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 (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

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 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

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?

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

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

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

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.

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.

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?

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

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

“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 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:

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)

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

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.