Product Manufacturing CHEN 4470 – Process Design Practice Dr. Mario Richard Eden Department of Chemical Engineering Auburn University Lecture No. 22 – Introduction to Six Sigma in Product Manufacturing March 28, 2013 Contains Material Developed by Dr. Daniel R. Lewin, Technion, Israel

Instructional Objectives  Be able to define the Sigma Level of a manufacturing process  Know the steps followed in product design and manufacture (DMAIC)  Be able to qualitatively analyze a process for the manufacture of a product and know how to identify the CTQ step using DMAIC

Example: The Electronics Food Chain Product Development Source: Dataquest 1999 data Electronic Equipment and Systems $988 B Semiconductors $170 B Materials and $33 B Equipment Semiconductor

IC Production Capability –Moore’s Law Product Development

Device Complexity Trends Product Development Chip Area DeviceYearTransistors per Chip (cm 2 ) K K K M1.8 Pentium M2.9 Pentium Pro M2.9

Technology vs. Economics Product Development Physical Limit Economic Limit The budget always runs out before the physical limits are reached. Capability Cost

Technology vs. Economics (Continued) Product Development Physical Limit Economic Limit Capability Cost New Physical Limit Innovation!!

Implications of Blind Faith in Moore’s Law –Fear is that exponential growth is only the first half of an “S” shaped curve Product Development Revenue Time

Industry Drivers (Push vs. Pull) –Market requires (push): Smaller feature sizes desired Larger chip area desired Improved IC designs lead to innovations –IC industry delivers (pull): Lower cost per function (higher performance per cost) New applications are enabled to use chips with new capabilities Higher volumes produced Product Development

Definition –6  = “Six Sigma” –SSLW: Chapter 25SSL: Chapter 19 Description –Structured methodology for eliminating defects, and hence, improving product quality in manufacturing and services. –Aims at identifying and reducing the variance in product quality, and involves a combination of statistical quality control, data analysis methods, and the training of personnel. Six Sigma 1:15

Statistical Background –  is the standard deviation (SD) of the value of a quality variable, x, a measure of its variance, assumed to be normally distributed: –Assume Lower Control Limit LCL =  - 3 , and Upper Control Limit UCL =  + 3  : Six Sigma 2:15 Average Standard Deviation   + 3   - 3 

Statistical Background (Continued) –At SD = , the number of Defects Per Million Opportunities (DPMO) below the LCL in a normal sample is: Six Sigma 3:15 In a normal sample, the DPMO will be the same above the UCL. The plot shows f(x) for  = 2.

Methodology –In accepted six-sigma methodology, a worst-case shift of 1.5  in the distribution of quality is assumed, to a new average value of   Six Sigma 4:15 In this case, the DPMO above the UCL = 66,807, with only DPMO = 3 below the LCL (  = 2).

Methodology (Continued) –However, if  is reduced by ½ (  = 1), so that the new LCL =  - 6 , and UCL =  + 6 , the DPMO for normal and abnormal operation are now much lower: Six Sigma 5:15

Sigma Level vs. DPMO Six Sigma 6:15 Sigma Level DPMO , , , , , ,

Simple Example: Computing the Sigma Level –On average, the primary product from a specific distillation column fails to meet its specifications during five hours per month of production. Compute its sigma level. Solution –The chart on slide 15 gives the Sigma level as 3.8  Six Sigma 7:15

Computing Throughput Yield –For n steps, where the number of expected defects in step i is DPMO i, the defect-free throughput yield (TY) is: –If the number of expected defects in each step is identical, then TY is: Six Sigma 8:15

Simple Example: Computing Throughput Yield –In the manufacture of a device involving 40 steps, each step is operating at 4  (DPMO=6,210): –This means that 22% of production is lost to defects! –Corresponding to approximately 220,000 units per million produced (DPMO  220,000) –The chart on slide 15 gives the Overall Sigma level as 2.3  Six Sigma 9:15

Monitoring and Reducing Variance –A five-step procedure is followed - Define, Measure, Analyze, Improve, and Control - DMAIC: Define –A clear statement is made defining the intended improvement. –Next, the project team is selected, and the responsibilities of each team member assigned. –To assist in project management, a map is prepared showing the Suppliers, Inputs, Process, Outputs and Customers (referred to by the acronym, SIPOC). Six Sigma 10:15

Define (Continued) –Example: A company producing PVC tubing by extrusion needs to improve quality. A SIPOC describing its activities might look like this: Six Sigma 11:15

Measure –The Critical To Quality (CTQ) variables are monitored to check their compliance with the UCLs and LCLs. –Most commonly, univariate statistical process control (SPC) techniques, such as the Shewart chart, are utilized. –The data for the critical quality variables are analyzed and used to compute the DPMO and the sigma level. –Example: Continuing the PVC extrusion example, suppose this analysis indicates operation at 3 , with a target to attain 5  performance. Six Sigma 12:15

Analyze –To increase the sigma level, the most significant causes of variability are identified, assisted by a systematic analysis of the sequence of manufacturing steps. –This identifies the common root cause of the variance. –Example: In the PVC extrusion example, a list of possible causes for product variance includes: Variance in quality of PVC pellets Variance in volatiles in pellets Variance in steam heater operating temperature Six Sigma 13:15

Improve –Having identified the common root cause of variance, it is eliminated or attenuated by redesign of the manufacturing process or by employing process control. –Example: Continuing the PVC tubing example, suggestions to how the variance in product quality can be reduced include: Redesign the steam heater. Install a feedback controller to manipulate the steam valve to enable tighter control of the operating temperature. Combination of the above. Six Sigma 14:15

Control –After implementing steps to reduce the variance in the CTQ variable, this is evaluated and maintained. –Thus, steps M, A, I and C in the DMAIC procedure are repeated to continuously improve process quality. –Note that achieving 6  performance is rarely the goal, and seldom achieved. Six Sigma 15:15

Methodology –The DMAIC procedure is combined with ideas specific to product design to create a methodology that assists in applying the six-sigma approach to product design. –A five-step procedure is recommended: 1.Define project 2.Identify requirements 3.Select concept 4.Develop design 5.Implement design Six Sigma for Design 1:3

Step 1: Define Project –The market opportunities are identified. –A design team is assigned and resources are allocated. –Often, project timeline is summarized in a Gantt chart. Step 2: Identify Requirements –As in DMAIC, the requirements of the product are defined in terms of the needs of customers. Step 3: Select Concept –Innovative concepts for the new design are generated, first by “brainstorming.” –The best are selected for further development. Six Sigma for Design 2:3

Step 4: Develop Design –Often several teams work in parallel to develop and test competing designs, making modifications as necessary. –The goal is to prepare a detailed design, together with a plan for its management, manufacture, and quality assurance. Step 5: Implement Design –The detailed designs in Step 4 are critically tested. –The most promising design is pilot-tested and if successful, proceeds to full-scale implementation. Six Sigma for Design 3:3

Summary – Six Sigma  Define the Sigma Level of a manufacturing process (Increased losses [DPMO] means decreased sigma level).  Apply DMAIC in product design and manufacture.  Qualitatively analyze a process for the manufacture of a product and know how to identify the CTQ step using DMAIC. On completion of this part, you should:

Next Lecture – April 9 –Final reports and oral presentations Other Business