Total Quality Management Dr. Nur Aini Masruroh Hidden costs of poor quality Reprocessing Rejects Sorting Inspection Customer returns Warranty Downgrading expenses of product Competitor Lost sales Process downtime Extra inventory Lost discounts Damaged goods Overtime to correct errors Loss of good will Paperwork errors Delays Obsolete inventory Premium freight costs Incorrect orders shipped Customer allowances Extra process capacity Competitor Competitor Lakukan sesuatu sebelum semuanya terlambat!! 3 aini@ugm.ac.id Definition of Quality What is Quality? What are measures of quality for this product? Conformance to agreed requirements - Agreed between? Fitness for purpose/use ISO 8402 states that TQM is a: 4 “Management approach of an organisation, centered on quality, based on the participation of all its members and aiming at long term success through customer satisfaction, and benefits to all members of the organisation and to society” aini@ugm.ac.id Total Quality Management (TQM): Defined Total quality management is defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer 5 aini@ugm.ac.id TQM Wheel Customer satisfaction 6 aini@ugm.ac.id Nature of Quality Dimensions of Quality Determinants of Quality Costs of Quality 7 aini@ugm.ac.id Best-In-Class and World-Class Customers’ expectations of quality are not the same for different classes of products or services. Best-in-class quality means being the best product or service in a particular class of products or services. Being a world-class company means that each of its products and services are considered best-in-class by its customers. 8 aini@ugm.ac.id Some Dimensions of Product Quality Performance–relative to customer’s intended use Features–special characteristics Reliability–likelihood of breakdowns, malfunctions Serviceability–speed/cost/convenience of servicing Durability–amount of time/use before repairs Appearance–effects on human senses Customer service–treatment before/during/after sale Safety – user protection before/during/after use 9 aini@ugm.ac.id Determinants of Quality Quality of design – products/service designed based on customers’ expectations and desires Quality capability of production processes – processes must be capable of producing the products designed for the customers Quality of conformance – capable processes can produce inferior product if not operated properly Quality of customer service – a superior product does not mean success; must have quality service also Organization quality culture – superior product and service requires organization-wide focus on quality 10 aini@ugm.ac.id Costs of Quality 11 Prevention costs - reducing the potential for defects Appraisal costs - evaluating products Internal failure - of producing defective parts or service External costs - occur after delivery aini@ugm.ac.id TQM Encompasses entire organization, from supplier to customer Stresses a commitment by management to have a continuing, company-wide, drive toward excellence in all aspects of products and services that are important to the customer. 12 aini@ugm.ac.id Prevention Versus Detection This is a central theme of all Quality approaches Detection allows a mistake or error to become a DEFECT! 13 Passed on to next process undetected Much more difficult to identify Costs more to put right Difficult to identify the root cause Can often pass through the system to the external customer! Preoccupied with the OUTPUT from the process aini@ugm.ac.id TQM Principles 14 Internal customer supplier relationship Continuous Improvement Teamwork Employee participation/ development Training and education Suppliers and customers integrated into the process Honesty, sincerity & care aini@ugm.ac.id Starting TQM is like pushing a boulder up a mountain…. hard work!! Along the way its gets harder… People get left behind… Fall out… and it feels like you’re the only one trying! But eventually it gets to a point when the process gathers speed and becomes unstoppable!!! 15 aini@ugm.ac.id Improvement Quality Improvement process Position before Renewal points and possible loss of gains Introduction of TQM Time 16 aini@ugm.ac.id Quality Improvement process Continuous Improvement Improvement Many small improvements Position before Introduction of TQM Renewal points and possible loss of gains Time 17 aini@ugm.ac.id Permanency of TQM Levels of TQM adoption 6. World Class 5. Award Winners 4. Improvers 3. Tool-pushers 1. Uncommitted 2. Drifters Level of TQM adoption 18 aini@ugm.ac.id Levels of TQM Adoption Uncommitted Not yet started a formal process of quality improvement ISO9000 registered, but not utilised effectively Basic tools & techniques in use from customer pressure No long term plan for quality improvement Not aware of major benefits to be gained through quality Uncommitted to any long term plan Don’t really see any benefit to quality improvement 19 aini@ugm.ac.id Levels of TQM Adoption Drifters 20 Existing quality programme for up to 3 years Aware of ‘Received Wisdom’ of quality gurus Initial programme probably fizzled out Ready for different approach but unsure which Quality improvement still perceived as a programme rather than a process Drift from one programme to another Change of approach sparked by new senior executive aini@ugm.ac.id Levels of TQM Adoption Tool-Pushers More experience of quality improvement; up to 5 years Will have experience of SPC, Quality Circles, and other Quality Planning tools such as QFD, FMEA Early success with one approach but has now fallen in to disuse Not all members of the management team are committed to quality; lack of supportive training Looking for quick fixes and latest management fad Right kind of signal to customers, but under surface a fire-fighting culture still remains 21 aini@ugm.ac.id Levels of TQM Adoption Improvers Extensive experience of ongoing quality improvement Understand that TQM is not a short sprint Adopted a continuous improvement approach ‘Quality Champion’ culture starting to emerge Company wide education in place and ongoing Demonstrable progress made in critical business areas Still perhaps reliant on a few key individuals Moving in the right direction, but realize they have a long way to go 22 aini@ugm.ac.id Levels of TQM Adoption Award Winners Capable of competing for major quality awards EFQM Quality Award, MBNQA, Deming Prize Leadership culture ingrained throughout the organisation Several successful organizational changes taken place Fully participative organizational culture TQM view sincerely as a way of managing the business This level marks the end of the TQM apprenticeship Fewer than 200 companies world-wide 23 aini@ugm.ac.id Levels of TQM Adoption World Class Total integration of quality improvement and business strategy Demonstrates sustained award winning performance 24 Japan Quality Control medal award All employees share pursuit of never ending customer satisfaction TQM not just a business strategy, but a way of life Possibly fewer than 15 companies can be considered at this level world-wide aini@ugm.ac.id Deming’s Fourteen Points 6-25 Create consistency of purpose Lead to promote change Build quality into the products Build long term relationships Continuously improve product, quality, and service Start training Emphasize leadership aini@ugm.ac.id Deming’s Points - continued 6-26 Drive out fear Break down barriers between departments Stop haranguing workers Support, help, improve Remove barriers to pride in work Institute a vigorous program of education and selfimprovement Put everybody in the company to work on the transformation aini@ugm.ac.id Quality Loss Function High Loss Unacceptable Loss Poor Fair Good Best Low Loss Frequency Target-oriented quality yields more product in the "best" category Conformance-oriented quality keeps products within 3 standard deviations Lower 27 Target Upper Distribution of Specifications for Products Produced aini@ugm.ac.id Target Specification Example A study found U.S. consumers preferred Sony TV’s made in Japan to those made in the U.S. Both factories used the same designs & specifications. The difference in quality goals made the difference in consumer preferences. Japanese factory (Target-oriented) Freq. LSL 28 Target USL U.S. factory (ConformanceX oriented) aini@ugm.ac.id Quality Loss Function; Distribution of Products Produced Quality Loss Function (a) High loss Unacceptable Loss (to producing organization, customer, and society) Low loss Target-oriented quality yields more product in the “best” category Target-oriented quality brings products toward the target value Conformanceoriented quality keeps product within three standard deviations Poor Fair Good Best Frequency Lower 29 Target Specification Upper Distribution of specifications for product produced (b) aini@ugm.ac.id PDCA Cycle 30 4.Act: Implement the plan 1.Plan: 3.Check: Is the plan working 2.Do: Test the plan Identify the improvement and make a plan aini@ugm.ac.id Tools of TQM Tools for generating ideas Tools to organize data Pareto charts Process charts (Flow diagrams) Tools for identifying problems 31 Check sheet Scatter diagram Cause and effect diagram Histograms Statistical process control chart aini@ugm.ac.id Seven Tools for TQM 32 aini@ugm.ac.id Pareto Analysis of Wine Glass Defects (Total Defects = 75) 60 88% 54 50 97% 93% 100% 100% 72% 80% 40 60% 30 40% 20 12 5 10 4 2 0 0% Scratches 72% 33 20% Porosity Nicks Contamination 16% 5% 4% Causes, by percent total defects Misc. aini@ugm.ac.id 3% Cumulative Percent Frequency (Number) 70 Process Chart Shows sequence of events in process Depicts activity relationships Has many uses 34 Identify data collection points Find problem sources Identify places for improvement Identify where travel distances can be reduced aini@ugm.ac.id Statistical Process Control (SPC) Uses statistics & control charts to tell when to adjust process Developed by Shewhart in 1920’s Involves 35 Creating standards (upper & lower limits) Measuring sample output (e.g. mean wgt.) Taking corrective action (if necessary) Done while product is being produced aini@ugm.ac.id Statistical Process Control (SPC) Statistical technique used to ensure process is making product to standard All process are subject to variability Natural causes: Random variations Assignable causes: Correctable problems 36 Machine wear, unskilled workers, poor material Objective: Identify assignable causes Uses process control charts Quality Characteristics Variables Attributes Characteristics that you measure, e.g., weight, length May be in whole or in fractional numbers Continuous random variables 37 Characteristics for which you focus on defects Classify products as either ‘good’ or ‘bad’, or count # defects e.g., radio works or not Categorical or discrete random variables Process Control Charts Plot of Sample Data Over Time Sample Value 80 Sample Value UCL 60 40 Average 20 LCL 0 1 5 9 13 Time 38 17 21 Control Chart Purposes Show changes in data pattern e.g., trends Show causes of changes in data Assignable causes Data outside control limits or trend in data Natural causes 39 Make corrections before process is out of control Random variations around average Theoretical Basis of Control Charts Central Limit Theorem As sample size gets large enough, sampling distribution becomes almost normal regardless of population distribution. X X 40 Theoretical Basis of Control Charts Properties of normal distribution 99.7% of al l x fall within 3 x 95.5% of al l x fall within 2 x x x 41 Control Chart Types Continuous Numerical Data Control Charts Categorical or Discrete Numerical Data Variables Charts R Chart 42 X Chart Attributes Charts P Chart C Chart X Chart Type of variables control chart 43 Interval or ratio scaled numerical data Shows sample means over time Monitors process average Example: Weigh samples of coffee & compute means of samples; Plot Control Chart for Samples of 9 Boxes Variation due to assignable causes 17=UCL Variation due to natural causes 16=Mean 15=LCL 1 44 2 3 4 5 6 7 8 Sample Number 9 10 11 12 Variation due to assignable causes Out of control X Chart Control Limits UCL x x A R From Table on p.48 Range for sample i LCL x x A R Mean for sample i n xi x i n 45 n Ri R # Samples i 1 n Factors for Computing Control Chart Limits S am p le S ize, n M ean F acto r, A 2 U p p er R an g e, D 4 L o w er R an g e, D 3 2 1 .8 8 0 3 .2 6 8 0 3 1 .0 2 3 2 .5 7 4 0 4 0 .7 2 9 2 .2 8 2 0 5 0 .5 7 7 2 .1 1 5 0 6 0 .4 8 3 2 .0 0 4 0 7 0 .4 1 9 1 .9 2 4 0 .0 7 6 8 0 .3 7 3 1 .8 6 4 0 .1 3 6 9 0 .3 3 7 1 .8 1 6 0 .1 8 4 10 0 .3 0 8 1 .7 7 7 0 .2 2 3 12 0 .2 6 6 1 .7 1 6 0 .2 8 4 0 .1 8 4 46 R Chart Type of variables control chart Shows sample ranges over time 47 Interval or ratio scaled numerical data Difference between smallest & largest values in inspection sample Monitors variability in process Example: Weigh samples of coffee & compute ranges of samples; Plot R Chart Control Limits UCL R D 4 R LCL R D 3 R n Ri R i 1 n 48 From Table on p. 48 Range for Sample i # Samples Steps to Follow When Using Control Charts 49 1. Collect 20 to 25 samples of n=4 or n=5 from a stable process and compute the mean. 2. Compute the overall means, set approximate control limits,and calculate the preliminary upper and lower control limits.If the process is not currently stable, use the desired mean instead of the overall mean to calculate limits. 3. Graph the sample means and ranges on their respective control charts and determine whether they fall outside the acceptable limits. 4. Investigate points or patterns that indicate the process is out of control. Assign causes for the variations. 5. Collect additional samples and revalidate the control limits. Mean and Range Charts Complement Each Other 50 Patterns to Look for in Control Charts 51 Deciding Which Control Chart to Use Using an X and R chart: Using the P-Chart: 52 Observations are variables Collect 20-25 samples of n=4, or n=5, or more each from a stable process and compute the mean for the X chart and range for the R chart. Track samples of n observations each. We deal with fraction, proportion, or percent defectives Observations are attributes that can be categorized in two states Have several samples, each with many observations Assume a binomial distribution unless the number of samples is very large – then assume a normal distribution. Deciding Which Control Chart to Use Using a C-Chart: 53 Observations are attributes whose defects per unit of output can be counted The number counted is often a small part of the possible occurrences Assume a Poisson distribution Defects such as: number of blemishes on a desk, number of typos in a page of text, flaws in a bolt of cloth Why Transformations to Total Quality Do Not Persist? Managers are not accountable to their people for the quality of their leadership/management Michael Beer Harvard Business School & Center for Organizational Fitness 54 aini@ugm.ac.id