This document outlines a presentation on achieving success with telematics programs. It discusses developing a telematics strategy by starting with a clear business problem and defining metrics. It recommends running a pilot program to test solutions and define requirements. It also emphasizes creating an operational playbook to continuously improve processes based on telematics data. A case study example shows how focusing on dump cycle times through equipment changes and process improvements led to significant cost savings.
A presentation to an IQPC conference in April of 2009. Demonstrates a process to help leaders move project forward by clearly defining a business justification for their projects.
Part of OFX Academy Course: Improving Line Performance
http://academy.optimumfx.com/course/improving-line-performance/
Improving Packaging Line Performance – Using the correct Data and Drill Down Analysis
A presentation to an IQPC conference in April of 2009. Demonstrates a process to help leaders move project forward by clearly defining a business justification for their projects.
Part of OFX Academy Course: Improving Line Performance
http://academy.optimumfx.com/course/improving-line-performance/
Improving Packaging Line Performance – Using the correct Data and Drill Down Analysis
Doing Analytics Right - Designing and Automating AnalyticsTasktop
There is no “one-sized fits all” of development analytics. It is not as simple as “here are the measures you need, go implement them.” The world of software delivery is too complex, and software organizations differ too significantly, to make it that simple. As discussed in the first webinar, the analytics you need depend on your unique business goals and environment.
That said, the design of your analytics solution will still require:
* The dashboards,
* the required data, and
* an appropriate choice of analytical techniques and statistics to apply to the data.
This webinar will describe a straightforward method for finding your analytic solution. In particular, we will explain how to adapt the Goal, Question, Metric (GQM) method to development processes. In addition, we will explain how to avoid “the light is brighter here” analytics anti-pattern: the idea that organizations tend to design metrics programs around the data they can easily get, rather than figuring out how to get the data they really need.
Aliur Rahman, Experian, - Setting up an Optimisation FunctionMezzo Labs
Setting up an Optimisation Function by Aliur Rahman with Experian, presented at Mezzo Lab's 'Getting to Grips with Data-Driven Optimisation' event in November 2016.
Full function trade promotion management in the cloud built on the Salesforce.com platform. Trade Funds Management, Trade Promotion Optimization, Settlements and reporte/dashboards.
Webinar: Proactive Strategies for Finding and Fixing Performance IssuesJennifer Finney
Proactive Strategies for Finding and Fixing Performance Issues
Video
Most organizations have internal processes to address performance problems, but they are typically reactive—occurring only after online performance has already been degraded. Relying on a defensive strategy to address performance problems can negatively impact end-users and revenue before you’re able to identify and solve the issue.
This web seminar will explain how to customize performance practices to build proactive internal testing processes. These methods will help you detect and solve performance problems before they make it into production and cost you money and customers.
You will learn how to:
Customize your test processes using the latest performance tools
Get ahead of issues with real-user and synthetic monitoring
Prevent negative user experiences that impact the bottom line
Solve performance issues proactively, before they become problems for your users
Reliability programs should support strategic corporate objectives
Relevant Key Performance Indicators
Measure program effectiveness
Provide believable data
KPI management and other Reporting options in Maximo
Resource Management Maturity - Does Your Resource Management Practice Work Fo...Unanet
How mature is YOUR resource management practice?
Only 25% of respondents in our most recent GAUGE survey said they have reached a “Very Mature” level of resource management practice.
This means that the vast majority of firms just like yours have a lot of improvements that can be made.
Download the slides to take a look at how Nalas transformed their resource management practice.This is a great presentation, no matter if you think you are managing your resources really well, or if you could make some improvements.
You will learn:
*Where you fall on the resource management maturity scale (level 1-5)
*What a practical deployment of an enterprise resource management practice looks like from Nalas
*How you can move up the maturity scale
To learn more, visit www.unanet.com
APM Center of Excellence Drives Improved Business Results at Itau UnibancoCA Technologies
Improving the quality of applications and the overall customer experience is a key focus for Itau. This presentation will discuss the APM Center of Excellence
process and how this approach lead to better response times using fewer resources and improved business results while delighting both clients and applications support teams.
For more information on DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Doing Analytics Right - Designing and Automating AnalyticsTasktop
There is no “one-sized fits all” of development analytics. It is not as simple as “here are the measures you need, go implement them.” The world of software delivery is too complex, and software organizations differ too significantly, to make it that simple. As discussed in the first webinar, the analytics you need depend on your unique business goals and environment.
That said, the design of your analytics solution will still require:
* The dashboards,
* the required data, and
* an appropriate choice of analytical techniques and statistics to apply to the data.
This webinar will describe a straightforward method for finding your analytic solution. In particular, we will explain how to adapt the Goal, Question, Metric (GQM) method to development processes. In addition, we will explain how to avoid “the light is brighter here” analytics anti-pattern: the idea that organizations tend to design metrics programs around the data they can easily get, rather than figuring out how to get the data they really need.
Aliur Rahman, Experian, - Setting up an Optimisation FunctionMezzo Labs
Setting up an Optimisation Function by Aliur Rahman with Experian, presented at Mezzo Lab's 'Getting to Grips with Data-Driven Optimisation' event in November 2016.
Full function trade promotion management in the cloud built on the Salesforce.com platform. Trade Funds Management, Trade Promotion Optimization, Settlements and reporte/dashboards.
Webinar: Proactive Strategies for Finding and Fixing Performance IssuesJennifer Finney
Proactive Strategies for Finding and Fixing Performance Issues
Video
Most organizations have internal processes to address performance problems, but they are typically reactive—occurring only after online performance has already been degraded. Relying on a defensive strategy to address performance problems can negatively impact end-users and revenue before you’re able to identify and solve the issue.
This web seminar will explain how to customize performance practices to build proactive internal testing processes. These methods will help you detect and solve performance problems before they make it into production and cost you money and customers.
You will learn how to:
Customize your test processes using the latest performance tools
Get ahead of issues with real-user and synthetic monitoring
Prevent negative user experiences that impact the bottom line
Solve performance issues proactively, before they become problems for your users
Reliability programs should support strategic corporate objectives
Relevant Key Performance Indicators
Measure program effectiveness
Provide believable data
KPI management and other Reporting options in Maximo
Resource Management Maturity - Does Your Resource Management Practice Work Fo...Unanet
How mature is YOUR resource management practice?
Only 25% of respondents in our most recent GAUGE survey said they have reached a “Very Mature” level of resource management practice.
This means that the vast majority of firms just like yours have a lot of improvements that can be made.
Download the slides to take a look at how Nalas transformed their resource management practice.This is a great presentation, no matter if you think you are managing your resources really well, or if you could make some improvements.
You will learn:
*Where you fall on the resource management maturity scale (level 1-5)
*What a practical deployment of an enterprise resource management practice looks like from Nalas
*How you can move up the maturity scale
To learn more, visit www.unanet.com
APM Center of Excellence Drives Improved Business Results at Itau UnibancoCA Technologies
Improving the quality of applications and the overall customer experience is a key focus for Itau. This presentation will discuss the APM Center of Excellence
process and how this approach lead to better response times using fewer resources and improved business results while delighting both clients and applications support teams.
For more information on DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Introduction
• Mark Monroe
• Summit Materials Performance Team
• Lead Telematics Strategist
• 13 Years Analysis, Project Management, Reporting,
and Implementation Experience
3. Session Objectives
• Define Your Telematics Strategy
• How to Run a Pilot
• Create a Blueprint for Future Success
• Real Life Case Study
6. Define and Execute Telematics Strategy
• Start with a business problem or hypothesis
– “I want to reduce idle time”
– “I want to automate meter hour readings”
– “I want to improve efficiency”
– “I want to improve safety by measuring travel speed and location”
– “We need to comply with a regulation (eLog, DVIR)
Pro Tip: Know your requirements before speaking with suppliers
7. Define What to Measure?
• What must be measured to solve business problem?
Location & Hour
Meters
• Location
• Speed
• Meter Hours
• Idle Time
Health Data
• Health alerts
• Fuel Burn
Productivity
• Cycle Time
• Production Tons
• Tons/Hour, Tons/Load
• Idle Time
• Fuel Consumption
Compliance/Safety
• eLog
• DVIR
• Speeding
• Abnormal events
(braking, speeding,
sharp turn, etc.)
8. “When performance is measured, performance
improves. When performance is measured and reported
back, the rate of improvement accelerates.”
–Thomas S. Monson
9. How are you going to measure?
• What Technology is Required?
– OEM vs. Aftermarket
– ECM vs. Battery connection vs. No connection
• What is your fleet makeup?
• What frequency do you need to receive the data?
– Real time, near real time, events only, daily
• Reporting: how will you act on the data?
Let supplier help you answer these questions
10. Example: Idle Time
• Business Problem: reduce idle time across fleet of Ready Mix Trucks
• What to measure: Idle Time (primary KPI)
– Definition: Amount of time an engine powered on and unit is non-productive
• How will it be measured?
– Utilize a basic telematics device connected to a battery
– Location and engine status
– Idle when engine is on and don’t move over 1,000 feet in 2 minutes
– Report on a weekly and monthly basis
11. Global Strategy
Objective Technology
Required
Opportunity Ease of
Implementation
Cost
Reduce Idle Time Simple High High Low to Medium
Health Alerts OEM/Advanced Medium Low Medium to High
#3
#4
#5
P
r
i
o
r
i
t
y
Global strategy will help identify easy wins and opportunities to use
one system to achieve multiple objectives
13. How to run a successful pilot
• Have a telematics strategy
• Identify 2-3 suppliers who can help you accomplish
strategy
• Engage most promising vendor for a pilot
• Define pilot success criteria
• Define timeline
• Define KPIs
• Define reporting requirements
14. Sample Pilot Requirements
• Timeline:
– Installation
– Baseline Data Collection (min 2 weeks)
– Action Phase Data Collection (min 2 weeks)
• KPI Definitions:
KPI Name
Measurement
Intent
KPI Definition /
Formula
Frequency
of Update
Units of
Measure Notes / Assumptions
Target
Value
KPI 1 Daily, Weekly
KPI 2 Monthly
KPI 3
Weekly,
Monthly
15. Sample Pilot Requirements (cont.)
• Reporting Requirements by role
Org. Role GM Equipment Executive Site Supervisors Corporate
KPI 1
Weekly,
Monthly Weekly Weekly Daily
Daily, Weekly,
Monthly
KPI 2
Weekly,
Monthly Weekly Weekly Daily
Daily, Weekly,
Monthly
KPI 3
Weekly,
Monthly Weekly Weekly Daily
Daily, Weekly,
Monthly
18. Now that you are capturing the data,
what are you going to do about it?
–Telematics handbook or playbook
–Define the process
–Roles and responsibilities (who, what, when, and
resulting actions)
–How to communicate feedback to achieve success
19. Sample Operational Playbook
• Weekly meetings with Pit Crew & Jaw Operator –
Focus on one new KPI per week
• Week 1 – Truck Load Counts
– Set goal for safe dump counts (based on telematics data)
– Discuss ways to effect dump counts
• Shot performance
• Bottlenecks
• Haul Road condition
– Best practice sharing from operator with best load counts
• Week 2 – Load Times
– Set goal for safe load time (based on data)
– Discuss ways to safely effect load times
• Proper spotting/positioning by loader operator
• Ways to avoid bunching
• Good shot/bad shot
• Discuss best practices
21. Case Study - Background
• Telematics program began during November of 2017
• Original Fleet was:
– One 650 Komatsu Excavator
– Three John Deere 460 Articulated Trucks
• Initial Cycle Time KPI’s
– Truck Exchange time – 02:15 (okay)
– Truck Load Time – 01:50 (good – less than 0:30/pass)
– Primary Wait Time – 6:50 (terrible)
– Idle Time – 28% (excellent)
22. Case Study Opportunity 1
• Initially let the operators know that the system was in place – did
not share any data.
• Management focused on one KPI - dump and maneuver – and
looked to improve it.
– Enlarged dump area for more maneuverability
– Separated the plant feed loader from the trucks
– Improved communication between the loader and the trucks in the
dump area
– Trained the loader operator to perform better dump area maintenance
Result: Reduced cycle time by 2:00
23. Case Study Opportunity 2
• As dump area improved, noticed exchange time
extending
• Downsized excavator from 650 to 490 Komatsu
– Smaller size did not hurt total cycle time – added one pass
but reduced exchange time
– Management focus shifted to haul road maintenance, again
noticed exchange time extending
• Reduced number of haul trucks to two
~$190/hr cost reduction $300-400k annual savings
24. Conclusion
• To find success with a telematics program, one must begin with a
hypothesis, well defined metrics, and a defined methodology to
achieve success
• Understand your technology needs
• Start simple and work your way towards complex
• Have a playbook
• Installing a telematics system is the easy part. What do you do with
the data?
25.
26. Questions
• How many pilots did it take for your company to find
success?
• What are some challenges around the pilot model
you’ve described?
• What success have you seen in your organization?
