The document provides deliverables for the Define and Measure phases of a Six Sigma project. In the Define phase, it defines project scope and goals, including a project charter, SIPOC diagram, process map, and stakeholder communication plan. It also establishes milestones and a timeline. In the Measure phase, it outlines plans to collect data on the key process (Y) of average handling time, including operational definitions, measurement methods, and a measurement system analysis to ensure reliability. It will analyze the data to identify potential factors (X's) that influence the process and check for their impact on the key process.
A telecom company was able to see a significant reduction in the handle time after incorporating MK's recommendation based on extensive data analytics leading to huge cost reduction for their centers
Project Storyboard: Reducing Cycle Time for Bid Tab Creationby 33%GoLeanSixSigma.com
Think it's impossible to make a significant change in government? Well, it's not easy, but GoLeanSixSigmna.com Black Belt Pamela Kuehling made a real difference from the inside. She overturned countless "honest wrong beliefs" with facts and data by modeling the Lean Six Sigma approach in her work and won over the doubters.
Initially the overall procurement process was the focus, but when that proved to be too big, she scoped it down to a more manageable project that can now be the basis for an expanded improvement. Her improvement was meaningful, but a bigger victory was in bringing critical and logical thinking into an environment often impacted by less scientific influences!
A telecom company was able to see a significant reduction in the handle time after incorporating MK's recommendation based on extensive data analytics leading to huge cost reduction for their centers
Project Storyboard: Reducing Cycle Time for Bid Tab Creationby 33%GoLeanSixSigma.com
Think it's impossible to make a significant change in government? Well, it's not easy, but GoLeanSixSigmna.com Black Belt Pamela Kuehling made a real difference from the inside. She overturned countless "honest wrong beliefs" with facts and data by modeling the Lean Six Sigma approach in her work and won over the doubters.
Initially the overall procurement process was the focus, but when that proved to be too big, she scoped it down to a more manageable project that can now be the basis for an expanded improvement. Her improvement was meaningful, but a bigger victory was in bringing critical and logical thinking into an environment often impacted by less scientific influences!
This Template is created for helping the quality or continuous improvement professionals to generate a step by step problem solving report, which include the guidance on each steps in a 8D process, also include the templates of popular quality tools such as 5-Why and Fishbone Diagram.
想学习六西格玛?可以看看ucourse.org的网上课程。
http://ucourse.org/ssgb
Project Closure Process Steps PowerPoint Presentation Slides SlideTeam
If you are looking for professional templates and slides to prepare a professional PPT on project closure process steps? Well if yes, then download our ready to use project closure process steps PowerPoint presentation slides. Showcase the performance of your company project to the customer using this project closure process steps PPT presentation. Using these project management presentation templates, you will be able to confirm if the team members have met all sponsor and consumer needs. This closing a project PowerPoint presentation includes essential topics such as project brief, project description, project timeline, project progress summary, project status report, and project health card. It also covers a slide on project dashboard, project closure report, work breakdown structure, and project conclusion report-performance analysis, deadline, budget/costs. With the help of the project description presentation PPT, you will be able to represent throughout the progress of the project. Use of stunning graphics and visuals will help you describe the different project stages performance. Download this project closure checklist PPT presentation. Ensure a clean baton change with our Project Closure Process Steps PowerPoint Presentation Slides. They allow flawless accomplishment.
Global 8D Problem Solving Process Training ModuleFrank-G. Adler
The 8D Problem Solving Process Training Module v8.0 includes:
1. MS PowerPoint Presentation including 206 slides covering the Global 8D Problem Solving Process & Tools, a Case Study, and 7 Workshop Exercises.
2. MS Word Problem Solving Process Case Study
3. MS Excel 8D Problem Solving Process Worksheet Template
4. MS Excel Process Variables Map Template, Process FMEA Template, and Process Control Plan Template
5. MS Word 8D Problem Solving Process Report Template
Service revamp lean six sigma black belt projectSumit K Jha
Project- Service revamp
Type- Lean Six Sigma Black Belt Project
Outline- To improve the entire process of getting purchase orders, purchasing, manufacturing, warehousing and installation
Tools/Framework- Six Sigma concepts such as SIPOC, fish bone analysis, control charts and hypothesis testing; statistical tools, Microsoft Dynamic AX
Role- Project manager
Outcome- The successful completion of the project yielded in cost savings of INR 1.61 crores
Ensure that the best possible level of service quality and availability is maintained with this Incident Management Powerpoint Presentation Slides. Showcase the activities within the incident management procedure by incorporating this incident detection and recording PPT visuals. Determine how quickly a resolution of the incident is required by using this professionally designed investigation and analysis PPT graphic. Present the primary ITIL management roles with the help of our incident closure PowerPoint infographics. Also, determine the relative impact of an issue on business processes by taking the aid of the resolution and record the PPT template. Take the advantage of this problem management PowerPoint layout to determine the level of risk by considering the category of probability against consequence severity. Showcase the procedures to deal with the potential problems using the incident monitoring PPT templates. Download problem reporting and communication PPT presentation to restore a normal service operation as quickly as possible. https://bit.ly/3jH7J6u
Basic 8D Problem Solving Tools & Methods - Part 1Tony Alvarez
I've taught many workshops on basic problem solving over the years at various companies. This 3 part presentation collects tools and methods that I've found useful and that most people tend to be able to put into practice quickly. Problem solving is ground that has been covered by many people many times in the past and this presentation builds on that work, incorporates my experience and hopefully integrates it in a way that provides some new insights. This is the 1st of a 3 part presentation.
Root Cause Analysis and Corrective ActionsHannah Stewart
A snapshot of 5 of the most popular root cause analysis methods for EHS incident investigation, plus how to manage follow up corrective and preventive actions effectively. Read the full report here: https://www.pro-sapien.com/resources/downloads/root-cause-analysis/
This Template is created for helping the quality or continuous improvement professionals to generate a step by step problem solving report, which include the guidance on each steps in a 8D process, also include the templates of popular quality tools such as 5-Why and Fishbone Diagram.
想学习六西格玛?可以看看ucourse.org的网上课程。
http://ucourse.org/ssgb
Project Closure Process Steps PowerPoint Presentation Slides SlideTeam
If you are looking for professional templates and slides to prepare a professional PPT on project closure process steps? Well if yes, then download our ready to use project closure process steps PowerPoint presentation slides. Showcase the performance of your company project to the customer using this project closure process steps PPT presentation. Using these project management presentation templates, you will be able to confirm if the team members have met all sponsor and consumer needs. This closing a project PowerPoint presentation includes essential topics such as project brief, project description, project timeline, project progress summary, project status report, and project health card. It also covers a slide on project dashboard, project closure report, work breakdown structure, and project conclusion report-performance analysis, deadline, budget/costs. With the help of the project description presentation PPT, you will be able to represent throughout the progress of the project. Use of stunning graphics and visuals will help you describe the different project stages performance. Download this project closure checklist PPT presentation. Ensure a clean baton change with our Project Closure Process Steps PowerPoint Presentation Slides. They allow flawless accomplishment.
Global 8D Problem Solving Process Training ModuleFrank-G. Adler
The 8D Problem Solving Process Training Module v8.0 includes:
1. MS PowerPoint Presentation including 206 slides covering the Global 8D Problem Solving Process & Tools, a Case Study, and 7 Workshop Exercises.
2. MS Word Problem Solving Process Case Study
3. MS Excel 8D Problem Solving Process Worksheet Template
4. MS Excel Process Variables Map Template, Process FMEA Template, and Process Control Plan Template
5. MS Word 8D Problem Solving Process Report Template
Service revamp lean six sigma black belt projectSumit K Jha
Project- Service revamp
Type- Lean Six Sigma Black Belt Project
Outline- To improve the entire process of getting purchase orders, purchasing, manufacturing, warehousing and installation
Tools/Framework- Six Sigma concepts such as SIPOC, fish bone analysis, control charts and hypothesis testing; statistical tools, Microsoft Dynamic AX
Role- Project manager
Outcome- The successful completion of the project yielded in cost savings of INR 1.61 crores
Ensure that the best possible level of service quality and availability is maintained with this Incident Management Powerpoint Presentation Slides. Showcase the activities within the incident management procedure by incorporating this incident detection and recording PPT visuals. Determine how quickly a resolution of the incident is required by using this professionally designed investigation and analysis PPT graphic. Present the primary ITIL management roles with the help of our incident closure PowerPoint infographics. Also, determine the relative impact of an issue on business processes by taking the aid of the resolution and record the PPT template. Take the advantage of this problem management PowerPoint layout to determine the level of risk by considering the category of probability against consequence severity. Showcase the procedures to deal with the potential problems using the incident monitoring PPT templates. Download problem reporting and communication PPT presentation to restore a normal service operation as quickly as possible. https://bit.ly/3jH7J6u
Basic 8D Problem Solving Tools & Methods - Part 1Tony Alvarez
I've taught many workshops on basic problem solving over the years at various companies. This 3 part presentation collects tools and methods that I've found useful and that most people tend to be able to put into practice quickly. Problem solving is ground that has been covered by many people many times in the past and this presentation builds on that work, incorporates my experience and hopefully integrates it in a way that provides some new insights. This is the 1st of a 3 part presentation.
Root Cause Analysis and Corrective ActionsHannah Stewart
A snapshot of 5 of the most popular root cause analysis methods for EHS incident investigation, plus how to manage follow up corrective and preventive actions effectively. Read the full report here: https://www.pro-sapien.com/resources/downloads/root-cause-analysis/
PROJECT STORYBOARD: Project Storyboard: Reducing Underwriting Resubmits by Ov...GoLeanSixSigma.com
GoLeanSixSigma.com Black Belt Tyson Simmons project to reduce underwriting package defects and subsequent re-submission demonstrates some great points. His team voted to narrow down potential root causes and noted them with dots on their Fishbone Diagram. Then the big "Oh darn!" When they tested the suspected root causes (analyst and submitter), neither of them proved to be statistically significant.
What do you do when all of your root causes prove to be false? You go back and look for more which is what Tyson did. The red dots on the Fishbone Diagram suggested the next possible root cause, which did prove out. Nice job, Tyson, for sticking with the process and shooting right past your goal!
– Bill Eureka, GoLeanSixSigma.com Master Black Belt Coach
This presentation covers the entire aspects of 6 sigma quality methodology. You can have this presentation as a reference to anything related to 6 sigma. This is one of the best material to be refereed before the implementation of 6 sigma in your organization, whether it is in service sector or in manufacturing..
Recorded webinar: http://bit.ly/1uVqMJC
Subscribe: http://www.ksmartin.com/subscribe
Purchase the book: http://www.bit.ly/VSM
These are slides from a webinar done with APICS Heartland on the topic of Value Stream Mapping.
This webinar covers:
• How to use value stream mapping as an organizational transformation & leadership alignment tool
• How to plan for a value stream mapping activity
• The mechanics of mapping, including key metrics
for office/service/knowledge work
• How to create an actionable Value Stream Transformation Plan
PROJECT STORYBOARD: Reducing Software Bug Fix Lead Time From 25 to 15 daysGoLeanSixSigma.com
GoLeanSixSigma.com Green Belt Eduardo Torres did a great job of cutting waste out of the process of fixing software bugs. The use of software is growing fast, and with no known way to guarantee new software is error-free, rapidly fixing bugs found is critical. Eduardo not only cut nearly 40% of the process time, but also cut the variability in half, greatly improving reliability!
– Susan Tighe, GoLeanSixSigma.com Master Black Belt
Coach
---
Eduardo Torres is a Senior Project Manager and Lean Six Sigma Green Belt with expertise in the Telecommunications Field. For his Green Belt Project, he decided to tackle the long lead time for software bug fixes – reducing the total lead time from 25 to 15 days!
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
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.
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.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
2. Define Deliverables
DEFINE
Project Mapping and Pre DMAIC Analysis
Project Charter
Terms & Acronyms Used
ARMI/RASIC
Communication Plan
Process Map (Flow Chart)
SIPOC/COPIS
3. Project Mapping
DEFINE
Customer Sample Comments
Key Output Characteristics
Important to Customer (CTQ's)
Mr. Vineet Unny, Process owner of
Advance Health.
He is worried as the C-Support is a new LOB which started 5 months
ago, and after completing ramp, team is not achieving the desired
AHT target. Moreover, company have to pay extra cost by OT so that
employees achieve the required call volume for the day.
Need to reduce AHT
Mr Rahul Gupta, Process Manager of
C-Support LOB.
Looking at the daily performance of the team, we are still not able
reduce the AHT. We have to work on the employee Handling time. So
that, we can amuse our customer and fulfil their needs.
Increase the Process performance by reducing
handling time.
Advance Health Pvt. Ltd. Process improvement Data Type : Continuous
4. Graphical Summary
DEFINE
6.00
5.25
4.50
3.75
3.00
2.25
1.50
Median
Mean
3.6
3.5
3.4
3.3
3.2
3.1
3.0
1st Q uartile 2.1500
Median 3.2400
3rd Q uartile 4.7150
Maximum 5.9900
3.2121 3.5940
2.9700 3.5801
1.3042 1.5756
A -Squared 2.84
P-V alue < 0.005
Mean 3.4031
StDev 1.4271
V ariance 2.0365
Skewness 0.14667
Kurtosis -1.18654
N 217
Minimum 1.0200
A nderson-Darling Normality Test
95% C onfidence Interv al for Mean
95% C onfidence Interv al for Median
95% C onfidence Interv al for StDev
95% Confidence Intervals
Summary for HT
C-SAT
LOB
AHT
Performance
8
6
4
2
0
99.9
99
95
90
80
70
60
50
40
30
20
10
5
1
0.1
HT
Percent
Mean 3.403
StDev 1.427
N 217
AD 2.838
P-Value <0.005
Probability Plot of HT
Normal
Inference: As we have seen that the AHT of the LOB, the handling time is hovering around 3.24, which is not good for the process . We have
seen the huge difference between the AHT when we have done the normality test for the same. We have seen that there are more than 40%
of employees whose AHT are above 4.00 . So we are aiming our Target around 2.80 for LOB and Process improvement.
5. Project Charter
Business Case- Advance Health is a Health Care Insurance company which provide various types of policy to their
customer. C-Support is one of the LOB where we provide customer support via call and verify patient details and
provide information related to covered and non-covered services. C-Support LOB facing issue related to AHT and it
leads to customer dissatisfaction due to not resolving queries on time. As per our analysis if we will improve the
AHT and make it to 2.80 then we can save approximately 1. 3 million dollars yearly.
Problem Statement- After analysis of the last months data it is observed that our process AHT is now 3.24 which is
way behind the process target of 3.00. If we calculate in monetary term then we have deprivation of $2,593,66.7 in
last 5 months. Moreover, we have to give overtime to our employees to achieve the desired target which is also the
cost for the company. If we will not control the AHT in coming months then it will impact more to our company
revenue.
Goal Statement- We will reduce the AHT to 2.80 till 19th April ‘20.
In Scope- Gurgaon Site, C-Support LOB
Out Scope- Rest other site and LOB.
DEFINE
7. Terms & Acronyms Used
DEFINE
Indicators Definition
AHT Average Handling Time
Production No. of Calls employee takes
Target Total production that need to be done
CSAT Customer Satisfaction
C-Support LOB
TAT Turn Around Time
OC Office Communicator
SLA Service Level Agreement
8. ARMI
DEFINE
When Populating the Stakeholder, consider the ARMI:
• A= Approver of team decisions
• R= Resource or subject matter expert (ad hoc)
• M= Member of team
• I= Interested Party who will need to be kept informed
Key Stakeholders Define Measure Analyze Improve Control
Mr. Unny (Process Owner) I,A I I I I,A
Operation Managers I,M I,M I,M I,M I,M
Green Belt (GB) I,R,M I,R,M I,R,M I,R,M I,R,M
Training & Quality Team I,R I,R I,R I,R I,R
Team Leaders, SME & QA’s I,R,M I,R,M I,R,M I,R,M I,R,M
IT Department I I I I,M I
9. RASIC
DEFINE
RASIC Chart for Define & Measure
Activities
DPE
Process
Manager
MBB(Coach)
Green
Belt
Quality
&
Training
Team
Team
Leader,
SME,
QA
IT
Department
Team
Member’s
Collect VOC from all stakeholders I,A,C I,S I,C R S S S C
Conduct Stakeholder analysis - - I,C R - - - -
Collect data for the last 12 months - - I,C R - - - -
Analysis of data I I,S I,C R - - - C
Report out on the Pre DMAIC Analysis I I,S I,C R - - - -
Create Project Charter - I,S I,C R S S - -
Send Charter for Executive Approval A - I,C R - - - -
Approve Charter A - I,C R - - - -
Build SIPOC - I,S I,C R - - - -
Build Process Map - I,S I,C R - - - -
Build the data collection plan I I I,C R C,S S - -
Get the DCP Approved I I I,C R C,S - - -
Approve DCP A I I,C R C,S - - -
Collect Data I I I,C R C,S - - C
Validate data - I I,C R C,S - - C
Publish next steps to stakeholder - I I,C R - - - -
• Responsible (R) : Solely and directly responsible for the activity (Owner) - Includes approving authority (A)
• Approve (A) : Reviews and assures that the activity is being done as per expectations
• Support (S) : Provides the necessary help and support to the owner
• Inform (I) : Is to be kept informed of the status/progress being made
• Consult (C) : Is to be consulted for this activity for inputs
10. Communication Plan
DEFINE
Message Audience Media Who When
Project Charter Sr. DPE & DPE E-mail, Call & OC ME 28th
Jan ‘21
Team Meeting Team Member (All Stakeholders) E-mail Invite ME Alternate Days
Project Progress – 1st
Phase Team Member (All Stakeholders) E-mail, OC ME 31st Jan’21
Mitigate Review – 2nd
Phase Approvers E-mail ME 5th
Feb’21
Technology Change – Process
Requirement
IT & Ops E-mail ME & Process Manager Tentative
Project Progress – 3rd
Phase Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 17th
Feb’21
Project Progress – 4th
Phase Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 1st
March ’21
Project Progress- Improvement
Trending
Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 10th
March ‘21
Project Progress – 4th
Phase Team Member (All Stakeholders) E-mail & Team Discussion ME & OPS Team Manager 22nd March ’21
11. Process Map
DEFINE
Start
Patient visit hospital for treatment
and call CCE for policy verification
Call picks by the Agents. Agent
greets and probe the required
verification and reason for making
the call
CCE will verify the patient
eligibility for the DOS
is Agents
able to
verify the
eligibility
Escalate the issue.
CCE verify if services
are covered or not
Is agents
able to
verify the
services.
End
Yes
No
No
Yes
Caller provides the patient
details for policy verification
12. COPIS
DEFINE
Customer Output Process Input Supplier
Patient visit hospital for treatment
Caller will provide the patient
details over call.
CCE will verify the patient eligibility
for the date of service.
After eligibility, policy holder will verify
the service taken by the patient by
submitting scanned copy.
CCE will verify the service is covered or
not by inputting details in application.
CCE will escalate the issue if he/she is
not able to resolved the issue.
Call Close
Patient details
Scanned Copy of
Services taken.
Contract Details.
Caller
Resolved
WIP
Decline
Caller
Respective
Department
Management
14. Data Collection Plan
MEASURE
Y Operational Definition Defect Definition
Performance
Standard
Specification Limit Opportunity
Average Handling Time of
team per week
Start time and End time for each call is
captured by avaya. Calculated call
duration for each call by subtracting
start time from end time. Calculate
duration in a week and divide that by no
of calls taken in a week.
If AHT of team for any
given week is greater
than 2.80.
2.80 minutes
USL- 3.00
LSL- N/A
Every Week
Y Data Type
Unit of
Measurement
Decimal Places
Database
Container
Existing/New
Database
To date- From Date
Average Handling
Time of team per
week
Continuous Minutes 2 Excel Existing 31st
Dec 01st
Oct
Data Items
Needed
Formula to be
Used
Equipment Used
for Measurement
Equipment
Calibration Info
Responsibility Training Need
Operator
Information
Hold Time, Probing
Time, Resolution Time,
Summary Time
Total time spend on a
call in a week / No of
calls taken in a week
Avaya
NA SME Yes Team A
Mode of Collecting Data
15. Measurement System Analysis
MEASURE
EFFECTIVENESS
OP2
OP1
100
90
80
70
60
50
Appraiser
Percent
95.0% C I
Percent
OP2
OP1
100
90
80
70
60
50
Appraiser
Percent
95.0% C I
Percent
Date of study:
Reported by:
Name of product:
Misc:
Assessment Agreement
Within Appraisers Appraiser vs Standard
EFFECICIENCY
10 existing samples were picked and measured by 2 different operators and master calibrator(standard). Each operator has
measured each sample twice.
AIAG: Automotive Industry Action Group
1. AAA>=90%, Accept
2. 70%=<AAA<90%, Your Call
3. AAA<70%, Reject
MSA: Pass
Practice Purpose Only
16. Measurement System Analysis
MEASURE
Minitab Descriptive Statistics Rule
Rule
Description
Acceptable Result
A
R&R % of
Tolerance
< 10%
(9.65)
Pass
B
% Contribution
(R&R Std
deviation)
Smaller than
Part to Part
Variation
(.93)
Pass
C
Number of
distinct
categories
>=4
(14)
Pass
Overall Gage Result – “MSA Passed”
Gage R&R (ANNOVA)– Crossed
17. Process Capability
MEASURE
DPMO
• Discrete data
Z - SCORE
• Numerical (Continuous, Count, %age)
• Normal Distribution
• At least one specification applicable
Cp, Cpk
• Continuous Data
• Normal Distribution
• Both specification applicable
18. Process Capability-DPMO
DPMO: Defects Per Million Opportunities
DPMO= DPO*1000000 = 0.5*1000000 = 500000
DPO: Defects Per Opportunity
DPO=Total number of defects/Total opportunities
=10/20=0.5
%age Fail=DPO*100=0.5*100=50%
%age Pass = 100%-%age Fail = 100%-50%=50%
Calls Script Verification Enquiry resolved Tag
Call 1 P P P P
Call 2 P P P F
Call 3 P P F F
Call 4 P F F F
Call 5 F F F F
PPM: Parts Per Million
PPM= DPU*1000000 = 0.8*1000000 = 800000
DPU: Defects Per Unit
DPU=Total number of defective units/Total units audited = 4/5 = 0.8
Practice Purpose Only
20. Analyze Deliverables
ANALYZE
Identify Potential Factors
Fishbone
DCP for Potential Factors
Basic Analysis for Project Y
Checking for Impact of Factors on Y
Hypothesis Summary
MSA results of Impacting Factors
22. DCP for Potential Xs
ANALYZE
Potential Cause Type of Data Collection Method Test to be Used
Visualization plot
Used
Handling Time Continuous Automated 1 Sign Test Box Plot
Case Type Discrete Automated Mann Whitney Box Plot
Sub Query Discrete Automated Moods Median Box Plot
Product(Policy Type) Discrete Automated Moods Median Box Plot
Supervisor Discrete Automated Mann Whitney Box plot
Shift Discrete Automated Moods Median Box Plot
Gender Discrete
Automated Mann Whitney
Box Plot
Tenure Continuous
Automated
Regression / Co-relation Scatter
Hold Time Continuous Automated Regression / Co-relation Scatter
Probing Time Continuous Automated Regression / Co-relation Scatter
Resolution Time Continuous Automated Regression / Co-relation Scatter
Outlining Data Collection Steps for Xs
23. Basic Data Analysis for Project Y
ANALYZE
Randomness Study
Randomness & Shape Study
Normality Study
As per Run Chart, Clustering, Trend, Mixture, Oscillation P
value is > .05, which means our HT data is random and
stable.
As per Normality Test, P value is < .05, which
means HT data is non – normal.
24. 1 Sample Sign Test on Handling Time(Y)
ANALYZE
1 Sample Sign Test
Sign test of median = 2.800 versus > 2.800
N Below Equal Above P Median
217 82 1 134 0.0003 3.240
Inference – There are total 217 data
points of Y, and if we compare data
according to our proposed median
there are 134(61.75%) data points
which are above than the target.
Moreover, P value is < .05, which
means alternate hypothesis(Ha) is true
and according to it there is significant
impact
25. Checking for Impact of x1 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
N Median
HT_Complaint 17 2.970
HT_Inquiry 200 3.285
Point estimate for ETA1-ETA2 is -0.270
95.0 Percent CI for ETA1-ETA2 is (-1.060,0.440)
W = 1685.5
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at
0.5016
The test is significant at 0.5016 (adjusted for ties)
Inference – As ETA1 and ETA2 is significant at
.5016, which means P value is > .05 and hence null
hypothesis is true.
Mann-Whitney Test
26. Checking for Impact of x2 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Chi-Square = 7.74 DF = 5 P = 0.171
Sub Query Type N<= N> Median Q3-Q1
Claims query related grievance 0 4 4.01 1.40
Deductions in claims 2 1 3.16 2.30
Delay in claims settlement 4 1 1.96 1.47
Early Claim Settlement 3 3 3.23 2.84
Reimbursement Not received 0 1 4.95 *
Rejections in Claims 7 3 3.20 2.47
Status 93 95 3.29 2.56
Individual 95.0% CIs
Sub Query Type --------+---------+---------+--------
Claims query related grievance (----*--------)
Deductions in claims (--*---------------)
Delay in claims settlement (---*-------------)
Early Claim Settlement (-----------*------------------)
Reimbursement Not received
Rejections in Claims (---------*-----------)
Status (-*---)
--------+---------+---------+--------
2.4 3.6 4.8
Overall median = 3.24
* NOTE * Levels with < 6 observations have confidence < 95.0%
Inference – As P value is > 0.05, hence Ho applied
and there is no significant impact between the sub
query type.
Mood median test for HT
27. Checking for Impact of x3 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Mood median test for HT
Chi-Square = 15.51 DF = 15 P = 0.415
Individual 95.0% CIs
Product (Policy No) ---+---------+---------+---------+---
Citibank Optima Restore Floater
Dengue Care (--------*-------)
Easy Health Floater Standard (--*---)
Easy Health Floater Standard Two Year (----------*---)
Easy Health Group- Floater- Canara (--------*------)
Easy Health Group- Individual- Canara (---*------)
Easy Health Group Floater Indian Overseas Bank (---------*--------
--)
Easy Health Group Individual Indian Overseas Bank
Easy Health Individual Exclusive
Easy Health Individual Premium
Easy Health Individual Standard (-*------)
Easy Health Individual Standard Two Year (---*-----)
Group Health Floater (--------*-----------------)
Group Health Individual (---*----------)
Individual Personal Accident Standard (---------------*
Optima Cash- Gold (-*-)
Optima Cash- Gold Two Year
Optima Restore Floater (----*---)
Optima Restore Floater Two Years (------*----)
Optima Restore Individual (------------*------)
Optima Restore Individual Two Years (-----*----)
---+---------+---------+---------+---
1.5 3.0 4.5 6.0
Inference:- Since P value is > 0. 05, which means
Null hypothesis is true, and hence there is no
signigicant impact between Product types
Mood Median Test: HT versus Product
(Policy No)
28. Checking for Impact of x4 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Supervisor N Median
HT_Danish 103 2.5300
HT_Kanwarpreet 114 4.1950
Point estimate for ETA1-ETA2 is -1.3700
95.0 Percent CI for ETA1-ETA2 is (-1.7298,-
0.9599)
W = 8257.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is
significant at 0.0000
The test is significant at 0.0000 (adjusted for
ties)
Inference:- As per MW hypothesis
test between supervisors, P value is <
0.05, which means there is significant
impact.
Mann-Whitney Test and CI: HT_Supervisors
29. Checking for Impact of x5 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Mood Median Test: HT versus Shift
Mood median test for HT
Chi-Square = 2.40 DF = 2 P = 0.302
Individual 95.0% CIs
Shift N<= N> Median Q3-Q1 ---+---------+---------
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Overall median = 3.24
Inference:- Since P value is > .05, which
means Ho is true and there is no
significant impact.
30. Checking for Impact of x5 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Results for: gender
Mann-Whitney Test and CI: HT_Female, HT_Male
N Median
HT_Female 171 2.8500
HT_Male 46 5.4100
Point estimate for ETA1-ETA2 is -2.5700
95.0 Percent CI for ETA1-ETA2 is (-2.8999,-2.2501)
W = 14786.0
Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at
0.0000
The test is significant at 0.0000 (adjusted for ties)
Inference:- Since P value is < .05, which
means Ha is true and there is significant
impact.
31. Checking for Impact of x5 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Mood Median Test: HT versus Shift
Mood median test for HT
Chi-Square = 2.40 DF = 2 P = 0.302
Individual 95.0% CIs
Shift N<= N> Median Q3-Q1 ---+---------+---------+---------+---
Evening 31 41 3.65 2.53 (------------*---------)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Overall median = 3.24
Inference:- Since P value is > .05, which means Ho is true and
there is no significant impact.
The regression equation is
HT = 7.32 - 0.787 Tenure
Predictor Coef SE Coef T P
Constant 7.3183 0.2989 24.49 0.000
Tenure -0.78715 0.05835 -13.49 0.000
S = 1.05261 R-Sq = 45.8% R-Sq(adj) = 45.6%
Analysis of Variance
Source DF SS MS F P
Regression 1 201.67 201.67 182.01 0.000
Residual Error 215 238.22 1.11
Total 216 439.88
Inference:- Since P vale < .05, which
means Ha is true and there is
significant impact between HT and
Tenure.
Regression Analysis: HT versus Tenure
32. Checking for Impact of x5 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Mood Median Test: HT versus Shift
Mood median test for HT
Chi-Square = 2.40 DF = 2 P = 0.302
Individual 95.0% CIs
Shift N<= N> Median Q3-Q1 ---+---------+---------
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Overall median = 3.24
Inference:- Since P value is > .05, which
means Ho is true and there is no
significant impact.
The regression equation is
HT = 2.13 + 2.67 Hold Time
Predictor Coef SE Coef T P
Constant 2.1282 0.2344 9.08 0.000
Hold Time 2.6745 0.4539 5.89 0.000
S = 1.32722 R-Sq = 13.9% R-Sq(adj) = 13.5%
Analysis of Variance
Source DF SS MS F P
Regression 1 61.156 61.156 34.72 0.000
Residual Error 215 378.726 1.762
Total 216 439.882
Inference:- As per the test P value is <0.05,
which means Ha is true and there is significant
impact between HT and hold time.
Regression Analysis: HT versus Hold Time
33. Checking for Impact of x5 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Mood Median Test: HT versus Shift
Mood median test for HT
Chi-Square = 2.40 DF = 2 P = 0.302
Individual 95.0% CIs
Shift N<= N> Median Q3-Q1 ---+---------+---------
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Overall median = 3.24
Inference:- Since P value is > .05, which
means Ho is true and there is no
significant impact.
The regression equation is
HT = 0.207 + 11.6 Probing Time
Predictor Coef SE Coef T P
Constant 0.20708 0.06849 3.02 0.003
Probing Time 11.6445 0.2294 50.76 0.000
S = 0.396975 R-Sq = 92.3% R-Sq(adj) = 92.3%
Analysis of Variance
Source DF SS MS F P
Regression 1 406.00 406.00 2576.31 0.000
Residual Error 215 33.88 0.16
Total 216 439.88
Inference:- Since P valus is <.05 and R-
Sq>62%, which means there is strong
impact.
Regression Analysis: HT versus Probing Time
34. Checking for Impact of x5 on Y
ANALYZE
Graphical Depiction
Statistical Interpretation of Relationship
Mood median test for HT
Chi-Square = 2.40 DF = 2 P = 0.302
Individual 95.0% CIs
Shift N<= N> Median Q3-Q1 ---+---------+---------
+---------+---
Evening 31 41 3.65 2.53 (------------*-------
--)
Morning 38 35 3.17 2.38 (--------*---------)
Night 40 32 3.09 2.66 (---------*--------------)
---+---------+---------+---------+---
2.80 3.20 3.60 4.00
Overall median = 3.24
Inference:- Since P value is > .05, which
means Ho is true and there is no
significant impact.
The regression equation is
HT = 2.52 + 0.523 Resolution Time
Predictor Coef SE Coef T P
Constant 2.5239 0.2203 11.46 0.000
Resolution Time 0.5232 0.1188 4.40 0.000
S = 1.36995 R-Sq = 8.3% R-Sq(adj) = 7.8%
Analysis of Variance
Source DF SS MS F P
Regression 1 36.377 36.377 19.38 0.000
Residual Error 215 403.504 1.877
Total 216 439.882
Inference:- Since R-Sq is 8.3, which
means there is weak impact between
HT and Resolution time.
Regression Analysis: HT versus Resolution Time
35. Hypothesis Summary
ANALYZE
Summary of Impacting Factors
S. No. Factor p – Value Graphical Tool Used Inference Next Steps
1 Handling Time .0003 Box Plot Ha Improve
2 Case Type .5016 Box Plot Ho -
3 Sub Query
0.171
Box Plot H0 -
4 Product(Policy Type)
0.415
Box Plot H0 -
5 Supervisor
0.0000
Box plot Ha Improve
6 Shift .302 Box Plot Ho -
7 Gender
0.000
Box Plot Ha Improve
8 Tenure
0.000
Scatter Ha Improve
9 Hold Time
0.000
Scatter Ha Improve
10 Probing Time
0.000
Scatter Ha Improve
11 Resolution Time
0.000
Scatter Ha Improve
36. Improve Deliverables
IMPROVE
Screening of the Impacting Factors
Action Plan for Improving the Factors
Basic Analysis of Improved Y
Pre–Post Analysis of Project Y
Pre-Post Analysis of Factor(s)
Improve Summary – Take Aways
FMEA on Action Plan
37. Screening of Impacting Factors
IMPROVE
To Improve
AHT
(A)
Customer
Importance
(B)
Expected
total
project cost
(C)
Likelihood
of the
success
(D)
Expected
contributio
n to profit
(E)
Applicabali
ty to other
areas
(F)
Project
priority
number
(G)
Project
Order
Supervisor 7 9 9 7 5 19,845 2
Gender 5 3 7 8 9 7560 6
Tenure 7 2 9 9 7 7938 5
Hold Time 9 7 8 7 8 28,224 1
Probing Time 7 7 5 7 8 13,720 4
Resolution Time 8 5 8 5 9 14,400 3
38. Action Plan for Improving the Factors
IMPROVE
S. No. Pain Area Root Cause Improvement Idea
Implementation
Owner
Implementation
Status
1. Supervisor Lacking Skills
Should have trained on
basic skills before moving
anyone to people manager
position.
Process HR Pending
2. Gender Training
Need to improve interview
process so hiring should
be done based on skills not
on gender biasness.
HR/Manager Pending
3. Tenure Ramp Up
Need to increase Ramp
time for fresh hires, so they
can expertise in the
product.
Training Team Pending
4. Hold Time System
Speed up the device with
regular maintenance,
IT Team Pending
5. Probing Time Support
Need to provide extra
support and session.
TL/SMEs Pending
6. Resolution Time Checklist
There should be checklist
template to provide
resolution timely
SMEs Pending
39. FMEA for Action Plan
IMPROVE
FAILURE MODE AND EFFECT ANALYSIS
Process Step Failure mode Effect on EDR
Severity
Occurrence
Detection
RPN
Risk management
strategy
Risk treatment plan
Responsibility
End
date
Residual Risk
Severity
Occurrence
Detection
RPN
S*O*D (RMS) (RTP) S*O*D
50. Control Deliverables
CONTROL
Control Plan & FMEA on Control Plan
Time Series Study of Y – Pre & Post
Control Charts & Inference for Y – Pre & Post
Basic Analysis of Improved Y
Establish Process Capability
Control Charts & Inference (for X1, X2, X3…)
Cost Benefit Analysis and Sign Off
51. Control Plan & FMEA on Control Plan
CONTROL
What’s Controlled Goal/Spec Limits Control Method
Who/What
Measures
Where Recorded
Decision Rule /
Corrective Action
SOP
62. Advance Innovation Group
www.advanceinnovationgroup.com
F-39, Sector 6
Noida, UP – 201301
India
Advance Innovation Group
3 continents. One team.
AIG is headquartered in Boston, Massachusetts and maintains several consulting and training delivery centers across Asia Pacific including India. Asia Pacific operations is headquartered at
Noida, India with several offices and training facilities.
Global offices allow us closer client contact to better serve your needs, while enriching our services with global perspective and experience.