The Planning Fallacy (Project Overruns)
•Download as PPTX, PDF•
2 likes•842 views
A summary of a Freakonomics Podcast on Why all your Projects are always late - and What to do about it. Podcast Link: http://pca.st/xU8w
Report
Share
Report
Share
Recommended
Predicting Moscow Real Estate Prices with Azure Machine Learning
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data.
Nest Business Model Presentation
My team created a new business model for Nest Labs using the business model canvas. We decided to introduce a new service to be integrated with Nest Protect and other Nest products. This service, called SafeNest, will operate as a safety and security system for a low subscription fee. To use SafeNest customers must have a Nest Protect. Hardware used to enhance the SafeNest service are sensors and a specialty Nest/Jawbone bracelet we invented called Nestlet.
The purpose of this service is to increase consumer investment in the Nest system. As the leader and pioneer of the "Internet of Things," our strategy is to grab as much Home Automation market share as possible, as quickly as possible before our Apple launches the HomeKit. This service will also steal market share from the Home Safety and Security market (ADT, LifeAlert).
Machine Learning Deep Learning AI and Data Science
What is Machine Learning
What is Deep Learning
What is Data Science
What is Artificial Intelligence
Artificial Intelligence Career In 2023
The strongest demand for experts in AL/ML is on the rise worldwide. Bloomberg says, the global artificial intelligence market size was valued at USD 59.67 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 39.4% to reach USD 422.37 billion by 2028.
AI and Data Science.pdf
AI and Data Science Revolutionizing Industries and Shaping the Future
The document discusses how rapid advancements in artificial intelligence are disrupting industries globally. It outlines key developments in AI's history and applications that are streamlining tasks through automation, enabling personalized experiences and improved customer service, and poised to revolutionize healthcare. However, as AI becomes more prevalent, ethical and regulatory challenges also emerge regarding data privacy, bias, and other implications. The future potential of AI is limitless as it transforms additional sectors like transportation, education, energy, and the environment through applications such as autonomous vehicles.
Toyota environmental challenge 2050
The Toyota Environmental Challenge 2050 (Challenge 2050) is a set of six challenges that aim to go beyond reducing negative environmental impacts to generating net positive impacts on the planet and society. After significant research and internal and external collaboration, Toyota Motor Corporation (TMC, Toyota's parent corporation located in Japan) declared these six issues in 2015. The objectives, which apply to all Toyota businesses worldwide, are the most stringent and inspiring environmental pledges this firm has ever made.
Sun modeling
Designing systems to use Cassandra can be difficult for programmers used to relational databases, normalisation and entity relationship diagrams. One of the first steps in designing the tables for Cassandra is to model the CQL commands that will be run and use these to designate tables, but how to get these statements from the users requirements? Sun modelling is a technique used by designers of OLAP systems to gather requirements from users without having to explain the intricacies of Star Schema. In this talk we'll introduce Sun modelling and show how it can be simply modified to gather requirements for CQL queries and hence table design.
Machine Learning in Healthcare and Life Science
Machine Learning in Healthcare and Life ScienceIDEAS - Int'l Data Engineering and Science Association
The document discusses machine learning in healthcare and life sciences. It provides an overview of machine learning techniques like classification, regression, and clustering. It then discusses IBM's Watson Data Platform and Data Science Experience for developing and deploying machine learning models at scale. The document presents a case study on using deep learning for lung cancer detection from medical images. It concludes with recommendations for applying machine learning including the importance of data and shortening development cycles.Recommended
Predicting Moscow Real Estate Prices with Azure Machine Learning
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data.
Nest Business Model Presentation
My team created a new business model for Nest Labs using the business model canvas. We decided to introduce a new service to be integrated with Nest Protect and other Nest products. This service, called SafeNest, will operate as a safety and security system for a low subscription fee. To use SafeNest customers must have a Nest Protect. Hardware used to enhance the SafeNest service are sensors and a specialty Nest/Jawbone bracelet we invented called Nestlet.
The purpose of this service is to increase consumer investment in the Nest system. As the leader and pioneer of the "Internet of Things," our strategy is to grab as much Home Automation market share as possible, as quickly as possible before our Apple launches the HomeKit. This service will also steal market share from the Home Safety and Security market (ADT, LifeAlert).
Machine Learning Deep Learning AI and Data Science
What is Machine Learning
What is Deep Learning
What is Data Science
What is Artificial Intelligence
Artificial Intelligence Career In 2023
The strongest demand for experts in AL/ML is on the rise worldwide. Bloomberg says, the global artificial intelligence market size was valued at USD 59.67 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 39.4% to reach USD 422.37 billion by 2028.
AI and Data Science.pdf
AI and Data Science Revolutionizing Industries and Shaping the Future
The document discusses how rapid advancements in artificial intelligence are disrupting industries globally. It outlines key developments in AI's history and applications that are streamlining tasks through automation, enabling personalized experiences and improved customer service, and poised to revolutionize healthcare. However, as AI becomes more prevalent, ethical and regulatory challenges also emerge regarding data privacy, bias, and other implications. The future potential of AI is limitless as it transforms additional sectors like transportation, education, energy, and the environment through applications such as autonomous vehicles.
Toyota environmental challenge 2050
The Toyota Environmental Challenge 2050 (Challenge 2050) is a set of six challenges that aim to go beyond reducing negative environmental impacts to generating net positive impacts on the planet and society. After significant research and internal and external collaboration, Toyota Motor Corporation (TMC, Toyota's parent corporation located in Japan) declared these six issues in 2015. The objectives, which apply to all Toyota businesses worldwide, are the most stringent and inspiring environmental pledges this firm has ever made.
Sun modeling
Designing systems to use Cassandra can be difficult for programmers used to relational databases, normalisation and entity relationship diagrams. One of the first steps in designing the tables for Cassandra is to model the CQL commands that will be run and use these to designate tables, but how to get these statements from the users requirements? Sun modelling is a technique used by designers of OLAP systems to gather requirements from users without having to explain the intricacies of Star Schema. In this talk we'll introduce Sun modelling and show how it can be simply modified to gather requirements for CQL queries and hence table design.
Machine Learning in Healthcare and Life Science
Machine Learning in Healthcare and Life ScienceIDEAS - Int'l Data Engineering and Science Association
The document discusses machine learning in healthcare and life sciences. It provides an overview of machine learning techniques like classification, regression, and clustering. It then discusses IBM's Watson Data Platform and Data Science Experience for developing and deploying machine learning models at scale. The document presents a case study on using deep learning for lung cancer detection from medical images. It concludes with recommendations for applying machine learning including the importance of data and shortening development cycles.Machine Learning for Non-technical People
Machine learning is one of the most promising and most difficult to understand fields of the modern age. Here are the slides from Slater Victoroff's (CEO of indico) talk at General Assembly Boston for non-technical folks on how to separate the signal from the noise -- stay tuned for the next time he speaks:
https://generalassemb.ly/education/machine-learning-for-non-technical-people
Intro/Overview on Machine Learning Presentation -2
Intro/Overview on Machine Learning Presentation -2,
project on Machine Learning ,Intro/Overview on Machine Learning Presentation
Biz Model for ARM
My students use ideas from my class to describe a business model for ARM, which is a successful provider of microprocessor cores for mobile phones. They describe the value proposition, customer selection, value capture, scope of activities, and method of strategic control for ARM's entry into the microprocessor market for PCs.
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Confusion matrix and classification evaluation metrics
This document discusses classification evaluation metrics and their limitations. It introduces the confusion matrix and metrics calculated from it such as precision, recall, F1-score, and accuracy. The summary highlights that these metrics can be "hacked" and misleading. More robust alternatives like balanced accuracy and MCC are presented that account for true negatives and are not as affected by class imbalance. Comprehensive reporting of multiple metrics from different perspectives is recommended for fully understanding a model's performance.
Text clustering
Text clustering involves grouping text documents into clusters such that documents within a cluster are similar to each other and dissimilar to documents in other clusters. Common text clustering methods include bisecting k-means clustering, which recursively partitions clusters, and agglomerative hierarchical clustering, which iteratively merges clusters. Text clustering is used to automatically organize large document collections and improve search by returning related groups of documents.
Applying Machine Learning to Agricultural Data
This document discusses applying machine learning techniques to agricultural data. It describes a software tool called WEKA that allows experimenting with different machine learning algorithms on real-world datasets. As a case study, the document examines using machine learning to infer rules for culling less productive cows from dairy herd data. Several machine learning methods were tested on the data and produced encouraging results for using machine learning to help solve agricultural problems.
Classification vs clustering
Classification and clustering are two methods of organizing objects into groups based on their features. Classification involves assigning objects to predefined classes based on their attributes, while clustering aims to group similar objects together without predefined labels. Classification uses supervised learning with training data containing class labels, while clustering is unsupervised and does not use pre-labeled data. Different algorithms such as decision trees and Bayesian classifiers are used for classification, while k-means, expectation maximization, and other methods are typically applied to clustering.
Global Database on Crop Wild Relatives
Poster describing a global occurrence database of over 5 million records of the distributions of crops and their wild relatives, including taxonomic and geographic information.
Introduction to Data Science (Data Summit, 2017)
This document summarizes an introduction to data science presentation by Joe Caserta and Bill Walrond of Caserta Concepts. Caserta Concepts is an internationally recognized data innovation and engineering consulting firm. The agenda covers why data science is important, challenges of working with big data, governing big data, the data pyramid, what data scientists do, standards for data science, and a demonstration of data analysis. Popular machine learning algorithms like regression, decision trees, k-means clustering and collaborative filtering are also discussed.
Importance of Artificial Intelligence.pdf
Coddle Technologies provide a complete spectrum of software development services delivering innovative solutions for Startups, SMBs and Enterprises.
Link :https://www.coddletech.com/
Free OpManager training Part 4 - Monitoring Network Performance and Network Maps
Free OpManager training Part 4 - Monitoring Network Performance and Network MapsManageEngine, Zoho Corporation
This document summarizes a training session on fault management and IT automation using OpManager. It includes an agenda covering alarm severity levels, threshold violation alarms, alarms from event logs, SNMP traps, syslog alarms, and notifications. It also discusses using IT workflows to automate problem remediation.Machine Learning Final presentation
The document presents a machine learning model to predict disease severity and outcomes in COVID-19 patients. It discusses using various machine learning algorithms like random forest, support vector machine, classification and regression trees, genetic algorithm, and artificial neural networks on COVID-19 patient data. The objectives are to build a model that can predict the spread of the virus in the next 7 days and analyze current conditions in India to see if it will face a crisis like other countries. Literature on existing COVID-19 machine learning studies is reviewed and research gaps are identified like improving data collection. The Italy dataset with 14 attributes is used and the different algorithms' accuracies will be calculated to identify the best approach.
Ai in finance
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
Artificial Intelligence (AI) in media applications and services
The document discusses artificial intelligence (AI) and its applications in media. It provides an overview of key AI concepts like machine learning, neural networks, and deep learning. It then discusses various use cases for AI in media like content understanding, automatic content generation, and monitoring. As an example, it presents a proof of concept for using AI to detect trends from social media images and videos by recognizing concepts and objects. Finally, it outlines some open research topics for applying AI in broadcast media like verification of content authenticity, speech recognition for dialects, customizing content for different distribution channels, and improving training data diversity.
Artificial Inteligence: the begining
This presentation briefly discusses the following topics:
What is Artificial Intelligence ?
Aim of AI
Need for AI
What is intelligence?
Objectives of AI research
AI research Scope
Role of Tools in AI
Multi and Cross disciplinary approach
Applications of AI
AI Science
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
Generative AI: Shifting the AI Landscape
This document discusses generative AI and its potential impacts. It provides an overview of generative AI capabilities like one model for all tasks, emergent behaviors, and in-context learning. Applications discussed include materials discovery, process monitoring, and battery modeling. The document outlines a vision for 2030 where generative AI becomes more general purpose and powerful, enabling new industries and economic growth while also raising risks around concentration of power, misuse, and safe and ethical development.
module
The document discusses the project preparation phase, which aims to identify the project work, establish goals and objectives, and set up efficient decision-making. It involves conceptualizing the project, establishing goals and objectives, issuing a project charter, outlining an implementation strategy, developing cost estimates, identifying risks, defining roles and responsibilities, and holding a kickoff meeting. The preparation phase comes after identifying solutions and allows checking preconditions before moving to the planning stage.
Relationship Forecasting
The document discusses relationship forecasting and why it is better than traditional budgeting approaches. Some key points:
1) Forecasting focuses on what is likely to happen rather than target-setting, and uses a range to capture uncertainty rather than a single number.
2) Considering best- and worst-case scenarios through a range helps have more honest, meaningful discussions about opportunities and risks.
3) Relationship forecasting emphasizes building trust between parties to improve forecast accuracy, which benefits the overall organization.
4) A variety of statistical tools from simple conversations to more advanced models like Monte Carlo simulations can help quantify probabilities within a forecast range.
Only in fairytales are emperors told they are naked
The document discusses project governance and why it often fails. It provides two approaches to implementing effective project governance - a top-down approach where a small senior board makes decisions, and a bottom-up approach that focuses on common metrics, maturity, and early intervention. Both approaches aim to identify and stop failing projects early. Effective governance requires frank discussion and honest assessment of a project's likelihood of success.
More Related Content
What's hot
Machine Learning for Non-technical People
Machine learning is one of the most promising and most difficult to understand fields of the modern age. Here are the slides from Slater Victoroff's (CEO of indico) talk at General Assembly Boston for non-technical folks on how to separate the signal from the noise -- stay tuned for the next time he speaks:
https://generalassemb.ly/education/machine-learning-for-non-technical-people
Intro/Overview on Machine Learning Presentation -2
Intro/Overview on Machine Learning Presentation -2,
project on Machine Learning ,Intro/Overview on Machine Learning Presentation
Biz Model for ARM
My students use ideas from my class to describe a business model for ARM, which is a successful provider of microprocessor cores for mobile phones. They describe the value proposition, customer selection, value capture, scope of activities, and method of strategic control for ARM's entry into the microprocessor market for PCs.
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Confusion matrix and classification evaluation metrics
This document discusses classification evaluation metrics and their limitations. It introduces the confusion matrix and metrics calculated from it such as precision, recall, F1-score, and accuracy. The summary highlights that these metrics can be "hacked" and misleading. More robust alternatives like balanced accuracy and MCC are presented that account for true negatives and are not as affected by class imbalance. Comprehensive reporting of multiple metrics from different perspectives is recommended for fully understanding a model's performance.
Text clustering
Text clustering involves grouping text documents into clusters such that documents within a cluster are similar to each other and dissimilar to documents in other clusters. Common text clustering methods include bisecting k-means clustering, which recursively partitions clusters, and agglomerative hierarchical clustering, which iteratively merges clusters. Text clustering is used to automatically organize large document collections and improve search by returning related groups of documents.
Applying Machine Learning to Agricultural Data
This document discusses applying machine learning techniques to agricultural data. It describes a software tool called WEKA that allows experimenting with different machine learning algorithms on real-world datasets. As a case study, the document examines using machine learning to infer rules for culling less productive cows from dairy herd data. Several machine learning methods were tested on the data and produced encouraging results for using machine learning to help solve agricultural problems.
Classification vs clustering
Classification and clustering are two methods of organizing objects into groups based on their features. Classification involves assigning objects to predefined classes based on their attributes, while clustering aims to group similar objects together without predefined labels. Classification uses supervised learning with training data containing class labels, while clustering is unsupervised and does not use pre-labeled data. Different algorithms such as decision trees and Bayesian classifiers are used for classification, while k-means, expectation maximization, and other methods are typically applied to clustering.
Global Database on Crop Wild Relatives
Poster describing a global occurrence database of over 5 million records of the distributions of crops and their wild relatives, including taxonomic and geographic information.
Introduction to Data Science (Data Summit, 2017)
This document summarizes an introduction to data science presentation by Joe Caserta and Bill Walrond of Caserta Concepts. Caserta Concepts is an internationally recognized data innovation and engineering consulting firm. The agenda covers why data science is important, challenges of working with big data, governing big data, the data pyramid, what data scientists do, standards for data science, and a demonstration of data analysis. Popular machine learning algorithms like regression, decision trees, k-means clustering and collaborative filtering are also discussed.
Importance of Artificial Intelligence.pdf
Coddle Technologies provide a complete spectrum of software development services delivering innovative solutions for Startups, SMBs and Enterprises.
Link :https://www.coddletech.com/
Free OpManager training Part 4 - Monitoring Network Performance and Network Maps
Free OpManager training Part 4 - Monitoring Network Performance and Network MapsManageEngine, Zoho Corporation
This document summarizes a training session on fault management and IT automation using OpManager. It includes an agenda covering alarm severity levels, threshold violation alarms, alarms from event logs, SNMP traps, syslog alarms, and notifications. It also discusses using IT workflows to automate problem remediation.Machine Learning Final presentation
The document presents a machine learning model to predict disease severity and outcomes in COVID-19 patients. It discusses using various machine learning algorithms like random forest, support vector machine, classification and regression trees, genetic algorithm, and artificial neural networks on COVID-19 patient data. The objectives are to build a model that can predict the spread of the virus in the next 7 days and analyze current conditions in India to see if it will face a crisis like other countries. Literature on existing COVID-19 machine learning studies is reviewed and research gaps are identified like improving data collection. The Italy dataset with 14 attributes is used and the different algorithms' accuracies will be calculated to identify the best approach.
Ai in finance
Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
Artificial Intelligence (AI) in media applications and services
The document discusses artificial intelligence (AI) and its applications in media. It provides an overview of key AI concepts like machine learning, neural networks, and deep learning. It then discusses various use cases for AI in media like content understanding, automatic content generation, and monitoring. As an example, it presents a proof of concept for using AI to detect trends from social media images and videos by recognizing concepts and objects. Finally, it outlines some open research topics for applying AI in broadcast media like verification of content authenticity, speech recognition for dialects, customizing content for different distribution channels, and improving training data diversity.
Artificial Inteligence: the begining
This presentation briefly discusses the following topics:
What is Artificial Intelligence ?
Aim of AI
Need for AI
What is intelligence?
Objectives of AI research
AI research Scope
Role of Tools in AI
Multi and Cross disciplinary approach
Applications of AI
AI Science
The computational infrastructure is becoming a vast interconnected fabric of formal methods, including per a major shift from 2d grids to 3d graphs in machine learning architectures
The implication is systems-level digital science at unprecedented scale for discovery in a diverse range of scientific disciplines
Generative AI: Shifting the AI Landscape
This document discusses generative AI and its potential impacts. It provides an overview of generative AI capabilities like one model for all tasks, emergent behaviors, and in-context learning. Applications discussed include materials discovery, process monitoring, and battery modeling. The document outlines a vision for 2030 where generative AI becomes more general purpose and powerful, enabling new industries and economic growth while also raising risks around concentration of power, misuse, and safe and ethical development.
What's hot (19)
Intro/Overview on Machine Learning Presentation -2
Intro/Overview on Machine Learning Presentation -2
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...
Confusion matrix and classification evaluation metrics
Confusion matrix and classification evaluation metrics
Free OpManager training Part 4 - Monitoring Network Performance and Network Maps
Free OpManager training Part 4 - Monitoring Network Performance and Network Maps
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Artificial Intelligence (AI) in media applications and services
Artificial Intelligence (AI) in media applications and services
Similar to The Planning Fallacy (Project Overruns)
module
The document discusses the project preparation phase, which aims to identify the project work, establish goals and objectives, and set up efficient decision-making. It involves conceptualizing the project, establishing goals and objectives, issuing a project charter, outlining an implementation strategy, developing cost estimates, identifying risks, defining roles and responsibilities, and holding a kickoff meeting. The preparation phase comes after identifying solutions and allows checking preconditions before moving to the planning stage.
Relationship Forecasting
The document discusses relationship forecasting and why it is better than traditional budgeting approaches. Some key points:
1) Forecasting focuses on what is likely to happen rather than target-setting, and uses a range to capture uncertainty rather than a single number.
2) Considering best- and worst-case scenarios through a range helps have more honest, meaningful discussions about opportunities and risks.
3) Relationship forecasting emphasizes building trust between parties to improve forecast accuracy, which benefits the overall organization.
4) A variety of statistical tools from simple conversations to more advanced models like Monte Carlo simulations can help quantify probabilities within a forecast range.
Only in fairytales are emperors told they are naked
The document discusses project governance and why it often fails. It provides two approaches to implementing effective project governance - a top-down approach where a small senior board makes decisions, and a bottom-up approach that focuses on common metrics, maturity, and early intervention. Both approaches aim to identify and stop failing projects early. Effective governance requires frank discussion and honest assessment of a project's likelihood of success.
Whose project is it anyway?
Most traditional methodologies hold that a business case is something that a project manager inherits and that its responsibility sits with a sponsor, project executive or even a governance board of some sort. However the project manager can, and should, play a critical role in assessing and critiquing the business case to guard against project failure..
The Role of Acquisitions in Corporate Growth
This document discusses acquisition basics and how they apply to everyday business. It provides three ways for a business to grow: through new capabilities, brands, geographies; through new products or customers at 12-15% growth; or through price increases at 4-6% growth. However, 70% of acquisitions fail to create value due to inadequate planning, resources, communication, or misaligned incentives. The document outlines characteristics of successful acquirers, including having the right perspective, prioritization, planning, and process. It also discusses focusing efforts on integration post-close to prevent issues like delays in asset appraisals negatively impacting reported results.
What's the colour of your project?
This document provides questions and documents to consider when determining the actual status of a project, program, or portfolio. It advises sponsors to ignore reported colors and percentages and look for real, tangible information. Key things to look for include clearly defined success criteria, an honest finish date, evidence that status reports reflect reality, and documentation of budgets, resources, risks, and responsibilities. Examining critical paths, integration plans, and meeting notes can also help determine if a project is where it claims to be. The goal is to have a true understanding of status rather than just pretty colors or estimates.
Module Five strengthens your business implementation plan through .docx
Module Five strengthens your business implementation plan through an analysis of crosscultural, economic, and geopolitical factors that may impact the business environment and concept by allowing you to explicate the assumptions behind your business implementation plan and detail your contingency plan.
Understanding the ways in which globalization factors such as the cross-cultural and geopolitical implications influence your implementation plan is critical for success. Even if your idea or concept is limited to one specific country or part of a country, various crosscultural, economic, and geopolitical elements will interact with your business implementation plan.
For instance, your business implementation plan may target potential customers in a country that is heavily dependent on the manufacturing sector. Many manufacturing industries rely on global demand for the products they manufacture. Geopolitical and global economic circumstances have the potential to dramatically affect demand for your product or service even if it is only available in one country.
Beyond this, you or others may ultimately offer the product or service in multiple countries. For these and other reasons, your plan must analyze the cross-cultural, economic, and geopolitical factors that may affect the business environment or concept. Janakova and Magdolen (2013) support this when they argue that political turmoil and many other factors lead to a situation in which organizations operate in a globally interdependent environment. Global realities will affect your business implementation plan, so it is good to outline these realities.
There are at least two perspectives from which to understand the geopolitical and global economic environments. First, you should understand the ways in which these can negatively affect your product or service. The goal is to have a clear idea of how to appropriately respond to negative events to insulate your financial stakeholders from such events. The second perspective is how these geopolitical and global economic environments can positively affect your product or service. The approach here is to have an idea of how to take advantage of opportunities that occur during changes in global political or economic environments.
Understanding cross-cultural factors means more than just determining how to operate within the context of other countries. Janakova and Magdolen (2013) state that in many business and organizational environments, leaders and others interact with people from different cultures who also have cross-cultural backgrounds to consider. Within this framework, Nardon and Steers (2014) proposed that cross-cultural leadership involves understanding how cross-cultural dynamics may differ from context to context and also identifying how to leverage cross-cultural dynamics. A comprehensive business implementation plan will consider cross-cultural, economic, and geopolitical factors that may impact the business environment and ...
Project Success 'Orange Paper'
The document summarizes the results of a survey about factors that contribute to successful projects. The key findings are:
1) The survey found that delivering expected benefits and objectives were the top criteria for defining a successful project. However, benefits management was not commonly practiced on projects.
2) Having strong stakeholder management and project planning were seen as the most crucial technical factors for success. However, benefits management was not identified as a top technical factor despite its importance in defining success.
3) There were some differences in perspectives across project roles - benefits management was more important higher in the organization, while risk management was prioritized more lower down. This suggests a need for better communication and alignment of priorities.
Presentation on 'Why cant people estimate' event, 23rd June 2016
Estimates are often biased due to human optimism and a focus on internal factors rather than external benchmarks. Parametric models using historical data from analogous projects can help mitigate bias by providing an "outside view". Calibrating models to actual data and considering cost impacts across the total life cycle can further improve estimates.
Essay On Planning
Essay on Planning Retirement
Essay about project Planning
Succession Planning Essay
Essay on Plan for Success
Essay on Wedding Planning
Planning Process Paper
Management Planning Essay
Unseen
The document discusses reasons for IT project failure and success. It argues that putting people before process is key to success. Specifically, it recommends creating a sense of shared purpose and excitement around the project ("halo effect"), avoiding last-minute crunches due to resource constraints ("student syndrome"), and addressing dysfunctional team behaviors outlined in Lencioni's "Five Dysfunctions of a Team" model. Process-heavy approaches that neglect people and team dynamics are more likely to lead to failure.
Improving Connections & Performance
The document discusses establishing an employee program called "The Program" to improve engagement and performance by providing opportunities for employees to contribute their talents and discretionary efforts. The Program would involve employees submitting project ideas, voting on projects, and working in teams over 3-month rounds to develop solutions. It aims to tap into employee dreams and passions to increase value delivery and engagement.
Martin folwer
Martin Fowler is a software developer, author, and speaker known for his work in agile software development. He helped create the Agile Manifesto in 2001 and popularized concepts like dependency injection. Fowler believes that estimation is valuable when it helps teams make significant decisions around resource allocation, coordination, and responding to change. He advocates for continuous integration and continuous deployment to help teams build and deliver software more rapidly.
Case study 1 (zaileha,suryani,anis,ilyana)
The NOMIS prison project failed for several reasons:
1) There was a lack of alignment between IT and other corporate entities, with the project being separated from organizational management issues.
2) Optimism bias led to unrealistic expectations about resources, timelines, and benefits. Project timelines and costs were underestimated.
3) Poor project management included inadequate oversight, unclear roles and responsibilities, poor planning, and weak change control. The project was too large in scope given the resources allocated to it.
The project was doomed to fail as early as the initial planning stages due to unrealistic timelines and budgets set due to optimism bias. However, management did not realize the full extent of issues until 2007 when
Case study 1 (zaileha,suryani,anis,ilyana) version 2
The NOMIS prison project failed due to poor planning and management issues. It was doomed to fail as early as 2007 when costs had exceeded £155 million and the project was two years behind schedule. The most difficult contributing factor to prevent was optimism bias, where project managers overestimated benefits and underestimated costs and timelines. Management failed to properly plan resources, establish governance structures, and monitor finances. The large scale of the project required thorough strategic planning and contingency planning that was lacking.
Mindset of Prog Mgr v0.1
The document discusses the mindset of a successful program manager. It describes the program manager's role in operating between the external stakeholder environment and internal team environment. The program manager must translate between these two worlds which have different perspectives - stakeholders focus on the journey and outcomes, while teams focus on work and deliverables. Adopting this mindset helps the program manager address common problems like connecting vision to work, aligning stakeholders, ensuring commitment to needed changes, and structuring organization and processes to support both environments. Understanding and managing the dynamics between the external and internal environments is key to effective program management.
FusionPoint: 11 signs your marketing analytic project may be in trouble
This presentation is intended for people managing sales and marketing analytic and reporting projects. It discusses situations that may disrupt your project and lists a few risk mitigation strategies. Whether you are initiating a BI, Pricing, Promotion, Assortment, Dashboard, Sorecard or Demand Planning project, identifying and managing these situations can increase your chance of success.
Pereira diamond benefits management model - Q&A
The document summarizes questions and answers from a webinar on applying the Pereira benefits management model. Key points include:
- The Pereira model can partially be applied to public sector organizations, focusing on costs and efficiency. A social ROI model is being developed.
- Benefits estimation should prioritize legal compliance, business growth, cost reduction, then efficiency increase.
- Revenue-generating projects fall under the business growth dimension but cost reductions also directly impact results.
- Benefits have dynamic behavior over a project lifecycle so cannot be considered fully achieved/not achieved without ongoing tracking.
- Organizations should be benefits-driven, not cost-driven, though cost estimation is
X Lerant Presentation On Integrating Strategy And Budgeting Pdf
The document discusses creating a culture of accountability in budgeting by directly linking budgets to organizational strategies. It outlines several problems such as strategies not being well documented or understood, and budgets being based on history rather than strategic goals. Solutions proposed include developing clear strategic plans, building consensus around budgeting to strategy, establishing accountability, and assigning roles to ensure the budget supports strategic initiatives. The document provides recommendations for implementing these solutions through steps such as setting goals for each strategic initiative and allowing users to budget for initiatives instead of just cost centers.
ANI | Webinar | Why great leaders must unlearn to succeed today | Barry O'Rel...
The document discusses common problems that organizations face when trying to scale innovation through large project-based work. It provides examples from a bank that was struggling to complete its "Golden Data" initiative to create a single source of data across the organization after 2.5 years of work. The key mistakes made were: 1) Thinking big but starting big and trying to do everything at once, 2) Using siloed teams with different priorities, 3) Lacking a shared understanding of success, 4) Focusing on outputs rather than outcomes, 5) Giving individuals and teams too many competing priorities, 6) Having too many initiatives in progress at once, and 7) Tying personal brands and bonuses to specific initiatives rather than overall impact
Similar to The Planning Fallacy (Project Overruns) (20)
Only in fairytales are emperors told they are naked
Only in fairytales are emperors told they are naked
Module Five strengthens your business implementation plan through .docx
Module Five strengthens your business implementation plan through .docx
Presentation on 'Why cant people estimate' event, 23rd June 2016
Presentation on 'Why cant people estimate' event, 23rd June 2016
Case study 1 (zaileha,suryani,anis,ilyana) version 2
Case study 1 (zaileha,suryani,anis,ilyana) version 2
FusionPoint: 11 signs your marketing analytic project may be in trouble
FusionPoint: 11 signs your marketing analytic project may be in trouble
X Lerant Presentation On Integrating Strategy And Budgeting Pdf
X Lerant Presentation On Integrating Strategy And Budgeting Pdf
ANI | Webinar | Why great leaders must unlearn to succeed today | Barry O'Rel...
ANI | Webinar | Why great leaders must unlearn to succeed today | Barry O'Rel...
Recently uploaded
From Concept to reality : Implementing Lean Managements DMAIC Methodology for...
The Ready-Made Garments (RMG) industry in Bangladesh is a cornerstone of the economy, but increasing costs and stagnant productivity pose significant challenges to profitability. This study explores the implementation of Lean Management in the Sampling Section of RMG factories to enhance productivity. Drawing from a comprehensive literature review, theoretical framework, and action research methodology, the study identifies key areas for improvement and proposes solutions.
Through the DMAIC approach (Define, Measure, Analyze, Improve, Control), the research identifies low productivity as the primary problem in the Sampling Section, with a PPH (Productivity per head) of only 4.0. Using Lean Management techniques such as 5S, Standardized work, PDCA/Kaizen, KANBAN, and Quick Changeover, the study addresses issues such as pre and post Quick Changeover (QCO) time, improper line balancing, and sudden plan changes.
The research employs regression analysis to test hypotheses, revealing a significant correlation between reducing QCO time and increasing productivity. With a regression equation of Y = -0.000501X + 6.72 and an R-squared value of 0.98, the study demonstrates a strong relationship between the independent variables (QCO downtime and improper line balancing downtime) and the dependent variable (productivity per head).
The findings suggest that by implementing Lean Management practices and addressing key productivity inhibitors, RMG factories can achieve substantial improvements in efficiency and profitability. The study provides valuable insights for practitioners, policymakers, and researchers seeking to enhance productivity in the RMG industry and similar manufacturing sectors.
All the Small Things - XP2024 Bolzano/Bozen
Originally presented at XP2024 Bolzano
While agile has entered the post-mainstream age, possibly losing its mojo along the way, the rise of remote working is dealing a more severe blow than its industrialization.
In this talk we'll have a look to the cumulative effect of the constraints of a remote working environment and of the common countermeasures.
Neal Elbaum Shares Top 5 Trends Shaping the Logistics Industry in 2024
In the ever-evolving world of logistics, staying ahead of the curve is crucial. Industry expert Neal Elbaum highlights the top five trends shaping the logistics industry in 2024, offering valuable insights into the future of supply chain management.
Maximize Your Efficiency with This Comprehensive Project Management Platform ...
In today's work environment, staying organized and productive can be a daunting challenge. With multiple tasks, projects, and tools to juggle, it's easy to feel overwhelmed and lose focus. Fortunately, liftOS offers a comprehensive solution to streamline your workflow and boost your productivity. This innovative platform brings together all your essential tools, files, and tasks into a single, centralized workspace, allowing you to work smarter and more efficiently.
9 Ways Managers Kill Morale (and What to Do Instead)
This presentation, "The Morale Killers: 9 Ways Managers Unintentionally Demotivate Employees (and How to Fix It)," is a deep dive into the critical factors that can negatively impact employee morale and engagement. Based on extensive research and real-world experiences, this presentation reveals the nine most common mistakes managers make, often without even realizing it.
The presentation begins by highlighting the alarming statistic that 70% of employees report feeling disengaged at work, underscoring the urgency of addressing this issue. It then delves into each of the nine "morale killers," providing clear explanations and illustrative examples.
1. Ignoring Achievements: The presentation emphasizes the importance of recognizing and rewarding employees' efforts, tailored to their individual preferences.
2. Bad Hiring/Promotions & Broken Promises: It reveals the detrimental effects of poor hiring and promotion decisions, along with the erosion of trust that results from broken promises.
3. Treating Everyone Equally & Tolerating Poor Performance: This section stresses the need for fair treatment while acknowledging that employees have different needs. It also emphasizes the importance of addressing poor performance promptly.
4. Stifling Growth & Lack of Interest: The presentation highlights the importance of providing opportunities for learning and growth, as well as showing genuine care for employees' well-being.
5. Unclear Communication & Micromanaging: It exposes the frustration and resentment caused by vague expectations and excessive control, advocating for clear communication and employee empowerment.
The presentation then shifts its focus to the power of recognition and empowerment, highlighting how a culture of appreciation can fuel engagement and motivation. It provides actionable takeaways for managers, emphasizing the need to stop demotivating behaviors and start actively fostering a positive workplace culture.
The presentation concludes with a strong call to action, encouraging viewers to explore the accompanying blog post, "9 Proven Ways to Crush Employee Morale (and How to Avoid Them)," for a more in-depth analysis and practical solutions.
innovation in nursing practice, education and management.pptx
MSc nursing 2 nd year Government college of nursing Rajnandgaon Chhattisgarh
Small Business Management An Entrepreneur’s Guidebook 8th edition by Byrd tes...
Small Business Management An Entrepreneur’s Guidebook 8th edition by Byrd test bank.docx
https://qidiantiku.com/test-bank-for-small-business-management-an-entrepreneurs-guidebook-8th-edition-by-mary-jane-byrd.shtml
Designing and Sustaining Large-Scale Value-Centered Agile Ecosystems (powered...
Is Agile dead? It depends on what you mean by 'Agile'. If you mean that the organizations are not getting the promised benefits because they were focusing too much on the team-level agile "ways of working" instead of systemic global improvements -- then we are in agreement. It is a misunderstanding of Agility that led us down a dead-end. At Org Topologies, we see bright sparks -- the signs of the 'second wave of Agile' as we call it. The emphasis is shifting towards both in-team and inter-team collaboration. Away from false dichotomies. Both: team autonomy and shared broad product ownership are required to sustain true result-oriented organizational agility. Org Topologies is a package offering a visual language plus thinking tools required to communicate org development direction and can be used to help design and then sustain org change aiming at higher organizational archetypes.
A comprehensive-study-of-biparjoy-cyclone-disaster-management-in-gujarat-a-ca...
Disaster management;
Cyclone Disaster Management;;
Biparjoy Cyclone Case Study;
Meteorological Observations;
Best practices in Disaster Management;
Synchronization of Agencies;
GSDMA in Cyclone disaster Management;
History of Cyclone in Arabian ocean;
Intensity of Cyclone in Gujarat;
Cyclone preparedness;
Miscellaneous observations - Biparjoy cyclone;
Role of social Media in Disaster Management;
Unique features of Biparjoy cyclone;
Role of IMD in Biparjoy Prediction;
Lessons Learned; Disaster Preparedness; published paper;
Case study; for disaster management agencies; for guideline to manage cyclone disaster; cyclone management; cyclone risks; rescue and rehabilitation for cyclone; timely evacuation during cyclone; port closure; tourism closure etc.
m249-saw PMI To familiarize the soldier with the M249 Squad Automatic Weapon ...
M249 Saw marksman PMIThe Squad Automatic Weapon (SAW), or 5.56mm M249 is an individually portable, gas operated, magazine or disintegrating metallic link-belt fed, light machine gun with fixed headspace and quick change barrel feature. The M249 engages point targets out to 800 meters, firing the improved NATO standard 5.56mm cartridge. The SAW forms the basis of firepower for the fire team. The gunner has the option of using 30-round M16 magazines or linked ammunition from pre-loaded 200-round plastic magazines. The gunner's basic load is 600 rounds of linked ammunition. The SAW was developed through an initially Army-led research and development effort and eventually a Joint NDO program in the late 1970s/early 1980s to restore sustained and accurate automatic weapons fire to the fire team and squad. When actually fielded in the mid-1980s, the SAW was issued as a one-for-one replacement for the designated "automatic rifle" (M16A1) in the Fire Team. In this regard, the SAW filled the void created by the retirement of the Browning Automatic Rifle (BAR) during the 1950s because interim automatic weapons (e.g. M-14E2/M16A1) had failed as viable "base of fire" weapons.
Early in the SAW's fielding, the Army identified the need for a Product Improvement Program (PIP) to enhance the weapon. This effort resulted in a "PIP kit" which modifies the barrel, handguard, stock, pistol grip, buffer, and sights.
The M249 machine gun is an ideal complementary weapon system for the infantry squad platoon. It is light enough to be carried and operated by one man, and can be fired from the hip in an assault, even when loaded with a 200-round ammunition box. The barrel change facility ensures that it can continue to fire for long periods. The US Army has conducted strenuous trials on the M249 MG, showing that this weapon has a reliability factor that is well above that of most other small arms weapon systems. Today, the US Army and Marine Corps utilize the license-produced M249 SAW.
Recently uploaded (10)
From Concept to reality : Implementing Lean Managements DMAIC Methodology for...
From Concept to reality : Implementing Lean Managements DMAIC Methodology for...
Neal Elbaum Shares Top 5 Trends Shaping the Logistics Industry in 2024
Neal Elbaum Shares Top 5 Trends Shaping the Logistics Industry in 2024
Maximize Your Efficiency with This Comprehensive Project Management Platform ...
Maximize Your Efficiency with This Comprehensive Project Management Platform ...
9 Ways Managers Kill Morale (and What to Do Instead)
9 Ways Managers Kill Morale (and What to Do Instead)
innovation in nursing practice, education and management.pptx
innovation in nursing practice, education and management.pptx
Small Business Management An Entrepreneur’s Guidebook 8th edition by Byrd tes...
Small Business Management An Entrepreneur’s Guidebook 8th edition by Byrd tes...
Designing and Sustaining Large-Scale Value-Centered Agile Ecosystems (powered...
Designing and Sustaining Large-Scale Value-Centered Agile Ecosystems (powered...
A comprehensive-study-of-biparjoy-cyclone-disaster-management-in-gujarat-a-ca...
A comprehensive-study-of-biparjoy-cyclone-disaster-management-in-gujarat-a-ca...
m249-saw PMI To familiarize the soldier with the M249 Squad Automatic Weapon ...
m249-saw PMI To familiarize the soldier with the M249 Squad Automatic Weapon ...
The Planning Fallacy (Project Overruns)
- 1. The Planning Fallacy Why AllYour Projects Are Always Late – andWhat to Do About It Freakonomics Radio Overview http://pca.st/xU8w info@c2group.us
- 2. Optimism Bias Overconfidence The Planning Fallacy Coordination Neglect Procrastination Adaptiv e Makes us Feel Better We are Rewarded for it (as a society) Failure to recognize how hard it is to put stuff together when others are involved. Complexities of combined outputs Impulse Control Why Does it Happen? What Goes into It? The Basics (our biases)
- 3. Danny Conoman Nobel Price - Economics His Thoughts: The Planning Fallacy Strategic Misrepresentation Deliberate – Incentivized to misrepresent the business case. Project to look good on paper to increase changes of getting funded. Underestimating the cost and overestimating the benefits. Create an intentional high benefit/cost ratio. Why Does it Happen? What Goes into It? Something a Little More Complicated (politics) “If you realistically present to people what can be achieved in solving a problem, they will find it completely uninteresting.” “You can’t get anywhere without some degree of overpromising.”
- 4. What are the Results of The Planning Fallacy’s Output? Incorrectly Defined Project Expectations Under-Estimated Project Scope (Schedule/Budge t) People are hired to meet unrealistic budgets and schedules. When not met, people lose their jobs or opportunities for promotions. Companies contracted lose their credibility or contracts as the accountability is distributed. The Human ImpactsThe Intangible Impacts As budgets and schedules slip, people begin to blame one another. Initiatives can change from completing a project to protecting and maintaining a job/credibility (reputation). Cost and Schedule impacts are tremendous during this phase of a project. New teams typically come in and the schedule and scope are reset. Stress and reputations can take a toll on health and future career or business outcomes. Too often Projects are Started by people who never finished them.
- 5. Develop & Maintain a Database Algorithms Mitigating for: The Planning Fallacy Strategic Contract Incentives Price-in the Optimism Bias & use Reference Class Forecasting Analytics on how much the schedule and estimate are underestimated for this type of project. Incentivize for targets met and punish for targets not met. Now What? What are we Supposed to Do? If on average, similar projects go over budget by X%, add same X% to planned budget/schedule. Overcome Algorithm Aversion. Use Data instead of Human Judgement to make Forecasts. Identify & Compare to Similar References Too often Projects are Started by people who never finished them.