# J0444 OPERATION MANAGEMENT Six Sigma Pert 12 Universitas Bina Nusantara.

## Presentation on theme: "J0444 OPERATION MANAGEMENT Six Sigma Pert 12 Universitas Bina Nusantara."— Presentation transcript:

J0444 OPERATION MANAGEMENT Six Sigma Pert 12 Universitas Bina Nusantara

History Carl Frederick Gauss (1777-1885) introduced the Normal Curve concept. Walter Shewhart (1920): Six Sigma as a measurement standard in product variation Bill Smith, an engineer from Motorola terminologized the “Six Sigma” In the late 1970's, Mikel Harry, a senior engineer at Motorola's Government Electronics Group (GEG), began to experiment with problem solving through statistical analysis. Using his methodology, GEG began to show dramatic results Dr. Mikel Harry and Richard Schroeder, were responsible for creating the unique combination of change management and data- driven methodologies that transformed six sigma from a simple quality measurement

 Metric Benchmark Vision Philosophy Method Tool Symbol Goal Value Letter in the Greek Alphabet. Used to Describe the Distribution of Any Process. The “Sigma Value” is a Metric. It Indicates How Well a Business Process is Performing. “Six Sigma” is a Philosophy Aimed at Increasing the Sigma Value of All Business Processes. Konsep Six Sigma

Measure of Quality Process For Continuous Improvement Enabler for Culture Change What is Six Sigma

Measure of Quality Example #1: Manufacturing Steel Rolling Mill Sheet Thickness is a CTQ (Critical to Quality Parameter) Nominal Thickness = 1000 mm Minimum Spec = 950 mm Maximum Spec= 1050 mm Scrap Production averages 100 meter / Coil

Measure of Quality Steel Strip Thickness Quite some Variation -Ending up as Scrap Upper Specification Limit No Less Than 950 mm Lower Specification Limit No More Than 1050 mm Scrap

Measure of Quality Let’s Look at some Basic Statistics Mean Thickness = 993 mm Standard Deviation = 25 mm Upper Specification Limit Lower Specification Limit Mean Thickness 993 mm Standard Deviation 25 mm On Average it’s OK - it’s a Variation issue

Measure of Quality How Capable is our Process to Produce within Spec? Sigma Rating = Spec Width / 2* SD UpperSpecificationLimitLowerSpecificationLimit Spec Width (1050-950) 100 mm Standard Deviation 25 mm = 100 / 50 = 100 / 502=

Measure of Quality Reducing Variation is Clearly the Key to Improving Process Capability Upper Specification Limit Lower Specification Limit Std Dev Spec Width 22 100  m 25  m

Measure of Quality Reducing Variation is Clearly the Key to Improving Process Capability Upper Specification Limit Lower Specification Limit Std Dev Spec Width 33 100  m 17  m

Measure of Quality Reducing Variation is Clearly the Key to Improving Process Capability Upper Specification Limit Lower Specification Limit Std Dev Spec Width 44 100  m 12  m

Measure of Quality Reducing Variation is Clearly the Key to Improving Process Capability Upper Specification Limit Lower Specification Limit Std Dev Spec Width 55 100  m 10  m

Measure of Quality Reducing Variation is Clearly the Key to Improving Process Capability Upper Specification Limit Lower Specification Limit Std Dev Spec Width 66 100  m 8  m

Measure of Quality SpecStandardSigmaDPMO % WidthDeviationLevelIn Spec Upper Specification Limit Lower Specification Limit 22 100 25 2308,500 69.1 Unit : Each Measurement Defect : Measurement out of Spec Defect Opportunities per Unit : 1 Quality expressed as DPMO ( Defects per Million Opportunities) 6 Sigma Lingo

Measure of Quality Upper Specification Limit Lower Specification Limit 3333 SpecStandardSigmaDPMO % WidthDeviationLevelIn Spec 100 25 2308,500 69.1 100 17 3 66,800 93.3 Unit : Each Measurement Defect : Measurement out of Spec Defect Opportunities per Unit : 1 Quality expressed as DPMO ( Defects per Million Opportunities) 6 Sigma Lingo

Measure of Quality Upper Specification Limit Lower Specification Limit 44 SpecStandardSigmaDPMO % WidthDeviationLevelIn Spec 100 25 2308,500 69.1 100 17 3 66,800 93.3 100 12 4 6,200 99.4 Unit : Each Measurement Defect : Measurement out of Spec Defect Opportunities per Unit : 1 Quality expressed as DPMO ( Defects per Million Opportunities) 6 Sigma Lingo

Measure of Quality Upper Specification Limit Lower Specification Limit 55 SpecStandardSigmaDPMO % WidthDeviationLevelIn Spec 100 25 2308,500 69.1 100 17 3 66,800 93.3 100 12 4 6,200 99.4 100 10 5 233 99.98 Unit : Each Measurement Defect : Measurement out of Spec Defect Opportunities per Unit : 1 Quality expressed as DPMO ( Defects per Million Opportunities) 6 Sigma Lingo

Measure of Quality Unit : Each Measurement Defect : Measurement out of Spec Defect Opportunities per Unit : 1 Quality expressed as DPMO ( Defects per Million Opportunities) 6 Sigma Lingo Upper Specification Limit Lower Specification Limit SpecStandardSigmaDPMO % WidthDeviationLevelIn Spec 100 25 2308,500 69.1 66 100 17 3 66,800 93.3 100 12 4 6,200 99.4 100 10 5 233 99.98 100 8 6 3 99.9997

Example #2: Product Delivery PT X deliver their products to it’s customer five times, their delivery time data are 21 days, 15 days, 12 days, 10 days, and 2 days. The AVERAGE (Mean) of Their Delivery Time is: 21 + 15 + 12 + 10 + 2 = 60/5 = 12 DAYS PT Y deliver their products to it’s customer five times, their delivery time data are 14 days, 12 days, 12 days, and 10 days. The AVERAGE (Mean) of Their Delivery Time is: 14 + 12 + 12 + 12 + 10 = 60/5 = 12 DAYS Measure of Quality

Upper Specification Limit Upper Specification Limit Lower Specification Limit Lower Specification Limit Upper Specification Limit Upper Specification Limit Lower Specification Limit Lower Specification Limit PT X The AVERAGE (Mean) of Their Delivery Time is: 12 DAYS But… Standard Deviation = 7.0 PT Y The AVERAGE (Mean) of Their Delivery Time is: 12 DAYS And…. Standard Deviation = 1.4

Measure of Quality Baseline 12 24 14 7 16 8 20 25 14 10 11 30 16 15.8 Improved (?) 27 7 15 4 18 6 23 6 2 24 2 6 5 11.2 Mean SD 7.0 9.0 BUT….our customer only feels the VARIANCE,….and cancel the next orders! What the Company Feels 11.215.8 What Customer Feel Using mean-based thinking, we improve average performance by 29%, and break out the champagne….. Example #2: Service Time

Measure of Quality Improved (?) 11 10 12 11 12 10 11.07 Mean SD 0.76 but UNFORTUNATELY, what the customer wants is 9 days (or what competitors can do is 9 days)….so it is not variance issue anymore, but now about the Process Centering issue Now it is improved….the Mean is 11, and the STD is below 1….

23 Variation is the enemy! Variation reduction = Defect reduction Six Sigma process = Defect reduction until 3.14 out of 1 Million products/process Quality measurement = measurement of defect on the process/products

24 Variation Reduction Goal: Reduce Process Width-Variation is the Enemy A Process is “A Distribution of Distributions”

25 The Athletic View of Performance Consider a goalkeeper who plays 50 games a year and faces 40 shots on goal each game. A defect is when the opposition scores. A 6s goalkeeper will be scored against once in every 147 years

US airline fatality rate: 6s Is 6 Sigma Impossible? US airline baggage handling: 30-50,000 lost items per million, ~ 3 - 3.5  Average Companies: 30-50,000 defects per million ~ 3 - 3.5  World-Class Companies: <1000 defects per million 5 - 5.5 

Reduce Spread Reduce Spread Center Process Center Process Off-Target X X X X X X X X X On-Target X X X X X X X X X X Variation X X X X X X X X X X X X Nature Of The Problem Process for Continuous Improvement

Y = f {X 1, X 2, X 3, …X n } Output (Dependent Variable) Process (Independent Variables)

29 Identifying Variation Sources Y = f ( X 1, X 2, X 3, X 4, …, X n-2, X n-1, X n ) OUTPUT Y Process X1X1 X2X2 X3X3 X4X4 X n-2 X n-1 XnXn Inside the Box Xs Controllable Outside the Box Xs Un-Controllable

Six Sigma approach is focus on process… Fixing process so they will produce perfection on products and services

Define Measure Analyze Improve Control For improve existing process/products DMAIC Six Sigma Methodology

Define Measure Analyze DesignVerify For new process/products (sometime called DFSS, Design For Six Sigma) DMADV Six Sigma Methodology

33 Define Phase Voice of Customer (VOC)? Problems? Unit, Defects, Opportunities?

34 Measure Phase Collect data baseline Identify frequency of defects? Baseline process capability? Target or benchmark process capability?

35 Analyze Phase Why, when, where defects occurs? Data analysis Process analysis Identify Vital X’s

36 Improve Phase How to reduce defects? Fix problems Collect improved data

37 Control Phase Calculate new process capability Verify statistically improvement made Implement process control

 Establishing these factors provides the seeds of success.  They need to be integrated consistently to fit each business.  They are all necessary for the best result  The most powerful success factor is “committed leadership.” Committed and Involved Leadership Business Process Framework Six Sigma Projects Strategy Integration Full Time 6 Sigma Team Leaders Incentives & Accountability Quantifiable Measures & Results The 6 Sigma Success Factors

What Is Six Sigma Projects? Project needs to be done by Every Employee (Green Belt) Project using Six Sigma methodology (DMAIC/DMADV) Project that making Improvement (DPMO reduction) Project that has a measurable unit and defect Project that has a measurable impact Project that start with Customer (internal/external) CTQ

Harvesting the Fruit of Six Sigma Sweet Fruit - 6s Design for Six Sigma (DFSS) Bulk of Fruit - 4 to 5s Process Improvement Six Sigma Tools Low Hanging Fruit - 3 to 4s Basic Quality Tools Ground Fruit - up to 2s Logic and Intuition Process Entitlement Start With Low Hanging Fruit

Download ppt "J0444 OPERATION MANAGEMENT Six Sigma Pert 12 Universitas Bina Nusantara."

Similar presentations