service quality unit 11 & chapter 6. ever wonder what 99.9% meant? is a goal of 99.9% good...
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![Page 1: 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](https://reader036.vdocuments.mx/reader036/viewer/2022062407/56649d625503460f94a43c8a/html5/thumbnails/1.jpg)
Service quality
Unit 11 & Chapter 6
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Ever wonder what 99.9% meant?
Is a goal of 99.9% good enough?
1 hour of unsafe drinking water every month2 unsafe plane landings per day at O’Hare Airport in Chicago16,000 pieces of mail lost by the U.S. Post Office every hour.
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Ever wonder what 99.9% meant?
20,000 incorrect prescriptions every year 500 incorrect operations each week50 babies dropped at birth every day22,000 checks deducted from the wrong bank account each hour32,000 missed heart beats per person each year
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What is Service Quality?
Identify a “quality” service Discuss why it is high quality
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Garvin’s 8 Dimensions of Quality
PerformancefeaturesReliabilityConformanceDurabilityServiceabilityAestheticsPerceived Quality
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Schonberger’s Additional 4 Dimensions of Quality
Quick ResponseQuick change expertiseHumanityValue
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Quality toolbox Quality toolbox (no shortage of topics for MGT 667)
1992 Baldrige winner’s Texas Instruments DSEG1992 Baldrige winner’s Texas Instruments DSEG (now Raytheon TI Systems) (now Raytheon TI Systems)
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Quality Management Tool Box
Variation SPC (control charts), Process capability (Cpk), Design of Experiments, Taguchi, acceptance sampling, Gauge R&R, other statistical tools
Mistakes mistake-proofing (poka-yoke), Just culture, StandardizationErgonomics, Human factors engineering
CultureQuality 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)
ComplexityProcess Mapping,Design for Manufacturability & assembly, Root cause analysis, FMEA, Fault trees, Quality Function Deployment, Focused factories, Group technology, Smart simple design, 5s, visual systems
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Mistake-proofing tool flowchartMistake-proofing tool flowchart
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Best thinking on Service Quality:
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Service Quality Model
Financial Services -- focus group basedA.K.A. Gap Analysis, SERVQUALCompares customer perceptions with
customer expectations (Gap #5)Gap #5 = function of Gaps #1, #2, #3, #4
Here’s how the looks...
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customer
Personal needs Past Experience
Expected service
Perceived Service
Service Delivery
ManagementPerceptions of
Customer Expectations
Service QualitySpecifications
External Communication
to Customers
provider
Word-of-mouthcommunications
Gap #5
Gap #3
Gap #4
Gap #2
Gap #1
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Gap #1: Lack of market researchInadequate upward communicationToo many levels of management
Gap #2: Inadequate management communication of service qualityPerception of infeasibilityInadequate task standardizationAbsence of goal setting
GAPS #1 and #2
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Gap #3: 1) Role ambiguity and conflict2) Poor employee or technology job fit3) inappropriate control systems4) Lack of perceived control 5) Lack of teamwork
Gap #4: 1) Inadequate horizontal communication2) Propensity to overpromise
GAPS #3 and #4
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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.
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Change the design by mistake-proofing
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If it is worthwhile to mistake-proof yo-yos…
…What else would it be worth mistake-proofing?
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Exercise:
Can you think of examples of mistake-proofing in your car?
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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. 1994. Sloan management review (spring): 35-44.
1998, John R. Grout
1/3 of customer complaints relate to problems caused by the customer themselves
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Server Poka-yokes 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
Task
Treatment Tangibles
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Examples
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
Task
Treatment Tangibles
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Customer Poka-yokes 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
Preparation
Resolution
Encounter
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Examples
Preparation 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
Preparation
Resolution
Encounter
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Have you ever…
Shot a rifle?Played darts?Shot a round of golf?Played basketball?
Emmett
Jake
Who is the better shot?
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Variability
The world tends to be bell-shaped
Most outcomes
occur in the middle
Fewer in the “tails”
(lower)
Fewer in the “tails” (upper)
Even very rare outcomes are
possible(probability > 0)
Even very rare outcomes are
possible(probability > 0)
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Variability
Add up the dots on the dice
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Sum of dots
Pro
ba
bili
ty 1 die
2 dice
3 dice
Here is why: Even outcomes that are equally likely (like dice), when you add them up, become bell shaped
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“Normal” bell shaped curve
Add up about 30 of most things and you start to be “normal”
Normal distributions are divide upinto 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
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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:
RAX 2
RDLCL 3 RDUCL 4
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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)
n
ppp
)1(3
cc 3
n
uu 3
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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
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Process capability (Cpk)
Good quality: defects are rare (Cpk>1)
Poor quality: defects are common (Cpk<1)
Cpk measures “Process Capability”
If process limits and control limits are at the same location, Cpk = 1. Cpk ≥ 2 is exceptional.
μtarget
μtarget