Choosing the right process improvement tool for your project.
Learn how an experienced engineer decides when simulation is the right tool for his projects,
and when it isn't.
With the evolution of process improvement software, it can be difficult to decide the right tool for the job. Using something too powerful and complex can be a lengthy and unnecessary process, but underestimating the depth of analysis required and choosing something too simplistic early in a project can result in repeated work later.
Queueing theory is the mathematical study of waiting lines, or queues. A queueing model is constructed so that queue lengths and waiting time can be predicted
Generalized linear models (GLMs) and gradient boosting machines (GBMs) are two of the most widely used supervised learning approaches in all of commercial data science. GLMs have been the go-to predictive and inferential modeling tool for decades, but important mathematical and computational advances have been made in training GLMs in recent years. This talk will contrast H2O’s implementation of penalized GLM techniques with ordinary least squares and give specific hints for building regularized and accurate GLMs for both predictive and inferential purposes. As more organizations begin experimenting with and embracing algorithms from the machine learning tradition, GBMs have come to prominence due to their predictive accuracy, the ability to train on real-world data, and resistance to overfitting training data. This talk will give some background on the GBM approach, some insight into the H2O implementation, and some tips for tuning and interpreting GBMs in H2O.
Patrick's Bio:
Patrick Hall is a senior data scientist and product engineer at H2O.ai. Patrick works with H2O.ai customers to derive substantive business value from machine learning technologies. His product work at H2O.ai focuses on two important aspects of applied machine learning, model interpretability and model deployment. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning.
Prior to joining H2O.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick is the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Queueing theory is the mathematical study of waiting lines, or queues. A queueing model is constructed so that queue lengths and waiting time can be predicted
Generalized linear models (GLMs) and gradient boosting machines (GBMs) are two of the most widely used supervised learning approaches in all of commercial data science. GLMs have been the go-to predictive and inferential modeling tool for decades, but important mathematical and computational advances have been made in training GLMs in recent years. This talk will contrast H2O’s implementation of penalized GLM techniques with ordinary least squares and give specific hints for building regularized and accurate GLMs for both predictive and inferential purposes. As more organizations begin experimenting with and embracing algorithms from the machine learning tradition, GBMs have come to prominence due to their predictive accuracy, the ability to train on real-world data, and resistance to overfitting training data. This talk will give some background on the GBM approach, some insight into the H2O implementation, and some tips for tuning and interpreting GBMs in H2O.
Patrick's Bio:
Patrick Hall is a senior data scientist and product engineer at H2O.ai. Patrick works with H2O.ai customers to derive substantive business value from machine learning technologies. His product work at H2O.ai focuses on two important aspects of applied machine learning, model interpretability and model deployment. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning.
Prior to joining H2O.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick is the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Introduction
What is ML, DL, AL?
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Advantages & Disadvantages
Definition: According to Arthur Samuel (1950) “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed”.
Machine learning is the study and design of algorithms which can learn by processing input (learning samples) data.
The most widely used definition of machine learning is that of Carnegie Mellon University Professor Tom Mitchell: “A computer program is said to learn from experience ‘E’, with respect to some class of tasks ‘T’ and performance measure ‘P’ if its performance at tasks in ‘T’ as measured by ‘P’ improves with experience ‘E’”.
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Using the program SAS Enterprise Miner and applications of classification algorithms, including decision trees, regression, neural networks, and random forests to create different types of classification models to predict the shopping intent of visitors to the website columbia.com.tr
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
In this tutorial, we will learn the the following topics -
+ The Curse of Dimensionality
+ Main Approaches for Dimensionality Reduction
+ PCA - Principal Component Analysis
+ Kernel PCA
+ LLE
+ Other Dimensionality Reduction Techniques
How does Netflix recommend movies? In this presentation we go over a very common technique for recommendations called matrix factorization to predict what rating a user will give a movie. It sounds like a complicated mathematical concept, but all it consists of is finding a set of intermediate features such as action, comedy, etc., and using them to help us determine the ratings.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on the Fundamentals of Deep Recommender Systems.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/_M5S0Njmc_c
SCM-APO-PP/DS-Production Planning and Detailed SchedulingAJAY
APO Production Planning and Detailed Scheduling is a set of functionalities around Inhouse Production Planning, External Procurement Planning, Resource Scheduling and Sequence Optimization.
PPDS is primarily divided in two areas - Production Planning and Detailed Scheduling. This component has the highest amount of integration between APO and the OLTP (R/3 or ERP) system for real-time transaction data transfer back and forth. This is made possible by a standard interface from SAP named Core Interface Function.
PP/DS is more used for finite optimization at a plant level by optimizing the resources, materials and manpower. It considers all the finite constraints in more detail and allows the planners to generate a day-to-day finite schedule, resource loading charts etc.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
When should I use simulation?
Choosing the right process improvement tool for your project.
Learn how an experienced engineer decides when simulation is the right tool for his projects,
and when it isn't.
With the evolution of process improvement software, it can be difficult to decide the right tool for the job. Using something too powerful and complex can be a lengthy and unnecessary process, but underestimating the depth of analysis required and choosing something too simplistic early in a project can result in repeated work later.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Introduction
What is ML, DL, AL?
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Advantages & Disadvantages
Definition: According to Arthur Samuel (1950) “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed”.
Machine learning is the study and design of algorithms which can learn by processing input (learning samples) data.
The most widely used definition of machine learning is that of Carnegie Mellon University Professor Tom Mitchell: “A computer program is said to learn from experience ‘E’, with respect to some class of tasks ‘T’ and performance measure ‘P’ if its performance at tasks in ‘T’ as measured by ‘P’ improves with experience ‘E’”.
Decision Tree
Definition
Why Decision Tree?
Basic Terminology
Challenges
Random Forest
Definition
Why Random Forest
How does it work?
Using the program SAS Enterprise Miner and applications of classification algorithms, including decision trees, regression, neural networks, and random forests to create different types of classification models to predict the shopping intent of visitors to the website columbia.com.tr
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
In this tutorial, we will learn the the following topics -
+ The Curse of Dimensionality
+ Main Approaches for Dimensionality Reduction
+ PCA - Principal Component Analysis
+ Kernel PCA
+ LLE
+ Other Dimensionality Reduction Techniques
How does Netflix recommend movies? In this presentation we go over a very common technique for recommendations called matrix factorization to predict what rating a user will give a movie. It sounds like a complicated mathematical concept, but all it consists of is finding a set of intermediate features such as action, comedy, etc., and using them to help us determine the ratings.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on the Fundamentals of Deep Recommender Systems.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/_M5S0Njmc_c
SCM-APO-PP/DS-Production Planning and Detailed SchedulingAJAY
APO Production Planning and Detailed Scheduling is a set of functionalities around Inhouse Production Planning, External Procurement Planning, Resource Scheduling and Sequence Optimization.
PPDS is primarily divided in two areas - Production Planning and Detailed Scheduling. This component has the highest amount of integration between APO and the OLTP (R/3 or ERP) system for real-time transaction data transfer back and forth. This is made possible by a standard interface from SAP named Core Interface Function.
PP/DS is more used for finite optimization at a plant level by optimizing the resources, materials and manpower. It considers all the finite constraints in more detail and allows the planners to generate a day-to-day finite schedule, resource loading charts etc.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
When should I use simulation?
Choosing the right process improvement tool for your project.
Learn how an experienced engineer decides when simulation is the right tool for his projects,
and when it isn't.
With the evolution of process improvement software, it can be difficult to decide the right tool for the job. Using something too powerful and complex can be a lengthy and unnecessary process, but underestimating the depth of analysis required and choosing something too simplistic early in a project can result in repeated work later.
Predicting Azure Churn with Deep Learning and Explaining Predictions with LIMEFeng Zhu
Although deep learning has proved to be very powerful, few results are reported on its application to business-focused problems. Feng Zhu and Val Fontama explore how Microsoft built a deep learning-based churn predictive model and demonstrate how to explain the predictions using LIME—a novel algorithm published in KDD 2016—to make the black box models more transparent and accessible.
“Machine Learning in Production + Case Studies” by Dmitrijs Lvovs from Epista...DevClub_lv
Epistatica is a data science spin-off from VIA SMS R&D SERVICES, searching its niche in European markets.
Dmitrijs is head of credit risk with VIA SMS R&D SERVICES, a fintech company, and member of the board at Epistatica, holds a PhD from RAS Institute for Information Transmission Problems and analyzed data for over 12 years.
This guide will help you get started with Innoslate, the full lifecycle systems engineering tool. It will take you through developing your requirements, creating model, simulating your models, and keeping traceability through the entire project.
Tech-Talk at Bay Area Spark Meetup
Apache Spark(tm) has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment. How do I embed what I have learned into customer facing data applications. Like all things in engineering, it depends.
In this meetup, we will discuss best practices from Databricks on how our customers productionize machine learning models and do a deep dive with actual customer case studies and live demos of a few example architectures and code in Python and Scala. We will also briefly touch on what is coming in Apache Spark 2.X with model serialization and scoring options.
Testing the impact of policy decisions using simulationSIMUL8 Corporation
With many factors and risks to consider, identifying the impact of policy change can be a challenge.
Learn why simulation is used to make evidence-based policy decisions, improve program outcomes and deliver services more efficiently to the public.
Using real-life examples from healthcare to smart cities, Tom Stephenson shows the benefits of using simulation for evaluating policy changes.
In this webinar session, Dr Tracey England, Mathematical Modeller and Research Fellow at ABCi, shared three case studies of how simulation software has supported healthcare improvements at Aneurin Bevan University Health Board.
Learn how Memorial Health System have utilized simulation to answer facility planning questions – saving unnecessary costs, avoiding delays in construction, and improving patient safety and satisfaction.
Graham Prellwitz and Lance Millburg discuss the benefits of using SIMUL8 for validating healthcare facilities ahead of finalizing building plans and construction.
In this on-demand webinar session, you'll learn 4 recommendations for successful simulation projects and see how these have been applied across a range of planning projects.
Laboratories must be able to deliver quality results, at the lowest cost, within the shortest time frame.
In this webinar learn how simulation can be used to improve laboratory flow.
Watch the webinar recording: https://www.simul8healthcare.com/case-studies/improving-laboratory-flow-with-simulation
Tom Stephenson, Senior Healthcare Consultant at SIMUL8 Corporation, will discuss his experience of designing laboratory simulations and share best practice techniques.
Through real examples, you'll learn how SIMUL8 has been used to test laboratory improvements, including:
- Assessing the impact and ROI of new machinery
- Selecting optimal layouts
- Understanding how the current system will cope with demand changes
- Testing total lab automation
Merging Cath Labs: Using simulation to design a solution and understand the i...SIMUL8 Corporation
Learn how Boston Scientific used simulation to test the impact of merging Cath Labs from two different sites in a Canadian hospital.
In this live webinar session, Boston Scientific's Yixin Wang will discuss how simulation formed a key part of the change process, engaged clinicians and administrators in the redesign, and ensured consensus on the best solution.
You'll learn how the teams worked together to understand the complexities of future demand from the local population, procedure types and timings, staffing, scheduling, as well as determining the optimum design for the combined unit.
In highly congested hospitals it may be common for patients to overstay at Intensive Care Units (ICU) due to blockages and imbalances in capacity.
Watch the webinar in full at: https://www.simul8healthcare.com/case-studies/releasing-icu-bed-capacity
Reece Holbrook, Technical Fellow at <b>Medtronic</b>, discusses how simulation is being used to turn available data from clinical trials into actionable insights for hospital electrophysiology lab managers. Watch the webinar in full: https://www.simul8healthcare.com/case-studies/medtronic-bringing-data-to-life
Simulation modeling of pre/post bed needs for an Interventional PlatformSIMUL8 Corporation
Architect Frank Zilm discusses how simulation software was used to explore the implementation of an interventional platform concept, integrating surgery, cardiac procedures, interventional radiology and endoscopy services, at Saint Louis University Hospital.
Redefining the care team to meet Population Health objectivesSIMUL8 Corporation
Dr. Phil Smeltzer from The Medical University of South Carolina demonstrates an interactive simulation that helps physicians adopt a population health mindset.
In the third webinar of the series, Max builds on the example simulation in Sessions 1 & 2 and shows how you can control the simulation using spreadsheets, and how to link Excel to the simulation. Find out more at: http://www.SIMUL8.com/the-complete-guide-to-simul8-success
The second webinar in the series, "The Complete Guide to SIMUL8 Success." Max Guild talks us through how to get results fast using SIMUL8. Full webinar recording: http://simul8.com/the-complete-guide-to-SIMUL8-success
Improving Eye Care Outpatient Services with SimulationSIMUL8 Corporation
David Southern and Dr. Eren Demir of Pathway Communications demonstrate how simulation used to forecast demand and improve the clinical management of retinal diseases.
The first webinar in the series, "The Complete Guide to SIMUL8 Success." Max Guild talks us through how to get results fast using SIMUL8. Full webinar recording: http://simul8.com/the-complete-guide-to-SIMUL8-success-webinars
Cheryl Davenport, Director of Health and Care Integration at Leicestershire County Council, talks about how simulation is helping to evaluate how emergency hospital admissions can be reduced.
Jacquie White, Deputy Director of NHS England Long Term Conditions, Older People & End of Life Care and Claire Cordeaux SIMUL8 Executive Director for Health & Social Care were invited by Centers for Medicare & Medicaid Services to discuss how NHS England work in chronic disease.
Lance Millburg, Senior Lean Six Sigma Project Manager talks us through how Memorial Health System built their simulation team from the ground up into a nationally recognized program in 2 years.
Jacquie White, Deputy Director of NHS England Long Term Conditions, Older People & End of Life Care and Dr Eileen Pepler, Academic, Researcher and Consultant in the Canadian Healthcare will discuss how NHS England work in chronic disease is being translated into a Canadian context.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
2. Introductions
Brittany Hagedorn, MBA,
CSSBB
- SIMUL8’s Healthcare Lead
for North America
- Experienced Six
Sigma Blackbelt and
Healthcare Consultant
- Here to answer your questions at the end
3. Introductions
Brian Harrington, CSSBB
- 20 years in simulation at
Ford Motor Company
- Experienced Six
Sigma Blackbelt and
Simul8 Manufacturing Consultant
- Director of MTN-SIM, a
simulation specialist consulting firm
- Our presenter for today
5. Manufacturing Dilemma
• Any product development process
involves extensive prototyping;
• Yet, costly manufacturing production
systems are typically not prototyped
7. System Design
•
•
•
•
•
•
•
Plant Layout
Effects of introducing new equipment
Location and sizing of inventory buffers
Location of inspection stations
Optimal number of carriers, pallets
Resource planning
Protective capacity planning
Biggest Bang for the Dollar!
Contains Operational Procedures &
Performance Metrics.
8. Operational Procedures
• Production Scheduling - Choice of scheduling
and dispatching rules
• Control strategies for material handling
equipment
• Shift patterns and planned downtime
• Impact of product variety and mix
• Inventory Analysis
• Preventative maintenance on equipment
availability
Continuous Improvement
9. Performance Evaluation
• Throughput Analysis (capacity of the
system, identification of bottlenecks); Jobs
per Hour
• Time-in-System Analysis
• Assessment of Work-in-process (WIP)
levels
• Setting performance measure standards;
OEE
If you can measure it, you can manage it!
11. Why Simulation?
•
•
•
•
•
Competition drives the following:
Leaner production environment
Shorter product development cycles
Narrower profit margins
Flexible Manufacturing (1 Facility, 1
Process, Multiple Models)
12. Types of Simulation
• Mathematical Modeling
– e.g. Queuing Theory
• Monte Carlo Simulation
– e.g. Excel based models
• Discrete Event Simulation
– e.g. Using simulation software
14. Question Time:
Which of the following Simulation techniques
do you use:
1. Math, Queuing Theory
2. Excel Based, Monte Carlo
3. Discrete Event Simulation
4. None
16. A Queuing System
Input Source
Service Process
Queue
Arrival
Process
Service
Mechanism
Jockeying
Queue
Balking
Reneging
Served Customers
Queue Structure
17. Queuing Concepts
Relationships for M/M/C
1
Po =
C-1
Σ
n=0
(λ/µ)
n!
n
+ (λ/µ)
c!
c
cµ
(
)
cµ - λ
c
Lq =
(λ/µ) (λ µ) Po
(c – 1)! (cµ – λ) 2
λ = mean arrival rate
µ= mean service rate
C = number of parallel servers
ρ = utilization
These are messy to calculate by
hand, but are very easy with
appropriate software or a table.
18. Queuing Concepts
A Comparison of Single Server Models
2
M/G/1 L =
q
M/D/1 L q =
M/M/1 L =
q
λ σ
2
2
+ (λ/µ)
2(1 - λ/µ)
(λ/µ)
2
2(1 - λ/µ)
2
(λ/µ)
(1 - λ/µ)
Note that
M/D/1 is
½ of M/M/1
19. Benefits & Common Uses
Proven mathematical models of queuing behavior;
the underlying framework of more comprehensive
models.
• Computer Networks – data buffering before
loss of data transmission
• Healthcare – optimizing staffing levels
according to patient arrivals
• Traffic & Parking lots – Traffic lights, toll booths
• Service Industry – Number of servers, checkouts, lanes, ATM machines, etc.
20. Limitations on Queuing Models
• What if:
– we don’t have one of these basic models?
– we have a complex system that has segments
of these basic models and has other
segments that do not conform to these basic
models?
• Then – simulate!
21. Excel Based Simulations
• Uses Data Table functions
• Each Row might be one iteration of a simulation
• Each Col is a random variable generated in the
simulation
• RAND(), VLOOKUP(), COUNTIF(), NORMINV()
• Calculation & Iteration
• >>> Using VBA to bring in Probability functions
22. Monte Carlo Simulation
• Named after the gaming tables of Monte Carlo
• Also referred to as a Static Simulation Model in
that it is a representation of a system at a
particular point in time
• In contrast, a Dynamic Simulation is a
representation of a system as it evolves over
time
• Might be accomplished using Excel and the
Random()
23. Monte Carlo Simulation
A Simple Example
Day
RN
Demand Units
Sold
Units
Unsold
Units
Short
Sale
s
Rev
Return
s
Rev
Unit
Cost
Good
Will
Profit
$
1
10
16
16
2
0
4.80
0.16
2.70
0.00
2.26
2
22
16
16
2
0
4.80
0.16
2.70
0.00
2.26
3
24
17
17
1
0
5.10
0.08
2.70
0.00
2.48
4
42
17
17
1
0
5.10
0.08
2.70
0.00
2.48
5
37
17
17
1
0
5.10
0.08
2.70
0.00
2.48
6
77
18
18
0
0
5.40
0.00
2.70
0.00
2.70
7
99
20
18
0
2
5.40
0.00
2.70
0.14
2.56
8
96
20
18
0
2
5.40
0.00
2.70
0.14
2.56
9
89
19
18
0
1
5.40
0.00
2.70
0.07
2.63
10
85
19
18
0
1
5.40
0.00
2.70
0.07
2.63
Avg
2.50
Where do these numbers come from?
24. Benefits & Common Uses
Proven technique that captures random
behavior (at a specific point in time); can go
further than mathematical solutions.
• Business risk assessment
– Demand & Profit
• Sizing of a market place
– Consumption rate
• Project schedules (best case, worst case)
25. Limitations & Disadvantages
• Stochastic, but static! Usually the time
evolution of a manufacturing system is
significant!
• Excel based models, soon start to use
VBA, and become very complicated
• Might require 1000’s of iterations; Data
Tables become slow
• Difficult to communicate results to
management.
27. Benefits of using DES Simulation
• Mathematical & Excel based models only go so
far
• Less difficult than mathematical methods
• Adds lot of “realism” to the model. Easy to
communicate to end users and decision makers
• Time compression
• Easy to “scale” the system and study the effects
• User involvement results in a sense of
“ownership” and facilitates implementation
Sim Tree
28. Manufacturing Models
• The element that the system evolves over time
is important
• Contain several complicated queuing systems
• Internal process steps are significant to achieve
the desired result
• Conditional build signals (Batch, In-Sequence)
• Several sources of stochastic
behavior
• Contain several shared
resources and conditional
decisions
31. DES Building Blocks
The 8 Core Building Blocks: Start Point, Queue, Activity, Conveyor,
Resource, and End Point. Then the Logical aspect Labels & Conditional
Statements.
32. 8 is all you Need
1. Work Item Types: Can represent parts,
carriers, signals, phone calls, just about
anything that requires a “Label Profile”.
2. Activities: Work Centers, machines, tasks,
process steps, anything that requires a “Cycle
Time”.
3. Storage Areas: Buffers, de-couplers, banks,
magazines, anything that requires a finite space
to occupy over time.
4. Conveyors: Moving parts from pt A to pt B;
Number of parts & Speed of conveyor.
33. …8 is all you Need…
5. Resources: Manpower, crews, forklifts, tugs;
anything that require a certain resource to be
present.
6. End Pt: Keep track of statistics and free
memory!
7. Labels: The attributes of a Work Item.
8. Visual Logic: The ability to create conditional
statements; variables, loops, commands &
functions.
34. Question Time…
How do you use 6-Sigma techniques within
your current role?
1. I don’t use 6-Sigma
2. I use 6-Sigma on specific types of
projects
3. I use 6-Sigma on all my projects
4. I use an integrated toolset which includes
6-Sigma
36. Less is More using 6-Sigma
DES Steps:
• Objective, Assumptions, Data Collection, Build Model,
Verify, Validate, Experimentation, Results
DMAIC or DMADV steps:
• Define, Measure, Analyze, Improve, Control
• Define, Measure, Analyze, Design, Verify
Very similar steps!
37. Y=f(x’s) Transfer Function
Six Sigma focuses on Key Input Factors (x’s) to deliver
your Response.
All of the x’s can be measured & controlled to increase
accuracy & precision of hitting your Target (Y).
Trivial Many (N’s)
Inputs (N’s & X’s)
System/Process
Vital Few (X’s)
Output (Y)
38. The P-Diagram
The P-Diagram not only helps engineers to define the Key Parameters for
a robust design, but also acts as an excellent communication tool for
team reviews.
39. Leverage Statistical Distributions!
• Curve fit your data! Instead of using lengthy
spreadsheets.
• Black-box; entire segments of the model can be
collapsed using distributions.
• If using empirical datasets, drop them into a
“Probability Profile Distribution”
40. Graph your Data!
One of the most basic steps in 6-Sigma; Exploit your data!
Stat-Fit for
SIMUL8
41. Use Known Distributions
The data collection phase of modeling can be the
lengthiest and most time consuming.
Downtime (MTBF & MTTR); such as Exponential &
Erlang respectively.
Cycle times often use a Fixed distribution; that is the
“Design Cycle Time”.
42. Steady State
A common data collection error is to capture all
data points, and attempt to force them into one
distribution.
– Filter out the outliers; usually catastrophic points
are outside the scope of the steady state system.
42
43. Concluding Thoughts
• Queuing Theory & Monte Carlo Simulations can meet
your specific objectives in certain applications. Yet, can
become overwhelming when pulling them beyond their
intent.
• Most Manufacturing, Healthcare objectives go much
further beyond these capabilities. Where the dynamic
aspects of time are critical!
• Discrete Event Simulation is a user friendly tool that is
built on the foundations of queuing theory & statistical
sampling.