This document discusses a study that created a technology roadmap to explore delivering services for digital streaming over broadband. The roadmap included layers for market analysis, product analysis, technology analysis, and research and development. It also emphasized the importance of considering the environment and policy factors. As part of the study, the authors conducted a benchmarking effort and market segmentation to identify customer value drivers. They then presented a product technology analysis and scenario planning to help identify opportunities and inform the roadmap. The process demonstrated in the paper can be used as a standard approach for developing technology roadmaps for organizations providing similar broadband services.
Data Governance from a Strategic Management PerspectiveBoris Otto
This document summarizes a presentation on data governance from a strategic management perspective. It discusses data governance as a dynamic capability that allows companies to address changing market needs by integrating, reconfiguring, gaining and releasing resources. It provides examples of how different companies have implemented and evolved their data governance over time, with some facing challenges integrating governance into daily operations. Effective double-loop learning and changing perceptions of data management are identified as important success factors for improving data governance maturity.
DESIGN, DEVELOPMENT & IMPLEMENTATION OF ONTOLOGICAL KNOWLEDGE BASED SYSTEM FO...IJDKP
This document summarizes an article that describes the design and development of an ontological knowledge-based system to support reconfigurable assembly lines in the automotive industry. The system uses an ontology to represent the relationships between products, processes, and resources. It aims to facilitate rapid reconfiguration of assembly lines in response to changing product requirements. The system is intended to help automotive companies address challenges like increasing competition, complex products and processes, and the need to adapt quickly to changes and new customer requirements.
Data Quality as a Business Success FactorBoris Otto
The document discusses data quality as a business success factor. It provides two case studies: (1) At automotive supplier ZF Friedrichshafen AG, consistent and accurate master data is required for customer relationship management. (2) At Bayer CropScience, root causes of poor data quality were identified, including a lack of data quality training and heterogeneous data maintenance tools. The document emphasizes that corporate data quality management relates to business strategy and should follow a lifecycle approach. Benefits of improved data quality can include inventory savings and reduced costs of obsolete records.
Evolution of data governance excellencepatriziapesce
1) The document discusses data governance design options for large enterprises, including a line organization, staff organization per business unit, shared service center, and externalization models.
2) It provides two examples of how companies have implemented data governance: a high tech company that used a central function and a chemical company that used a shared service center for governance and operational responsibility.
3) Key principles for an effective governance design are discussed, including being global, shared, governing, service-oriented, managed, and empowered. The evolution from a shared service to outsourced data management processes is also covered.
The document discusses making data quality an operational way of life through rigorous governance and quality processes. It explains that operational data quality requires continuous validation of data accuracy, currency, and auditability. It also requires governance to ensure data relevance considering internal/external changes. The key aspects of operational data quality are repeatable validation workflows, tracking exceptions, using baselines to assess reasonability, and categorizing exceptions to address data quality issues.
This document discusses the hurdles and enablers to adopting software product line practices in large corporate organizations, specifically large banks. It identifies some key hurdles including: different business units perceiving little return on investment for cross-unit product lines; and difficulties motivating investment and changing funding models. It proposes some enabling mechanisms that are showing positive results, such as aligning product lines with strategic business goals and establishing executive sponsorship. Large banks present additional challenges to product line adoption due to their multiple divisions, legacy systems, and focus on short-term profits over long-term IT strategies.
This document discusses a study that created a technology roadmap to explore delivering services for digital streaming over broadband. The roadmap included layers for market analysis, product analysis, technology analysis, and research and development. It also emphasized the importance of considering the environment and policy factors. As part of the study, the authors conducted a benchmarking effort and market segmentation to identify customer value drivers. They then presented a product technology analysis and scenario planning to help identify opportunities and inform the roadmap. The process demonstrated in the paper can be used as a standard approach for developing technology roadmaps for organizations providing similar broadband services.
Data Governance from a Strategic Management PerspectiveBoris Otto
This document summarizes a presentation on data governance from a strategic management perspective. It discusses data governance as a dynamic capability that allows companies to address changing market needs by integrating, reconfiguring, gaining and releasing resources. It provides examples of how different companies have implemented and evolved their data governance over time, with some facing challenges integrating governance into daily operations. Effective double-loop learning and changing perceptions of data management are identified as important success factors for improving data governance maturity.
DESIGN, DEVELOPMENT & IMPLEMENTATION OF ONTOLOGICAL KNOWLEDGE BASED SYSTEM FO...IJDKP
This document summarizes an article that describes the design and development of an ontological knowledge-based system to support reconfigurable assembly lines in the automotive industry. The system uses an ontology to represent the relationships between products, processes, and resources. It aims to facilitate rapid reconfiguration of assembly lines in response to changing product requirements. The system is intended to help automotive companies address challenges like increasing competition, complex products and processes, and the need to adapt quickly to changes and new customer requirements.
Data Quality as a Business Success FactorBoris Otto
The document discusses data quality as a business success factor. It provides two case studies: (1) At automotive supplier ZF Friedrichshafen AG, consistent and accurate master data is required for customer relationship management. (2) At Bayer CropScience, root causes of poor data quality were identified, including a lack of data quality training and heterogeneous data maintenance tools. The document emphasizes that corporate data quality management relates to business strategy and should follow a lifecycle approach. Benefits of improved data quality can include inventory savings and reduced costs of obsolete records.
Evolution of data governance excellencepatriziapesce
1) The document discusses data governance design options for large enterprises, including a line organization, staff organization per business unit, shared service center, and externalization models.
2) It provides two examples of how companies have implemented data governance: a high tech company that used a central function and a chemical company that used a shared service center for governance and operational responsibility.
3) Key principles for an effective governance design are discussed, including being global, shared, governing, service-oriented, managed, and empowered. The evolution from a shared service to outsourced data management processes is also covered.
The document discusses making data quality an operational way of life through rigorous governance and quality processes. It explains that operational data quality requires continuous validation of data accuracy, currency, and auditability. It also requires governance to ensure data relevance considering internal/external changes. The key aspects of operational data quality are repeatable validation workflows, tracking exceptions, using baselines to assess reasonability, and categorizing exceptions to address data quality issues.
This document discusses the hurdles and enablers to adopting software product line practices in large corporate organizations, specifically large banks. It identifies some key hurdles including: different business units perceiving little return on investment for cross-unit product lines; and difficulties motivating investment and changing funding models. It proposes some enabling mechanisms that are showing positive results, such as aligning product lines with strategic business goals and establishing executive sponsorship. Large banks present additional challenges to product line adoption due to their multiple divisions, legacy systems, and focus on short-term profits over long-term IT strategies.
This document summarizes a research paper that studied critical processes for managing supplier involvement in new product development. The researchers developed an analytical framework that identifies both long-term strategic processes and short-term operational processes related to supplier involvement. They conducted a multiple-case study of supplier collaborations within a copier and printer manufacturer. Their findings demonstrated that coherent planning and execution of both strategic and operational activities is important for achieving both short-term project objectives and long-term benefits of supplier involvement in product development.
This document discusses the challenges of materials management for engineering, procurement, and construction (EPC) projects and how an effective materials management system can help. It notes that materials management has become increasingly challenging with bigger, more complex projects. An integrated materials management system can streamline procurement, improve integration between engineering and procurement, provide visibility across the supply chain, and help ensure materials arrive on time. The document presents SmartPlant Materials as a software solution that leading EPC companies like WorleyParsons and CB&I have implemented to gain these benefits and improve project execution and efficiency.
A Data-driven Maturity Model for Modernized, Automated, and Transformed ITbalejandre
This document presents a research-based maturity model for measuring organizations' progress in IT transformation. The model segments organizations into four levels of maturity based on surveys of 1,000 IT executives about their infrastructure, processes, and relationships. Only a small percentage have achieved the highest levels of modernized infrastructure, automated processes, and business-IT alignment needed for digital transformation. Higher maturity is correlated with improved agility, efficiency, innovation funding, and business outcomes. Adopting modern data center technologies, automated processes, and DevOps practices can help organizations progress to more mature states.
This paper examines, through the use of plant-level data, whether R&D’s productivity impact is contingent on the distance of a plant’s productivity from the industry’s technological frontier. R&D is specified as an accumulated stock from R&D investments. We analyse the productivity effect of a plant’s own R&D as well as the productivity impact of the plant’s parent firm’s and other firms’ proximity-weighted R&D stocks. The results show that a plant’s own and a parent firm’s R&D have a positive productivity impact and that the former impact decreases as the distance from the industry’s technological frontier increases. Furthermore, the productivity effect of other firms’ proximity-weighted R&D is, on average, positive, but this impact increases in the distance from the technological frontier. Another important finding is that all the plants tend to converge towards the industry’s technological frontier despite the size of external R&D spillovers.
Cognizant SAP Manufacturing Success Report 2014Cognizant
The document discusses key trends in the UK manufacturing sector based on a report called "Manufacturing Success 2014". It finds that while UK manufacturers are optimistic about growth prospects, many struggle with interconnectedness. Specifically:
- Over 3/4 of UK manufacturers are optimistic about growth but less than half are able to capitalize on opportunities from connecting people, businesses, and things within their organizations.
- Multi-generational workforces and changing customer demands present challenges around flexibility, skills, and understanding customer needs that manufacturers must address to accelerate growth.
- Supply chain issues have negatively impacted over 60% of manufacturers, highlighting the need for improved visibility and resilience across interconnected supply networks.
Technical Data Management from the Perspective of Identification and Traceabi...ijtsrd
In a Manufacturing Industry, be it of any scale, the entity of utmost importance is the technical data. As the quantum of the generation of such necessary data is large, it paves the way to the need of establishing a data management tool such that would aid ease of access and clarity of thought. Such a tool may be in the form of software or in the form of a set personal routine or procedure that is sincerely adhered to. Technical data literally forms the backbone of the Industrys progress. Just like the nervous system is highly dependent on the well being of the backbone, almost all the departments in an Industry are highly reliant on the Technical Data Pool available. This paper highlights the importance of Technical data management from the key perspective of identification wherein a document can be easily identified and traceability wherein the document can be quickly traced for the origin as well as the locations where it is currently used. Certain recommendations shall be appended for a reference towards improved functioning of various departments in the Manufacturing Industry. A conclusion shall thereafter be drawn highlighting the utility and importance of Technical Data Management. Gourav Vivek Kulkarni "Technical Data Management from the Perspective of Identification and Traceability in the Manufacturing Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26389.pdfPaper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/26389/technical-data-management-from-the-perspective-of-identification-and-traceability-in-the-manufacturing-industry/gourav-vivek-kulkarni
This document discusses flexibility of infonomics (knowledge management) in industrial production under Industry 4.0. It outlines a vision for Industry 4.0 where implicit human knowledge can be digitized and integrated into smart, connected systems. Currently, most of this knowledge remains undigitized. The document presents initial findings from a research project investigating how technical communication and knowledge management must adapt to effectively support Industry 4.0. Key findings include the need to shift from static to dynamically evolving knowledge, capture more implicit knowledge, and develop standards like iiRDS to enable intelligent, contextualized information delivery across systems. Overall, the document argues more work is needed to realize the full potential of flexibility through digitizing implicit human knowledge in industrial production.
This document reviews the journal article "Big Data in Design and Manufacturing Engineering". It begins by defining big data and its characteristics. It then discusses the benefits of big data in design and manufacturing such as defect tracking, improved supply planning, and optimized manufacturing processes. Applications of big data in various industries are presented along with methods and technologies used. Challenges of big data like data management and privacy are also reviewed. The document concludes that big data can provide valuable insights if the right tools and questions are used to analyze large, diverse datasets.
Improving Risk Evaluation and Mitigation StrategyCognizant
For life sciences companies, improving risk evaluation and mitigation strategy (REMS) is critical to adhere to FDAAA and other regulatory hurdles; here's our Microsoft SharePoint-based approach for improving document management and sharing and upgrading REMS.
This white paper discusses how companies can apply data science insights to improve products and operations. It describes the typical data science project lifecycle, including problem definition, data collection, model building and testing. However, many companies struggle to deploy models into production applications. The paper argues that data science teams need tools that allow models to be easily updated and redeployed without disrupting operations. The Yhat platform aims to streamline this process and help companies more quickly turn insights into data-driven products.
The document discusses the importance of developing a big data plan. It states that while exploiting big data is an important source of competitive advantage, many companies struggle due to technical and organizational challenges. It recommends that companies craft a big data plan that focuses on three elements: assembling and integrating data from various sources, selecting analytic models that can optimize operations and predict business outcomes, and creating intuitive tools that help employees make use of the analytic outputs. Developing such a plan will help companies prioritize investments and initiatives to harness big data effectively.
This report summarizes interviews with 28 business leaders about challenges and opportunities of big data. Key findings include: 1) While there is potential for profits from big data, caution is needed as data does not automatically lead to profits. 2) Different business models exist including data users, suppliers, and facilitators. 3) Practical obstacles like poor data quality and political issues around data sharing must be addressed. 4) To succeed, businesses need a clear business model making data central and a plan to generate value from data.
The document summarizes a meeting of the National Defense Industry Association's Manufacturing Division that discussed establishing a pilot institute for the National Network for Manufacturing Innovation. Dr. Jennifer Fielding and Ed Morris presented on the National Additive Manufacturing Innovation Institute (NAMII), a public-private partnership led by the National Center for Defense Manufacturing and Machining. NAMII would establish shared additive manufacturing facilities and technology to bridge the gap between research and production. The summaries highlighted benefits of additive manufacturing for national defense and commercial applications.
Piloting procter & gamble from decicion cockpitsniz73
P&G developed business analytics solutions called Business Sphere and Decision Cockpits to provide executives and employees predictive insights. Business Sphere integrates data from various sources using algorithms and models. The first project, Business Sufficiency, gives executives predictions about market share and performance 6-12 months in the future. It reveals what is happening now, why, and what actions can be taken. Decision Cockpits eliminate debates over data and allow focus on improving business. These tools changed decision making by providing standardized, real-time data to enable faster, better decisions and anticipate future events.
Strategic Foresight at Deutsche TelekomRené Rohrbeck
This document describes Deutsche Telekom's approach to strategic foresight and roadmapping. It discusses three major tools used for continuous scanning: the Technology Radar identifies emerging technologies; the Product and Service Radar assesses competitors' offerings; and Customer Foresight identifies customer needs and trends. The Technology Radar tracks 60 technologies and classifies them by relevance and development phase. Information comes from an international scout network. Roadmapping guides innovation by exploring new markets and products/services. Continuous scanning is essential in a fast-changing environment to detect threats and opportunities.
The document discusses the challenges facing process manufacturers and what they should look for in an ERP system. It summarizes key challenges as ageing infrastructure, high costs, increased customer demands for faster development times, and new environmental/safety standards. It recommends looking for an ERP system with strong process manufacturing functionality like formula/recipe management, quality control, lot tracking, production scheduling, and regulatory compliance. Case studies from various companies demonstrate how an ERP system from Sage helped them address challenges and support growth.
Innovation is achieved when product specific process knowledge is discovered ...Meghana Ransing
This document discusses the 7Epsilon methodology for continual process improvement and zero defect manufacturing in foundries. It focuses on knowledge retention, reuse, and discovery through analyzing in-process data and developing a knowledge repository. A case study is presented on how to apply the 7Epsilon approach to identify process-specific knowledge and reduce defects in a low alloy steel foundry's melting process by analyzing fracture test data to determine the optimal chemistry specifications. The goal is to help foundries reduce costs by tapping into existing process knowledge and establishing effective knowledge management strategies.
EMC Isilon: A Scalable Storage Platform for Big DataEMC
This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
Data Science Course in Paschim Vihar (1).pptxamitk971644
Want to excel in data science? Enroll in our cutting-edge course at Dynamic FutureTech in Paschim Vihar. Get hands-on experience and launch your career today!
Data Science: Unlocking Insights and Transforming IndustriesUncodemy
Data science is an interdisciplinary field that encompasses a range of techniques, algorithms, and tools to extract valuable insights and knowledge from data.
This document summarizes a research paper that studied critical processes for managing supplier involvement in new product development. The researchers developed an analytical framework that identifies both long-term strategic processes and short-term operational processes related to supplier involvement. They conducted a multiple-case study of supplier collaborations within a copier and printer manufacturer. Their findings demonstrated that coherent planning and execution of both strategic and operational activities is important for achieving both short-term project objectives and long-term benefits of supplier involvement in product development.
This document discusses the challenges of materials management for engineering, procurement, and construction (EPC) projects and how an effective materials management system can help. It notes that materials management has become increasingly challenging with bigger, more complex projects. An integrated materials management system can streamline procurement, improve integration between engineering and procurement, provide visibility across the supply chain, and help ensure materials arrive on time. The document presents SmartPlant Materials as a software solution that leading EPC companies like WorleyParsons and CB&I have implemented to gain these benefits and improve project execution and efficiency.
A Data-driven Maturity Model for Modernized, Automated, and Transformed ITbalejandre
This document presents a research-based maturity model for measuring organizations' progress in IT transformation. The model segments organizations into four levels of maturity based on surveys of 1,000 IT executives about their infrastructure, processes, and relationships. Only a small percentage have achieved the highest levels of modernized infrastructure, automated processes, and business-IT alignment needed for digital transformation. Higher maturity is correlated with improved agility, efficiency, innovation funding, and business outcomes. Adopting modern data center technologies, automated processes, and DevOps practices can help organizations progress to more mature states.
This paper examines, through the use of plant-level data, whether R&D’s productivity impact is contingent on the distance of a plant’s productivity from the industry’s technological frontier. R&D is specified as an accumulated stock from R&D investments. We analyse the productivity effect of a plant’s own R&D as well as the productivity impact of the plant’s parent firm’s and other firms’ proximity-weighted R&D stocks. The results show that a plant’s own and a parent firm’s R&D have a positive productivity impact and that the former impact decreases as the distance from the industry’s technological frontier increases. Furthermore, the productivity effect of other firms’ proximity-weighted R&D is, on average, positive, but this impact increases in the distance from the technological frontier. Another important finding is that all the plants tend to converge towards the industry’s technological frontier despite the size of external R&D spillovers.
Cognizant SAP Manufacturing Success Report 2014Cognizant
The document discusses key trends in the UK manufacturing sector based on a report called "Manufacturing Success 2014". It finds that while UK manufacturers are optimistic about growth prospects, many struggle with interconnectedness. Specifically:
- Over 3/4 of UK manufacturers are optimistic about growth but less than half are able to capitalize on opportunities from connecting people, businesses, and things within their organizations.
- Multi-generational workforces and changing customer demands present challenges around flexibility, skills, and understanding customer needs that manufacturers must address to accelerate growth.
- Supply chain issues have negatively impacted over 60% of manufacturers, highlighting the need for improved visibility and resilience across interconnected supply networks.
Technical Data Management from the Perspective of Identification and Traceabi...ijtsrd
In a Manufacturing Industry, be it of any scale, the entity of utmost importance is the technical data. As the quantum of the generation of such necessary data is large, it paves the way to the need of establishing a data management tool such that would aid ease of access and clarity of thought. Such a tool may be in the form of software or in the form of a set personal routine or procedure that is sincerely adhered to. Technical data literally forms the backbone of the Industrys progress. Just like the nervous system is highly dependent on the well being of the backbone, almost all the departments in an Industry are highly reliant on the Technical Data Pool available. This paper highlights the importance of Technical data management from the key perspective of identification wherein a document can be easily identified and traceability wherein the document can be quickly traced for the origin as well as the locations where it is currently used. Certain recommendations shall be appended for a reference towards improved functioning of various departments in the Manufacturing Industry. A conclusion shall thereafter be drawn highlighting the utility and importance of Technical Data Management. Gourav Vivek Kulkarni "Technical Data Management from the Perspective of Identification and Traceability in the Manufacturing Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26389.pdfPaper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/26389/technical-data-management-from-the-perspective-of-identification-and-traceability-in-the-manufacturing-industry/gourav-vivek-kulkarni
This document discusses flexibility of infonomics (knowledge management) in industrial production under Industry 4.0. It outlines a vision for Industry 4.0 where implicit human knowledge can be digitized and integrated into smart, connected systems. Currently, most of this knowledge remains undigitized. The document presents initial findings from a research project investigating how technical communication and knowledge management must adapt to effectively support Industry 4.0. Key findings include the need to shift from static to dynamically evolving knowledge, capture more implicit knowledge, and develop standards like iiRDS to enable intelligent, contextualized information delivery across systems. Overall, the document argues more work is needed to realize the full potential of flexibility through digitizing implicit human knowledge in industrial production.
This document reviews the journal article "Big Data in Design and Manufacturing Engineering". It begins by defining big data and its characteristics. It then discusses the benefits of big data in design and manufacturing such as defect tracking, improved supply planning, and optimized manufacturing processes. Applications of big data in various industries are presented along with methods and technologies used. Challenges of big data like data management and privacy are also reviewed. The document concludes that big data can provide valuable insights if the right tools and questions are used to analyze large, diverse datasets.
Improving Risk Evaluation and Mitigation StrategyCognizant
For life sciences companies, improving risk evaluation and mitigation strategy (REMS) is critical to adhere to FDAAA and other regulatory hurdles; here's our Microsoft SharePoint-based approach for improving document management and sharing and upgrading REMS.
This white paper discusses how companies can apply data science insights to improve products and operations. It describes the typical data science project lifecycle, including problem definition, data collection, model building and testing. However, many companies struggle to deploy models into production applications. The paper argues that data science teams need tools that allow models to be easily updated and redeployed without disrupting operations. The Yhat platform aims to streamline this process and help companies more quickly turn insights into data-driven products.
The document discusses the importance of developing a big data plan. It states that while exploiting big data is an important source of competitive advantage, many companies struggle due to technical and organizational challenges. It recommends that companies craft a big data plan that focuses on three elements: assembling and integrating data from various sources, selecting analytic models that can optimize operations and predict business outcomes, and creating intuitive tools that help employees make use of the analytic outputs. Developing such a plan will help companies prioritize investments and initiatives to harness big data effectively.
This report summarizes interviews with 28 business leaders about challenges and opportunities of big data. Key findings include: 1) While there is potential for profits from big data, caution is needed as data does not automatically lead to profits. 2) Different business models exist including data users, suppliers, and facilitators. 3) Practical obstacles like poor data quality and political issues around data sharing must be addressed. 4) To succeed, businesses need a clear business model making data central and a plan to generate value from data.
The document summarizes a meeting of the National Defense Industry Association's Manufacturing Division that discussed establishing a pilot institute for the National Network for Manufacturing Innovation. Dr. Jennifer Fielding and Ed Morris presented on the National Additive Manufacturing Innovation Institute (NAMII), a public-private partnership led by the National Center for Defense Manufacturing and Machining. NAMII would establish shared additive manufacturing facilities and technology to bridge the gap between research and production. The summaries highlighted benefits of additive manufacturing for national defense and commercial applications.
Piloting procter & gamble from decicion cockpitsniz73
P&G developed business analytics solutions called Business Sphere and Decision Cockpits to provide executives and employees predictive insights. Business Sphere integrates data from various sources using algorithms and models. The first project, Business Sufficiency, gives executives predictions about market share and performance 6-12 months in the future. It reveals what is happening now, why, and what actions can be taken. Decision Cockpits eliminate debates over data and allow focus on improving business. These tools changed decision making by providing standardized, real-time data to enable faster, better decisions and anticipate future events.
Strategic Foresight at Deutsche TelekomRené Rohrbeck
This document describes Deutsche Telekom's approach to strategic foresight and roadmapping. It discusses three major tools used for continuous scanning: the Technology Radar identifies emerging technologies; the Product and Service Radar assesses competitors' offerings; and Customer Foresight identifies customer needs and trends. The Technology Radar tracks 60 technologies and classifies them by relevance and development phase. Information comes from an international scout network. Roadmapping guides innovation by exploring new markets and products/services. Continuous scanning is essential in a fast-changing environment to detect threats and opportunities.
The document discusses the challenges facing process manufacturers and what they should look for in an ERP system. It summarizes key challenges as ageing infrastructure, high costs, increased customer demands for faster development times, and new environmental/safety standards. It recommends looking for an ERP system with strong process manufacturing functionality like formula/recipe management, quality control, lot tracking, production scheduling, and regulatory compliance. Case studies from various companies demonstrate how an ERP system from Sage helped them address challenges and support growth.
Innovation is achieved when product specific process knowledge is discovered ...Meghana Ransing
This document discusses the 7Epsilon methodology for continual process improvement and zero defect manufacturing in foundries. It focuses on knowledge retention, reuse, and discovery through analyzing in-process data and developing a knowledge repository. A case study is presented on how to apply the 7Epsilon approach to identify process-specific knowledge and reduce defects in a low alloy steel foundry's melting process by analyzing fracture test data to determine the optimal chemistry specifications. The goal is to help foundries reduce costs by tapping into existing process knowledge and establishing effective knowledge management strategies.
EMC Isilon: A Scalable Storage Platform for Big DataEMC
This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
Data Science Course in Paschim Vihar (1).pptxamitk971644
Want to excel in data science? Enroll in our cutting-edge course at Dynamic FutureTech in Paschim Vihar. Get hands-on experience and launch your career today!
Data Science: Unlocking Insights and Transforming IndustriesUncodemy
Data science is an interdisciplinary field that encompasses a range of techniques, algorithms, and tools to extract valuable insights and knowledge from data.
STS. Smarter devices. Smarter test systems.Hank Lydick
This document provides an overview of trends in automated test and measurement. It discusses how semiconductor companies are using real-time data analytics to reduce manufacturing test costs by harvesting production test data. It also discusses how test management software is becoming more important for handling new programming languages. Additionally, it discusses how RFIC companies are reusing IP and standardizing hardware to reduce costs and time to market across the product design cycle from characterization to production.
This document discusses trends in automated test systems and strategies. It covers topics like harvesting production test data through real-time analytics, challenges of life-cycle management for long-term projects due to software obsolescence and compatibility issues, and how off-the-shelf test executives can help address the influx of new programming languages. It also discusses standardizing platforms across product design cycles to reduce costs, and adopting modular solutions to validate high-frequency components economically.
This document discusses how to deliver real business impact through analytics by taking a business process view. It recommends understanding end-to-end business processes to design analytics enablement, focusing on providing visibility, managing effectiveness, executing actions, and repeating the process. It also recommends dissecting the data-to-insight process, choosing the right operating model for a shared analytics organization, and ensuring stakeholders are aligned around an agile strategy. Taking this approach can help harness data and analytics to generate material business impact.
This document discusses the evolution of enterprise data platforms and introduces the concept of a data mesh as a potential next-generation architecture. It makes the following key points:
- Traditional centralized data platforms like data warehouses and data lakes have limitations around scalability and organizational bottlenecks as data use cases increase.
- A data mesh proposes a decentralized architecture with "domain ownership of data" to address these challenges. It advocates for data to be treated as a product and shared across organizational boundaries.
- A data mesh aims to enable rapid development of data use cases at scale, improve data quality/trustworthiness, and efficiently govern data - seen as the three pillars for increasing value from data.
- Many companies are
Data Science for Beginners: A Step-by-Step IntroductionUncodemy
Data science is a dynamic and rapidly evolving field that has gained immense importance in recent years. It involves the extraction of meaningful insights and knowledge from large and complex datasets. If you are new to data science, this step-by-step introduction will provide you with a solid foundation and explain why pursuing a data science certification course.
Advanced analytics uses sophisticated techniques like machine learning, data mining, and predictive modeling to gain deeper insights from data beyond traditional business intelligence. While executives see the potential benefits, most companies are unsure how to implement advanced analytics. The document recommends starting with targeted efforts to build models from existing data sources and transform organizational culture, rather than massive overhauls. This balanced approach can help companies develop analytics capabilities and maintain flexibility as technologies and opportunities evolve.
Accelerating Time to Success for Your Big Data Initiatives☁Jake Weaver ☁
1. The document discusses the challenges of implementing big data initiatives, including sizing infrastructure, finding skilled professionals, and managing changing priorities over time.
2. It recommends partnering with a managed services provider to simplify big data implementation and gain expertise, flexibility, and time-to-market benefits.
3. The CenturyLink big data solutions suite includes managed Hadoop and analytics platforms to optimize data storage, integration, and analysis for customers.
This document discusses drivers of technological change and contains several sections. It covers topics like core competencies, development strategy, strategic portfolio management, and innovation in the service industry. Key drivers mentioned include technological innovation, globalization, mobility, big data analysis, and social media. The document emphasizes characteristics of good organizational strategy like being flexible, responsive, creative, challenging, realistic, and focused. It also discusses focusing strategic portfolio management on meeting strategic objectives. Finally, it defines innovation in the service industry as including both product and process innovation for firms in service sectors.
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Data is a key enabler of digital transformation and innovation. It fuels new digital processes and solutions. To benefit from data, organizations must first define and organize core master data and then acquire the right competencies to analyze and combine both structured and unstructured internal and external data. This will allow organizations to discover innovative solutions through a "data-lab" approach and trials. Ensuring high quality master and process data is also important to enable seamless experiences across systems.
This document discusses challenges and solutions related to big data implementation. Some key challenges mentioned include reluctance to invest in big data strategies, integrating traditional and big data, and finding professionals with both big data and domain skills. The document recommends starting small with proofs of concept and taking an iterative approach to derive early benefits from big data before making larger investments. It also stresses the importance of having an enterprise-wide data strategy and acquiring various skills needed for big data projects.
Report on strategic rules of Information System for changing the bases of com...Md. Khukan Miah
Achieving advantages requires broad IS management and user dialogue plus imagination. The process is complicated by the fact that many IS products are strategic though the potential benefits are very subjective and not easily verified. Often a strict ROI focus by senior management may turn attention toward narrow, well-defined targets as opposed to broader strategic opportunities that are harder to analyze.
Analytics Unleashed_ Navigating the World of Data Science.pdfkhushnuma khan
The 21st century has witnessed an unprecedented explosion in the volume, variety, and velocity of data. This deluge of information, often referred to as “Big Data,” has spurred the emergence of Data Science as a crucial discipline. Data Science integrates statistical methodologies, advanced programming, and domain expertise to analyze and interpret complex datasets. Its applications span diverse sectors, including business, healthcare, finance, and technology.
This document discusses Oracle's approach to big data and information architecture. It begins by explaining what makes big data different from traditional data, noting that big data refers to large datasets that are challenging to store, search, share, visualize, and analyze due to their volume, velocity, and variety. It then provides an overview of big data architecture capabilities and describes how to integrate big data capabilities into an organization's overall information architecture. The document concludes by outlining some key big data use cases and best practices for organizations adopting big data.
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Research and Development Digitalization with Data Intelligence in mind.
1. White Paper
R&D Digitalization with
Data Intelligence in Mind
A Strategy for Chemicals & Materials Innovation
Max Petersen
Associate Vice President of Chemicals & Materials Marketing
November 2019
2. White Paper: R&D Digitalization with Data Intelligence in Mind
2
For Chemicals & Materials Innovation
Turning Scientific Data into Value
Max Petersen
CONTENTS
Abstract P 3
Challenges in Chemicals & Materials
R&D
P 3
Why Digitalize in the First Place? P 3
Why is R&D Digitalization so Difficult in
Chemicals & Materials?
P 5
Defining an R&D Digitalization Strategy
that Works
P 5
P 6
P 9
Implementing a Data-Centric R&D
Platform
Conclusion
Call to Action P 10
Associate Vice President of Chemicals and Materials Marketing
Max Petersen is the AVP of Chemicals and Materials Marketing at Dotmatics. In this role, Max is responsible for developing
Dotmatics’ strategy for the chemicals & materials industries.
Max has over 20 years of experience in informatics and simulation technologies in chemicals, materials and life sciences
applications. He held business consulting and various technology leadership positions and also managed a lab automation
company. He holds a Ph.D. in Physics from Fritz-Haber-Institute of the Max-Planck-Society and a masters in Physics from Technische
Universität Berlin.
4. White Paper: R&D Digitalization with Data Intelligence in Mind
4
Collaboration and externalization: Specialty chemical
companies often sit in the center of an integrated innovation
supply chain. They must source raw ingredients (often with
highly variable specs) and produce materials that need to
match narrow specification ranges provided by their customers.
Innovation tasks may be handed off to 3rd parties or may come
from mergers or acquisitions. Here, a digitalization infrastructure
needs to provide data standardization and openness to
facilitate the free exchange of information.
Based on this, a cohesive digitalization strategy builds value by
a. putting enabling technologies into place (cloud/SaaS
infrastructure, a single easily maintainable code base, etc.),
b. establishing personal productivity tools (automation of non-
value-added tasks),
c. developing operational excellence (data standardization, best
practices, data-driven decision making), and finally
d. accelerating new product development (NPD).
In order to support this strategy, R&D digitalization needs to
fulfill 3 key requirements: It needs to enable data capture (“data-
in”), underpin operational excellence (“data-centric platform”),
and ensure data can be provisioned to scientists, management
and data analytics experts (“data-out”). See Figure 1.
Figure 1 - Value model for R&D digitalization.“Data-in”objectives generally align to either enabling on personal productivity improvement goals, while platform capabilities enable
operational excellence. Finally,“data-out”capabilities are required for accelerated new product development (NPD), which coincides with highest value creation.
6. White Paper: R&D Digitalization with Data Intelligence in Mind
6
Implementing a Data-Centric R&D Platform
Considering the key role of data availability, a data-driven
platform needs to be at the center of any strategic digitalization
project. A data-centric platform allows for a unified view on
all data and allows for the implementation of user roles and
workflows. This requirement goes beyond an application-driven
platform that has the sole purpose to integrate an application
portfolio. Nevertheless, any platform should be open and able
to integrate 3rd party data sources & applications.
In order to implement the vast variability of R&D workflows
that can be found in materials innovation, configuration-driven
interfaces are a way to create experiment templates that
reflect domain data models. This means that all data types that
belong to a specific experimental workflow can be grouped
together and exposed as needed. With this approach end user
adoption is simplified as e.g. lab technicians are only exposed
to data fields relevant to their work. It also means that data
siloes are eliminated, as an experiment template now ties
together multiple data sources, such as structural, composition,
performance and analytical characterization data.
To support data analytics initiatives and to allow scientists to
innovate faster, digitalization needs “data-out” capabilities, i.e.
the ability to perform queries against all aspects of a domain
model. The query capabilities are best combined with a data
visualization system that can help with data exploration,
knowledge extraction and decision support. Interactivity with
the data platform is key, e.g. to allow for design of experiment
(DoE) or reporting capabilities.
Figure 2: Mapping high-level objectives of R&D digitalization to infrastructure requirements requires a comprehensive view on lab digitalization, data intelligence and containment
of R&D IT infrastructure complexity.
8. White Paper: RD Digitalization with Data Intelligence in Mind
8
“Dotmatics has also invested in building a cloud-first IT
infrastructure, which means that the same system can be
run on-premise, on the cloud or hosted, providing distinct
advantages in scalability, availability, accessibility and security.”
process control data to help scale-up. In regulatory or customer
complaint scenarios, easy accessibility of research data is vital
for quick turnarounds.
In order to manage IT infrastructure complexity, the platform
needs to provide various functions, including a data federation
service to simplify tying in 3rd party data sources that can then
be accessed by “data-out” technologies. Dotmatics has also
invested in building a cloud-first IT infrastructure, which means
that the same system can be run on-premise, on the cloud or
hosted, providing distinct advantages in scalability, availability,
accessibility and security.
A key distinction between a data-centric platform approach
and an application portfolio is how workflows are implemented.
Within an application-centric approach, the end users carry
out their work by accessing functionalities within a set of
applications with rigid user interfaces. This often leads to end
user adoption and change management issues when ways of
working undergo a significant shift.
In the other hand, in a data-centric platform, workflows are
configured with the applications acting behind the scenes
to fulfill specific tasks. This is illustrated in Figure 6 (overleaf).
For example, knowledge management solutions may be
used at various stages throughout the innovation lifecycle by
many different roles (marketing, planning, task coordination,
pilot, registration, regulatory). Configurability means that
each of these roles can have specialized interfaces tailored
to their needs. The same is true for lab technicians that
access inventory, sample management and request handling
functionality from dedicated interface configurations that
closely match pre-digitalization workflows.
Figure 5: Mapping of RD digital excellence to functional application areas.
10. Head Office UK
Phone: +44(0)1279 654 123
Fax: +44(0)1279 653 088
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UK
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Fax: +81 3 4577 1481
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101-0031 Tokyo
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Phone: +82 31 278 7038
Fax: +82 31 278 7039
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16226, Korea
Contact Dotmatics
dotmatics.com
WP-1911-01
Further Reading
1. https://www.accenture.com/us-en/blogs/chemicals-and-
natural-resources-blog/digital-disruption-in-the-lab-the-case-
for-rd-digitalization-in-chemicals
2. https://www.mckinsey.com/industries/chemicals/our-
insights/digital-in-chemicals-from-technology-to-impact#
3. https://www.businesschemistry.org/article/?article=245
4. https://cen.acs.org/articles/95/i39/Digitalization-comes-
materials-industry.html
5. https://www.mckinsey.com/business-functions/operations/
our-insights/prepare-for-rds-connected-future
Call to Action
Dotmatics is a fast-growing, science-focused company that
develops its entire software suite by a co-located team of
programmers and scientists. With a global network of support
offices, Dotmatics prides itself on a partnership approach to
engaging with science-driven organizations worldwide.
We invite you to contact us in order to discuss your specific
RD goals and needs. With 15 years of experience in RD
digitalization and a highly skilled team of scientists, project
management experts and product development professionals,
we can provide you with the expertise and technology needed
to successfully implement your digitalization projects.
Seoul, South Korea
Head Office, UK
Tokyo, Japan
Rome, Italy
Australia
Boston , US
San Diego , US
Paris, France