This presentation explores the use of data to evaluate ergonomic risk factors and how PG&E collected this data to help create an algorithm that accurately predicts the risk of ergonomic discomfort.
Optimizing Data Synthesis and Visualization in Real-Time Decision-MakingCSSI_Inc
CSSI’s Kim Bender was a speaker at 2014's AMS Summer Community Meeting: Improving Forecasts and the Communication of Forecasts. Kim was a member of the panel on “Synthesizing Forecasting Information” which discussed the plethora of information forecasters have to guide their decisions.
Root cause analysis training for beginnersBryan Len
Root cause analysis training for beginners.For what reason Do You Need Root Cause Analysis Training?
On the off chance that you are associated with critical thinking, at any level, in your organization:
To begin with, you have to figure out how to get to the base of every issue to dispose of them forever.
You would likewise need to figure out how to build up a viable restorative activity design and preventive arrangement for every issue or occurrence keeping in mind the end goal to evade a similar issue from happening once more
Root Cause analysis training for beginners shows all of you the above in addition to gives you the chance to hone what you realized on genuine situations in class to guarantee you are prepared to backpedal and apply your insight and abilities at working environment.
Audience :
TONEX root cause analysis training for beginners is a 2-day course designed for:
Quality personnel
R&D team
Production engineers and managers
Design team
All the individuals whose job involve problem solving, safety, reliability, quality control, operations and logistics, and production
Root cause analysis training for beginners course covers the devices and methods to follow issues down to the root cause. Such "in reverse" looking system is helpful particularly in the conditions where there is in excess of one noteworthy cause related with an issue. Some of the time the numerous causes are autonomous of each other, while now and again they all connected together and consequently expelling one cause can bring about settling the entire issue. Root cause analysis training for beginners hands-on workshop will show you how to distinguish every one of the causes, find their association with each other, their consequences for the framework, expel them for all time, and set up preventive activities to keep away from them from happening again in future.
Learn more about Root cause analysis training for beginners
https://www.tonex.com/training-courses/root-cause-analysis-training-for-beginners/
An Ishikawa or cause-and-effect diagram provides a systematic way to visualize the potential causes of a problem or effect. It was developed by Kaoru Ishikawa in 1943 and resembles a fishbone with the problem stated at the head and categories of causes branching out from it. The diagram encourages group participation to determine the root causes of a problem in an orderly format. It helps teams focus on potential causes rather than symptoms and identifies areas for improvement by highlighting causes that appear repeatedly or can be measured and addressed.
Va root cause analysis for process improvementsourias
This document outlines an eight-step model for conducting a root cause analysis to improve processes in a Division of Family Services. The model expands on the basic Define, Measure, Analyze, Improve, Control process improvement model. The eight steps are: 1) Define the problem, 2) Collect and analyze data, 3) Understand the process, 4) Identify possible causes, 5) Identify and select possible solutions, 6) Implement the solution, 7) Evaluate the effects, and 8) Communicate and institutionalize the change. This model is intended to standardize root cause analyses and provide greater detail to aid in learning and implementing process improvements.
Semi-quantitative approach to risk analysisRiskTracer
This document discusses approaches to semi-quantitative risk analysis. It defines risk as potential negative consequences and opportunity as potential positive consequences. Scenarios are defined as chains of causal events. Risk and opportunity are modeled as sets of scenarios, probabilities, and consequences. Challenges include specifying scenarios, estimating probabilities which are prone to biases, and assessing consequences. Experts' judgments on likelihoods and consequences will vary and should be mapped to probability and impact scales. Histograms can show if judgments are unimodal or multimodal to determine if a group estimate is appropriate or more information is needed.
Causal inference for complex exposures: asking questions that matter, getting...Ellie Murray
Slides from Dec 3, 2021 talk at University of Minnesota School of Public Health, Epidemiology department.
Lecture topic: how do we ask good causal questions & once we've got our questions framed, how do we answer them?
Lecture recording will be posted to YouTube - date tbd.
Analysis of "A Predictive Analytics Primer" by Tom DavenportAloukik Aditya
This document provides an overview of predictive analytics. It explains that predictive analysis uses past data to predict future outcomes. It emphasizes that the quality of the underlying data is crucial, as poor or unrepresentative data can negatively impact predictive models. The document also notes that assumptions used in models are important and can become invalid over time as behaviors change. It concludes by highlighting some key questions managers should ask analysts to better understand the limitations and validity of predictive analytics results.
Unstructured interviews, reference checks, and years of work experience are poor predictors of employee performance, collectively explaining only about 24% of on-the-job performance. Structured interviews, cognitive tests, and work samples are better predictors, collectively explaining about 81% of performance, but are not widely adopted. The best individual predictors are work samples, cognitive tests, and structured interviews, each explaining 26-29% of performance.
Optimizing Data Synthesis and Visualization in Real-Time Decision-MakingCSSI_Inc
CSSI’s Kim Bender was a speaker at 2014's AMS Summer Community Meeting: Improving Forecasts and the Communication of Forecasts. Kim was a member of the panel on “Synthesizing Forecasting Information” which discussed the plethora of information forecasters have to guide their decisions.
Root cause analysis training for beginnersBryan Len
Root cause analysis training for beginners.For what reason Do You Need Root Cause Analysis Training?
On the off chance that you are associated with critical thinking, at any level, in your organization:
To begin with, you have to figure out how to get to the base of every issue to dispose of them forever.
You would likewise need to figure out how to build up a viable restorative activity design and preventive arrangement for every issue or occurrence keeping in mind the end goal to evade a similar issue from happening once more
Root Cause analysis training for beginners shows all of you the above in addition to gives you the chance to hone what you realized on genuine situations in class to guarantee you are prepared to backpedal and apply your insight and abilities at working environment.
Audience :
TONEX root cause analysis training for beginners is a 2-day course designed for:
Quality personnel
R&D team
Production engineers and managers
Design team
All the individuals whose job involve problem solving, safety, reliability, quality control, operations and logistics, and production
Root cause analysis training for beginners course covers the devices and methods to follow issues down to the root cause. Such "in reverse" looking system is helpful particularly in the conditions where there is in excess of one noteworthy cause related with an issue. Some of the time the numerous causes are autonomous of each other, while now and again they all connected together and consequently expelling one cause can bring about settling the entire issue. Root cause analysis training for beginners hands-on workshop will show you how to distinguish every one of the causes, find their association with each other, their consequences for the framework, expel them for all time, and set up preventive activities to keep away from them from happening again in future.
Learn more about Root cause analysis training for beginners
https://www.tonex.com/training-courses/root-cause-analysis-training-for-beginners/
An Ishikawa or cause-and-effect diagram provides a systematic way to visualize the potential causes of a problem or effect. It was developed by Kaoru Ishikawa in 1943 and resembles a fishbone with the problem stated at the head and categories of causes branching out from it. The diagram encourages group participation to determine the root causes of a problem in an orderly format. It helps teams focus on potential causes rather than symptoms and identifies areas for improvement by highlighting causes that appear repeatedly or can be measured and addressed.
Va root cause analysis for process improvementsourias
This document outlines an eight-step model for conducting a root cause analysis to improve processes in a Division of Family Services. The model expands on the basic Define, Measure, Analyze, Improve, Control process improvement model. The eight steps are: 1) Define the problem, 2) Collect and analyze data, 3) Understand the process, 4) Identify possible causes, 5) Identify and select possible solutions, 6) Implement the solution, 7) Evaluate the effects, and 8) Communicate and institutionalize the change. This model is intended to standardize root cause analyses and provide greater detail to aid in learning and implementing process improvements.
Semi-quantitative approach to risk analysisRiskTracer
This document discusses approaches to semi-quantitative risk analysis. It defines risk as potential negative consequences and opportunity as potential positive consequences. Scenarios are defined as chains of causal events. Risk and opportunity are modeled as sets of scenarios, probabilities, and consequences. Challenges include specifying scenarios, estimating probabilities which are prone to biases, and assessing consequences. Experts' judgments on likelihoods and consequences will vary and should be mapped to probability and impact scales. Histograms can show if judgments are unimodal or multimodal to determine if a group estimate is appropriate or more information is needed.
Causal inference for complex exposures: asking questions that matter, getting...Ellie Murray
Slides from Dec 3, 2021 talk at University of Minnesota School of Public Health, Epidemiology department.
Lecture topic: how do we ask good causal questions & once we've got our questions framed, how do we answer them?
Lecture recording will be posted to YouTube - date tbd.
Analysis of "A Predictive Analytics Primer" by Tom DavenportAloukik Aditya
This document provides an overview of predictive analytics. It explains that predictive analysis uses past data to predict future outcomes. It emphasizes that the quality of the underlying data is crucial, as poor or unrepresentative data can negatively impact predictive models. The document also notes that assumptions used in models are important and can become invalid over time as behaviors change. It concludes by highlighting some key questions managers should ask analysts to better understand the limitations and validity of predictive analytics results.
Unstructured interviews, reference checks, and years of work experience are poor predictors of employee performance, collectively explaining only about 24% of on-the-job performance. Structured interviews, cognitive tests, and work samples are better predictors, collectively explaining about 81% of performance, but are not widely adopted. The best individual predictors are work samples, cognitive tests, and structured interviews, each explaining 26-29% of performance.
This document provides an overview of problem analysis techniques. It discusses identifying the problem, specifying it in terms of identity, location, timing and magnitude. It also covers investigating the problem by looking at distinctions and changes between what is and is not occurring. The final stages discussed are testing the most probable cause by matching it to the observed effects through logic, and then verifying the likely cause independently proves it produced the observed effect. Problem analysis provides a systematic approach to explaining situations where expected performance is not being achieved and the cause is unknown.
Applied research aims to solve current problems faced by managers through timely solutions, while basic research builds knowledge by understanding how to solve organizational problems. Research involves defining a clear problem statement, establishing a theoretical framework, generating testable hypotheses, analyzing collected data statistically, carefully measuring observations, and deducing conclusions from the data analysis results.
Understanding & Managing Variation: Use of Computer SimulationSIMUL8 Corporation
SIMUL8's Brittany Hagedorn joins Mike Stoecklein of the ThedaCare Center for Healthcare Value to discuss the importance of managing variability and how computer simulation can contribute to the ongoing efforts of many healthcare systems to embrace Lean.
Cause and effect analysis was developed in the 1960s by Kaoru Ishikawa to help identify the root causes of problems. It uses a diagram called an Ishikawa or fishbone diagram to map the potential causes for an effect or problem. The technique helps conduct a thorough analysis by considering all possible causes across major contributing factors. The steps involve identifying the problem or effect, determining key factors, brainstorming potential causes within each factor, and analyzing the diagram to investigate the most likely causes.
The document discusses accident investigation as an aspect of a total hazard control system. It outlines who should be involved in investigations, what should be examined, and types of analysis that can be used. Cause and effect diagrams or fishbone diagrams are described as a tool to uncover the root causes of accidents by breaking the problem down into major causal categories.
1. The document discusses the attributes and qualities of an effective researcher. It notes that researchers must have intellectual curiosity, be honest, and think critically.
2. Key qualities of a good researcher include being research-oriented, efficient, scientific, effective, analytic, responsive, creative, and honest. Researchers must also have an analytical mind, strong communication skills, and be able to stay calm under pressure.
3. The document emphasizes that good researchers are curious, quick thinkers who are committed to their work and pay close attention to detail. They understand basic statistics and can work well in a team environment.
This document introduces meta-analysis, which combines data from multiple studies to better estimate the true effect of an intervention or exposure. It explains that meta-analysis directly analyzes effect sizes rather than p-values and statistically synthesizes all effects. Finally, it notes that publication bias, where negative results are less likely to be published, can be identified using a funnel plot in a meta-analysis.
Hardwiring Safety 7 Tips For Changing Cultureladukepc
The document outlines seven tools for changing an organization's safety culture: 1) weekly safety inspections by supervisors and safety champions, 2) job safety analyses, 3) hazard investigation teams to review inspections and incidents, 4) a safety scorecard for balanced measurement, 5) safety scoreboards and strategy meetings, 6) a hazard tracking database for accountability and decision making, and 7) safety strategy teams to own safety.
The cause and effect diagram, also known as a fishbone diagram or Ishikawa diagram, was invented in 1943 by Kaoru Ishikawa to help identify potential causes for a problem. It maps out causes in categories related to a problem or effect. The major purpose is to generate a comprehensive list of possible causes through brainstorming to help understand and solve problems. To create one, the effect is written and main categories of causes are connected. Then detailed potential causes are added as branches in each category. Variations include diagrams for production processes or listing causes before structuring them in the diagram.
The document discusses fishbone analysis (also known as Ishikawa diagram), which is a tool for systematically analyzing the potential causes of quality, process, or project problems. It provides the following key points:
1. Dr. Kaoru Ishikawa invented the fishbone diagram to systematically analyze the effects and causes that contribute to those effects.
2. The fishbone diagram looks like the skeleton of a fish, with the "head" representing the problem/effect and the "bones" and sub-branches representing categories of causes and specific potential causes.
3. Basic steps for constructing a fishbone diagram include identifying the problem/effect, categorizing potential cause factors, brainstorming specific causes within
The document discusses various topics related to test management, including organizing test teams, independent and integrated testing, test plans, estimates and strategies, test progress monitoring and control, configuration management, risks and testing, and incident management. Specifically, it examines the roles of test leaders and testers, factors that influence test estimates, selecting test strategies, using configuration management to deliver proper test releases, considering likelihood and impact to assess risk levels, and writing incident reports to log unexpected test results.
Our inner thoughts and perceptions are shaped by various factors like context, perspective, purpose, intent, memory, experience and knowledge which form a data set in our brain. This data set informs our perception of reality and also affects our intentions. However, intentions do not always translate into actions due to intervening factors like fear, threat, mood or past experiences that can alter our intent and motivation. To bridge the gap between intentions and actions, people resort to strategies like drawing on their own and others' experiences, employing empathy to understand different values, and allowing emotions to help make choices.
The five whys tool is used to analyze causes and effects through asking why up to five times to get to the root cause of a problem quickly. Starting with a defined problem, you ask why it exists and then why those reasons exist, tracing it back to its origin. The tool can also work in reverse, starting with a solution and tracing out potential effects to check for unwanted side effects and ways to improve the solution. The visual result is a deductive mind map that shows the hierarchical relationship between a problem and its underlying causes or a solution and its potential effects.
This document is a cartoon guide to causal inference by Ellie Murray. It discusses how causal inference aims to estimate what would happen if aspects of the world were different, such as through randomized experiments or statistical methods. It notes that intention-to-treat effects from randomized trials require no assumptions, while per-protocol effects require assumptions like no unmeasured confounding and positivity. Well-defined interventions are also important for consistency. The goal of causal inference is to understand what would happen under counterfactual scenarios, like if we could travel back in time.
This document discusses behavior modification assessment and treatment programs. It covers the following key points:
1) Behavioral assessment focuses on gathering objective data about behaviors rather than suspicions. Baseline data is collected to establish stability before treatment.
2) An intake phase determines if the client is in the right treatment setting and informs them about the program policies. Crisis situations require immediate treatment.
3) Treatment programs are developed based on a functional assessment that identifies the controlling variables influencing problem behaviors. Programs are evaluated based on objective behavior data to determine effectiveness.
The document discusses the limitations of people analytics and using algorithms to evaluate employees. It notes that personality tests and metrics may make incorrect assumptions or focus on the wrong factors. People are complex and cannot be fully analyzed like data. The best predictor of future performance is a person's past as evaluated by someone who knows them, not by algorithms. The document advocates treating employees in a holistic way by providing an supportive environment and opportunities for development, rather than solely focusing on past achievements or quarterly targets.
A comment in Nature, signed by over 800 researchers, called for a rise up against statistical significance. This was followed by a special issue of The American Statistician aimed at halting the use of the term "statistically significant", and new guidelines for statistical reporting in the New England Journal of Medicine. These slides discuss the broader context of the "p-value crisis" and alternatives for communicating the conclusions after statistical analyses.
Target audience: Medical researchers; Scientists involved in conducting or interpreting analyses and communicating the results of scientific research, as well as readers of scientific publications.
Learning objectives:
To understand the context of the reproducibility crisis in medical research.
To learn about problems with p-values and alternatives to report findings.
To understand how (not) to interpret significant and insignificant findings.
To learn how to communicate research findings in a modest, thoughtful, and transparent way.