Presentation on "Benchmarking Genesis" by Basma A. Bargal during the 6th International Benchmarking Conference organized by Dubai Quality Group from 6-7 March 2012 at Al Bustan Rotana Dubai
Benchmarking is a process of comparing an organization's performance metrics and processes to industry best practices from other companies. The document discusses definitions of benchmarking from various authors, the history and development of benchmarking, key principles of benchmarking, and the benchmarking process. It provides an overview of benchmarking, including why organizations implement benchmarking, how it has evolved over time, potential legal issues, and ethics considerations for proper benchmarking.
Handover Consulting - HR Process FrameworkAli AlJabari
Handover Consulting provides HR consulting services including developing comprehensive HR process frameworks. They have experience developing frameworks for various company types and business sectors across the Middle East. Their process involves assessing clients' current HR systems, identifying gaps, developing integrated policies and procedures compliant with labor laws, and training clients on the new framework. This ensures clients have consistent, optimized processes from hire to retire.
This document outlines the objectives, concepts, types, process, and reasons for success or failure of benchmarking. It defines benchmarking as comparing products, services, or processes to industry best practices to establish performance goals and improve. There are three main types of benchmarking: internal, competitive, and functional. The benchmarking process involves selecting a process for improvement, identifying benchmarking partners, implementing changes, and measuring results. Reasons for benchmarking include improving processes, maintaining leadership, and addressing problems. Key steps are selecting processes and measures to prioritize for benchmarking. Commitment, proper planning and implementation are important for success, while lack thereof can lead to benchmarking efforts failing.
Employee retention refers to employers' efforts to retain employees in their workforce. While retention can be represented by a simple statistic like retention rate, it also relates to the strategies employers use to retain talent. The goal is usually to decrease costs associated with turnover like training and recruitment. Employers can analyze data and implement concepts from organizational behavior to improve retention rates. They may also aim for "positive turnover" by retaining only high performers. Theories like Herzberg's help explain factors like motivators and hygiene factors that influence satisfaction and retention. Common retention strategies include competitive benefits, incentives, internal development opportunities, and engagement surveys.
Presentation on "Benchmarking Genesis" by Basma A. Bargal during the 6th International Benchmarking Conference organized by Dubai Quality Group from 6-7 March 2012 at Al Bustan Rotana Dubai
Benchmarking is a process of comparing an organization's performance metrics and processes to industry best practices from other companies. The document discusses definitions of benchmarking from various authors, the history and development of benchmarking, key principles of benchmarking, and the benchmarking process. It provides an overview of benchmarking, including why organizations implement benchmarking, how it has evolved over time, potential legal issues, and ethics considerations for proper benchmarking.
Handover Consulting - HR Process FrameworkAli AlJabari
Handover Consulting provides HR consulting services including developing comprehensive HR process frameworks. They have experience developing frameworks for various company types and business sectors across the Middle East. Their process involves assessing clients' current HR systems, identifying gaps, developing integrated policies and procedures compliant with labor laws, and training clients on the new framework. This ensures clients have consistent, optimized processes from hire to retire.
This document outlines the objectives, concepts, types, process, and reasons for success or failure of benchmarking. It defines benchmarking as comparing products, services, or processes to industry best practices to establish performance goals and improve. There are three main types of benchmarking: internal, competitive, and functional. The benchmarking process involves selecting a process for improvement, identifying benchmarking partners, implementing changes, and measuring results. Reasons for benchmarking include improving processes, maintaining leadership, and addressing problems. Key steps are selecting processes and measures to prioritize for benchmarking. Commitment, proper planning and implementation are important for success, while lack thereof can lead to benchmarking efforts failing.
Employee retention refers to employers' efforts to retain employees in their workforce. While retention can be represented by a simple statistic like retention rate, it also relates to the strategies employers use to retain talent. The goal is usually to decrease costs associated with turnover like training and recruitment. Employers can analyze data and implement concepts from organizational behavior to improve retention rates. They may also aim for "positive turnover" by retaining only high performers. Theories like Herzberg's help explain factors like motivators and hygiene factors that influence satisfaction and retention. Common retention strategies include competitive benefits, incentives, internal development opportunities, and engagement surveys.
Introduction of Online Machine Learning AlgorithmsShao-Yen Hung
This document summarizes a paper presentation for an SDM course in 2016 on ad click prediction from an online machine learning perspective. It discusses challenges with big data including memory and time requirements. It then summarizes several online learning algorithms - Truncated Gradient (2009), Forward-Backward Splitting (FOBOS, 2009), Regularized Dual Averaging (RDA, 2010), Follow-the-Regularized Leader Proximal (FTRL-Proximal, 2011) - and how they address sparsity and regularization. It also demonstrates an R package for FTRL-Proximal and references several related papers.
This document discusses online optimization algorithms. It begins with an introduction to online learning and its advantages over batch learning. It then provides background knowledge on relevant concepts like convex functions, gradients, loss functions, and regularization. It explains the differences between batch gradient descent and stochastic gradient descent. The document proceeds to describe several online optimization algorithms: Simple Coefficient Rounding (SCR), Truncated Gradient (TG), Forward-Backward Splitting (FOBOS), Regularized Dual Averaging (RDA), and Follow The Regularized Leader (FTRL). It provides detailed explanations of how SCR, TG, and FOBOS generate sparsity and their updating rules.
The document discusses Spark, an open-source cluster computing framework. It compares Spark to Hadoop, noting that Spark uses in-memory computing while Hadoop uses disk-based computing. It also discusses using Spark with R programming through RStudio and compares Spark to other technologies like Storm and Samza. Google Trends graphs are shown demonstrating the increasing popularity of Spark compared to Hadoop. References for learning more about Spark on R, Storm vs Spark vs Samza, and a Spark on R tutorial are provided at the end.
This document provides an introduction to Hadoop. It describes that Hadoop was created by Doug Cutting in 2006 at Yahoo to address large datasets. It discusses the key components of Hadoop including HDFS for storage and MapReduce for processing. HDFS uses a master/slave architecture with a NameNode and DataNodes to store and replicate blocks of data across nodes. MapReduce allows distributed processing of data across clusters using a map and reduce function. The document outlines the architecture and functions of core Hadoop components like HDFS and MapReduce.
Introduction of Online Machine Learning AlgorithmsShao-Yen Hung
This document summarizes a paper presentation for an SDM course in 2016 on ad click prediction from an online machine learning perspective. It discusses challenges with big data including memory and time requirements. It then summarizes several online learning algorithms - Truncated Gradient (2009), Forward-Backward Splitting (FOBOS, 2009), Regularized Dual Averaging (RDA, 2010), Follow-the-Regularized Leader Proximal (FTRL-Proximal, 2011) - and how they address sparsity and regularization. It also demonstrates an R package for FTRL-Proximal and references several related papers.
This document discusses online optimization algorithms. It begins with an introduction to online learning and its advantages over batch learning. It then provides background knowledge on relevant concepts like convex functions, gradients, loss functions, and regularization. It explains the differences between batch gradient descent and stochastic gradient descent. The document proceeds to describe several online optimization algorithms: Simple Coefficient Rounding (SCR), Truncated Gradient (TG), Forward-Backward Splitting (FOBOS), Regularized Dual Averaging (RDA), and Follow The Regularized Leader (FTRL). It provides detailed explanations of how SCR, TG, and FOBOS generate sparsity and their updating rules.
The document discusses Spark, an open-source cluster computing framework. It compares Spark to Hadoop, noting that Spark uses in-memory computing while Hadoop uses disk-based computing. It also discusses using Spark with R programming through RStudio and compares Spark to other technologies like Storm and Samza. Google Trends graphs are shown demonstrating the increasing popularity of Spark compared to Hadoop. References for learning more about Spark on R, Storm vs Spark vs Samza, and a Spark on R tutorial are provided at the end.
This document provides an introduction to Hadoop. It describes that Hadoop was created by Doug Cutting in 2006 at Yahoo to address large datasets. It discusses the key components of Hadoop including HDFS for storage and MapReduce for processing. HDFS uses a master/slave architecture with a NameNode and DataNodes to store and replicate blocks of data across nodes. MapReduce allows distributed processing of data across clusters using a map and reduce function. The document outlines the architecture and functions of core Hadoop components like HDFS and MapReduce.
1. 思考技術(2)
1
Institute of Manufacturing Information and Systems (製造資訊與系統研究所)
Institute of Engineering Management (工程管理碩士在職專班)
National Cheng Kung University (國立成功大學)
主題:隱而未見的顯而易見
指導教授:李家岩 博士
報 告 者:洪紹嚴
日期:2015/11/26