1. Measure Up! Data Analytics and Libraries Alan Safer CSU Long Beach Alan.Safer@csulb.eduAlan.Safer@csulb.edu. Lesley Farmer CSU Long Beach Lesley.Farmer@csulb.edu 1

2. 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? 2

3. What data do you collect? 3

4. 4 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

5. What tools do you use to collect data? 5

6.  Surveys  Web statistics  Circulation statistics  Interviews and interviews  Observation  LibQual / PibPAS  Flowfinity  Document collecting 6

7. 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 7

8. 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) 8

9. Graphs  Box Plots  Stem and Leaf Plots  Histograms/Bar Graphs  Pareto Charts  Pie Charts  Time Series Plot  Outlier assessment 9

10. How do the data connect with your library’s goals? 10

11. The Answer May Be Data Analytics >> Decisions Y Dependent Output Effect Symptom Monitor Response Why should we test or inspect Y, if we know this relationship? X1... XN Independent Input-Process Cause Problem Control Factor To get results, should we focus our behavior on the Y or X ? f (X) Y=

12. Basic Implementation Roadmap Understand and Define Entire Value Streams Deploy Key Business Objectives - Measure and target (metrics) - Align and involve all employees - Develop and motivate Define, Measure, Analyze, Improve Identify root causes, prioritize, eliminate waste, make things flow and pulled by customers Control -Sustain Improvement -Drive Towards Perfection Identify Customer Requirements Vision (Strategic Business Plan) Continuous Improvement (DMAIC) Identify Customer Requirements

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14. 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. 14

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 $ 15

16. Measure Phase  Current process capabilities through flow charts, performance matrixes, data collection sheets 16

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18. Analyze Phase  ID root causes of problems in order to eliminate or reduce them  Tools: fishbone diagram, histogram, Pareto chart, XmR chart 18

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22. 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 solutions based on impact, cost, support

23. 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 23

24. Control Phase  New quality standards  Responsibility/timeline for implementation  Method to measure user satisfaction  Methods to measure process control and capability  Progress reports 24

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26. 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 26

27. Over to You…  Areas for improvement?  Ways to incorporate data analytics?  And who are good data analytics partners? 27

28. Readings  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. 28

29. Sample Data Analytics Tools 29

30. SIPOC chart 30

31. Balanced Scorecard 31

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33. Decision Tree 33

34. Process Capacity 34

35. Chapter 7 35 Introduction to Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2012 John Wiley & Sons, Inc. Actions taken to improve a process

36. Chapter 5 36 Introduction to Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2012 John Wiley & Sons, Inc. 1. Histogram or stem-and-leaf plot 2. Check sheet 3. Pareto chart 4. Cause-and-effect diagram 5. Defect concentration diagram 6. Scatter diagram 7. Control chart Control Chart Examples

37. Stem-and-Leaf Plot 37

38. Scatter Diagram 38

39. Defect Concentration Diagram 39

40. Failure Analysis 40

41. Chapter 1 41 Introduction to Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2012 John Wiley & Sons, Inc.

42. DMADV: for new projects  D efine design goals (client demands, library goals)  M easure and identify CTQs (characteristics that are C ritical T o Q uality): product capabilities, production process capability, risks  A nalyze to develop and design alternatives  D esign details (and optimize)  V erify the design 42

43. Next Steps  Let’s work together!  Lesley.Farmer@csulb.edu  Alan.Safer@csulb.edu 43

44. 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?) Practical Problem 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 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 Problem Control 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 Identify Problem 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  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? Problem Solving PlanExecute Plan Crane Co. Op. Ex. Methodology Originated by MBBs; D. Braasch, J. Davis, R. Duggins, J. O’Callaghan, R. Underwood, I. Wilson Operational Excellence Methodology Based in part on Six Sigma Methodology developed by GE Medical Systems and Six Sigma Academy, Inc.