Manufacturing Handbook of Best Practices 2011 Part 4 pptx

69 4 DFMA/DFSS John W. Hidahl Design for manufacture and assembly (DFMA) and design for Six Sigma (DFSS) are complementary approaches to achieving a superior product line that maximizes quality while minimizing cost and cycle time in a manufacturing environment. DFMA is a methodology that stresses evolving a design concept to its absolute simplest configuration. It embodies ten simple rules, which can have an incredible impact on minimizing design complexity and maximizing the use of cost-effective standards. DFSS applies a statistical approach to achieving nearly defect-free prod- ucts. It uses a scorecard format to quantify the parts, process, performance, and software (if applicable) capabilities or sigma level. It facilitates the effective design of a product by aiding the selection of (1) suppliers (parts), (2) manufacturing and assembly processes (process), (3) a system architecture and design (performance), and (4) a software process (software) that minimizes defects and thus produces a high-quality product in a short cycle time. 4.1 DESIGN FOR MANUFACTURE AND ASSEMBLY (DFMA) The DFMA methodology consists of six basic considerations and ten related rules, as shown in Table 4.1. DFMA is intended to increase the awareness of the engineering design staff to the need for concurrent product and process development. Several studies have proven that the design process is where approximately 80% of a product’s total costs are determined. Stated differently, the cost of making changes to a product as it progresses through the product development process increases by orders of magni- tude at various stages. For instance, if the cost of making a change to a product during its conceptual design phase is $1000, then the cost of making the same change after the drawings are released and the initial prototype is fabricated is approximately $10,000. If this same change is not applied until the production run has started, the cost impact will be approximately $100,000. If the need for the design change is not recognized until after the product has been purchased by the consumer or delivered to the end user, the total cost for the change will be approximately 1000 times as great as if it had been implemented during the conceptual design review. In addition to driving product cost, design is also a major driver of product quality, reliability, and time to market. In today’s marketplace, customers are seeking the best value for their investment, and the most effective way to incorporate maximum value into a product’s design disclosure is through the use of DFMA. SL3003Ch04Frame Page 69 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC 70 The Manufacturing Handbook of Best Practices 4.1.1 S IMPLICITY Simplicity is the first design consideration, and it bridges the first five DFMA commandments, namely, (1) minimize the number of parts, (2) minimize the use of fasteners, (3) minimize reorientations, (4) use multifunctional parts, and (5) use modular assemblies. There are several approaches that can be used to minimize the part count in a design, and specific workbook and software techniques have been developed on this, but the driving principles revolve around three questions: (1) Does the part move? (2) Does the part have to be made from a different material than the other parts? and (3) Is the part required for assembly or disassembly? If the answer to all three is no, then that part’s function can be combined with another existing part. Using this approach progressively, existing assemblies that were not based upon DFMA principles can often be redesigned to eliminate 50% or more of their existing parts count. Reduced part counts yield (1) higher reliability; (2) lower configuration management, manufacturing, assembly, and inventory costs; (3) fewer opportunities for defects; and (4) reduced cycle times. Minimizing the use of fas- teners has several obvious advantages, and yet it is the most frequently disregarded principle of DFMA. Excessive fasteners in a design are often the result of engineering design uncertainty, and are often justified as offering flexibility, adjustment, quick component replacement, or modularity. The reality is that excessive fasteners increase the cost of assembly, increase inventory costs, reduce automation opportu- nities, reduce product reliability, and contribute to employee health risks such as TABLE 4.1 DFMA Considerations and Commandments Considerations 1. Simplicity 2. Standard materials and components 3. Standardized design of the product itself 4. Specify tolerances based on process capability 5. Use of the materials most processed 6. Collaboration with manufacturing personnel The Ten Commandments 1. Minimize the number of parts 2. Minimize the use of fasteners 3. Minimize reorientations 4. Use multifunctional parts 5. Use modular subassemblies 6. Standardize 7. Avoid difficult components 8. Use self-locating features 9. Avoid special tooling 10. Provide accessibility SL3003Ch04Frame Page 70 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC DFMA/DFSS 71 carpal tunnel syndrome. Prototype designs may require additional fasteners and interfaces to test various design or component options, but the production design should be stripped of any excessive fasteners. The five why ’s approach as used commonly in root cause analysis is recommended for testing the minimal requirements for fasteners. Unless one of the sequential answers to, “ Why do we need this fastener?” can be traced directly to a stated operational requirement, the fastener(s) should be elimi- nated from the production design disclosure. With respect to minimizing reorienta- tions during assembly, the guiding principles are to create a design that can be easily assembled (with a minimum amount of special tooling) and to always use gravity to aid you in assembly. Minimizing the number of fasteners will obviously contribute toward minimizing the number of reorientations necessary. The use of multifunc- tional parts is a primary method of reducing the total parts count, thus enhancing design simplicity. Similarly, the use of modular subassemblies is a good design method to predesign for continuous product improvement through block upgrades and similar product line enhancements over time. As new technology moves into practice and becomes cost effective, modular subassemblies can be easily replaced to provide expanded capabilities, higher processing speeds, or more economical (market competitive) modular substitutions. Although modular subassemblies may increase the total part count of the original product, the added ease and speed of implementing improvements are a positive trade-off for many products or product families. 4.1.2 U SE OF S TANDARD M ATERIALS C OMPONENTS AND D ESIGNS The second and third design considerations, standard material and components and standardized design of the product, are described by the sixth commandment: stan- dardize. Design reuse is one of the most cost-effective methods used in the design process. By defining company- or product family-related standard materials, standard parts, and specific design process standards, the product cost and time to market will be reduced, while reliability and customer value will be maximized. The key element in standardization is establishing the discipline within the organization to keep the standards current and readily available to the product development team, and enforcing their effective and consistent use. 4.1.3 S PECIFY T OLERANCES The fourth design consideration is specifying or establishing design tolerances based upon process capability rather than the typical design engineer’s affinity for closely toleranced parts. This approach is embodied in the seventh design commandment: avoid difficult components. The most effective way to apply this consideration is through the concurrent product development team environment where the design engineer and the manufacturing (producibility) engineer work collaboratively to ensure that the designed parts can be efficiently manufactured without excessive costs or scrapped material. This imposes the requirement that the manufacturing engineer have full knowledge of the process capabilities of in-house equipment and processes, as well as supplier equipment and processes. SL3003Ch04Frame Page 71 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC 72 The Manufacturing Handbook of Best Practices 4.1.4 U SE OF C OMMON M ATERIALS The fifth design consideration is use of the materials most processed. This simply means that materials that are commonly machined or processed in some manner within the company or within the company’s supplier base should be the first materials of choice for the various components. Exotic or state-of-the-art processes and materials should be avoided whenever possible to preclude extended process development activities associated with low process capability, which typically increase cost and cycle time while reducing quality and reliability. 4.1.5 C ONCURRENT E NGINEERING C OLLABORATION The sixth and final design consideration is collaboration with manufacturing per- sonnel. As identified previously, it is essential that the design team include cross- functional personnel such as manufacturing engineers, quality engineers, and procure- ment specialists to ensure that all the appropriate design trade-offs are properly analyzed and selected throughout the product development process by the experts in the respective disciplines involved. The traditional “Throw the design over the wall to manufacturing when engineering is done with it” approach is guaranteed to produce product attributes that contribute to higher production costs and extended time to market. The other three design commandments that remain to be described are (8) to use self-locating features, (9) to avoid special tooling, and (10) to provide accessi- bility. The use of self-locating features is an assembly aid that can dramatically reduce assembly costs and cycle time. Parts that naturally nest together or contain self-centering geometries reduce the handling, alignment, reorientation, and inspec- tion costs of assembly. Automated assembly processes in particular benefit tremen- dously from self-locating features to minimize the tooling and fixturing often required to ensure proper part alignment during assembly. Similarly, the avoidance of special tooling is a key consideration in complex assembly processes. Special tooling should be used only when other design elements or part geometries cannot incorporate self-locating features. Special tooling harbors an extensive array of hidden costs when fully analyzed. In addition to the cost of designing, fabricating, checkout, inventory, maintenance, spares, and planned replacement of special tool- ing, it can also add substantial cycle time to the assembly process. The added cycle time can accrue from issuing it from stores, moving it, installing it, and then verifying its proper placement, alignment, attachment, and operation over its intended design life. The final commandment is to provide accessibility, which implies the need for maintenance, inspection, part adjustment, part replacement, or other product access requirements over its design life. The key here is to define the requirements for accessibility based on the customers’ (end-users’) needs and the product develop- ment team’s comprehensive vision of the product’s possible applications, as well as its growth or evolution in the future. This requires a balance between satisfying current minimum needs and anticipating the most likely future needs, while still keeping the design simplicity DFMA consideration in mind. All the aforementioned DFMA considerations and commandments should be applied as an integrated and balanced approach in the design process. A well- documented product development process, in combination with clearly defined team SL3003Ch04Frame Page 72 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC DFMA/DFSS 73 member roles and responsibilities, will greatly improve the application of DFMA in most organizations. 4.2 DESIGN FOR SIX SIGMA (DFSS) DFSS methodology encompasses all the DFMA principles and adds proven statis- tical techniques to drive the design process, and thus the product, to lower defect counts. The typical DFSS statistical applications in design include (1) tolerance analysis, (2) process mapping, (3) use of a product scorecard, (4) design to unit production costs, and (5) design of experiments. 4.2.1 S TATISTICAL T OLERANCE A NALYSIS Statistical tolerance analysis employs a root-sum-squared approach to evaluating tolerancing requirements in lieu of the more traditional “worst-case analysis.” Its methodology is based on the statistical fact that the probabilities of encountering the worst-case scenario are extremely remote. For instance, if an assembly involves the interfacing of four different parts, and each part is known to have a ±3 sigma dimensional capability, then the defect probability can be calculated to be 2.7 in 1000, or 0.0027. By applying statistics, the probability of encountering the worst- case situation can be calculated to be 5 in 100 billion or 0.0000000000534. This clearly demonstrates the ultraconservatism of this approach and the consequent extremely tightly toleranced part call-outs required to achieve it. Tightly toleranced parts have inherent hidden manufacturing costs associated with them, because they dictate detailed inspection requirements and often require scrap or rework of a significant percentage of the manufactured parts. Most of these scrapped or reworked parts would have, in fact, worked perfectly well, but were rejected due to excessively demanding part tolerancing. A product generally consists of both parts and processes. This relationship means that to be successful you should seek to understand both the upstream and downstream capabilities of the various processes that will be used to produce the product. A product must be designed to not only meet the customer’s requirements, but must also comple- ment the process capabilities of the manufacturing company and its supplier base. It is unlikely that a company will ever reach a goal of Six Sigma quality without under- standing the capability of the entire supply (or value) chain. Design teams must under- stand and properly apply the process capabilities of their manufacturing facilities and those of their suppliers in order to repeatedly produce near zero-defect products. Process capability data are the enabling links needed to create robust designs. The preferred graphical method of describing the key process capabilities and how they relate to the overall product manufacturing activity is through the process map. 4.2.2 P ROCESS M APPING Six Sigma process-mapping techniques encompass several statistical measures of process performance and capabilities in addition to the typical process flows and related process operation information. As you will see, this information is extremely useful when a team of individuals has been assigned to improve a process. Let’s SL3003Ch04Frame Page 73 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC 74 The Manufacturing Handbook of Best Practices start with some of the common vocabulary used in process mapping to become familiar with the terminology (Table 4.2). Now that the basic terms have been defined, why do you suppose a process map is important when improving an existing process or implementing a new one? There are several visual features that a process map provides to aid a team’s understanding of the operations involved in a given process: 1. A process map allows everyone involved in improving a process to agree on the steps it takes to produce a good product or service. 2. A map will create a sound starting block for team breakthrough activities. 3. It can identify areas where process improvements are needed most, such as the identification and elimination of non-value-added steps, the poten- tial for combining operations, and the ability to assist with root-cause analysis of defects. 4. It will identify areas where data collection exists and ascertain its appro- priateness. 5. The map will identify potential X’s and Y’s, leading to determining the extent to which various x’s affect the y’s through the use of designed experiments. 6. The map serves as a visual living document used to monitor and update changes in the process. 7. It acts as the baseline for an XY matrix and a process failure modes and effects analysis (PFMEA). A Six Sigma process map for a manufacturing operation is shown in Figure 4.1. The map was created by a focused team working on a product-enabling process. The team consisted of operators, maintenance technicians, design engineers, material and process engineers, shop floor supervisors, and operations managers. The basic elements of this process map include (1) the process boundaries, (2) the major operations involved, (3) process inputs, (4) process outputs, and (5) the process metrics. There are several steps that must be followed to create a valid process map, as outlined in Table 4.3. TABLE 4.2 Process Mapping Vocabulary Process map: a graphical representation of the flow of a process. A detailed process map contains information that is beneficial to improving the process, i.e., cycle times, quality, costs, inputs, and outputs. Y: key process output variable; any item or feature on a product that is deemed to be “customer” critical, referred to as “y1, y2, y3.” X: key process input variable; any item which has an impact on Y, referred to as “x1, x2, x3.” Controllable X: knob variable; an input that can be easily changed to measure the effect on a Y. Noise X: inputs that are very difficult to control. S.O.P. X: standard operating procedure; clearly defined and implemented work instructions used at each process step. XY matrix: a simple spreadsheet used to relate and prioritize X’s and Y’s through numerical ranking. SL3003Ch04Frame Page 74 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC DFMA/DFSS 75 The key statistical information often described on a Six Sigma process map includes the defects per unit (DPU) at each operation step, rolled throughput yield (RTY), and key process capability (CPk) values. The design team needs to analyze these process parameters and understand their influence on RTY in order to design quality into the product rather than attempting to inspect quality into the product. 4.2.3 S IX S IGMA P RODUCT S CORECARD The Six Sigma product scorecard is an excellent method for applying process capability information to the conceptual phase as well as subsequent phases of the design evolu- tion. The scorecard is derived from the Six Sigma requirements for process definition, measurement, analysis, improvement, and control. By individually analyzing four ele- ments of a design (parts, process, performance, and software), scorecard sigma levels can be identified. Initial scorecard values can be used to evaluate conceptual design alternatives and to influence the downselect criteria; refined scorecards can be used to aid trade studies to optimize the baseline design configurations. In these design studies, product sigma levels can be evaluated as independent variables that drive cost, schedule, and other critical parameters. Baseline design selection at an overall 3 Sigma level, for instance, would yield 66,807 parts per million (ppm) defective, whereas achievement of a 6 Sigma design level would yield only 3.4 ppm defective, or a ratio of approximately 20,000 to 1 in improved quality! An example of a Six Sigma product scorecard is shown in Figure 4.2. This summary-level scorecard includes the four assembly level evaluation elements: parts, process, performance, and software, with the software element being nonapplicable for this simple mechanical configuration. Note that for each of the elements, the DPU estimate and the opportunity counts are described for each major subassembly. These are then totaled near the bottom of the table, and first time sigma, DPU/oppor- tunity, sigma/opportunity long term and short term are all calculated through algorithms built into the Excel spreadsheet. Each element results in a separate short-term sigma TABLE 4.3 Steps to Creating a Process Map Step 1: Define the scope of the process you need to work on (actionable level). Step 2: Identify all operations needed in the production of a “good” product or service (include cycle time and quality levels at each step). Step 3: Identify each operation above as a value-added or non-value-added activity. A value added operation “transforms the product in a way that is meaningful to the customer.” Step 4: List both internal and external Y’s at each process step. Step 5: List both internal and external X’s at each process step. Step 6: Classify all X’s as one or more of the following: • Controllable (C) • Standard operating procedures • Noise Step 7: Document any known operating specifications for each input and output. Step 8: Clearly identify all process data-collection points. SL3003Ch04Frame Page 75 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC 76 The Manufacturing Handbook of Best Practices that is used as the design basis for most applications. The minimum sigma value for any of the elements constitutes the design sigma limitation. Unless all the elements are fairly equivalent in value, the overall sigma score will be heavily influenced by the lowest element sigma value. Each of the elements uses a separate worksheet accessible through the Excel worksheet tabs at the bottom of the spreadsheet layout. The parts worksheet shown in Figure 4.3 is completed by defining all the major purchased or manufactured individual parts that will make up the assembly or subassembly. This is most easily accomplished through the use of a bill of materials, or parts listing. The supplier, part number, part description, quantity, part defect rate in ppm defective, and the total DPU, an alternate description for ppm, are all defined. A separate worksheet is completed for each major subassembly to be built by manufacturing. The overall intent of this methodology is to drive the previously FIGURE 4.1 Solid rocket motor strip winding process map. CT = cycle time, DPU = defects per unit, MBOM = manufacturing bill of materials, NVA = non-value added, RTY = rolled throughput yield, SOP = standard operating procedures, VA = value addeed, X = input variables, Y = output variables. Receive Material DPU=.01 CT=2.0 hrs X’s Y’s NVA Quality of Material Technician, SOP, Specifications Material Handler, SOP, 40°F Cold Box, Proper Storage Material Handler, SOP, Forklift Technician, SOP, Controllers, Barrel Temp., ScrewTemp., Head Temp., Hopper Temp., Rollaformer Temp. Technician, SOP, Rollaformer Profile Technician, SOP, Diode Settings Material Conforms to Spec Material Conforms to Spec Material Conforms to Spec Material Received at Strip Winder Preheated Operating System Thickness of Strip meets Requirements Width of Strip meets Requirements Verify and Test Material Quality Properties DPU=.001CT=40.0 hrs Transport and Store in 40°F Cold Box DPU=.001 CT=4.0 hrs Issue Material per MBOM to floor DPU=.001 CT=2.0 hrs Preheat Temperature Control Unit DPU=.001 CT=1.0 hrs Set Gap on Upper/Lower Rollaformers DPU=0.05 CT=1.0 hrs Set Diode (width) on the Controller DPU=.001 CT=.01 hrs NVA NVA NVA NVA NVA NVA Technician, SOP, Material Condition, Machine Settings Technician, SOP, Material Condition, Machine Settings Technician, SOP, Material Condition, Machine Settings Technician, SOP, Material Condition, Machine Settings Technician, SOP, Material Conveyance System X’s Y’s Material Feed Intiated Material pre- conditioned System at Acceptable Pressure Range Hot Strip Molded Molded Strip ready for Application at Winder NVA VA SCRAP Acceptable Strip at Rollaformers? No Yes Convey Strip to Application System Extruder Charged at Steady-State Pressure DPU=.001 CT=.05 hrs Strip Formed at Rollaformers DPU=.001 CT=.05 hrs VA VA VA Final RTY=92.5% Material Conditioned at Extruder DPU=.001 CT=.10 hrs Feed Insulation Material into Extruder DPU=.01 CT=.050 hrs SL3003Ch04Frame Page 76 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC DFMA/DFSS 77 FIGURE 4.2 Six Sigma product scorecard. Date AI&T Cost $2,599 DPU Part Number Critical Path Cycle Time 0 DPMO Name ACME Raw Process Multiplier 8.29 Sigma Period of Data Part (σ) Process (σ) Performance (σ) Assembly DPU Opp. Count Parts Cost DPU Opp. Count Labor Cost Cycle Time (min.) Total Time (min. VA Time (min.) Scan Drive 0.1649 1 $2,500 37.9711 2680 $99 250 290 35 8.29 0.07667732 2191 Antenna Receiver Electronics System Totals 0.1649 1 $2,500 37.9711 2680 99 250 290 35 8.29 0.0767 2191 First Time Sigma 1.03 <-6 1.45 RTY 84.8% 0.0% 92.6% DPU/Opp 0.1649 0.0142 0.0000 Sigma/Opp 1.03 2.20 3.98 4/1/00 - 04/04/00 xxxxxxxx 2.42 38.2127 7843.3 DPU Opp. CountRPM Date AI&T Cost $2,599 DPU Part Number Critical Path Cycle Time 0 DPMO Name ACME Raw Process Multiplier 8.29 Sigma Period of Data Part (σ) Process (σ) Performance (σ) Assembly DPU Opp. Count Parts Cost DPU Opp. Count Labor Cost Cycle Time (min.) Total Time (min. VA Time (min.) Scan Drive 0.1649 1 $2,500 37.9711 2680 $99 250 290 35 8.29 0.07667732 2191 Antenna Receiver Electronics System Totals 0.1649 1 $2,500 37.9711 2680 99 250 290 35 8.29 0.0767 2191 First Time Sigma 1.03 <-6 1.45 RTY 84.8% 0.0% 92.6% DPU/Opp 0.1649 0.0142 0.0000 Sigma/Opp 1.03 2.20 3.98 4/1/00 - 04/04/00 xxxxxxxx 2.42 38.2127 7843.3 DPU Opp. CountRPM SL3003Ch04Frame Page 77 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC 78 The Manufacturing Handbook of Best Practices described DFMA principles of fewer parts and part types into the design and to ultimately select quality suppliers and processes to manufacture the individual parts. The process worksheet portrayed in Figure 4.4 describes the assembly process information, much of which is taken directly from the process map previously considered. Here again, one worksheet per major assembly or subassembly is com- piled for each assembly level built by manufacturing. The process worksheet iden- tifies all the major internal processes used to build the product. The DFMA intent here is to use high quality processes and simplify the build process to the greatest practical extent. For each process step, the load center, cycle time, labor hours and cost, process target, specification or tolerance, upper specification limit (USL), lower specification limit (LSL), process mean value, standard deviation, process capability (CPk), number of applications, process opportunities, and product opportunities are all defined. From this information the spreadsheet algorithms are used to calculate the total number of product opportunities, average defects per opportunity, average yield per opportunity, average process sigma long term (LT), average process sigma short term (ST), as well as the total defects per unit, the rolled throughput yield, and the sigma (z) score. As evidenced by the amount of statistical process data required, this methodology involves extensive process capability data collection and knowledge to be used successfully. It requires taking the operator “black magic” out of the process capability equation, and replacing it with parametrically driven process knowledge and control features, which can be derived from design of experiments, and other Six Sigma methodologies. An example of the performance worksheet is presented in Figure 4.5. It is used to identify all the customer-focused, top-level system performance parameters, and to quantify the probability that the design configuration will successfully achieve them. Its intent is to quantifiably assess the design’s capability against the defined system-level requirements. It also provides insight into the production acceptance testing requirements and needed measurement system accuracy (MSA). The work- sheet lists the key customer-based performance parameters that can be obtained from a customer’s specification, a technical requirements document, or from a quality function deployment (QFD) process. It defines target values, units, upper specifica- tion limit (USL), lower specification limit (LSL), performance mean value, standard deviation, z score USL, z score LSL, rolled throughput yield, and DPU. A software worksheet is presented in Figure 4.6. It identifies the entire software build process, tracks defects found during each phase of the software development, and calculates the efficiency of each software phase in detecting and eliminating defects. It also provides a future extrapolation of overall delivered software quality, based on defect rates demonstrated during the build process. The top-level product scorecard results are calculated by algorithms internal to the spreadsheet using all the individual worksheet inputs. As previously identified, Figure 4.2 illustrates the combined results from this Six Sigma tool, and its influence on designing quality into the product. This methodology provides a powerful method of positively influencing the design process through the use of data and removes the mystery (or mystique) that surrounds many modern-day manufacturing facilities about their ability to produce high-quality products on a consistently repetitive basis. SL3003Ch04Frame Page 78 Tuesday, November 6, 2001 6:10 PM © 2002 by CRC Press LLC [...]... SL3003Ch04Frame Page 79 Tuesday, November 6, 2001 6:10 PM DFMA/DFSS Part Number Name Scan Drive Period of Data 4/ 1/00 Total Part Count 1 Avg Defects /Part 0.1 649 Avg Yield /Part 84. 8% Avg Part Sigma 1.03 FIGURE 4. 4 Six Sigma product scorecard — process worksheet © 2002 by CRC Press LLC Raw Process Multiplier $13 $13 $4 $88 Value Added Time Mean (minutes) $2 $2 $1 $15 Cycle Time - Std Dev (minutes) $11 $11 $4. .. 30 4 20 3 10 5 0 120 12.00 58.00 inf 1.50 The Manufacturing Handbook of Best Practices Process Step Form & Tin Identification Stencil Print Pick & Place xxxxxxxx Scan Drive 4/ 1/00 2680 0.0 141 7 98.59% 2.20 SL3003Ch04Frame Page 80 Tuesday, November 6, 2001 6:10 PM 80 Part Number Name Period of Data Total # of Product Opps Average Defects/Opp Average Yield/Opp Avg Process Sigma xxxxxx Scan Drive 4/ 04/ 00... Opportunities 3 647 16 347 5 Operation Opportunities 382 332 382 $99 $20 $119 $81 Total Unit Cost Total COQ Total Cost Total Variance # of Times Used Number of Defects Std Dev Mean USL Cpk . TDU Name Scan Drive Yield Period of Data 4/ 1/00 - Sigma Total Part Count 1 Avg Defects /Part 0.1 649 COQ Avg Yield /Part 84. 8% Part Cost Avg. Part Sigma 1.03 Variance Supplier Part No. Description Feature. & Tin 2306 Insp. 33 24 < 0 382 3 647 1 908 908 9. 547 105 14 -3.80 2.31 1 0.3 $37 $11 $2 $13 $200 $187 1 120 30 10 12.00 Identification 3 044 Insp. 344 5 0.56 332 16 1 0 0. 048 48 193 1.67 1.67 1 0.3. Planned Variance Ace 1 349 5 94- 1 Printed Wiring Board 1 291 48 1 649 48 0.1 649 1.03 $2,000 $500 $2,500 $2,000 ($500) Cost Data Measured FeaturePart Description $2,500 ($500) 0.1 649 84. 8% 1.03 $500 Defect