Service quality

Unit 11 & Chapter 6

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.

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

What is Service Quality?

 Identify a “quality” service  Discuss why it is high quality

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

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

Quality toolbox

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

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 (C pk ), Design of Experiments, Taguchi, acceptance sampling, Gauge R&R, other statistical tools

Mistakes

mistake-proofing (poka-yoke), Just culture, Standardization Ergonomics, Human factors engineering

Mistake-proofing tool flowchart

Best thinking on Service Quality:

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...

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

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

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

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.

Change the design by mistake-proofing

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

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

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 1/3 of customer complaints relate to problems caused by the customer themselves Source: make your service fail-safe. Chase, R. B., And D. M. Stewart. 1994. Sloan management review (spring): 35-44. 1998, John R. Grout

Server Poka-yokes

Task Treatment

 Task poka-yokes: 

Doing work incorrectly, not requested, wrong order, too slowly

Tangibles

 Treatment poka-yokes: 

Lack of courteous, professional behavior

 Tangible poka-yokes: 

Errors in physical elements of service

Examples

Task Treatment 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

Customer Poka-yokes

Preparation Encounter

 Preparation poka-yokes:

Resolution



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

Examples

Preparation Encounter

 Preparation poka-yokes: 

Appointment reminder calls



Student degree requirement checklist

Resolution

 Encounter poka-yokes: 

Height bar in amusement park



ATM using card swipe instead of insertion

 Resolution poka-yokes: 

Provide premium for completed survey

Have you ever…

 Shot a rifle?

 Played darts?

 Shot a round of golf?

 Played basketball?

Emmett

Who is the better shot?

Jake

Variability

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

Variability

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

Add up the dots on the dice

0.2

0.15

0.1

0.05

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Sum of dots

1 die 2 dice 3 dice

“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

Setting up control charts:

Calculating the limits

  Find A 2 on table (A 2 times R estimates 3 σ) Use formula to find limits for x-bar chart:

X



A

2

R

 Use formulas to find limits for R chart:

LCL



D

3

R UCL



D

4

R

Lots of other charts exist

P chart C charts For yes-no questions like “is it defective?” (binomial data) For counting number defects where most items have ≥1 defects (eg. custom built houses) U charts Cusum & EWMA Average count per unit (similar to C chart) Advanced charts

p

 3

p

( 1 

n p

)

c

 3

c u

 3

u n

“V” shaped or Curved control limits (calculate them by hiring a statistician)

Limits

 Process and Control 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

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