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Manufacturing Solutions: Connected Smart Factory & Products
INTRODUCTION AND OVERVIEWS
Intro 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 TIBCO Connected Intelligence Platform can help you speed up and automate problem identification, diagnosis and solution.
The TIBCO 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.
This image summarizes some of the key solutions our manufacturing customers use TIBCO for:
This solution brief summarizes the use cases and solutions customers implement with the TIBCO platform: Manufacturing Intelligence in the Age of Industry 4.0 and the IoT
For an overview of how TIBCO provides value to manufacturing customers, visit this TIBCO 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 TIBCO'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.
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, 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.
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: anomaly-detection-for-manufacturing-final.pdf
Analytics for Semiconductor Manufacturing
View the Slide Deck: tibco_semiconductor_manufacturing_analytics_distn.pdf
UNIFY for Manufacturing - Data Management & Virtualization
Unlock the Power of Any Data for Manufacturing - Part 1
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 TIBCO’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 TIBCO’s Intelligent Manufacturing models.
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 TIBCO’s Intelligent Manufacturing solutions can help you overcome challenges with data management.
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.
- Read the data sheet on Master IDMP Compliance with TIBCO EBX Master Data Management.
- View the Infographic on Master IDMP Compliance
CONNECT for Manufacturing - Data Access & Integration, Cloud, Blockchain
TIBCO’s Future of the Connected Digital Factory - Part 1.
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 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 do 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 to improve the lifespan of the manufacturing devices and reduce downtime.
- Machine learning models that 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 upon sensor readings and data correlation.
- Delivery of alerts and recommendations to appropriate personnel.
TIBCO’s Future of the Connected Digital Factory Deep Dive - Part 2.
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.
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.
Five Core Principles for the Connected Factory of the Future
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.
Blockchain for Digital Twins
Digital representations of devices, equipment, business processes, and even people, are being used by organizations across the world to meet some of their toughest business requirements. These so called “digital twins” are heavily dependent on data, but to provide maximum value, technologies such as blockchain and smart contracts may also be utilized. Blockchain-backed digital twins can be complex to develop and execute, but the right tools make it far easier. Technology-agnostic Project Dovetail software from TIBCO LABS is one such tool. Learn about:
- Use cases and applications for digital twins
- Key digital twin technology foundations
- Blockchain as a digital twin enabler
- Rich implementations involving blockchain with smart contracts
SOLUTIONS BY USE CASE AREA
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 TIBCO 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 TIBCO’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:
- Watch a demo of this application
- Try it out yourself in the TIBCO Spotfire Demo Gallery
- Download it from the TIBCO Community Exchange
- Learn more about it on this TIBCO Community Wiki page
Big Data Product Digital Twins
TIBCO 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 sensor 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 TIBCO 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
- View this slide deck
- Read this overview blog that features the system architecture
- Read this technical blog that details the TIBCO 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 Video. View Slides. Read Whitepaper.
Machine Learning - Effect of process measurements on product yield - shows nonlinearities & interactions
To learn more:
- Learn how TIBCO AutoML empowers citizen data scientists to quickly create meaningful machine learning applications
- Read the blog article on Creating a Big Data Analytic workflow that features use of a machine learning algorithm to understand a manufacturing big data product quality problem
- Visit the Machine Learning Wiki page
- 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 TIBCO Hyper-Converged Analytics required for a Next Generation Intelligent Digital Plant, along with a Blueprint which 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 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
See how TIBCO 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.
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. TIBCO Data Science has comprehensive capabilities for 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 #TIBCOFAST - 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 TIBCO powers the seven-time F1 world champion, Mercedes AMG Petronas racing.
- See more about how the Mercedes AMG Petronas F1 racing team uses Digital twins to optimize performance and maintenance strategies
- Read about opportunities for New Revenue Streams in the Automotive Industry with connected vehicles.
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. TIBCO 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 TIBCO 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 TIBCO Data Science software is the calculation engine supporting rules, alarms, and alerts.
- Read more about the solution here.
- Watch a short 2 minute demo of what the solution does here
- Watch a more complete demo of the solution and it's architecture here
- Try a live interactive demo on the Spotfire Demo Gallery
- Download this solution from the Exchange
Multivariate + Comprehensive Process Control Solutions
TIBCO Data Science (Statistica) has comprehensive out-of-the-box Process Control capabilities including Quality Control Charts, Multivariate Statistical Process Control and the Monitoring and Alerting Server 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 TIBCO Data Science with data visualization in Spotfire. View a comprehensive Process Control Monitoring and Alerting Solution here.
Watch a demo of Multivariate Statistical Process Control Capabilities
Using AI to detect complex anomalies in time series data
The TIBCO 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.
Anomalies and their component signatures in a time series dataset
The TIBCO anomaly detection solution leverages cutting edge Tensorflow Autoencoders to identify anomalous behavior in multivariate environments. This methodology maps many variables into a lower dimension and compares the predicted value to the actual readings; if the predicted value is far from the actual reading it is labeled as an anomaly. This approach also offers the user visual analyses to find causes of the anomalous behavior.
- Watch a demo of this AI App.
- Try the AI App for yourself and interact with the anomaly detection features.
- Watch a webinar on the use and creation of this App.
- Download this solution from the TIBCO Community Exchange.
- Visit the Anomaly Detection - Technology and Applications community page for more information about anomaly detection
The Hi 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 and predictive process modeling.
Here is an example of an Advanced Process Control application deployed by Hemlock Semiconductor
Comprehensive Industrial Statistics and Six Sigma
See a complete list and description of all TIBCO 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 value of their assets. Manufacturers can increase uptime, minimize costs, and optimize processes for expensive equipment on the factory floor. TIBCO Spotfire® helps organizations optimize maintenance schedules by monitoring and responding to key signals in sensor data. In general, fixed assets— vehicles, plants, and 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. TIBCO 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 TIBCO 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 TIBCO Streaming Analytics - TIBCO accelerators jump start building end-to-end analytics solutions. See what’s new and watch a demo of the TIBCO 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 feeds to identify trends and gain insight, and how to use real-time model execution for anomaly detection and classification.
To learn more:
- See the iSteer Predictive Maintenance solution
- Read a brief customer story from Bosch on Predictive Maintenance
- Download equipment accelerators from the TIBCO Exchange:
- Intelligent Equipment Accelerator - Capture and analyze sensor data in real-time
- High Tech Manufacturing Accelerator - Monitor equipment status & calculate OEE
- IoT Accelerator - Monitor sensor data from IoT devices
- Data Historian Accelerator - Capture real-time telemetry from data historians like OPC UA and OSI PI
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 caputres 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 video shows a deep dive into the supply chain-related real time inventory tracking, where you will learn about real time stock alerts, store deliveries, distribution center alerts, and transport logistics optimization.
Download the Continuous Supply Chain Accelerator from the TIBCO Exchange
Blockchain Opportunities in the Manufacturing Supply Chain
Manufacturers today face a number of logistics and supply chain challenges that could be overcome by systems providing a secure, tamper-resistant, single source of truth. Chief among these challenges is limited data sharing due to data security barriers among suppliers, shippers, manufacturers and test houses, an impediment to achieving optimal product quality and regulatory compliance. Additionally, inefficient and inadequate processes for tracking goods make it more difficult to isolate shipping problems, track faulty parts and verify product authenticity. Counterfeiting has become a serious problem that costs US-based semiconductor manufacturers $7.5 billion annually.
For more information:
Read this Whitepaper: Blockchain and Manufacturing: A Match Made in the Factory
Watch this Webinar: Blockchain and Manufacturing - A Match Made in the Factory
- Visit the TIBCO Blockchain Solutions page
6 Ways to Maximize Your Smart Supply Chain Data
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 manage the distribution, production, and planning of components and items aren’t working anymore. 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.
- Reliable, alternative suppliers
- Realistic customer demand
- Efficient material requirements planning (MRP)
- Optimization and reduction of production waste
- Better risk assessment
- Real-time AI and end-to-end tracking based on IoT
CUSTOMER SUCCESS STORIES
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.
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 TIBCO 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.
Texas Instruments: Actionable Insights Using TIBCO Spotfire at Texas Instruments - Texas Instruments uses Spotfire software for advanced analytics across the company — 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.
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 the 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.
Semiconductor Customer Success Stories
More Manufacturing Customer Success Stories
Download Manufacturing Industry templates, data functions and acclerators from the TIBCO Exchange.
Visit the Visualization, Analytics and Data Science Community Wiki Home Page: This is the starting point for an extensive, constantly expanding collection of linked Wiki pages covering TIBCO 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 TIBCO corporate Manufacturing page: Manufacturing Intelligence for the Connected Factory
Manufacturing content in the TIBCO Resource Library
... with content about Smart Manufacturing & Digital Transformation
- SEMI Smart Manufacturing Central
- McKinsey Industry Insights
- Centre for the Fourth Industrial Revolution
- Shaping the Future of Advanced Manufacturing and Production