfire, Administration, Partners, and more.  

    Visit the Spotfire corporate Manufacturing page: Manufacturing Intelligence for the Connected Factory

    Manufacturing content in the Spotfire Resource Library

    External Sites

    ... with content about Smart Manufacturing & Digital Transformation

    hyperconverged_analytics_in_manufacturing_v3_distn.pdf digital_twins_in_mfg_for_itm_-_tibco_distn.pdf anomaly-detection-for-manufacturing-final.pdf TIBCO Semiconductor Manufacturing Analytics Distn_Oct2022.pdf digital_twin_for_yield_v5_distn.pdf


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