Introduction to Statistical Quality Control, 5th edition

Report
Measure Up!
Data Analytics
and Libraries
Alan Safer CSU Long Beach
[email protected]
Lesley Farmer CSU Long Beach
[email protected]
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Does this sound familiar?
I
can’t get the articles I need!
 The catalog says the book is there, but I
can’t find it.
 What does it take to get a new book on
the shelf before it becomes old?
 No one uses our self-check out system.
 Should we subscribe to ebooks?
 Why isn’t online reference service used?
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What data do you collect?
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What data do you collect?
Circulation figures
Patron usage
Facilities usage
Computer usage
Internet usage
Reference consultations and fill
Library guides/bibliographies use
Instructional sessions
Website hits (including tutorials)
Database usage vs cost
ILL processing and turnaround time
Ordering, processing, cataloging, preservation, weeding workflow and time
Ebook usage vs cost
Library software usage vs cost
Staff scheduling
Equipment maintenance and repairs
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What tools do you use to
collect data?
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What tools do you use to
collect data?
 Surveys
 Web
statistics
 Circulation statistics
 Interviews and interviews
 Observation
 LibQual / PibPAS
 Flowfinity
 Document collecting
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What do you DO with that
data?
 Descriptive
statistics
 Analyze workflow for efficiency
 Reveal trends
 Benchmark efforts
 Control quality
 Do cost-benefit analysis
 Analyze student learning
 Optimize scheduling
 Optimize queuing
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Techniques
 Correlation
analysis
(for relationship between continuous variables)
 Multiple Regression(continuous response variable),
Logistic Regression(categorical response variable)
 Decision Trees
 Principle Components, Factor Analysis
 Hypothesis testing (paired tests, two sample tests,
ANOVA)
 Chi-Square tests of independence
(for relationship between categorical variables)
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Graphs
 Box
Plots
 Stem and Leaf Plots
 Histograms/Bar Graphs
 Pareto Charts
 Pie Charts
 Time Series Plot
 Outlier assessment
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How do the data connect
with your library’s goals?
The Answer May Be Data
Analytics >> Decisions
Y= f (X)
To get results, should we focus our behavior on the Y or X ?
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Y
Dependent
Output
Effect
Symptom
Monitor
Response
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•
X1 . . . XN
Independent
Input-Process
Cause
Problem
Control
Factor
Why should we test or inspect Y, if we know this relationship?
Basic Implementation
Roadmap
Identify Customer Requirements
Understand and Define
Entire Value Streams
Vision (Strategic Business Plan)
Deploy Key Business Objectives
- Measure and target (metrics)
- Align and involve all employees
- Develop and motivate
Continuous Improvement (DMAIC)
Define, Measure, Analyze, Improve
Identify root causes, prioritize, eliminate waste,
make things flow and pulled by customers
Control
-Sustain Improvement
-Drive Towards Perfection
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Case Study:
Arizona State University
 Study
ILL article borrowing process
 Why: improve service to meet increased
demand
 Drivers: customer expectations, cost
reduction, leverage technology
 Personnel: leadership, staff involvement
Voyles, J. F., Dols, L., & Knight, E. (2009). Interlibrary Loan Meets Six
Sigma: The University of Arizona Library's Success Applying Process
Improvement. Journal Of Interlibrary Loan, Document Delivery &
Electronic Reserves, 19(1), 75-94.
15
Define Phase
 Reduce
costs
 Focus on articles (many processes
possible)
 ID customer expectations relative to
turnaround time, scan quality, priority
value
 Fill 80% of article requests within 3 days
 Premise: no additional staff or $
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Measure Phase
 Current
process capabilities through flow
charts, performance matrixes, data
collection sheets
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Analyze Phase
 ID
root causes of problems in order to
eliminate or reduce them
 Tools: fishbone diagram, histogram,
Pareto chart, XmR chart
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Improve Phase
 Cause:
variations and delays in searching
and delivery on evenings/weekends
 Cause: lack of lender staff
evenings/weekends
 Cause: Choosing right ISSN
 Lags in searching difficult requests
 Pilot/evaluate
cost, support
solutions based on impact,
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Implemented Solutions
 Use
downtime of other evening/weekend
staff
 Replace student workers with FT/temp
staff
 Add staff hours on evenings/weekends
 Train
 Schedule search requests
 Encourage other libraries to increase
evening/weekend staff, and use ODYSSEY
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Control Phase
 New
quality standards
 Responsibility/timeline for implementation
 Method to measure user satisfaction
 Methods to measure process control and
capability
 Progress reports
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Lessons Learned
 Increased
cost for document supplier
wasn’t worth it
 Saved $2/request (even with more
requests)
 Use ILL system that tracks detailed data
including processing steps
 Get monthly data summary
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Over to You…
 Areas
for improvement?
 Ways to incorporate data analytics?
 And
who are good data analytics
partners?
Readings
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Agrawal, P. (2011). Application of ‘Six Sigma' in libraries for enhancing service quality. Intl. Journal of
Information Dissemination & Technology, 1(4).
Bentley, W. (2010). Lean six sigma secrets for the CIO. Boca Raton, FL: CRC Press.
Biranvand, A., & Khasseh, A. (2013). Evaluating the service quality in the Regional Information Center for
Science and Technology using the Six Sigma methodology. Library Management, 34(1/2), 56-67.
Chapman, J., & Lown, C. (2010). Practical ways to promote and support collaborative data analysis
projects. Code4lib, 12, 12-21.
Delaware Division of Libraries. (2006). Library success: A celebration of library innovation, adaptation &
problem solving, 149-153.
Dong-Suk, K. (2006). A study on introducing six sigma theory in the library for service competitiveness
enhancement. IFLA Conference Proceedings, 1-16.
Huber, J. (2011). Lean library management. New York: Neal-Schuman.
Jain, M. (2009). Delivering successful projects with TSP and Six Sigma. Boca Raton, FL: CRC Press.
Jankowski, J. (2013). Successful Implementation of Six Sigma to Schedule Student Staffing for Circulation
Service Desks. Journal Of Access Services, 10(4), 197-216.
Kastelic, M., & Peer, P. (2012). Managing IT services: Aligning best practice with a quality method.
Organizacija, 45(1), 31-37.
Kumi, S., & Morrow, J. (2006). Improving self service the Six Sigma way at Newcastle University Library.
Program: Electronic Library & Information Systems, 40(2), 123-136.
Kucsak, M. (2012). Bringing Six Sigma to the Library. Library Faculty Presentations & Publications (2012).
http://works.bepress.com/michael_kucsak/7/
Lientz, B., & Rea, K. (2002). Achieve lasting process improvement:.New York: Academic Press.
Murphy, S. (2009). Leveraging Lean Six Sigma to culture, nurture, and sustain assessment and change in
the academic library environment. College & Research Libraries, 70(3), 215-225.
Voyles, J. , Dols, L., & Knight, E. (2009). Interlibrary Loan Meets Six Sigma: The University of Arizona Library's
Success Applying Process Improvement. Journal Of Interlibrary Loan, Document Delivery & Electronic
Reserves, 19(1), 75-94.
29
Sample Data Analytics Tools
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SIPOC chart
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Balanced Scorecard
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Decision Tree
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Process Capacity
Quality Control,
7th Edition by
Douglas C.
Montgomery.
Copyright (c)
2012 John
Wiley & Sons,
Inc.
Chapter 7
Actions taken to improve a
process
35
Quality Control,
7th Edition by
Douglas C.
Montgomery.
Copyright (c)
2012 John
Wiley & Sons,
Inc.
Chapter 5
Control Chart Examples
1.
2.
3.
4.
5.
6.
7.
Histogram or stem-and-leaf plot
Check sheet
Pareto chart
Cause-and-effect diagram
Defect concentration diagram
Scatter diagram
Control chart
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37
Stem-and-Leaf Plot
38
Scatter Diagram
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Defect Concentration
Diagram
40
Failure Analysis
Quality Control,
7th Edition by
Douglas C.
Montgomery.
Copyright (c)
2012 John
Wiley & Sons,
Inc.
Chapter 1
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42
DMADV: for new projects
 Define
design goals (client demands, library
goals)
 Measure and identify CTQs (characteristics that
are Critical To Quality): product capabilities,
production process capability, risks
 Analyze to develop and design alternatives
 Design details (and optimize)
 Verify the design
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Next Steps
 Let’s
work together!
 [email protected][email protected]
Operational Excellence Methodology
Plan
Execute
Identify
Problem
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Strategic Link to Business Plan defined in Project Selection Process
Defined Business Impact with Op Ex Champion support
Structured Brainstorming at all organizational levels
Cause and Effect Diagrams identifying critical factors
Primary and Secondary Metrics defined and charted
Multi-Level Pareto Charts to confirm project focus
Practical
Problem
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Develop a focused Problem Statement and Objective
Develop a Process Map and/or FMEA
Develop a Current State Map
Identify the response variable(s) and how to measure them
Analyze measurement system capability
Assess the specification (Is one in place? Is it the right one?)
Problem
Definition
• Characterize the response, look at the raw data
• Abnormal? Other Clues? Mean or Variance problem?
• Time Observation • Spaghetti Diagram
• Takt Time
• Future State Maps
• Percent Loading
• Standard Work Combination
• Use Graphical Analysis, Multi-Vari, ANOVA and basic
statistical tools to identify the likely families of variability
Problem
Solution
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Problem
Control
Execute
Plan
Identify the likely X’s
5S
• Set Up Time Reduction (SMED)
Material Replenishment Systems
Level Loading / Line Leveling
Cell Design
• Visual Controls
Use Design of Experiments to find the critical few X’s
Move the distribution; Shrink the spread; Confirm the results
Mistake Proof the process (Poka-Yoke)
Tolerance the process
Measure the final capability
Place appropriate process controls on
the critical X’s
• Document the effort and results
• Standard Work
• TPM
Problem Solving
 What do you want to know?
 How do you want to see what it is that you need
to know?
 What type of tool will generate what it is that you
need to see?
 What type of data is required of the selected tool?
 Where can you get the required type of data?
Based in part on Six Sigma Methodology developed by GE Medical Systems and Six Sigma Academy, Inc.
Crane Co. Op. Ex. Methodology Originated by MBBs; D. Braasch, J. Davis, R. Duggins, J. O’Callaghan, R. Underwood, I. Wilson

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