Quality Management Six Sigma Quality An introduction
Six Sigma Quality An introduction Generally six sigma quality points to very high quality levels that defects are a rarity in operations It also points to A disciplined way of handling issues in operations A structured way of addressing quality issues A trajectory to an unambiguous destination in the quality management journey in an organization
Introduction About Six sigma quality The moment we talk about quality, the word Six sigma comes to our mind A number of progressive companies are working hard to build six sigma quality level Motorola and GE are supposed to have pioneered this concept of 6 sigma Dabbawallahs of Mumbai has baffled the business world with their six sigma quality standard in their operations involving delivering 200,000 tiffin boxes from home to work place and again from work place back home every day
What is six sigma? A mechanisms to deliver near zero defect in operations using principles of process control A defect is an unacceptable state of a product or a service for a customer Defect becomes an extraordinarily a rare event For example a few defects in a million potential opportunity in a service One or two defective parts in a million that was produced in a manufacturing shop
Why near zero defects? Criterion Business Customers Retail Customers Total No. of policies issued during the year 247,010 2,520,874 2,767,884 Error Rate 0.50% 1.10% 1.05% Defective Policies 1,235 27,730 28,965 This implies that at a nearly 99% quality level, 28,965 customers would have been unsatisfied with the service that they have received from the company during the year. Source: Company Presentation, Own Research
Why high levels of quality? Better Quality Management System Superior Quality control Fewer Disruptions in Operations Smoother Output Fewer Rework Less Indirect costs High quality Finished goods Less inventory Greater Productivity Less inventory, labour, indirect costs & better quality
Quality Management Changing Perceptions Yesterday It is often uneconomical to make quality improvements since it brings down productivity, increases cost and investment. Productivity goes up and cost comes down as quality goes up. This fact is known, but not necessarily to everyone. Today
Metrics for Quality Management PPM and DPMO If we want defects to really become an extraordinarily a rare event we can think of two measures: Manufacturing: Parts per million (PPM) defect rate Services: Defects per Million Opportunities (DPMO) Six sigma uses these two measures.
Defects Per Million Opportunities (DPMO) If in a process Number of opportunities for making a defect per unit of execution of that process = k Number of units of observation of the process = n Number of defects that occurred in that process during the observation = d d * k n DPMO then will be = * 1,000, 000
DPMO Computation Example A hotel in a tourist location Potential opportunities to make a defect in a check-in process = 11 No. of guests handled during a season = 1,250 Number of defects observed = 357 DPMO = = d k n 1,000,000 357 1,250 11 1,000,000 = 25, 963.
Premises of Quality Management Premise 1: All Quality initiatives must be continuous and data driven Premise 2: System of Quality is one of Prevention & Elimination Not Detection & Correction Premise 3: The Performance Standard is Zero Defects Premise 4: The responsibility for Quality lies primarily with those who produce & deliver products & services
Six Sigma Program A six sigma program requires certain enabling mechanisms for an organization A structured program for quality management & improvement Facilitating mechanisms for the Operations personnel to own, solve and obliterate the quality problems Organization structure and mandate for quality improvement issue on a continuous basis
DMAIC Methodology Define Measure Control Analyze Improve Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 575.
DMAIC Methodology Define Define the problem, the requirements, project scope, project charter Set goals for improvement Measure Identify variables to be measured, the type of measurement Data collection and synthesis Analyze Develop a set of tools for analysis Apply graphical tools of analysis Identify possible sources of variation and vital few root causes Explore means of eliminating them Improve Generate & validate improvement alternatives Creating new process maps for the process Control Develop control plan Establish revised standard measures to maintain performance Develop relevant training plans to maintain standards
Organization for six sigma In order that the organization sustainably improves the quality to near zero defect levels, A good organizational structure Mandate to make changes Ownership of processes and results and Continuous and closer review are required
Organization for six sigma Process Owner Supervisor or a manager who takes responsibility for various steps of a process that delivers some output to the customer. It could be the in a particular work area where the improvement project has been identified Team Leader & Members Team leader (the project leader) and the members will comprise of the employees in the chosen work area They will have day-to-day operational control of activities
Organization for six sigma In a six sigma organizational structure three terminologies are used to indicate these organizational entities. This includes Master Black Belt, Black Belt and Green Belt. The depth of training and experience differentiates these three.
Organization for six sigma Six sigma coach A consultant or a senior person in the organization who offers expert knowledge on various aspects of six sigma. This includes statistical tools, process design & analysis, change management, small group improvement, use of QC tools for improvement etc. Sponsor A member of the senior management who oversees the overall progress and implementation Helps the team refine the project scope, sorts out issues cutting across other parts of the organization, approves projects and provides the necessary support in terms of resources
Quality Management Total Quality Management
Quality Gurus Deming s contributions New perceptions to quality management Critical Role of Top Management Plan Do Check Act (PDCA) Cycle 14 point agenda for quality improvement Considered father of Japanese Quality Management Systems Highest Award in Japan named after him
Juran s Quality Trilogy Quality planning: the process of preparing to meet quality goals Quality control: the process of making quality goals during operations; importance of using statistical methods Quality improvement: the process of breaking through to unprecedented levels of performance
Philip Crosby Absolutes of Quality I Absolute: Definition of quality is conformance to standards II Absolute: The system of Quality is prevention III Absolute: The performance standard is zero defects IV Absolute: Measurement of Quality is the price of nonconformance V Absolute: There is no such thing as Quality Problem
Other quality gurus Karou Ishikawa Cause & Effect (Fishbone) Diagram Cause & Effect Diagram with Action Card (CEDAC) Shigeo Shingo Poka Yoke Genichi Taguchi Loss function Design of experiments
Quality Revolution in the 1980 s Salient features Alternative ideas about what constitutes good quality Newer methods to build quality into products and services that we offer New tools to assess performance of an organization with respect to quality Changed roles of middle managers and supervisors from one of control to facilitation of the process of building quality into the products and services
Total Quality Management (TQM) The definition points to four critical aspects of any good TQM program Role of Top Management Employee Involvement & Training Use of Tools & Techniques Development of a good quality system
Total Quality Management Elements Role of Top Management Quality System Employee Involvement Training & Team Work Tools & Techniques
Role of Top Management Total in TQM refers to every one, every where and every time. This will be possible only when the Top Management gets actively involved in this process Possible roles for Top Management Lead from the front by example Signal the importance of quality for the organization Help Middle Management resolve difficult trade-offs by providing guidance & directions
Employee Involvement It is about creating certain structures, culture and practices to make employee involvement a reality Build a culture of process ownership facilitate this process Role of middle management and experts go through some change Provide training on some tools & techniques that people can use in their work place to address quality issues Build a climate and culture for team working Put in a system of project by project continuous improvement
Elements of a Quality Assurance System Understand customer needs Translate them to meaningful measures for the operating system Mechanisms for identifying quality problems Tools & techniques for the employees For tracking problems to their root causes Identifying corrective measures Top Management Commitment to Quality Quality Assurance System Employee involvement for continuous focus on quality improvement Quality Certifications & Benchmarking exercises Documentation of all quality related initiatives for continuous learning & improvement Methods for preventing recurrence of problems Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 350.
Tools for Quality Management Available tools for Quality Management can be broadly categorized into two: Quality Management @ Operations Highlighting Problems Identifying Improvement Opportunities Analyzing problems & their root causes Quality Planning & Design Building Quality into Products & Services Strategic Planning
Quality Management Tools Purpose of Use Quality Control Quality Management Highlighting Problems Control Charts Identifying Improvement Opportunities Analyzing problems & their root causes Building Quality into Products & Services Histograms Check Sheets Pareto Diagrams Scatter Diagrams Graphs Cause & Effect (Fishbone) Diagram CEDAC Affinity Diagram Relationship Diagram Tree Diagram Matrix Diagram Matrix Data Analysis Process Decision Program Chart (PDPC) Arrow Diagram Poka Yoke (Fool Proofing) Strategic Planning Quality Function Deployment (QFD) Quality Costing
Number of occurences Causes for adjustment snags Number of occurrences Leakage 25 Missing 24 Fouling 5 Reworks 26 Poor routing 5 Loose fitting 15 Histogram Causes for adjustment snags 25 20 25.0 24.0 26.0 15 10 15.0 05 5.0 5.0 LEAKAGE MISSING FOULING REWORKS POOR Categories of problems ROUTING LOOSE FITTING 05 Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 339.
Number of occurences Cumulative occurences (%) Pareto Diagram Adjustment Snags Analysis 30.0 100.0 25.0 90.0 80.0 20.0 70.0 60.0 15.0 50.0 10.0 40.0 30.0 5.0 20.0 10.0 0.0 Reworks Leakage Missing Loose fitting Poor routing Fouling 0.0 Categories of problems Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 340.
Number of occurences Number of occurences Cumulative occurrences (%) Number of Causes for rework occurrences Lack of drawing clarity 23 Tooling problems 15 Process control issues 6 Design issues 33 Vendor related problems 23 Rework Analysis 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Lack of drawing clarity Causes for Rework Tooling problems Process control issues Categories of problems Design issues Vendor related problems 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Design issues Lack of drawing clarity Reworks Analysis Vendor related problems Tooling problems Categories of problems Process Control Issues 100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0
Cause and Effect Diagram A generic representation Materials Work methods Quality Equipment Labour Cause Effect Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 340.
Cause & Effect Diagram An example Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 328.
Cause Effect Diagram with Action Card (CEDAC) An example Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 341.
Poka Yoke Poka Yoke, which means fool proofing is a technique which works on the basic premise that several defects that creep into an operation are indeed avoidable Further, Errors & Defects have a Cause & Effect relationship Poka Yoke ensures that a defect once detected can be eliminated once and for all by modifying the process or design of the product or service
POKA YOKE An example
Matrix Diagram A two dimensional matrix to portray and analyze a problem at a strategic level Once the two dimensions are identified it lends itself to the analysis of the problem in a structured way A visual approach that helps management to identify problems and possible solutions
Performance of the company Worst Same Better Matrix Diagram: An Example Earth Moving Equipment Manufacturer A - Product cost B - Product quality C - Engg. Quality D - Enquiry lead time E - Mfg. lead time F - Delivery reliability G - Design flexibility H - Delivery flexibility I - Volume flexibility J - Service support Less Important Qualifying Importance of the Attribute Order Winning Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 343.
Quality Function Deployment (QFD) The four houses of quality - - - - - - - - Links customer needs to design attributes Links design attributes to actions firms can take Links actions to implementaction decisions Links implementaction to process plans Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 344.
House of Quality 5. Tradeoffs 2. Importance 3. Product characteristics 1. Customer requirements 4. Relationship matrix 6. Benchmarks 7. Technical assessment & target values Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 315.
House of Quality An illustration for a Restaurant Customer Requirements Correlation: ++: Strong Positive +: Positive + -: Negative -- --: Strong Negative + + + + Number of tables available Competitive Evaluation X- Own Company A - Competitor A B - Competitor B (5 is best) 1 2 3 4 5 Steaming hot 7 ++ ++ A B X Enough space to sit & eat 4 - ++ ++ X A B Less time during peak hours 6 - -- ++ + X B A Easy to carry home 2 ++ A X B Quick order processing 2 - -- + + X A B Importance Weighting 7 6 9 4 6 4 Importance Scale: Strong: 9 Medium: 3 Small: 1 Target Values Technical Characteristics Temperature of cooked item Maintain current Level Time taken to cook the food Reduce it by 10% of the current level Order processing time Reduce time to 2 minutes Thickness of packing material Maintain current level Number of service counters in peak time Increase the counters by one Maintain current level Technical Evaluation (5 is best) 5 4 X A,B A,B X 3 A X B X,B X,A,B 2 B X A A 1 Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 315.
Quality Management Statistical Process Control (SPC) - Fundamentals
SPC An Introduction Statistics is at the core of modern quality management Helps operationalize some decisions and keep performance and outcome with in limits Provides basic framework to systematically analyze the quality problem in various business processes A good mechanism to highlight either an existing quality problem or an impending problem
Variations in Business Processes Two types of variations occur in business processes; Common Causes & Assignable Causes Chance variations due to common causes causes due to random events that cannot be controlled Ambient temperature and humidity Normal wear and tear
Statistical Process Control Business processes always exhibit variations Filling a 500 gms detergent powder in a sachet Guest check-out time in a 5 star hotel SPC is a collective set of tools & techniques used to develop a quality assurance system that enables one to make meaningful sense of these variations
Assignable Causes Non-random variations due to assignable causes When observed variations are not statistically found to be due to random events, it clearly points to the existence of assignable causes Errors due to operator skill level differences Changes in the operating condition of an equipment Changes introduced in the standard operating procedure
Issues addressed thru SPC Key issues addressed in SPC based quality assurance system: How do we know whether the observed changes are due to random variations or assignable causes? How does one ensure that the random events are indeed rare events?
Quality Assurance using SPC Some terminologies: Designed Standard Centre of specification limits (Target) Upper Specification Limit (USL) Lower Specification Limit (LSL) (USL LSL): Desired tolerance This represents the Voice of the Customer
Voice of the Customer Examples Customer check-out time in a 5 star Hotel: 90 ± 20 Seconds Target = 90 seconds USL = 110 seconds LSL = 70 Seconds Desired tolerance is 70 110 Seconds Diameter of the pen manufactured: 8 ± 0.5 mm Target = 8.0 millimeter USL = 8.5 millimeter LSL = 7.5 millimeter Desired tolerance is 7.5 8.5 millimeter
Quality Assurance using SPC Some terminologies: Status of process Centre of the process (Process Average) Upper Control Limit (UCL) Lower Control Limit (LCL) (UCL LCL): Spread of the process This represents the Voice of the Process
SPC Attribute to study At the outset the questions that we need to address are: What is the attribute in a process that needs to be measured for the purpose of quality control? How should we measure for the purpose of analysis?
Characteristics for process control Some examples Type of Applications Characteristic for Measurement Manufacturing Number of defects in the product Conformance to test specifications Number of missing elements Service Systems Number of defects in various business processes Errors in processing documents Conformance to waiting time/lead time related specifications Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 581.
Choosing a characteristic Examples from service industry Time taken to complete the Settlement of claims in insurance Loan approval in a financial institution Patient admission process in a hospital Voice of the customer: 20 ± 6 Minutes How to measure the quality performance in this case?
Methods of measuring defects Method A: Count the number of occasions patients were indeed admitted after 26 minutes as defects in the process. In 100 observations, let us say there were 7 occasions this means the proportion of defects is 7% Method B: Make detailed measurements of the actual admission time in the 100 cases 24.95, 21.87, 25.45, 19.75 Use this data and do analysis
Measurement Methods Attribute Based Simple clustering of the characteristic into a few categories (such as good or bad) Measurements are easy to make, quick & less expensive Will reveal very little information about the process
Measurement Methods Variable Based Detailed observation of the characteristic (such as length, diameter, weight, time) This is called variable based Measurement will be expensive and more time consuming Will provide a wealth of information about the process
Types of Charts For attribute based measures we have p chart C chart For variable based measures we have R Chart Before we see the specifics of each of these let us get to know the process of setting up a control chart
Logic of Charts We use certain well known statistical principles pertaining to a random process The mean (which is the measure of central tendency) The Standard Deviation (which is a measure of dispersion) In a Normal Distribution, the area covered within ± 3 std. dev will be 99.73%
Normal Distribution
Logic of Charts What it means is that any variations happening in this range has a 99.73 probability that it is due to random events. Once we cross these limits the probability that the variation is due to random is so low that we begin to suspect there is an assignable cause This is an indication that the process may be out of control
Control Chart A generalized representation +3σ Upper Control Limit (UCL) Plot of sample data Process Average 3σ Lower Control Limit (LCL) Process in a state of Statistical Control Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 583.
Control Chart A generalized representation +3σ Out of control indication Upper Control Limit (UCL) Process Average 3σ Lower Control Limit (LCL) Process not in a state of Statistical Control
Quality Management Setting up a Control Chart
Setting up a process control system Choose the characteristic for process control Choose the Measurement method Attribute Based Variable Based P chart, c chart XChart, R Chart Choose the type of Control Chart Collect Data, Establish Control Limits Plot the data & Analyse Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 580.
Step 1: Choose the measurement characteristic: Diameter of a cylindrical component (cm) Step 2: Choose the measurement method Actual measurement of diameter (variable based) Step 3: Choose the Control Chart: Step 4: Decide on a Sampling Plan Step 5: Collect Data & Establish Control Limits
Data for the chart Sampling Plan Sample every 20 minutes Each time take five consecutive samples (Sample size is 5) Take 15 such samples Observations in each sub-group* Sub-groups 1 2 3 4 5 1 12.45 12.39 12.55 12.38 12.40 2 12.55 12.39 12.40 12.38 12.44 3 12.46 12.44 12.44 12.35 12.36 4 12.38 12.39 12.55 12.38 12.40 5 12.37 12.44 12.45 12.41 12.41 6 12.45 12.37 12.44 12.38 12.41 7 12.46 12.38 12.35 12.50 12.44 8 12.44 12.39 12.37 12.45 12.39 9 12.44 12.55 12.44 12.37 12.55 10 12.35 12.38 12.45 12.44 12.38 11 12.36 12.37 12.41 12.40 12.40 12 12.51 12.36 12.41 12.37 12.39 13 12.38 12.50 12.45 12.37 12.44 14 12.41 12.37 12.45 12.40 12.36 15 12.37 12.44 12.45 12.41 12.37 * All values in the table in centimeters Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 584.
Extract the process parameters Subgroups Observations in each sub-group Average Range 1 2 3 4 5 X (R) 1 12.45 12.39 12.55 12.38 12.40 12.434 0.17 2 12.55 12.39 12.40 12.38 12.44 12.432 0.17 3 12.46 12.44 12.44 12.35 12.36 12.410 0.11 4 12.38 12.39 12.55 12.38 12.40 12.420 0.17 5 12.37 12.44 12.45 12.41 12.41 12.416 0.08 6 12.45 12.37 12.44 12.38 12.41 12.410 0.08 7 12.46 12.38 12.35 12.50 12.44 12.426 0.15 8 12.44 12.39 12.37 12.45 12.39 12.408 0.08 9 12.44 12.55 12.44 12.37 12.55 12.470 0.18 10 12.35 12.38 12.45 12.44 12.38 12.400 0.10 11 12.36 12.37 12.41 12.40 12.40 12.388 0.05 12 12.51 12.36 12.41 12.37 12.39 12.408 0.15 13 12.38 12.50 12.45 12.37 12.44 12.428 0.13 14 12.41 12.37 12.45 12.40 12.36 12.398 0.09 15 12.37 12.44 12.45 12.41 12.37 12.408 0.08 Average of all 15 observations 12.417 0.119 * All values in the table in centimeters Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 584.
Establish Control Limits Table for selecting values for establishing the control limits for X and R Charts* Sample A 2 D 3 D 4 size (n) 2 1.880 0 3.268 3 1.023 0 2.574 4 0.729 0 2.282 5 0.577 0 2.114 6 0.483 0 2.004 7 0.419 0.076 1.924 8 0.373 0.136 1.864 9 0.337 0.184 1.816 10 0.308 0.223 1.777 * Source: Juran, J.M. and F.M. Gryna, (1995), Quality Planning and Analysis, Tata McGraw-Hill, 3 rd Edition, New Delhi, pp 385.
Establish Control Limits In our example, A 2 = 0.577; D 3 = 0; D 4 = 2.144 * All values in centimeters
XChart X-bar Chart Mean Diameter (cms) 12.50 12.49 12.48 12.47 12.46 12.45 12.44 12.43 12.42 12.41 12.40 12.39 12.38 12.37 12.36 12.35 12.34 Sample Means Centre Line UCL LCL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sample Number Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 585.
R Chart An example Mean Range (cms) 0.28 0.26 0.24 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 R Chart Sample Range Centre Line UCL LCL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sample Number Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 585.
P Chart Suppose the same cylinders are subjected to a much simpler testing of merely classifying them as defect When the cylinder is beyond the acceptable limits (too small or too big in diameter) it is classified as defect Sampling Plan is as follows: Sample 100 pieces every 30 minutes for testing Collect 12 such samples
P Chart Step 1: Choose the measurement characteristic: Diameter of a cylindrical component (cm) Step 2: Choose the measurement method Classify as good or bad (attribute based) Step 3: Choose the Control Chart: P Chart Step 4: Decide on a Sampling Plan Step 5: Collect Data & Establish Control Limits
Data for the chart Sampling Plan Sample no. Number of defects Sample every 30 minutes Each time take 100 consecutive samples Take 12 such samples 1 10 2 9 3 8 4 11 5 7 6 12 7 7 8 10 9 13 10 12 11 13 12 14 * All values in the table in centimeters Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 586.
Extract the process parameters Sample no. Number of p defects (%) 1 10 0.10 2 9 0.09 3 8 0.08 4 11 0.11 5 7 0.07 6 12 0.12 7 7 0.07 8 10 0.10 9 13 0.13 10 12 0.12 11 13 0.13 12 14 0.14 Average of all 12 observations 0.105 * All values in the table in centimeters
Establish Control Limits
P Chart p Chart p Centre Line UCL LCL 0.21 0.18 Proportion of defects 0.15 0.12 0.09 0.06 0.03 0.00 1 2 3 4 5 6 7 8 9 10 11 12 Sample No. Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 587.
C Charts Similar to p chart, Instead of proportion of defects, we merely count the number of defects Appropriate in certain situations Number of knots in a square meter of a cloth Number of scratches in a square meter of a smooth finished surface etc.
Computing the limits for C Chart
Quality Management Using the Control Charts
Using the Control Charts There are two questions that comes to our mind when it comes to using the control charts: Is the process of out of control? What are we supposed to do in that case? Is there a way we can detect an impending out of control situation much earlier?
Number of defects Process out of control Points outside the control limit c Chart c Centre Line UCL LCL 24 21 18 15 12 9 6 3 0 1 2 3 4 5 6 7 8 9 10 Sample No. Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 588.
Steps to be taken Step 1: Remove the outlier and re-compute the control limits (revise the chart parameters) Step 2: Perform a detailed investigation to explore any assignable causes for the drift in the performance Step 3: If there are no assignable causes, resume the process with revised control parameters Step 4: If there are assignable causes implement countermeasures, and resume the process Step 5: Stabilize the process, re-establish control limits and ensure the process is in control
Early Detection of Problems The other question pertains to early detection of an impending problem Over several years, some useful rules have been created that helps operating personnel to detect a possible drift in the process
Zones A, B and C Mean Zone A Zone B Zone C Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 590.
When to Stop the Process One point beyond Zone A Nine points in a row in Zone C or beyond Six points in a row, steadily increasing or decreasing Fourteen points in a row, alternating up & down Two out of three points in a row in Zone A or beyond Four out of five points in a row in Zone B and beyond Fifteen points in a row in Zone C
Predictive capability of processes Which process is better? Process B is better than Process A Spread of a process is indicative of its capability Lesser the spread better is the process Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 591.
Predictive capability of processes Which process is better? Offset Process A is better than Process B A process that is aligned closer to the desired target is likely to be more capable Process B Process A LSL Source: Mahadevan, B. (2015), Operations Management: Theory & Practice, Pearson Education, 3 rd Edition, pp 592. Target USL
Process Capability Process Capability is defined by the spread of the process Potential capability (C p ) is defined as the ratio of the difference in specification limits to the process spread C p = Specification Range Pr ocess Capability ( USL LSL) 6 Actual capability (C pk ) takes into consideration the extent to which the process has deviated from the desired target C pk = Min Pr ocess Centre LSL USL Pr ocess, 3 3 Centre
Process Capability & Defects Process Capability Index (C pk ) Total Products outside the specification limits 0.25 453,255 ppm 0.50 133,614 ppm 0.60 71,861 ppm 0.80 16,395 ppm 1.00 2,700 ppm 1.20 318 ppm 1.50 7 ppm 1.70 0.34 ppm 2.00 0.0018 ppm Source: Quality Planning & Analysis, Juran & Gryna, Chapter 17, 3e
Six sigma Organization C pk is a good measure to predict the defects coming out of a process It could be used to target improvements in the process Suppliers could be asked to submit their C pk levels and it can be continuously monitored A six sigma organization is one which is able to achieve a C pk value of 2
Quality Management Issues in Service Quality
Service Quality Issues Example A flight that is supposed to take off at 7.30 pm is getting delayed. The airline customer relationship officer has kept the passengers in the dark about the delay. Further, upon mounting pressure announces a departure time which never happened.
Service Quality Issues Example A week end program in a business school was a disaster as it was nowhere near the expectations of the participants A number of e-retailers in the US failed miserably during the Christmas season of 1999. They could not deliver the Christmas gift before Christmas. Instead they returned the money paid by the customers with a $ 50 add on to it and an apology note..
Intangibility Performances rather than objects, therefore precise specs. can be rarely set Cannot be counted, measured, inventoried, tested and verified in advance to assure quality Difficult to understand how consumers perceive & evaluate their services
Heterogeneity Performance vary from producer to producer, consumer to consumer, day to day Consistency of behavior from service personnel is difficult to assure What firms intend to deliver may be different from what the consumer receives
Simultaneity Not engineered in a plant and then delivered in tact to the consumer Quality occurs during service delivery while the consumer interacts with the service personnel Consumers input may be critical to quality The service firm may have less managerial control in real time
Service Quality Some considerations Service quality is A measure of how well the service delivered matches with expectations Pre-dominantly is a function of perceptions of the customers (Example of the weekend course in a Business School)
Service Quality Some considerations Quality evaluations are Not made solely on the outcome of the service They also involve evaluation of the process of delivery (Example of Airline Delays & the way it was handled) (Example of e-tailers inability to deliver Christmas Gifts) Points to difficulty in Service Recovery (after a failure)
Service Quality The five gaps model Expected Service Gap 5 Consumer Perceived Service Firm Gap 3 Service Delivery Gap 4 External Communications to Consumers Gap 1 Translation of perceptions into Service Qlty. Specs. Gap 2 Management perceptions of Consumer Expectations Source: Parasuraman, A., Zeithhaml, V.A. and Berry, L.L., (1985), A conceptual model of service quality & its implications for future research, Journal of Marketing, 49 (4), 41 50.
Gaps in Service Quality Why do they occur? Gap 1: Service firm executives may not always understand What the consumer wants? What features a service must have? What levels of performance? Gap 2: Means to meet the expectations absent Knowledge of consumer expectations exist but not the perceived means to deliver Absence of management commitment to quality Gap 3: Variability in employee performance Gap 4: Problems arising out of communication Firms tend to promise more in communications than what they deliver in reality Firms tend to neglect to inform consumers of special efforts to assure quality that are not visible to consumers Gap 5 = f (Gap 1, Gap 2, Gap 3, Gap 4)
Service Quality Concluding Remarks Service Quality is more challenging than product quality as it is a function of the perceptions of the customers Organizations can use the notion of gaps in service delivery to identify specific improvement opportunities in the service delivery process