1. Service quality
Unit 11 & Chapter 6

2. Ever wonder what 99.9% meant?
Is a goal of 99.9% good enough?
1 hour of unsafe drinking water every month
2 unsafe plane landings per day at O’Hare Airport in Chicago
16,000 pieces of mail lost by the U.S. Post Office every hour.

3. Ever wonder what 99.9% meant?
20,000 incorrect prescriptions every year
500 incorrect operations each week
50 babies dropped at birth every day
22,000 checks deducted from the wrong bank account each hour
32,000 missed heart beats per person each year

4. What is Service Quality?
Identify a “quality” service
Discuss why it is high quality

5. Garvin’s 8 Dimensions of Quality
Performance
features
Reliability
Conformance
Durability
Serviceability
Aesthetics
Perceived Quality

6. Schonberger’s Additional 4 Dimensions of Quality
Quick Response
Quick change expertise
Humanity
Value

7. Quality toolbox (no shortage of topics for MGT 667)
1992 Baldrige winner’s Texas Instruments DSEG (now Raytheon TI Systems)

8. Quality Management Tool Box
Complexity
Process Mapping,
Design for Manufacturability & assembly,
Root cause analysis, FMEA, Fault trees, Quality Function Deployment, Focused factories, Group technology, Smart simple design, 5s, visual systems
Culture
Quality awareness, Teams, Autonomous work groups, Baldrige quality award, ISO 9000, Deming, PDCA, Policy Deployment (Hoshin Kanri), Supplier Mgt & certification, Six sigma, Metrics/scorecards/ dashboards, Benchmarking, JIT/Lean mfg. Corrective action program, Kaizen events, Total Productive Maintenance (TPM), cost of quality, zero defects, ISO1400, EMS, Servqual (gap analysis)
Variation SPC (control charts), Process capability (Cpk), Design of Experiments, Taguchi, acceptance sampling, Gauge R&R, other statistical tools
READ SLIDE
we’ve already discussed errors and variance (SPC) Now let’s talk about complexity...
Mistakes mistake-proofing (poka-yoke), Just culture, Standardization
Ergonomics, Human factors engineering

9. Mistake-proofing tool flowchart

10. Best thinking on Service Quality:

11. Service Quality Model Financial Services -- focus group based
A.K.A. Gap Analysis, SERVQUAL
Compares customer perceptions with customer expectations (Gap #5)
Gap #5 = function of Gaps #1, #2, #3, #4
Here’s how the looks...

12. Customer Expectations
Personal needs
Past Experience
Expected service
Perceived Service
Service Delivery
Management
Perceptions of
Customer Expectations
Service Quality
Specifications
External
Communication
to Customers
provider
Word-of-mouth
communications
Gap #5
Gap #3
Gap #4
Gap #2
Gap #1

13. GAPS #1 and #2 Gap #1: Lack of market research
Inadequate upward communication
Too many levels of management
Gap #2: Inadequate management communication of service quality
Perception of infeasibility
Inadequate task standardization
Absence of goal setting

14. GAPS #3 and #4 Gap #3: 1) Role ambiguity and conflict
2) Poor employee or technology job fit
3) inappropriate control systems
4) Lack of perceived control
5) Lack of teamwork
Gap #4: 1) Inadequate horizontal communication
2) Propensity to overpromise

15. Change the design by mistake-proofing
Mistake-proofing is the use of process design features to facilitate correct actions, prevent simple errors, or mitigate the negative impact of errors.

16. Change the design by mistake-proofing

17. If it is worthwhile to mistake-proof yo-yos…
…What else would it be worth mistake-proofing?

18. Can you think of examples of mistake-proofing in your car?
Exercise:
Can you think of examples of mistake-proofing in your car?

19. Applications to Services
Server and customer errors impact service quality and must be managed
Focus on “front-office” customer interaction
“Back-office” important but more similar to manufacturing
Source: make your service fail-safe. Chase, R. B., And D. M. Stewart Sloan management review (spring):
1/3 of customer complaints relate to
problems caused by the customer themselves
1998, John R. Grout

20. Server Poka-yokes Task poka-yokes: Treatment poka-yokes:
Tangibles
Task poka-yokes:
Doing work incorrectly, not requested, wrong order, too slowly
Treatment poka-yokes:
Lack of courteous, professional behavior
Tangible poka-yokes:
Errors in physical elements of service

21. Examples Task poka-yokes: Treatment poka-yokes: Tangible poka-yokes:
Tangibles
Task poka-yokes:
Cash register buttons labeled by item (instead of price)
Tags to indicate order of arrival
Treatment poka-yokes:
Bell on shop door
Record eye color on bank transaction form (insure eye contact)
Tangible poka-yokes:
Paper strips around towels (indicate clean linens)
Envelope windows

22. Customer Poka-yokes Preparation poka-yokes: Encounter poka-yokes:
Resolution
Encounter
Preparation poka-yokes:
Failure to bring necessary materials, understand role, or engage correct service
Encounter poka-yokes:
Inattention, misunderstanding, or memory lapses
Resolution poka-yokes:
Failure to signal service failure, provide feedback, learn what to expect
Resolution: After service encounter has occurred Customer will evaluate experience, modify expectations and provide feedback.

23. Examples Preparation poka-yokes: Encounter poka-yokes:
Appointment reminder calls
Student degree requirement checklist
Encounter poka-yokes:
Height bar in amusement park
ATM using card swipe instead of insertion
Resolution poka-yokes:
Provide premium for completed survey
Resolution
Resolution: After service encounter has occurred Customer will evaluate experience, modify expectations and provide feedback.

24. Have you ever… Who is the better shot? Shot a rifle? Played darts?
Shot a round of golf?
Played basketball?
Emmett
Jake
Who is the better shot?

25. Variability The world tends to be bell-shaped Even very rare
outcomes are
possible
(probability > 0)
Fewer
in the
“tails”
(lower)
(upper)
Most outcomes
occur in the
middle

26. Variability Here is why:
Even outcomes that are equally likely (like dice), when you add them up, become bell shaped

27. “Normal” bell shaped curve
Add up about 30 of most things
and you start to be “normal”
Normal distributions are divide up
into 3 standard deviations on
each side of the mean
Once your that, you
know a lot about
what is going on ?
And that is what a standard deviation
is good for

28. Setting up control charts: Calculating the limits
Find A2 on table (A2 times R estimates 3σ)
Use formula to find limits for x-bar chart:
Use formulas to find limits for R chart:

29. Lots of other charts exist
P chart
C charts
U charts
Cusum & EWMA
For yes-no questions like “is it defective?” (binomial data)
For counting number defects where most items have ≥1 defects (eg. custom built houses)
Average count per unit (similar to C chart)
Advanced charts
“V” shaped or Curved control limits (calculate them by hiring a statistician)

30. Limits Process and Control limits: Specification limits: Statistical
Process limits are used for individual items
Control limits are used with averages
Limits = μ ± 3σ
Define usual (common causes) & unusual (special causes)
Specification limits:
Engineered
Limits = target ± tolerance
Define acceptable & unacceptable

31. Process capability (Cpk)
Good quality: defects are rare (Cpk>1) μ
target
Poor quality: defects are common (Cpk<1) μ
target
Cpk measures “Process Capability”
If process limits and control limits are at the same location, Cpk = 1. Cpk ≥ 2 is exceptional.