product costing guidelines andrew cmu

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product costing guidelines andrew cmuWe modeled the impact of the management levers relating to oversight, the intensity of design specialization, and the level of interaction with the customer. The study highlights the necessity of leveraging the interdependencies between the design and manufacturing phases in NPD. Management Decision, Vol. 45, No. 2 Is Integration Enough for Fast Product Development.Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more. Agree. For additive manufacturing to find commercial application, it must be cost competitive against traditional processes such as forging. Forecasting the production costs of future products prior to large-scale investment is challenging due to the limits of traditional cost accounting's ability to handle both the systemic process implications of new technologies and the cognitive biases in humans' additive and systemic estimates. Leveraging a method uniquely suited to these challenges, we quantify the production and use economics of an additively manufactured versus a traditionally forged GE engine bracket of equivalent performance for commercial aviation. Opportunities to further reduce costs include accessing lower material prices without compromising quality, producing vertical builds with equivalent performance to horizontal builds, and increasing process control so as to enable reduced testing. Given the conservative nature of our assumptions as well as our choice of part, these results suggest that there may be broader economic viability for additively manufactured parts, especially when systemic factors and use costs are incorporated. AM, a three-dimensional production process, utilizes computer-aided design (CAD) to build objects layer-by-layer primarily via an energy source, typically a laser or an electron beam.http://www.goldia.cz/data/cuore-automatic-vs-manual.xml

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Due to its low material waste and reduced material usage through part design, AM can reduce material requirements for parts made with expensive materials. AM production can also, through redesign, produce lighter weight parts than traditional processes. These lightweight designs can have complex geometries that without AM would be impossible or very difficult to produce. For industries with high fuel use, such as the aerospace and automotive industries, these lightweight designs may provide significant cost savings as a result of reduced fuel consumption.Forecasting the production costs of future products prior to large-scale investment is challenging due to the limits of traditional cost accounting's ability to handle both the systemic process implications of new technologies and cognitive biases in humans' additive and systemic estimates. Here, we leverage a method uniquely suited to these challenges: process-based cost modeling (PBCM). We focus on the case of a GE engine bracket for civil aviation applications. This simple part allows us to create a conservative estimate of metal AM (MAM) cost-competitiveness without the likely additional benefits of parts consolidation. We quantify the production and use economics of an additively manufactured versus a traditionally forged bracket of equivalent performance for commercial aviation. For a given design, the model calculates the most efficient production facilities—i.e., the inputs (material, labor, energy, capital, and building space)—required to achieve a desired annual output of “good” parts given the technology or organizational capabilities of a producer (e.g., per process step cycle times, yields, downtimes, scrap rates, and plant-wide operating parameters).http://mapect.com/upload/fckeditor/cuno-water-softener-manual.xml To achieve this goal, a PBCM maps the physical parameters of a new product or process design (for example, material and geometry requirements for the engine bracket) to its consequences for the production process (for example, cycle times, yields, and maintenance times), and in turn for the full manufacturing facility (here, machine and material requirements to achieve a desired output of good brackets). To transform this model of production for a new design to a model of cost, we simply multiply each of the inputs by its price (e.g., material prices, wages, electricity prices, machine prices, building prices, or land prices). With the complete set of decision rules and inputs, the outcomes of any model can be reproduced. The architecture of the model has no consequence for outcomes; it is simply how the decision rules and input data are organized so as to maximize the model's user-friendliness, user-transparency, and developer flexibility. These mathematical equations describe both the relationship between design decisions and their consequences for process and also the operating rules of the production facility. We base these rules on production methods we observe on the shop floor for existing designs as well as any methods specific to the emerging design or production process being modeled. This sensitivity analysis examines input ranges of several variables, including build orientation, material price, machine price, cycle times, and destructive testing to determine the effect of variation of each input on total unit cost. Fuel use depends not only on the equipment using the fuel, but also on many other factors such as maintenance practices, airport traffic, and pilot skills. We assume that a 1 reduction in an aircraft's operating empty weight would approximately translate into a 1 reduction in the fuel needed (Table 1 ).Assuming a constant fuel price, we compute the net present value (NPV) of those annual savings over 10?http://www.diamondsinthemaking.com/content/boss-bf-2-owners-manualyr using a 7 discount rate via the following equation: Plugging our best and worst fuel values found in Table 1 into Eq. (1), we obtain the ranges for the net present value of the fuel savings (see Table 4 ). The two last columns in Table 2 contain the estimated interval for the annual fuel savings from the reference document. This simple part allows us to create a conservative estimate of MAM cost-competitiveness without the likely additional benefits of parts consolidation. We sought a publicly available part design for which full data could be shared in a publication, and where both the traditional and MAM redesigned parts serve the same functional purpose. The case we chose, the “TJ2” aerospace engine bracket, which won second place in the GE Engine Bracket Challenge, provides an ideal case study. As can be seen in Fig. 2(b), the additive design would be extremely difficult or impossible to produce via traditional manufacturing. If this engine bracket were installed at four brackets per engine in all of those sales, then this would total an annual production volume of approximately 13,500. The specific commercial-grade machines at the university laboratory are from Arcam (S12 model) and EOS (M290 model). We collected data on three MAM machine suppliers. Two of these are equipment suppliers of DMLS technology and one of these is EBM technology. For the build step, we worked with each supplier and the academic lab to obtain build times for the particular build orientations, batch sizes, and production parameters required for the engine bracket. The base case build time for each system is estimated by each equipment's build simulation tool. Given the bracket design and the DMLS and EBM suppliers in the final analysis, only four of the postprocessing steps on which we collected data were in the end necessary for the AM analysis.https://ggccnet.com/images/carrera-go-manuals.pdf The postprocessing required between EBM and DMLS systems are slightly different: EBM does not require a heat treatment step for stress relief, but DMLS does. In our process flow for the bracket design, the remaining postprocessing steps (wire electrical discharge machining (EDM), hot isostatic press (HIP), and shot peening) are the same for the three equipment platforms. Finally, given AM's nascent role in aerospace production, destructive testing must be performed to ensure quality and repeatability. The forging process includes induction heating, forging, heat treatment, grinding, multi-axis milling, and wire EDM. In contrast to MAM, testing for forging is nondestructive, as is standard for forged aerospace parts, as forging is a well-documented process with high consistency and repeatability. Since production of the target annual production volume of brackets would take such a small amount of time (approximately 25 days), it is likely to be fully utilized in a production environment for other products the remainder of the year. The Forging Industry Association respondent confirmed that given how low the engine bracket volumes were and how fast the entire year's throughput would be completed, the forging machine would not sit for 11 months of the year without any other work put on it. Thus, we assume that the forged machine is producing other parts when not making the engine bracket. This lack of flexibility affects the economic viability of MAM for the production of parts at low volumes, precisely where MAM might be more competitive against traditional manufacturing techniques. These best, base, and worst case inputs for key cost drivers, based on our data collection, are shown in Tables 8A and 8B, which are available under the “ Supplemental Materials ” tab for this paper on the ASME Digital Collection. In addition, we were able to engage in a variety of informal conversations with MAM producers, industry experts, and equipment suppliers not represented in the final analysis. We use these informal conversations, combined with knowledge of past production in other areas, to bind our best and worst case scenarios for each process input. For MAM, inputs for which we collected best and worst case inputs were the main AM machine price, batch size (and thus build time), Ti64 powder price, reject and scrap rates, postprocessing machine price, postprocessing cycle time, and destructive testing (Table 8A, which is available under the “ Supplemental Materials ” tab for this paper on the ASME Digital Collection). For forging, inputs for which we were able to collect best and worst case inputs were Ti64 price, forging scrap rate, and forging reject rate (Table 8B, which is available under the “ Supplemental Materials ” tab for this paper on the ASME Digital Collection). Two decision rules specific to our modeling of MAM are important to note in the main text. First, our model accounts for the increased production volume required to account for destructive testing as a separate input on top of per-process-step reject rate. The yield hit from destructive testing is accounted for in the final step. We assume that 1:8 destructive testing to ensure performance is being maintained. AM manufacturers are currently producing parts for aerospace applications with sufficient reliability to have 1:8 destructive testing, and some are currently moving to destructively testing 1 in 100 parts. We therefore believe this to be a reasonable, if not conservative, starting assumption. In contrast, we assume nondestructive testing for forging. Thus, AM must make more parts per process step to have the same number of “good parts” as forging, and this is reflected in the production costs. Additionally, it is important to note that for the AM build step, cycle time includes warm up and cool down time in addition to the actual time the machine spends building the part. The intermittent spikes in production cost represent the model needing to add an additional piece of equipment (whether build or postprocessing) to achieve a certain number of good parts per year. As expected, the traditional hammer forging process is the cheapest option for producing the engine bracket (Fig. 4 ). Importantly, the additively manufactured design offers an approximately 80 weight savings over the traditional forged design. There are also slight cost differences between different suppliers within the same MAM technology, as seen in Fig. 5. That the AM process has less of a cost advantage over forging at these extremely low volumes might at first seem counterintuitive. This difference is due to the forging process being nondedicated, while the AM process is dedicated: FAA regulations allow multiple different parts to be run on the forging equipment, keeping it fully utilized, while FAA regulations allow only one part to be run per machine on the additively manufactured process.Given their respective build rates, reject rates, scrap rates, material prices, and machine prices, DMLS and EBM have a different set of cost drivers per good part. Main machine cost and material cost are the major cost drivers for the DMLS system, whereas main machine cost is the overwhelming driver of the EBM system (Fig. 6(a) ). One of the two DMLS systems does have lower material scrap rates, but this does not outweigh the higher material price in the base case comparison (e.g., DMLS still has higher material costs than EBM). Interestingly, if—as represented by the best case scenario—the necessary quality powders can be sourced more cheaply, as is being claimed by material vendors outside the AM equipment system suppliers, then the contribution of material to overall cost of the DMLS parts would drop considerably. In contrast, despite having faster base case build rates and slightly better base case yields than DMLS, the EBM's machine price is nearly twice that of DMLS (Table 6A, which is available under the “ Supplemental Materials ” tab for this paper on the ASME Digital Collection). Overall, the faster build and slightly better yields do not outweigh this higher price in the comparison (e.g., EBM still has higher main machine costs than DMLS), assuming that equivalent parts can be produced by both the DMLS and EBM systems. Overall, the EBM base case is, at 10,000 brackets per year, cheaper than the DMLS systems; however, given the range of input uncertainties, no conclusive distinction should be made. Further work will be necessary to better understand the trade-offs between powder size and source (powders offered by machine suppliers versus those offered directly by the powder producers), in addition to the effect of powder size on production yields and final part properties and thus performance. Likewise, batch size for this part is tied to build orientation; to increase the batch size, one must stand the parts up vertically. However, the effect of varied build orientation on material properties is still an ongoing research topic. Currently, due to challenges presented by part geometries, cost, and porosity for other testing mechanisms, destructive testing is necessary to characterize the mechanical properties of AM parts. For example, in the case of GE's fuel nozzle, initially one in every eight parts was lost to destructive testing. To better understand the cost-consequences of this type of destructive testing, and the opportunities to reduce cost by reducing destructive testing (through, for example, improved understanding of the relationship between process decisions and material characteristics), we incorporated into our model these additional losses of good parts due to destructive testing.DMLS shows the possibility for cost savings, though there is some overlap in the cost of vertically built versus horizontally built brackets given the uncertainty. Given these potential cost savings, it is worthwhile to work toward understanding and mitigating microstructural anomalies associated with a vertical build. Many studies have discussed the effects of part orientation and location on the build plate on the mechanical properties of the AM parts. Both future research and technological advancements in MAM (such as increased speed, reliability, quality, and expanded range of materials) will deepen understanding of the limits of build orientation and thus batch size. This lighter design provides significant savings to the part end user. An investigation of these kinds of benefits, combined with other areas for cost reduction, such as further equipment and process standardization, will be important areas for ongoing research in understanding how the design benefits of AM can be brought to market to be cost competitive with traditional manufacturing methods. For example, the latest EBM machine model, Q20, has room in the chamber to build 48 engine brackets if brackets are stacked on top of each other; however, it is unclear if the material microstructure of these engine brackets would be such that they would all be of sufficient quality. As such, it offers a conservative estimate of the benefits of reducing destructive testing, but a nonconservative estimate of the cost of having to do more destructive testing in MAM. Future work should model the costs versus potential added value of a range of testing interventions from destructive to nondestructive testing (with different impacts on end-part reliability). Indeed, we find that even in a simple engine bracket design without part consolidation, the cost per good bracket can be cheaper than forging once lifetime weight savings are taken into account, when using relatively conservative assumptions with respect to processing capability. The MAM systems we compared had similar unit costs given uncertainties despite significant difference between the processing methods of DMLS and EBM. High-impact opportunities to further reduce the cost of MAM versus forging include reducing material price without foregoing reliability and part quality, and increasing the number of parts that can fit per chamber (through vertical builds) without compromising quality or reliability. Thanks also to Parth Vaishnav for his input on the fuel savings analysis, as well as Kathryn J. Jackson, Anthony D. Rollett, and C. Freg Higgs III for insights throughout the process. Funding for this project was provided by the Defense Advanced Research Projects Agency, America Makes, National Science Foundation's Science of Science and Innovation Policy program, and the Portuguese Science and Technology Fund. For analyzing the economics of new technologies that vary significantly from existing products or processes, we find generative methods to be the most appropriate approach. While not necessary for this model, in a scenario where firms are changing the design or material frequently, our data collection suggests that machine setup times can range between half an hour and 5?h (if the machine requires a material change). That said, we recognize that, for example, in a military context in the field, AM and forging would not be on equal footing, since the military may wish to produce a repair part on-site, and would not have time or ability to send out for a new one from a forging house. Boeing, 2016, “ Airplane Characteristics for Airport Planning,” Airport Compatibility, Boeing, Inc., Seal Beach, CA, accessed June 30, 2016, 32. Anderson, D., 2006, “ Fuel Conservation: Operational Procedures for Environmental Performance,” Boeing Commercial Airplanes, Seattle, WA, accessed June 30, 2016, 33. General Electric, GrabCAD, 2013, “ GE Engine Bracket Challenge,” GrabCAD, General Electric, Cambridge, MA, accessed June 30, 2016, 34. United States Securities and Exchange Commission, 2015, “ General Electric Form 10-K,” U.S. Securities and Exchange Commission, Washington, DC, accessed June 30, 2016, 35. GAO-15-505SP. 44. Gibson, I., Rosen, D., and Stucker, B., 2014, Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, 2nd ed., Springer, New York. Both DMLS processes 1 and 2 follow the DMLS process flow. Fig. 3 View large Download slide AM model functionality for metal engine bracket. Both DMLS processes 1 and 2 follow the DMLS process flow. EBM exhibits a slightly narrower range of cost given best and worst case scenarios. Fig. 4 View large Download slide EBM and DMLS appear to have significant overlap in cost given uncertainty, with forging cheaper across all APV. EBM exhibits a slightly narrower range of cost given best and worst case scenarios. Forging remains cheaper by small margin. Fig. 5 View large Download slide DMLS systems show significant cost overlap given uncertainty of best and worst case scenarios noted in Table 8A, which is available under the “ Supplemental Materials ” tab for this paper on the ASME Digital Collection. Forging remains cheaper by small margin. Boeing, 2016, “ Airplane Characteristics for Airport Planning,” Airport Compatibility, Boeing, Inc., Seal Beach, CA, accessed June 30, 2016, 32. Anderson, D., 2006, “ Fuel Conservation: Operational Procedures for Environmental Performance,” Boeing Commercial Airplanes, Seattle, WA, accessed June 30, 2016, 33. General Electric, GrabCAD, 2013, “ GE Engine Bracket Challenge,” GrabCAD, General Electric, Cambridge, MA, accessed June 30, 2016, 34. United States Securities and Exchange Commission, 2015, “ General Electric Form 10-K,” U.S. Securities and Exchange Commission, Washington, DC, accessed June 30, 2016, 35. GAO-15-505SP. 44. Gibson, I., Rosen, D., and Stucker, B., 2014, Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, 2nd ed., Springer, New York. Uncertainty By continuing to use our website, you are agreeing to our privacy policy. For the US government organization, see Center for Medicare and Medicaid Innovation. Core activities Administered by the CMMI Institute, a subsidiary of ISACA, it was developed at Carnegie Mellon University (CMU). It is required by many U.S. Government contracts, especially in software development. CMU claims CMMI can be used to guide process improvement across a project, division, or an entire organization. CMMI defines the following maturity levels for processes: Initial, Managed, Defined, Quantitatively Managed, and Optimizing. Version 2.0 was published in 2018 (Version 1.3 was published in 2010, and is the reference model for the remaining information in this wiki article).CMMI models provide guidance for developing or improving processes that meet the business goals of an organization.The project consisted of members of industry, government and the Carnegie Mellon Software Engineering Institute (SEI). The main sponsors included the Office of the Secretary of Defense ( OSD ) and the National Defense Industrial Association.The CMM was developed from 1987 until 1997.The table below lists the seventeen CMMI core process areas that are present for all CMMI areas of interest in version 1.3.It addresses product and service development processes. It addresses supply chain management, acquisition, and outsourcing processes in government and industry. It addresses guidance for delivering services within an organization and to external customers. Trying to keep up with the industry the model also has explicit reference to agile aspects in some process areas.Appraisals are typically conducted for one or more of the following reasons:There are three classes of appraisals, A, B and C, which focus on identifying improvement opportunities and comparing the organization's processes to CMMI best practices. Of these, class A appraisal is the most formal and is the only one that can result in a level rating. Appraisal teams use a CMMI model and ARC-conformant appraisal method to guide their evaluation of the organization and their reporting of conclusions. The appraisal results can then be used (e.g., by a process group) to plan improvements for the organization.More modern approaches, that involve the deployment of commercially available, CMMI-compliant processes, can significantly reduce the time to achieve compliance. Since the release of the CMMI, the median times to move from Level 1 to Level 2 is 5 months, with median movement to Level 3 another 21 months.These results do not guarantee that applying CMMI will increase performance in every organization. A small company with few resources may be less likely to benefit from CMMI; this view is supported by the process maturity profile (page 10). They believe neither way is the 'right' way to develop software, but that there are phases in a project where one of the two is better suited. They suggest one should combine the different fragments of the methods into a new hybrid method. There are several CMMI roadmaps for the continuous representation, each with a specific set of improvement goals.The staged approach yields appraisal results as one of five maturity levels. The continuous approach yields one of four capability levels. The differences in these approaches are felt only in the appraisal; the best practices are equivalent resulting in equivalent process improvement results.CMMI for Development SCAMPI Class A Appraisal Results. Software Engineering Institute. Carnegie Mellon University Software Engineering Institute. 2006. Software Engineering Institute. Software Engineering Institute. 2011. Software Engineering Institute. Retrieved 28 October 2006. Software Engineering Institute. Archived from the original on 25 July 2011. Retrieved 15 March 2011. The complete SEI list of published SCAMPI appraisal results. By using this site, you agree to the Terms of Use and Privacy Policy. Our office hours will be 10 am until 2 pm. Only two members permitted in the office at any one time. At the present time you can access the building with your CMU ID card. Please monitor the website for any future updates. Thank you for your patience and stay safe. Staff and Board of Directors.As an alternative to “for-profit“ financial institutions, our goal is to deliver quality personalized service to our members - not to maximize the profit gained from customers in order to satisfy stockholders. Because we are non-profit and member-owned, money made in excess of expenses is returned to our membership through competitive loan rates, savings dividends, and the addition of new products and services when feasible. Mission Statement: Carnegie Mellon Federal Credit Union is a member-owned, cooperative financial institution formed to encourage savings, make loans at competitive rates, and provide a range of convenient and personalized services to the Carnegie Mellon community in the most cost-effective manner while maintaining financial stability. NCUA is one of the strongest of the federal insurance funds and issues strict operating guidelines for the credit unions it regulates. 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In her role as executive director, she brings over 13 years of admissions and recruiting experience to the Tepper School. Kelly holds a BA in Psychology from Grove City College, an MBA from George Mason University, and an MS-MIS degree from the University of Pittsburgh. Candidates with no or limited professional experience must be strong in other aspects of their MBA application to offset the lack of experience. The only exception is for applicants who have earned a degree at a university where the language of instruction is English. Choose recommenders who are able to provide specific and relevant information about your qualifications for MBA study. To be considered for admission, you need to submit a complete application portfolio that includes the following documents:Late August March 15 for full-time MBA applicants; June 1 for part-time MBA applicants We will consider applicants who hold a three-year undergraduate degree.

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