Software Quality. Unit 2. Advanced techniques

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1 Software Quality Unit 2. Advanced techniques

2 Index 1. Statistical techniques: Statistical process control, variable control charts and control chart for attributes. 2. Advanced techniques: Quality function deployment (QFD), Failure mode and effect analysis (FMEA), Poka-Yoke.

3 Statistical process control (SPC) Process stability observation trough statistical techniques. Continuous process improvement. GOALS: To study the way in which process variables can affect essential characteristic of the product or service.

4 Statistical process control (SPC) SPC is a tool which permits to predict, reduce and maintain variations within reasonable limits. It is possible to find out: Controllable variables (assignable causes): changes in the raw material, breakdown in the machinery... Uncontrollable variables (non assignable causes): random variables

5 Statistical process control (SPC) A process is said to be under statistical control when it is only affected by random variables due to non assignable causes. If it is also affected by assignable causes, it is said that the process is out of statistical control. In SPC variations are measured, it is study their causes and they are corrected.

6 Control Charts It is a plotting graph in which a central line and tolerance limits (upper and lower) appears. This lines permit to study the variation of a determine variable (plotted values) with respect to the central line. To do that, samples are periodically taken during the manufacturing process. The average and variability of the process are studied, and the process are kept under statistical control.

7 Size Control Charts Sample number Size Tolerance Control Chart UTL LTL Sample number

8 Control Charts Two types: By variable: the variation of a measurable characteristic is controlled. By attributes: it is controlled if a determined condition is satisfied.

9 Variable Control Charts Normal Distribution Gauss bell 66,26% 95,44% 99,73%

10 Variable Control Charts To know if a process following a Normal distribution is under statistical control, it is needed to calculate Upper and Lower x Control limits: LCS LCI With these limits, it is assured that the 99.73% of the measured values are within limits. x x 3 3

11 Size Variable Control Charts Sample number UCL UTL x LTL LCL

12 Control Charts for Attributes They are used when measurements are not possible, for example, scratches, damages, etc. These controls are made at the end of the process, when defects are presented. They are necessary to assure product quality. One of the most used is the p chart. The p chart is used to report the proportion of nonconforming unit in a sample or subgroup.

13 Control Charts for Attributes From data the proportion of nonconforming units are calculated for each one of the samples (p%) p%, the average of the p%, and control limits are then calculated. p%( 100 UCL p p% 3. n p%) p% ( 100 LCL p p% 3. n p%)

14 p% Control Charts for Attributes Sample number Number inspected Number nonconforming Proportion %p 5,0 5,0 2,5 12,5 5,0 10,0 2,5 5,0 2,5 7,5 p% = 5,75 UCL= 16,79 LCL= - 5,29 20,0 18,0 16,0 14,0 12,0 10,0 p Chart of nonconforming unit UCL 8,0 6,0 4,0 2,0 p% 0,

15 Process Capability It is said that a process or machine is capable when they meet specifications. Process capability is not only used to assure that process are under statistical control, but also to minimize the number of nonconforming units.

16 Length Process Capability A process with UCL > UTL (Upper Tolerance Limit) can produce nonconforming products Sample number UCL USL x LSL LC

17 Process Capability When a process follows a normal distribution, the process is in statistical control, process capability is equal to 6. Thus, if we are capable of designing processes so that UTL LTL > 6, it is possible to assure that practically all the products are in tolerance zone. LTI LTS 99,73% LTS-LTI

18 Capability Index Capability index is the relation between tolerance and process capability ( 6 ). UTL LTL C p 6σ If 1 process produces nonconforming units. C p If C p 1,33 process is capable, and values are in tolerance zone.

19 Capability Index A process with C p 1,33 can produce units out of limits, when the process is not centered. They are defined Upper and Lower capability index as follow: C pu UTL 3σ x C pl x - LTL 3σ

20 Capability Index A process is said to be centered iff: (C pl = C p = C pu ) If Cp > CpL, the process is off-center towards lower tolerance limit. If Cp > CpU, the process is off-center towards upper tolerance limit.

21 Sampling plans It is an acceptation by inspection technique. A shipment or lot is accepted if a small sample of the product meets specifications.

22 Sampling plans PROBLEMS: How to determine the size of the sample? Which criteria use to accept or reject the lot?

23 Sampling plans SOLUTIONS: Characteristic function of defect per lot. Specific software for determining UNE tables.

24 Quality Function Deployment (QFD) It is a very useful tool to: Know customer wishes. Define product or service characteristics and requirements. Reduce development time. Reduce complaints. Remove non valuable processes.

25 Quality Function Deployment (QFD) House of quality is the primary tool used in QFD. It is a matrix in which Customer Requirements (CR) are correlated with prioritized Technical Descriptors(TD).

26 Quality Function Deployment (QFD) Steps to build up the matrix: 1. List Customer Requirements (WHATs). 2. Group customer data. 3. Assign priorities to CR s. 4. List Technical Descriptors (HOWs). 5. Group technical descriptors (affinity diagram).

27 Quality Function Deployment (QFD) 6. Develop a relationship matrix between WHATs & HOWs. 7. Develop an interrelationship matrix between HOWs. 8. Competitive assessment (compare with current products in the market).

28 Quality Function Deployment (QFD) 9. Develop objective measures per Technical Descriptor (TD). 10.Establish objectives per TD. 11.Select TD of urgent attention.

29 Quality Function Deployment (QFD) Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

30 Failure Mode and Effect Analysis (FMEA) FMEA is a preventive and multidisciplinary teamwork technique that it is performed in a planned and systematically way in order to detect failures in a design, product or service.

31 Failure Mode and Effect Analysis (FMEA) TYPES of FMEA: Design FMEA: It is focused into product and component design. Will reduce development time and cost of manufacturing process. Process FMEA: It is utilized to identify potential process failure modes by ranking failures and helping to establish priorities according to relative impact on the internal or external customer.

32 Failure Mode and Effect Analysis (FMEA) OBJECTIVES: Recognize and evaluate the potential failure of a product or process and its effects. Identify actions that could eliminate or reduce the chance of the potential failure occurring. Analyze and evaluate the efficacy of the adopted actions and provided resources.

33 Failure Mode and Effect Analysis (FMEA) Steps to follow: 1. Set up the working team. 2. Define process / product functions. 3. Predict potential failure modes. 4. Identify potential failures effects. 5. Analyze possible failure causes.

34 Failure Mode and Effect Analysis (FMEA) 6. Identify current control systems. Steps to follow: 7. Determine assessment index per failure mode. 8. Plan improvement actions. 9. FMEA revision and traceability.

35 Failure Mode and Effect Analysis Example: (FMEA) Perform a product FMEA for a hair dryer fan. Step 2, define product functions. Component fan Function To impel the air To refresh the engine Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

36 Failure Mode and Effect Analysis (FMEA) Step3, predict potential failure modes. Component Function Failure mode fan To impel the air To refresh the engine Blade breakdown Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

37 Failure Mode and Effect Analysis (FMEA) Step 4, identify potential failure effects. Component Function Failure mode Failure effect fan To impel the air To refresh the engine Blade breakdown Temperature raising Hair dryer stops User s burns Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

38 Failure Mode and Effect Analysis (FMEA) Step 5, Analyze possible failure causes (cause-and-effect diagram). Component Function Failure mode Failure effect Failure cause fan To impel the air To refresh the engine Blade breakdown Temperature raising Hair dryer stops User s burns Defect in material composition Defective assembly Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

39 Failure Mode and Effect Analysis (FMEA) Step 6, Identify current control systems. Component Function Failure mode Failure effect Failure cause Controls fan To impel the air To refresh the engine Blade breakdown Temperature raising Hair dryer stops User s burns Defect in material composition Defective assembly Supplier certificate Automatic auto control Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

40 Failure Mode and Effect Analysis (FMEA) Step 7, determine assessment index per failure mode. Severity Occurrence Detection Criteria S Criteria O Criteria D Very slow, failure is not perceptible by customer Low, minor disruption to customer Moderate, it produces dissatisfaction in customer High, it produces high dissatisfaction in customer. Very high, it produces standards non conformity or safety problems. 1 Exceptionaly 1 Remote probability failure comes to customer 2-3 Hardly ever 2-3 Low probability failure comes to customer. 4-6 often 4-6 Moderate Frequently 7-8 High probability Almost ever 9-10 Very high probability

41 Failure Mode and Effect Analysis (FMEA) Step 7, determine assessment index per failure mode. Risk Priority Number Component Function Failure mode Failure effect Failure cause Current conditions Controls O S D RPN fan To impel the air To refresh the engine Blade breakdown Temperature raising Hair dryer stops User s burns Defect in material composition Defective assembly Supplier certificate Automatic auto control Image from the book CALIDAD. Pablo Alcalde San Miguel. Ed. THOMSON Paraninfo

42 Failure Mode and Effect Analysis (FMEA) Step 8, to plan improvement actions An individual or group is designed as responsible. A new assessment of failure modes with the actions taken are made. Concrete actions: Changes in product, service or process design Inspection or control increment

43 Failure Mode and Effect Analysis (FMEA) Step 9, FMEA revision and traceability. After corrective actions has been implemented, resulting severity, occurrence and detection ranks should be re-calculated. Periodic reviews are planned to update the FMEA.

44 Poka-Yoke It is a quality technique proposed by a Japanese engineer Shigeo Shingo in the sixties. The term means mistake-proofing. Poka-Yoke refers to a special inspection mechanism which is used primarily to detect and prevent causes of defects in a system. Poka-Yoke is a method of preventing errors by putting limits on how an operation can be performed in order to force the correct completion of the operation.

45 Poka-Yoke CERO DEFECTS A defect is the result or effect of a simple error. With 100% inspections in errors source, defects are removed. 100% inspections have a cost and it must be bounded by the expected profit.

46 Poka-Yoke Shingeo Shingo s CERO DEFECTS : Inspection in error source. 100% inspections using simple and cheap Poka-Yoke systems. Immediate action, operations are stopped when an error occurs. Moreover, they do not continue until cause is remove.

47 Poka-Yoke The ideal is to include Poka-Yokes from design stage. If we don t do that, we are not following the basic quality principle to do it right the first time. Usually, Poka-Yokes are used in combination with other techniques. FMEA is one of them. Through FMEA it is possible to deduce where must be included Poka-Yokes into the process.

48 Poka-Yoke SYSTEM Product or Service Design Process Design Product or Service FMEA Poka-Yoke Process FMEA Poka-Yoke CUSTOMER

49 Improvement Improvement Improvement Zero Defects Evolution 1 st Stage Process 2 nd Stage Etapa Process 3 rd Stage Process 4 th Stage Process 5 th Stage Process Error Error Error Error Error Defect Defect Defect Defect Inspections Inspections Inspections POKA-YOKE Zero Defects Customer Customer Customer Customer Customer Nonconforming products Conforming products Conforming products Conforming products Conforming products

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