Today’s analytics strategies must harness intelligence from increasingly distributed data environments, with important resources residing across the cloud, in your data center, and in connected devices sitting at the edge of your network. This means making sure you have a modern infrastructure in place that is optimized for analytics, scalable and able to grow over time. Implementing analytics is not a ‘once and done’ initiative – the type of analytics you need depends on business priorities and budgets. As your business evolves, so will your analytics environment.
Meeting the challenges to adopt visual production management systems hms-whit...Ariel Lerer
This White Paper will provide an essential understanding of different initiatives towards having a Visual Production Management system, (VPMS), in a manufacturing environment. Also insights about why? and how? to implement a VPMS, highlighting the benefits of taking these actions, and further across your environment creating a learning organization.
Download from www.hmswebsite.com/vpms-white-paper/
Top Five Secrets for a Successful Enterprise Mobile QA Automation StrategyCognizant
1) The document discusses five secrets for a successful enterprise mobile automation strategy: selecting automation tools wisely, enabling continuous delivery and rapid QA, automating beyond functional coverage, supporting a flexible execution environment, and using the right framework.
2) It emphasizes the importance of tool selection parameters like cross-platform support, new OS version support, and open integration.
3) Continuous delivery is key to increasing speed without compromising quality in a DevOps environment. This requires automation across the entire software development lifecycle.
4) Automation should go beyond functional testing to also validate nonfunctional parameters and customer experience under varying conditions.
5) The framework design should allow for efficient implementation, reuse, and adaptation to changes
Making a Quantum Leap with Continuous Analytics-Based QACognizant
By correlating analytics data across the IT lifecycle, enterprises can design and implement a level of testing that improves predictive mechanisms and anticipates ever-changing business needs.
Empirix's Top Metrics to Achieve Contact Center AssuranceAlex Johnson
This document discusses metrics for achieving contact center assurance and quality customer experience. It describes Six Sigma techniques used in manufacturing for quality assurance but notes these may not align well with agile software development models used by many contact centers today. The document then outlines some key metrics for contact centers, including critical-to-quality trees to identify customer needs and measure how well sub-processes meet those needs, and critical-to-customer metrics to determine what customers want and measure experience quality. It also discusses agile methodology metrics like burn down rate and velocity.
3P Production Preparation Process Overviewopexcreative
The document describes a 3P (Production Preparation Process) event, which is an intense 3-5 day workshop to design lean manufacturing processes. A cross-functional team collaborates in the 3P to develop waste-free systems focused on material and information flow. Through rapid prototyping and testing alternatives, the team aims to launch new products and processes quicker with reduced costs and built-in quality. The 3P follows phases of information gathering, creative development of alternatives, capturing the new process design, implementation, and ongoing continuous improvement.
Selecting a Software Solution: 13 Best Practices for Media and Entertainment ...Cognizant
When selecting commercial off-the-shelf software (COTS), companies in the increasingly digitally-based media and entertainment industry need to develop a detailed advance plan, obtain support from all stakeholders and continuously monitor vendor performance against critical expectations, best practices and business requirements.
Global Quality Workflow, The Transition from Manual to Automated Compliance P...Maetrics
The document summarizes a presentation on transitioning from manual to automated quality and compliance processes. It discusses defining workflow and automation, aligning the organization, gaining alignment, and return on investment. It provides examples of one company's journey to establish a quality culture, use standard models, implement master data management and capability maturity models, and realize increased sales and reduced working capital through improved quality. The presentation aims to help organizations define their aspirational state and determine a path to transition processes from manual to automated.
Meeting the challenges to adopt visual production management systems hms-whit...Ariel Lerer
This White Paper will provide an essential understanding of different initiatives towards having a Visual Production Management system, (VPMS), in a manufacturing environment. Also insights about why? and how? to implement a VPMS, highlighting the benefits of taking these actions, and further across your environment creating a learning organization.
Download from www.hmswebsite.com/vpms-white-paper/
Top Five Secrets for a Successful Enterprise Mobile QA Automation StrategyCognizant
1) The document discusses five secrets for a successful enterprise mobile automation strategy: selecting automation tools wisely, enabling continuous delivery and rapid QA, automating beyond functional coverage, supporting a flexible execution environment, and using the right framework.
2) It emphasizes the importance of tool selection parameters like cross-platform support, new OS version support, and open integration.
3) Continuous delivery is key to increasing speed without compromising quality in a DevOps environment. This requires automation across the entire software development lifecycle.
4) Automation should go beyond functional testing to also validate nonfunctional parameters and customer experience under varying conditions.
5) The framework design should allow for efficient implementation, reuse, and adaptation to changes
Making a Quantum Leap with Continuous Analytics-Based QACognizant
By correlating analytics data across the IT lifecycle, enterprises can design and implement a level of testing that improves predictive mechanisms and anticipates ever-changing business needs.
Empirix's Top Metrics to Achieve Contact Center AssuranceAlex Johnson
This document discusses metrics for achieving contact center assurance and quality customer experience. It describes Six Sigma techniques used in manufacturing for quality assurance but notes these may not align well with agile software development models used by many contact centers today. The document then outlines some key metrics for contact centers, including critical-to-quality trees to identify customer needs and measure how well sub-processes meet those needs, and critical-to-customer metrics to determine what customers want and measure experience quality. It also discusses agile methodology metrics like burn down rate and velocity.
3P Production Preparation Process Overviewopexcreative
The document describes a 3P (Production Preparation Process) event, which is an intense 3-5 day workshop to design lean manufacturing processes. A cross-functional team collaborates in the 3P to develop waste-free systems focused on material and information flow. Through rapid prototyping and testing alternatives, the team aims to launch new products and processes quicker with reduced costs and built-in quality. The 3P follows phases of information gathering, creative development of alternatives, capturing the new process design, implementation, and ongoing continuous improvement.
Selecting a Software Solution: 13 Best Practices for Media and Entertainment ...Cognizant
When selecting commercial off-the-shelf software (COTS), companies in the increasingly digitally-based media and entertainment industry need to develop a detailed advance plan, obtain support from all stakeholders and continuously monitor vendor performance against critical expectations, best practices and business requirements.
Global Quality Workflow, The Transition from Manual to Automated Compliance P...Maetrics
The document summarizes a presentation on transitioning from manual to automated quality and compliance processes. It discusses defining workflow and automation, aligning the organization, gaining alignment, and return on investment. It provides examples of one company's journey to establish a quality culture, use standard models, implement master data management and capability maturity models, and realize increased sales and reduced working capital through improved quality. The presentation aims to help organizations define their aspirational state and determine a path to transition processes from manual to automated.
It Strategie Als Speerpunt Voor Ondernemingsbeleid Erik FoekenHPDutchWorld
1) The document discusses how IT strategy should be a key focus for business policy and how the IT organization needs to be aligned with business goals of product leadership, customer intimacy, and operational excellence.
2) It argues that the IT supply chain is often organized in silos and that HP's Business Technology Optimization vision can help achieve alignment through integration and automation with control over the production process and delivery of business outcomes.
3) Examples are given showing potential benefits of BTO like higher project success rates, lower costs, and improved service levels.
Six Sigma originated in the 1980s at Motorola to improve quality standards. It aims to reduce defects to 3.4 per million opportunities through a DMAIC process of define, measure, analyze, improve, and control. Six Sigma statistical tools help analyze processes, identify root causes of defects, and design improvements. Implementing a customer satisfaction metric involves understanding customers, measuring satisfaction across an organization using frameworks like SERVQUAL, and collecting the voice of the customer.
GUIDE TO ERP IMPLEMENTATION FOR AGENCIES- For every organization, there comes a time when it outgrows
the capacities of its homegrown systems. Implementation
of ERP becomes essential for the functioning of such companies.
This document describes an automated framework for assessing legacy application portfolios. The four-step framework involves discovery, digital documentation, assessment, and recommendation. Key aspects include using a tool to inventory applications, create digital documentation, and evaluate technical and business value scores. Applications are then categorized based on these scores to develop modernization recommendations. The process aims to provide a comprehensive view of applications to inform strategic IT roadmapping and legacy modernization decisions.
This document discusses how Smart Process Works' Focus Suite can help healthcare organizations by:
1) Discovering opportunities to improve processes, productivity, and compliance within 30 days through visual process mapping and analytics.
2) Transforming workforce performance by identifying best practices and eliminating inefficiencies.
3) Evolving organizational productivity through continuous process improvement with minimal IT impact.
Biomedical engineering work is subjected to stringent regulatory constraints that mandate a robust engineering process that conforms to all pertinent regulatory guidelines and imperatives.
Software development is an important component of any engineering project and as such, it should be equally addressed and properly integrated with the overall engineering process. To that effect, the following software development process is proposed. This process attempts to be well grounded in the nature of innovative Biomedical engineering work. There are inherent significant technology risks related to the development of innovative biomedical devices. These risks must be correctly identified, and mitigated throughout the entire engineering process. The main benefit of the software development process presented here is its explicit management of software risk factors as recommended by modern successful software development practices.
Lean Startup for Healthcare: Workshop at Healthbox Orthogonal
This document discusses how to use Lean Startup principles and practices to innovate healthcare products faster. It introduces Lean Startup, which focuses on rapidly testing assumptions and reducing risks through customer feedback. Key aspects covered include building minimum viable products (MVPs), conducting problem and solution interviews, and the goal of achieving product-market fit by increasing customer lifetime value and decreasing customer acquisition costs after launching. The document provides an overview of the Lean Startup process and emphasizes getting customer input early through various validation techniques.
The document discusses the application of Lean Manufacturing principles to companies of various sizes. It argues that Lean can benefit both small and large companies by reducing waste, improving efficiency, quality and customer satisfaction. Specific Lean tools like 5S, Pareto charts and A3 reports are applicable regardless of company size. Case studies show how both large corporations and small businesses have successfully implemented Lean to improve profits, productivity and competitiveness.
According to our customer surveys and confirmed by industry statistics, manual testers spend 50-70% of their effort on finding and preparing appropriate test data. Considering the fact that manual testing still accounts for 80+% of test operation efforts, up to half (!) of the overall testing effort goes into dealing with test data.
Find out how Tosca Testsuite can help you to lower the maintenance effort of your test data and operating costs of your test environment while building an efficient test data management strategy.
The document summarizes a presentation titled "Measurement and Metrics for Test Managers" given by Rick Craig of Software Quality Engineering. The presentation covered various metrics that can be collected and analyzed by test managers such as defect density, defect arrival rates, and customer satisfaction surveys. It discussed challenges with metrics including obtaining buy-in from teams and potential biases in how metrics are designed and collected.
To be most effective, test managers must develop and use metrics to help direct the testing effort and make informed recommendations about the software’s release readiness and associated risks. Because one important testing activity is to “measure” the quality of the software, test managers must measure the results of both the development and testing processes. Collecting, analyzing, and using metrics is complicated because many developers and testers are concerned that the metrics will be used against them. Join Rick Craig as he addresses common metrics—measures of product quality, defect removal efficiency, defect density, defect arrival rate, and testing status. Learn the guidelines for developing a test measurement program, rules of thumb for collecting data, and ways to avoid “metrics dysfunction.” Rick identifies several metrics paradigms and discusses the pros and cons of each. Delegates are urged to bring their metrics problems and issues for use as discussion points.
Quality by Design is a systematic approach to development that begins with predefined objectives and emphasizes understanding of the product and process, efficient process control, and quality risk management to improve the overall manufacturing quality. Implementing the Quality by Design (QbD) principles in manufacturing has resulted in reduced operating costs, highly efficient manufacturing processes and better positioning companies to meet increasing regulatory expectations.
QbD explains how to prioritize process parameters for screening designs, design robust processes using statistical design of experiments (DoE), bridge the bench and the commercial design spaces using mixing and scale-up calculations, quantify process risk, select suitable process analytical technology tools (PAT) and more.
To know more about Quality by Design training worldwide,
please contact us at -
Email: support@invensislearning.com
Phone - US +1-910-726-3695,
Website: https://www.invensislearning.com
This document discusses production preparation process (3P), a cross-functional team approach for designing lean production processes for new or modified production lines. The 3P methodology involves bringing together members from planning, production, quality, logistics, engineering, and suppliers to understand the designed production process using prototypes and mock-ups. This helps test assumptions before equipment is ordered and installed. Key benefits of 3P workshops include a smooth start of production without major issues, avoiding surprises after production starts, and early alignment of stakeholders.
Iwsm2014 importance of benchmarking (john ogilvie & harold van heeringen)Nesma
The document discusses three cases where the International Software Benchmarking Standards Group (ISBSG) database was used to provide benchmarks and industry data for software project estimation, competitive analysis, and supplier performance measurement.
In the first case, a telecom company used ISBSG data to perform a reality check on an expert estimate for a new software project, which found the estimate to be optimistic. In the second case, a software company analyzed ISBSG data to assess the competitiveness of its bidding process. In the third case, an organization set productivity targets for an outsourced supplier based on ISBSG benchmarks.
The document discusses how using Overall Equipment Effectiveness (OEE) can drive operations excellence. OEE measures availability, performance, and quality to provide a framework for manufacturing process improvement. It identifies the key factors measured by OEE and establishes that achieving 85% OEE is considered "world class". Automating data collection is important for accurate OEE measurement.
The document discusses three topics:
1. Human Resource Management - How HR analytics can help resolve challenges in HR by making it more data-driven.
2. Water Management - New digital technologies can monitor water usage and help optimize water resource management.
3. Manufacturing Industry - Advanced analytics in manufacturing can help with predictive maintenance, quality testing, supply chain optimization, and product optimization to reduce costs and improve processes.
Agile manufacturing is an approach that emphasizes flexibility, responsiveness, and adaptability to meet changing customer demands. It focuses on quickly and efficiently producing customized products while minimizing waste and lead time. The key elements of an agile manufacturing system include modular production, cross-functional teams, rapid prototyping, flexible equipment, real-time data collection, lean principles, an adaptable workforce, and a customer-centric focus. Market forces like changing customer expectations, shortened product lifecycles, and volatile demand have increased the need for agile manufacturing.
It Strategie Als Speerpunt Voor Ondernemingsbeleid Erik FoekenHPDutchWorld
1) The document discusses how IT strategy should be a key focus for business policy and how the IT organization needs to be aligned with business goals of product leadership, customer intimacy, and operational excellence.
2) It argues that the IT supply chain is often organized in silos and that HP's Business Technology Optimization vision can help achieve alignment through integration and automation with control over the production process and delivery of business outcomes.
3) Examples are given showing potential benefits of BTO like higher project success rates, lower costs, and improved service levels.
Six Sigma originated in the 1980s at Motorola to improve quality standards. It aims to reduce defects to 3.4 per million opportunities through a DMAIC process of define, measure, analyze, improve, and control. Six Sigma statistical tools help analyze processes, identify root causes of defects, and design improvements. Implementing a customer satisfaction metric involves understanding customers, measuring satisfaction across an organization using frameworks like SERVQUAL, and collecting the voice of the customer.
GUIDE TO ERP IMPLEMENTATION FOR AGENCIES- For every organization, there comes a time when it outgrows
the capacities of its homegrown systems. Implementation
of ERP becomes essential for the functioning of such companies.
This document describes an automated framework for assessing legacy application portfolios. The four-step framework involves discovery, digital documentation, assessment, and recommendation. Key aspects include using a tool to inventory applications, create digital documentation, and evaluate technical and business value scores. Applications are then categorized based on these scores to develop modernization recommendations. The process aims to provide a comprehensive view of applications to inform strategic IT roadmapping and legacy modernization decisions.
This document discusses how Smart Process Works' Focus Suite can help healthcare organizations by:
1) Discovering opportunities to improve processes, productivity, and compliance within 30 days through visual process mapping and analytics.
2) Transforming workforce performance by identifying best practices and eliminating inefficiencies.
3) Evolving organizational productivity through continuous process improvement with minimal IT impact.
Biomedical engineering work is subjected to stringent regulatory constraints that mandate a robust engineering process that conforms to all pertinent regulatory guidelines and imperatives.
Software development is an important component of any engineering project and as such, it should be equally addressed and properly integrated with the overall engineering process. To that effect, the following software development process is proposed. This process attempts to be well grounded in the nature of innovative Biomedical engineering work. There are inherent significant technology risks related to the development of innovative biomedical devices. These risks must be correctly identified, and mitigated throughout the entire engineering process. The main benefit of the software development process presented here is its explicit management of software risk factors as recommended by modern successful software development practices.
Lean Startup for Healthcare: Workshop at Healthbox Orthogonal
This document discusses how to use Lean Startup principles and practices to innovate healthcare products faster. It introduces Lean Startup, which focuses on rapidly testing assumptions and reducing risks through customer feedback. Key aspects covered include building minimum viable products (MVPs), conducting problem and solution interviews, and the goal of achieving product-market fit by increasing customer lifetime value and decreasing customer acquisition costs after launching. The document provides an overview of the Lean Startup process and emphasizes getting customer input early through various validation techniques.
The document discusses the application of Lean Manufacturing principles to companies of various sizes. It argues that Lean can benefit both small and large companies by reducing waste, improving efficiency, quality and customer satisfaction. Specific Lean tools like 5S, Pareto charts and A3 reports are applicable regardless of company size. Case studies show how both large corporations and small businesses have successfully implemented Lean to improve profits, productivity and competitiveness.
According to our customer surveys and confirmed by industry statistics, manual testers spend 50-70% of their effort on finding and preparing appropriate test data. Considering the fact that manual testing still accounts for 80+% of test operation efforts, up to half (!) of the overall testing effort goes into dealing with test data.
Find out how Tosca Testsuite can help you to lower the maintenance effort of your test data and operating costs of your test environment while building an efficient test data management strategy.
The document summarizes a presentation titled "Measurement and Metrics for Test Managers" given by Rick Craig of Software Quality Engineering. The presentation covered various metrics that can be collected and analyzed by test managers such as defect density, defect arrival rates, and customer satisfaction surveys. It discussed challenges with metrics including obtaining buy-in from teams and potential biases in how metrics are designed and collected.
To be most effective, test managers must develop and use metrics to help direct the testing effort and make informed recommendations about the software’s release readiness and associated risks. Because one important testing activity is to “measure” the quality of the software, test managers must measure the results of both the development and testing processes. Collecting, analyzing, and using metrics is complicated because many developers and testers are concerned that the metrics will be used against them. Join Rick Craig as he addresses common metrics—measures of product quality, defect removal efficiency, defect density, defect arrival rate, and testing status. Learn the guidelines for developing a test measurement program, rules of thumb for collecting data, and ways to avoid “metrics dysfunction.” Rick identifies several metrics paradigms and discusses the pros and cons of each. Delegates are urged to bring their metrics problems and issues for use as discussion points.
Quality by Design is a systematic approach to development that begins with predefined objectives and emphasizes understanding of the product and process, efficient process control, and quality risk management to improve the overall manufacturing quality. Implementing the Quality by Design (QbD) principles in manufacturing has resulted in reduced operating costs, highly efficient manufacturing processes and better positioning companies to meet increasing regulatory expectations.
QbD explains how to prioritize process parameters for screening designs, design robust processes using statistical design of experiments (DoE), bridge the bench and the commercial design spaces using mixing and scale-up calculations, quantify process risk, select suitable process analytical technology tools (PAT) and more.
To know more about Quality by Design training worldwide,
please contact us at -
Email: support@invensislearning.com
Phone - US +1-910-726-3695,
Website: https://www.invensislearning.com
This document discusses production preparation process (3P), a cross-functional team approach for designing lean production processes for new or modified production lines. The 3P methodology involves bringing together members from planning, production, quality, logistics, engineering, and suppliers to understand the designed production process using prototypes and mock-ups. This helps test assumptions before equipment is ordered and installed. Key benefits of 3P workshops include a smooth start of production without major issues, avoiding surprises after production starts, and early alignment of stakeholders.
Iwsm2014 importance of benchmarking (john ogilvie & harold van heeringen)Nesma
The document discusses three cases where the International Software Benchmarking Standards Group (ISBSG) database was used to provide benchmarks and industry data for software project estimation, competitive analysis, and supplier performance measurement.
In the first case, a telecom company used ISBSG data to perform a reality check on an expert estimate for a new software project, which found the estimate to be optimistic. In the second case, a software company analyzed ISBSG data to assess the competitiveness of its bidding process. In the third case, an organization set productivity targets for an outsourced supplier based on ISBSG benchmarks.
The document discusses how using Overall Equipment Effectiveness (OEE) can drive operations excellence. OEE measures availability, performance, and quality to provide a framework for manufacturing process improvement. It identifies the key factors measured by OEE and establishes that achieving 85% OEE is considered "world class". Automating data collection is important for accurate OEE measurement.
The document discusses three topics:
1. Human Resource Management - How HR analytics can help resolve challenges in HR by making it more data-driven.
2. Water Management - New digital technologies can monitor water usage and help optimize water resource management.
3. Manufacturing Industry - Advanced analytics in manufacturing can help with predictive maintenance, quality testing, supply chain optimization, and product optimization to reduce costs and improve processes.
Agile manufacturing is an approach that emphasizes flexibility, responsiveness, and adaptability to meet changing customer demands. It focuses on quickly and efficiently producing customized products while minimizing waste and lead time. The key elements of an agile manufacturing system include modular production, cross-functional teams, rapid prototyping, flexible equipment, real-time data collection, lean principles, an adaptable workforce, and a customer-centric focus. Market forces like changing customer expectations, shortened product lifecycles, and volatile demand have increased the need for agile manufacturing.
How AI Can Be Leveraged In All Aspects Of TestingAlisha Henderson
QA has become an essential practice for businesses that are in the digital space. To achieve digital transformation businesses should embrace the latest technologies in their software development process and build a strong data engineering foundation to fuel innovation.
This document discusses the four pillars of analytics technology speed: development and discovery speed, data processing speed, deployment speed, and response speed. It provides examples of how each type of speed can impact business value. Development and discovery speed refers to how quickly analytics projects can be built and iterated on. Data processing speed is the ability to analyze large amounts of data quickly. Deployment speed is getting analytics solutions into production quickly. Response speed is delivering insights in real-time. The document argues that an effective analytics platform needs to provide speed across all four pillars.
Transform your business with our Legacy Platform Transformation services. Our team of experts can help you navigate digital trends to ensure your business stays ahead of the curve. Let us help you keep up with the changing times and capitalize on new opportunities.
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.
How to Improve Quality and Efficiency Using Test Data AnalyticsTequra Analytics
Discover 8 ways in our guide for advanced manufacturers.
Do you perform advanced manufacturing in an industry such as aerospace, automotive, medical devices or telecoms? Is product testing part of your manufacturing process? If you can answer yes to these questions, keep reading to learn how test data analytics can enable many improvements.
An AI-enabled predictive maintenance solution can help companies improve business performance by analyzing asset data to derive actionable insights. It can help reduce unplanned downtime by 11% on average, lower maintenance costs by 30%, and minimize breakdowns by up to 70%. An effective predictive maintenance solution should leverage existing backend technologies, apply models and algorithms to data to derive insights, and provide a flexible front-end dashboard integrated with existing tools.
The DevOps promise: IT delivery that’s hot-off-the-catwalk and made-to-lastPeter Shirley-Quirk
DevOps promises rapid delivery AND stable operations by integrating business, development, test, deployment and operations into a cohesive workflow with a rapid feedback cycle. So how is that possible?
Bahaa Abdul Hussein is a Fintech expert and shares his experiences with his audience through his blogs.
The economy and competition in the financial industry have created a global context that is nudging banks to create a new data frame that is in tune with new needs. Financial institutions must refurbish their reporting mechanisms, while balancing cost, quality and production.
In today’s globalized, competitive marketplace, being able to leverage technology to deliver faster turnaround times, meet lower pricing goals and provide customizable options can mean the difference between sustainability and irrelevancy. In this ebook, we’ll explore some of the leading solutions transforming the manufacturing industry:
- Automation for cost savings
- 3D printing for improved productivity
- Smart data for quality assurance
- Connectivity for safety and communication
- Security solutions to protect it all
Learn more: http://ms.spr.ly/6006Twegg
Ways on how to improve manufacturing operationsSameerShaik43
Smarter Contact has evolved the way businesses connect with customers and prospects through its innovative and easy-to-use SMS marketing platform. However, behind it is a backstory, a long history of determination as an immigrant to the US to a company that employs 30+ people around the world.
https://www.tycoonstory.com/tips/how-to-improve-manufacturing-operations/
Process improvement aims to make business processes more efficient and effective by identifying issues, analyzing root causes, and implementing changes. It benefits organizations by saving time and resources through streamlining tasks, improving results and customer satisfaction, and increasing transparency. Regular process improvement is important for companies to meet goals, address inefficiencies costing over $1 trillion annually, and adapt to changing market demands.
This White Paper describes different options and scenarios to be considered when choosing or implementing a machine vision system, and alerts for keys factors that maximize its success.
AI for workflow automation Use cases applications benefits and development.pdfmahaffeycheryld
AI for workflow automation optimizes business operations by automating repetitive tasks, improving efficiency, and reducing errors. It is used in various sectors for automating data entry, enhancing customer service with chatbots, and performing predictive maintenance. Key benefits include increased productivity, cost reduction, and improved accuracy. Implementing AI-driven workflows involves integrating machine learning, natural language processing, and robotic process automation tools. This enables businesses to focus on strategic activities, drive innovation, and maintain a competitive edge.
https://www.leewayhertz.com/ai-for-workflow-automation/
With shrinking production cycles, increasing demand for customized products, and a growing skills gap in the workforce, there are many pressures affecting the manufacturing industry. Technology offers many potential solutions, along with its own set of changes and challenges, including data overload.
Advanced analytics solutions can help address these issues. Some enterprises are already reaping the benefits, like automated supply chains and predictive maintenance, but often it’s unclear where to begin.
Learn how manufacturing analytics solutions can improve core production and supply chain operations like quality assurance and inventory optimization. With the right approach and tools, and using your existing technology investments, you can uncover potential insights and solutions in the information you already have.
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End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
2. It could be argued that today’s business
challenges are more acute than ever
before. Whether you’re creating a new
sales and marketing strategy, working
out how to beat the competition by
offering stand-out customer service,
or battling to overcome inefficient
internal processes, it’s essential to
act fast, decisively and accurately. In
a world that’s always connected and
with customers growing expectations
for instant, personalized service, any
delay or misstep can significantly impact
business performance.
Fortunately, the problem also contains the solution.
As technologies like the Internet of Things (IoT) and
social media emerge and develop, an ever-increasing
sea of data is being created. Data that sits in your own
systems, but also data that’s out in the public domain
– like news articles or public health records. If you
can harness this data and make sense of it, you’ll gain
deeper insights into your business and your customers.
You’ll be able to spot previously invisible patterns that
may transform decision making. You may even be able
to predict and influence what’s going to happen next.
Advanced analytics is the key to finding those needles
of transformational insights within your haystacks. In
this eGuide, we’ll explore five areas in which advanced
analytics techniques – such as machine learning
and prescriptive analytics – can help transform your
business, and we’ll explore some real-world examples
from Intel’s own experience and those of its customers.
About Intel IT
Intel IT is focused on using new technology to
create business value and act as a catalyst for
organizational transformation.
As part of its commitment to driving industry-
wide innovation, Intel IT has built its own
competency center for data analytics. This
team is tasked with optimizing Intel’s internal
processes through digital innovation, for which
the current focus is artificial intelligence (AI) and
machine learning. The center then repurposes the
technology to create products and solutions for
use by its ecosystem and customers.
3. I.Critical
Business
Processes
Intel’s own IT department has worked with various
business units within Intel to address some of the
procedural challenges and bottlenecks that are
common to many organizations, such as reducing
time-to-market for new products, and engaging more
effectively with customers.
Product Design
Business Issue
In today’s fast-paced economy, windows of opportunity
can be narrow and elusive, meaning business velocity is
critical. Being able to design and deliver products and
services to market in response to new opportunities or
changing customer demand is important, but being able
to do it faster than the competition is key to success.
For many companies, this can be a challenge when
faced with increasingly diverse or complex product
portfolios, limited headcount, or reduced budgets. They
must deliver more with less, but also faster, and without
any reduction in quality.
One of the biggest bottlenecks in product design and
development can be the time-consuming validation
process. Any new prototype must be checked, tested
and checked again to ensure it is up to standard. There
is little room for compromise as any errors that are
missed at this stage could result in huge expense or
reputational damage if they are replicated when full-
scale production begins.
The Analytics Answer
Advanced analytic techniques such as machine learning
can help speed up the validation process and other
aspects of product design by imitating and supporting
human validation capabilities.
To help enhance validation in its own computer chip
design process, Intel IT developed a machine-learning
platform called CLIFF, which is designed to uncover bugs
in prototypes. The platform quickly browses through
many thousands of historical test records to uncover
patterns, a task that would take human reviewers
thousands of hours, making it impractical to perform
manually. Compared to standard regression tests, CLIFF
validates the targeted functionalities sixty times more
and identifies 30 percent more new issues on each run1
.
By automating the testing process, CLIFF has
contributed greatly to Intel’s strategic goal of reducing
product validation time (which can typically take up
to 50 percent of the development cycle) and reducing
the number of iterations required. As a result, it has
significantly shortened time-to-market while also
improving product quality.
The platform does all this using a form of prescriptive
analytics. This means that it is able to not only
predict likely outcomes through its machine learning
algorithms, but also to inform and automate decisions
about how best to tailor the process for each test
moving forward.
Looking Ahead
The introduction of a prescriptive analytics tool like
CLIFF is the first step towards shrinking the time
needed for validation and so reducing your time-to-
market. The focus of this use case has been on relieving
human testers of the burden of conducting repetitive
4. but highly accuracy-critical tests through intelligent
automation. With CLIFF, Intel’s testers are able either
to work faster and more efficiently, or to re-focus their
efforts on more value-adding tasks.
As technologies evolve, Intel expects that subsequent
phases of this journey will create opportunities to not
simply improve existing processes, but to augment
human testers’ abilities and enable them to drive more
innovation, deliver more new products, and further
reduce time to market. The phases we expect to see
next include:
• Introducing an algorithm that will continuously
monitor each test that CLIFF runs to ensure it is
getting results. Any tests that are found not to be
adding any value can then be removed from the
process, meaning that each validation will be fully
covered while keeping inefficiency to a minimum.
• Helping testers streamline the debugging process by
using machine learning to determine the root cause
of any bugs that are identified. This will enable the
human testers to focus their energies on coming up
with creative fixes and solutions.
• Encouraging man-machine collaboration by
developing methods for people to give their analytics
systems more contextual information. Even the
smartest algorithm can only make decisions based
on the data it can access within a system. People have
a lot more peripheral context - for example, news
about other initiatives going on at the company this
week, or hearing from a colleague at the watercooler
about a new change that has just been made in
another area of the product. In the future, product
development teams (and others) will need ways for
workers to give this context to the system to enable it
to further augment their own roles – a virtuous circle
of continuous refinement.
5. of manufacturing processes – both internally and in
outsourced facilities – as well as new initiatives like the
personalization and customization of the product or
service portfolio, often on tight deadlines in response
to sudden market demand. This must all be achieved
while maintaining the highest quality levels, which can
be particularly difficult in the face of rising demand for
more diverse and complex offerings.
Where blanket approaches to quality control may have
sufficed before, this growing complexity means a
‘one size fits all’ approach no longer works. Each unit
—whether it’s a car, a couch or a computer chip—must
be thoroughly examined independently, which can slow
delivery and hinder efficiency.
Manufacturing and
Quality Control
Business Issue
Once a prototype has been approved and sent
into production, a new range of challenges present
themselves. With operations spanning order-taking,
resource procurement, manufacturing, and delivery, being
able to make rapid, insightful decisions in response to any
change in this delicate ecosystem is critical to maintaining
your competitive edge. The more complex the supply
chain, the more volatile it can be, and the greater the need
for this agility. As companies’ cycles of growth accelerate,
their manufacturing and supply chain processes must also
adapt to support new and expanding business models in
both existing and brand-new business units.
Conventional supply chain management approaches
often struggle to meet this new level of demand.
They must accommodate and facilitate the expansion
6. The Analytics Answer
Quality control in a typical manufacturing environment
often involves a number of different steps. Intel takes
the quality of its products very seriously, and so has
invested significant time and resources into ensuring
each one of these stages is as thorough and efficient as
possible. Analytics plays a key role, enabling more data
to be processed faster and with greater accuracy than
manual processes. For example, Intel IT has identified
a way to reduce the overall number of tests without
compromising quality. This approach works on the
assumption that, like people, each unit is different and
requires its own combination of tests and decisions.
Using analytics and machine learning to tailor the tests
conducted on each unit brings value comparable to
that of personalized medicine. In addition to increasing
quality, this tailored testing has shaved seconds off
this stage of the quality control process for each unit,
significantly speeding up the manufacturing process
while reducing costs.
By replicating these sorts of process improvements
across the whole manufacturing environment and every
step in the process, even small tweaks can add up to
big savings and efficiency gains.
Looking Ahead
Quality control measures in manufacturing typically
still require a lot of manual intervention and human
decision making. Experts must create content to be
used in each test, research root causes of any issues
they identify, and make decisions about what steps
to take to drive improvements. A next step in the use
of analytics in this process will be the application of
machine learning algorithms to relieve some of this
burden and automate certain tasks so humans can
focus on those aspects that machines cannot do. By
introducing this technology to its own manufacturing
environment, Intel expects to enable its employees to
become more productive, and as a company to be able
to test more products, faster.
7. Sales and Marketing
Business Issue
When your product or service is ready for market, the
baton is passed to the sales and marketing teams to find
the right customer base and give them a reason to buy.
In today’s sales and marketing landscape, knowing your
customer is everything. Buyers in both the consumer and
business-to-business arenas have come to expect more
proactive, tailored communications from the companies
they buy from, so it’s essential you stay relevant. This
means navigating increasingly complex data-source
ecosystems to create a holistic understanding of each
customer’s needs, interests and propensity to buy.
Customer profiles typically include multifaceted
information ranging from sales data to social media
communications, their browsing history on your
website, and recordings of conversations with your call
center agents. These disparate types of data are difficult
to manage and process, especially in real time. This
means sales agents may struggle to access the right
information at the right time to help them close a deal.
The challenge becomes even greater if your company
operates an indirect sales model, where relationships with
the customer may be through a partner, and may involve
a large number of people covering slightly different
areas. Your own complex data pools may be muddied by
data from partners, customer relationship management
(CRM) systems and public-facing information, which is
often incomplete or inaccurate. This makes it hard to
create that all-important clear, and up-to-date view of the
customer you need to drive your sales pipeline.
The Analytics Answer
Sales and marketing teams need to transform vast
quantities of data about their covered accounts into
trusted insights, in real time. By using advanced
analytics capabilities, you can empower them to
uncover fresh insights that were previously hidden
within their data. This can help them have more
effective conversations with their customers, discover
and convert more leads, and enhance existing
customers’ loyalty.
Intel IT worked to create a machine learning-based
market intelligence system and recommendation engine2
that helps its sales and marketing division identify
which of its resellers will connect most effectively with
customers in specific industries, and so enhance their
ability to engage with and support those partners.
The original tool, called Sales and Marketing Account
Recommendation Tool (SMART), provided the sales
team with information about each reseller and its
market, which products to offer them and opportunities
to cross- or up-sell, based on its understanding of what
has worked well with similar organizations.
A proof of concept (PoC) of the tool’s second phase
was recently completed in the EMEA online sales
center. Building on the original tool, the latest
version, Sales AI 2.0, now combines Intel’s CRM data
with unstructured public data sources like news
publications, patent filings, and information on hiring,
venture capital funding and merger and acquisitions.
8. Leveraging AI technology such as text analytics, the
revised tool can scan these extensive, disparate data
sources and convert them into actionable insights
for salespeople by imitating humans’ language and
reasoning capabilities. This volume of data would take
months for humans to process, by which time a lot of it
would be obsolete. Sales AI 2.0, by contrast, completes
the analysis in just a few hours.
The PoC was a resounding success: 81 percent of the
insights were found to be valuable and actionable by
the account managers involved in the trial, and over 90
percent have chosen to continue to use it.
Following the PoC, Intel has deployed the system to
cover its top 50,000 reseller customers in eight different
languages. As a result, twice as many resellers in the
engagement chain advanced from leads to qualified leads
in comparison with the rest of the sales pipeline. These
resellers also showed a three times higher click-through
rate for email newsletters, and completed Intel training at
a rate three times higher than the rest of the pipeline3
. In
2016, Intel IT’s complete activities increased Intel’s revenue
by approximately USD 480 million, with the SMART and
Sales AI 2.0 tools contributing around USD 100 million4
.
Looking Ahead
The effectiveness of the SMART tool has led 91 percent
of sales agents to continue to use it following the testing
cycles, and Intel IT now plans to universally deploy the
system to sales centers worldwide. With this tool in place,
Intel aims to provide salespeople with a virtual personal
assistant that will help them have more insightful and
productive conversations with their customers, and deliver
more value. What’s more, the efficiency gains of having all
this information provided to them proactively, when they
need it will also enable salespeople to increase both the
quantity and quality of their interactions.
Importantly, insights within a machine learning system
like this flow in both directions. Each time a salesperson
uses the tool, new insights are delivered back into
the system in return, enabling a constant process of
algorithm fine-tuning. To this end, the Sales Assists
tool is in a constant state of renewal, refinement and
improvement. In addition, Intel IT is also working to
capitalize on these insights to build email and other
capabilities to further improve dialogue with customers
and increase opportunities for engagement.
With humans teaching algorithms at the same time and
to the same degree as they improve our understanding,
capabilities and workflow, across the industry it will be
the ability to best hone technological advances and
optimize this symbiotic relationship that will define the
future of the field.
9. II.Product
andService
Innovation
Another area of focus for the Intel IT team is exploring
areas in which its technology and those of its ecosystem
partners can be used to drive analytics-based value for
customers in specific industries or wanting to adopt
particular new technologies. We’ll consider an example
of each of these below.
Healthcare and
Pharmaceuticals
Business Issue
According to Eroom’s Law, the cost of developing a
new drug approximately doubles every nine years.
The current average cost to develop a single new drug
is around USD2.5 billion5
, with average development
time being at least a decade6
. Each new medicine or
treatment strategy can make a huge difference for sick
patients, so the expensive and drawn-out development
process is far from ideal. It results from the fact that it
requires huge amounts of manual data collection, itself
a time-consuming task, which also has the potential
to generate inconsistencies due to a lack of objective
measurements and adherence to protocol.
The Analytics Answer
With technology innovations such as wearable devices
and advanced analytics, pharmaceutical companies
can run more in-depth and accurate clinical trials
faster and at a lower cost by enabling the continuous
remote monitoring of patients. There is huge interest
and potential in this area, with Ericsson* estimating
that 4 million patients will use remote monitoring
technologies by 20207
. Not only will this enable
patients to benefit from new drugs becoming available
sooner, but it is also expected to mean revenue per
drug will jump considerably.
The Michael J Fox Foundation is using a wearable
analytics platform, developed in collaboration
with Intel, to help in its mission to find a cure for
Parkinson’s disease8
. Teva Pharmaceuticals*9
has also
licensed the platform in a two-phase clinical trial
for a new drug to treat Huntington’s disease, a fatal
neurodegenerative condition.
In both cases, each patient receives a wearable
device equipped with accelerometer and gyroscope,
which continuously captures data about the patient’s
movement. This data is securely transmitted to the cloud,
where machine learning algorithms create objectives
measurements to accurately determine the drug’s
effectiveness. Some of this data is also given back to
the patient through a mobile app, which they can use
to check their own activity levels and get updates or
reminders about their medication and treatment plan.
By providing more objective, continuous information
about patient symptoms during clinical trials of a new
drug, the platform helps the Michael J Fox Foundation
and Teva Pharmaceuticals* improve the quality and cost
effectiveness of these trials.
Looking Ahead
The use of wearable technology is the beginning of an
exciting journey in analytics and healthcare. Providing
patients with always-on devices that are permanently
connected to the cloud means pharmaceutical
organizations can gain a vast amount more data
than has previously been possible when relying on
infrequent in-person check-ups with each patient. With
more data, and the right machine learning algorithms
and analytics processes, they can achieve results
and deliver new drugs to market faster and cheaper.
They are also able to use this ongoing data collection
to proactively push updates, guidance and other
communications to their patients, creating the effect of
having a physician looking out for them all day every
day, and encouraging patients to engage more actively
with their own care.
In the future, opportunities to develop this personalized
care approach will be huge, with other technologies
such as video also having a role to play in both remote
care delivery and deeper, more complex data gathering.
10. IoT Analytics
Business Issue
Across industries as diverse as manufacturing, retail
and transportation, the installation of IoT sensors has
made it possible to collect thousands of data points
about processes, products and people. All this new
data, whatever its source, has the potential to provide
greater business insight, but it is essential to have a
system in place that is equipped to make sense of such
large volumes of rapidly changing data and pull out the
actionable insights from the noise.
The Analytics Answer
Intel IT is using IoT analytics to help it create a vision
of the smart building of tomorrow at its Smart Building
and Venue Experience Center in Chandler, Arizona10
.
The center acts as a testing ground for new IoT use
cases as well as a working example of how IoT analytics
can make a difference to business and operational
efficiency today. Its aim is to develop IoT standards for
smart buildings that will provide us and others with a
blueprint for creating future smart offices, factories11
and other buildings.
All the building’s systems – including HVAC, lighting,
restrooms and the parking lot – are IoT-enabled. The
center uses blueprints for various smart facility venues,
developed by Intel IT and the Intel IoT Group, to create
repeatable use cases and results which are then shared
internally, with partners and with customers. For ex-
ample, one solution monitors the parking lot to predict
how busy it will be at any given time, helping facility
managers stay up-to-date while also enabling people
looking for a space to find it more easily.
In another area of the center, sensors that are installed
in restrooms are helping reduce maintenance costs and
improve user satisfaction. Sensors count how many
people have visited the restroom, sending an alert to
maintenance crews when they are needed – for exam-
ple when tissue, towel or soap dispensers need to be
refilled – and saving them from having to make trips to
check, which may turn out to be unnecessary.
Looking Ahead
An exciting area for further analytics innovation, IoT
gives us the opportunity to make machines work for
us. The patterns and insights found through predictive
analytics and machine learning today will underpin
the next step. As organizations evolve their analytics
capabilities towards more prescriptive use cases, they
will be able to automate many of the operational tasks
in manufacturing, facilities management and a range of
other areas that today take busy employees away from
their more valuable core roles. This will not only help
improve efficiency and cut costs, but it can also have a
positive impact on the user experience.
For example, data from temperature and humidity
sensors in a meeting room could be used to constantly
optimize the environment – adjusting the temperature
up or down, or even opening or closing windows – allow-
ing those in the room to concentrate on their meeting.
Or supply chain systems monitoring multiple internal
and external data sources could identify where spikes in
demand for a particular product are likely and automati-
cally divert larger stock volumes to the affected area.