Browse Topic: Six Sigma
A continuous effort to improve reliability and efficiency of processes is at the forefront of any successful business. One methodology that can have a crucial impact in this effort is Lean Six Sigma (LSS), which aims to reduce variability and wasteful activities within a company’s processes, in turn leading to improvements in areas such as customer satisfaction, employee morale, regulatory compliance, and profitability. In the medical device industry, where a seemingly minor error could be life-threatening, LSS can play a pivotal role in patient safety. This article presents a case study illustrating the benefits of LSS for a medical device manufacturing company, as well as one of its key customers.
In research and development of any automotive industry the main challenge is to virtually simulate probable failures rather than relying on physical testing which consumes time and resources. It is even more challenging when it comes to failure prediction of ABS plastic parts due to its complexity in material, behavior and assembly variations. ABS material is used extensively in automotive vehicles especially in motorcycles and scooters due to its visual and structural benefits at moderate cost. In this paper, the work showcases a methodology to predict failure of ABS parts. In order to do so, understanding the shortcomings in the current system is necessary. With the help of testing database history of various vehicles on proving grounds, root causes and drawback with respect to current simulation process are identified for failure of ABS parts. The input excitations from proving ground are probabilistic, thus, random vibration fatigue process is then introduced to calculate life. By correlating the simulation predicted life with testing, the required factor of safety to include residual stresses, assembly stresses are calculated and passing limit in terms of stress is derived for ABS parts to further reduce the simulation run time. Using six sigma methods, the accuracy in simulation is improved with which cycle time, prototype cost and testing time is reduced.
Ford CEO Jim Farley exposed significant product-development lapses during his company's fourth-quarter-2022 earnings call on February 2. Ford's 4Q profit performance was no-excuses dismal. Its causes, he stated, run deep. So, in front of investors and media, Farley boldly lifted Ford's PD skirt to reveal alarming management and process issues behind the dysfunction. The fire had to be lit. “We didn't know that our wiring harness for Mach-E was 1.6 kilometers longer than it needed to be,” Farley stated on the call. “We didn't know it's 70 pounds heavier and that that's [worth] $300 a battery. We didn't know that we underinvested in braking technology to save on the battery size.” Credit to CNN's Chris Isidore for roping these EV-specific details into a Feb. 3 story.
Climate change and global warming are the main threats to our planet. CO2 emissions contribute vastly to climate change and automobiles contribute to CO2 emissions. We can reduce the CO2 emissions from vehicles by various measures, out of which shredding weight is one of the solutions, and even for hybrid or electric vehicles there will be a need for weight reduction for the control of global CO2 emission. We can shred the weight of the automobiles by replacing the components with lighter materials or by optimizing the components by removing excessive material. It is not always possible to change the materials due to its mechanical, thermal properties, manufacturability etc. This leads to the other method which is removing excessive material. Today, we use different simulation tools like ANSYS for topology or shape optimization. During the traditional optimization, we perform the simulation, based on the available design limits and propose the best optimized design to the customer. This method doesn’t consider any uncertainty like manufacturing tolerances, material irregularities etc in the model. Hence the proposed design may not be said as a robust design. The proposed Robust Optimization Methodology takes into account the uncertainties and proposes the robust optimized design. Robust optimization has two stages. In the first stage, we need to calculate the standard deviation (sigma) of output results based on the uncertainty in input parameters. In the second stage, we use the calculated sigma and modify the optimization target based on the required confidence level (for e.g., 3*Sigma (σ) ➔ 99.73% Confidence level). In this paper we demonstrate the robust optimization methodology on a clutch damper drive plate and propose robust design which resulted in 15% reduction in weight.
The Bharat Stage VI emission norms in India is driving the use of more complex after treatment systems for diesel engines, to meet the stringent emission limits. The after-treatment system typically includes theSelective Catalytic Reduction (SCR) catalyst and the Diesel Oxidation Catalyst (DOC) - Diesel Particulate Filter (DPF) systems to reduce engine out emissions of Nitrogen Oxides (NOx), hydrocarbons (HC), and particulates respectively. For a durable functioning of the aftertreatment system, cleaning of these components at regular intervals is required, the process termed as ‘regeneration’. The most common industry technique for regeneration is to use the existing injectors in the engine, to dose the extra fuel which is burnt in the DOC for regeneration. This has been a cost effective and simpler technique compared to the external hydrocarbon dosing system. But the tradeoff involved with this in-cylinder dosing technique is the risk of fuel in oil (FIO). The extra fuel injected impinges the cylinder wall and eventually get mixed with engine oil thereby diluting it. The diluted oil increases the risk of early wear and impacts the durability of the engine. This paper emphasizes on parameters impacting fuel in oil dilution due to regeneration, associated tradeoffs and some system level analysis of the same. Through six sigma, the various factors impacting FIO is studiedexperimentally using a mid-range diesel engine in an engine dynamometer. Study also considers impact of regen frequency and application level impact on oil drain interval for some on-highway commercial vehicle applications. A system level approach is formulated to understand FIO which can be used in the early phase of design and calibration of aftertreatment system.
Aerostructures assembly (ASA) is a vital process in any aircraft production phase that integrates individual detail parts, sub-assemblies, major assemblies, components, and systems into a final deliverable, a completed aircraft structure fit for flight. ASA in an aircraft’s entire product life cycle represents more than half the cost and time that is a significant portion of the total aircraft production cost. ASA depends on highly skilled manual labor work across the global aerospace supply chain for various assembly processes and subprocesses required for assembling detail parts into sub-assemblies and components to achieve the design intent of the load-carrying aerostructure that is airworthy for the complete operational cycle till disposal of an aircraft. The assembly processes can significantly impact quality, safety, and reliability and can affect an aircraft structure’s performance and design intent. To mitigate the increase in defects due to non-standardization and to fulfill the need for robust process designs of the assembly processes, this technical paper proposes the enhancement of the 6M methodology as a Design for Assembly (DfA) tool for developing both detailed process designs and robust Compliance Assessment Guidelines (CAG) for evaluating the performance of these assembly processes. The 6M methodology is proposed as an Aerospace Design for Assembly (ADfA) tool using the Design For Six Sigma (DFSS) approach to Define, Measure, Analyze, Design, and Validate (DMADV) the requirements of the ASA process designs. The 6M enhanced framework is used to create as a process design tool, and the outputs of the research are converted in assessment guidelines, which were validated by a global aerospace accreditation body for its adoption in the aerostructures supply chain. This paper can also be used as a reference guide by ASA engineers to readily identify the factors that can impact any process, ensuring the highest quality and reliable aerostructures products to customers ensuring cost-effective On-Time Delivery (OTD).
Design for Six Sigma (DFSS) is an essential tool and methodology for innovation projects to improve the product design/process and performance. This paper aims to present an application of the DFSS Taguchi Method for an automotive/vehicle component. High-Pressure Vacuum Assist Die Casting (HPVADC) technology is used to make Cast Aluminum Front Shock Tower. During the vehicle life, Shock Tower transfers the road high impact loads from the shock absorber to the body structure. Proving Ground (PG) and washout loads are often used to assess part strength, durability life and robustness. The initial design was not meeting the strength requirement for abusive washout loads. The project identified eight parameters (control factors) to study and to optimize the initial design. Simulation results confirmed that all eight selected control factors affect the part design and could be used to improve the Shock Tower's strength and performance. Non-dynamic analysis Smaller-the-Better (STB) was used for this project with the primary objective of reducing stress in part, which contributes to the higher durability life. The number of Simulation decks was generated using the L18 Orthogonal Array. Optimized front shock tower design met the strength requirement for 10% higher magnitude washout loads. The Optimum Design improved part robustness by 9.6 dB (actual) compared to the initial design. Key control factors that will enhance part strength are; shock tower material type, rib thickness, material addition at the rib/collar junction, spacer plate gauge/diameter and collar height. One of the project's objectives was to study the effect of several Shock Tower materials for this application, including HPDC alloys such as Aural-2, Silafont-36, Magsimal-59 and magnesium AM60B, which showed improvement in part strength and durability. Besides, the strength and ductility of AM60 material can be further improved by adopting the High-Pressure Supper Vacuum Die Casting process.
Vehicle suspension parts are subjected to variable road loads, manufacturing process variation and high installation loads in assembly process. Seam welding can be considered as such process to connect more components and parts. Typical in a Mc Pherson suspension system stabilizer bar link is connected to the strut assembly through ball stud and clamped to a bracket welded to the outer strut tube. Cracks have been observed in the stabilizer bar link bracket welds of vehicles in the field, effecting the functionality of the suspension system. During preliminary phase of product development CAE assessment of the seam weld is carried out against road load data, if the design does not meet the targets enabler studies are carried out in an iterative approach. Various design variables (control factors) can be considered to carry out the iterations. Design for Six Sigma (DFSS) and Taguchi approach are used to carry out a parametric design study of the weld attachment to quantify the effect of various control factors over fatigue life. This can quantify the relation and weight factor of each control factor or variables effecting the fatigue life. A study performed using Taguchi approach can provide an optimized design solution which can provide better fatigue life performance against higher road loads.
Roof is one of the major subsystems of the Body-In-White Structure, which significantly affects the vehicle strength and durability performance criteria. The roof structure should meet the functional targets under the standard operating conditions. Roof design considering various parameters in the initial phase is beneficial in reducing the product timeline for the OEM. The first-time right approach provides an opportunity for Optimization and Cost benefits in the longer term. This paper provides the use of Design for Six Sigma techniques to arrive at a robust and optimum design for the standard roof structure. The roof structure is designed to meet the operating conditions for durability. Roof finite element models are developed with control factors that affect the structure design. Virtual Analysis is performed on the Standard roof structure models. Roof Performance is the contribution of multiple factors such as roof material, thickness, number of roof bows, positioning of the bows, number of beads in the roof, section and height of the roof bows. The paper looks at the contribution of each factor and shows how the combination of one or more factors can be effectively used to design an optimum and robust design that is light weight under the vehicle operating conditions. Standard roof is designed for Snow Load conditions. The final robust and optimized finite element design is validated for other durability loading conditions.
The aerospace industry had recently initiated the journey towards the transition to the Advanced Product Quality Planning (APQP) process, for the manufacturing and assembly process of their products in their supply chain, aiming to continually meet the rising delivery demand of the global aerospace industry and improve the quality and costs of current products and services. Of the various APQP process elements and requirements, one specific requirement is the application of Design For Manufacturing and Assembly (DFMA®) guidelines, early in the product design and development phase, aiming to design, develop, and analyze the designs for effective and efficient product realization. These guidelines, though widely used, are fairly new for the aerospace industry, and there is no standard framework readily available to aerospace organizations for the successful deployment of these guidelines in the Aerospace APQP process. The study in this technical paper is a continuation of the research work carried out on the enhanced Design For Manufacture (DFM) guidelines in the aerospace industry and focuses on the development of such a framework derived from the existing APQP and DfMA models that can be specifically enhanced to suit aerospace processes for the implementation and sustenance of the DFMA® guidelines in the Aerospace APQP product design and development phase. The study focuses on the importance of the framework for the application of DfMA principles in aerospace designs, highlighting the design intents, actions, and sources that support the design goals. The study analyzes the current challenges and opportunities that the design and supplier organizations can mitigate and adapt for a successful APQP transition from the current traditional manufacturing and assembly processes appropriate for all aerospace product designs and services. The framework methodology is developed using the Design For Six Sigma (DFSS) concept by defining, measuring, analyzing, designing, and validating the DfMA guidelines for successful implementation and sustenance in an aerospace environment. The framework proposed can be tested in an aerospace design manufacturing environment and used as a reference guide for organizations intending to effectively apply and sustain the DfMA element in the Aerospace APQP Phase 2 of Product Design and Development.
The main components present in the clutch disc assembly are friction facing, metallic disc, damper spring, drive plate, retainer plate, washers and hub. Among the parts, metallic disc is the weakest component present in the clutch system and moreover it is subjected to higher fatigue load during the vehicle operating condition. Hence it is necessary to make the design as more robust to withstand the worst loading conditions. The metallic disc is subjected to axial load, torque, speed and axial misalignment during vehicle operating condition. Through bench test, it was observed that higher severity in metallic disc was due to axial misalignment. Initially, metallic disc was tested for axial misalignment condition up to failure through bench test and the number of cycles were determined. Structural simulation was simulated as the same as bench test using ANSYS workbench 19.2. From this better correlation arrived between FEA and bench test. To make FEA result more robust, tolerance study was done using six sigma methodology. Therefore, this Numerical method was useful to obtain a sturdy design of metallic disc without investing much time through bench test. Optimization study was performed on metallic disc by considering all the possible design parameters without affecting the functionality of the component. A Novel Y shaped design profile was introduced in the metallic disc to increase the life cycle of the component, which is the main novelty of the current research work. This design pointed 15% and 20% reduction in stress and 10% and 62% least stiffness when equated with the initial design for gearbox side (GBS) and flywheel side (FWS) respectively. Hence the Novel Y shaped design was considered as robust design for the current disc assembly of the automotive application.
It is a challenging task to find an optimal design concept for a truck frame structure given the complexity of loading conditions, vehicle configurations, packaging and other requirements. In addition, there is a great emphasis on light weight frame design to meet stringent emission standards. This paper provides a framework for fast and efficient development of a frame structure through various design phases, keeping durability in perspective while utilizing various weight reduction techniques. In this approach frame weight and stiffness are optimized to meet strength and durability performance requirements. Fast evaluation of different frame configurations during the concept phase (I) was made possible by using DFSS (Design for Six Sigma) based system synthesis techniques. This resulted in a very efficient frame ladder concept selection process. Frame gauge optimization during the subsequent development phase (II) utilizing a newly developed damage based approach greatly reduced the number of design iterations relative to a typical stress based approach. In the light weighting phase (III) that followed, a method was established to effectively locate and optimize lightening holes using fatigue damage contours. In the final optimization phase (IV) custom Python® scripts were developed to optimize weld lengths at joints. This whole framework provides a fast and efficient way to optimize a frame structure for durability.
In this paper we present an integrated approach which combines analysis of the effect of simultaneous variations in model input parameters on component or system temperatures. The sensitivity analysis can be conducted by varying model input parameters using specific values that may be of interest to the user. The alternative approach is to use a structured set of parameters generated in the form of a DFSS DOE matrix. The matrix represents a combination of simulation conditions which combine the control factors (CF) and noise factors. CF’s are the design parameters that the engineer can modify to achieve a robust design. Noise factors include parameters that are outside the control of the design engineer. In automotive thermal management, noise factors include changes in ambient temperature, exhaust gas temperatures or aging of exhaust system or heat shields for example. The integrated approach, presented in this paper, provides powerful tools that can significantly reduce the total simulation time and helps to provide robust thermal protection scenarios. The relative importance of the CF’s can be estimated, and the least costly but effective design can therefore be considered. An example is illustrated for optimization of catalytic converter design in order to reduce its impact on surrounding component temperatures.
Robust engineering is an integral part of the quality initiative, Design For Six Sigma (DFSS), in most companies to enable good designs and products for reliability and durability. Taguchi’s signal-to-noise ratio has been considered as a good performance index for robustness for many years. An alternate approach that is direct and simple for measuring robustness is proposed. In this approach, robustness is measured in terms of an augmented output response and it is a composite index of variation and efficiency of a system. This formulation represents an engineering design intent of a product in a statistical sense, so engineers can understand, communicate, and resonate at ease. Robust formulations are illustrated and discussed with case studies for smaller-the-better, nominal-the-best, and dynamic responses. Confirmation runs of optimization show good agreement of the augmented response with the additive predictive models.
Active grille shutter (AGS) in a vehicle provides aerodynamic benefit at high vehicle speed by closing the front-end grille opening. At the same time this causes lesser air flow through the cooling module which includes the condenser. This results in higher refrigerant pressure at the compressor outlet. Higher head pressure causes the compressor to work more, thereby possibly negating the aerodynamic benefits towards vehicle power consumption. This paper uses a numerical method to quantify the compressor power consumed in different scenarios and assesses the impact of AGS closure on total vehicle energy consumption. The goal is to analyze the trade-off between the aerodynamic performance and the compressor power consumption at high vehicle speeds and mid-ambient conditions. These so called real world conditions represent highway driving at mid-ambient temperatures where the air-conditioning (AC) load is not heavy. AC system is modeled using 1D methodology and its performance simulated at system level. Net power consumed by the vehicle is computed for different scenarios using a robust comparison methodology. System model is validated against vehicle drive cell test data. Tests are conducted on a mid-size sport utility vehicle (SUV) equipped with a full face AGS. Simulations are then performed with the validated model using a Design for Six Sigma (DFSS) methodology to compute net power consumed by the vehicle by varying the blower setting, vehicle speed and ambient temperature. This paper provides guidelines regarding when to have the AGS closed or open for different noise factors considered.
Since the electronic shift lever detent system is used in vehicles on a large scale, it is urgent to solve the problem of robustness parameter design of the shift quality of SLDS under the uncertain dynamic parameters and manufacturing tolerances. We Build the MBD model of shift lever detent system, selecte the evaluation indicator for shifting quality and propose a two-stage method which associates the deterministic optimization of grey relational grade with the robustness parameter optimization of six sigma, in the early stage of product quality design. We use the grey relational grade to take the place of SNR in deterministic optimization, and compute the the optimal combination of controllable factors and their levels. The controllable parameters of shift lever detent system include three parameters that determine the detent profile structure parameters, spring parameters and contact pair parameters. The deterministic optimization of grey relational grade provides initial values for the six sigma optimization, and the six sigma optimization solves the problem of tolerance design of controllable factors under the uncertainty of noise factors in further. The test results of the experiment validate the rationality and correctness of the MBD model of shift lever detent system, the selected controllable parameters, and the two-stage optimization method. The results also prove these models, parameters and methods, which can solve the problem of robust design of the shift quality. What's more, The two-stage method proposed in this paper is more applicable to the problem of dynamic robust design with discrete-continuous parameters.
Nowadays development of automotive HVAC is a challenging task wherein thermal comfort and safety are very critical factors to be met. HVAC system is responsible for the demisting and defrosting of the vehicle’s windshield and for creating/maintaining a pleasing environment inside the cabin by controlling airflow, velocity, temperature and purity of air. Fog or ice which forms on the windshield is the main reason for invisibility and leads to major safety issues to the customers while driving. It has been shown that proper clear visibility for the windshield could be obtained with a better flow pattern and uniform flow distribution in the defrost mode of the HVAC system and defrost duct. Defroster performance has received significant attention from OEMs to meet the specific global performance standards of FMVSS103 and SAE J902. Therefore, defroster performance is seriously taken into consideration during the design of HVAC system and defroster duct. The HVAC unit provides hot air to the defroster duct which is blowing high velocity air to the windscreen to clear the frosting. Currently as a traditional defrost duct design process, multiple flow simulation needs to be carried out for various design configurations of defrost duct through CFD analysis until the performance targets are achieved during the design cycle and it is very time consuming. In this paper, the focus is to develop defrost duct modelling using parameterization technique and optimize the defrost duct system to meet the performance requirements through robust optimization Design for Six Sigma (DFSS) methodology to reduce the design time, cost, size and weight of the system. Parametric modelling technique is used for designing the defrost duct through design software to reduce the design time for simulation. A 3-dimensional model (3D) of a car cabin with full a HVAC system was developed using Star-CCM+® to predict the performance of the system in the windshield. DFSS methodology helps in finding out the optimized design factors of defrost duct to meet the performance targets such as pressure drop, airflow and velocity at windshield aim points simultaneously. The optimized defrost duct design results were compared with the baseline defrost duct design results and the improvement in performance results is achieved by more than 60%.
An Aircraft’s assembly process plays a vital part in its design, development and production phases and contributes to about half of the Total cost spent in its entire product lifecycle. Design For Assembly (DFA®) principles have been one of the proven effective methodologies in Automotive and Process industries. Use of DFA® principles have resulted in proactively simplifying and optimizing engineering designs with reduced product costs, and improved efficiencies in product design and performance. Standardization of Assembly guidelines is vital for “Design and Build” and “Build-To-Print” manufacturing supplier organizations. However, Standardizing design methodologies, through use of proven tools like Advanced Product Quality Planning, (APQP) are still in the initial stages in Aerospace part and process design processes. Thus, there is a tremendous opportunity for research on the application of the existing DFA® guidelines to optimize Engineering Aerospace Assembly processes aiming to simplify, standardize design methodologies by building on existing industry practices which have a common platform for design communication and are easy to adopt within the existing process/systems. This technical paper is to discuss the framework for application of DFA® principles and design guidelines specifically aimed for engineering optimization of Aerospace Assembly Process Designs. The Aerospace DFA® implementation framework proposed in this paper is based on the study on the application of the existing DFA® guidelines proven and used in other Process industries to Aerospace Part and Process Design and development. This paper collates the findings, experiences and learnings gained during the study collated from a research point of view using Six Sigma methodology DMAIC and DMADV. This paper also focuses on the use and publication of this research outputs on Aerospace industry applicable DFA® guidelines, which can be used as a reference for emerging Aerospace designers in their future and current designs.
Multi-layered, high-density polyethylene (HDPE) fuel tanks are increasingly being used in automobiles due to advantages such as shape flexibility, low weight and corrosion resistance. Though, HDPE fuel tanks are perceived to be safer as compared to metallic tanks, the material properties are influenced by service temperature. At higher temperatures (more than 80oC), plastic fuel tanks can soften, sag and eventually spill out the fuel, while the extreme cold (less than -20°C) can lead to potential cracking problems. Damage may also occur due to accidental drop while handling or due to an impact from a flying shrapnel. This can be catastrophic due to flammability of the fuel. The objective of this work is to characterize and develop a failure model for the plastic fuel tank material to simulate damage and enhance predictive capability of CAE for chassis and safety load cases. Different factors influencing the material properties such as service temperature, rate of deformation, state of stress etc. were considered to develop a characterization and modelling strategy for the HDPE fuel tank material. Samples cut-out from different regions of the fuel tank were subjected to various tests such as tensile test at different strain rates viz. 0.01/s, 0.1/s, 1/s, 10/s and 100/s, compression, shear, flexure and instrumented dart impact tests at different temperatures, -40°C, 23°C and 85°C. Simulation of damage was accomplished via progressive damage and failure modeling capability available in ABAQUS. Ductile damage initiation criteria and equivalent plastic displacement for damage evolution were considered. The parameters of the failure model were optimized using Design for Six Sigma (DFSS) principles. The material model was validated by comparing simulation results with test at coupon and component levels.
Drive cycles have been an integral part of emission tests and virtual simulations for decades. A drive cycle is a representation of running behavior of a typical vehicle, involving the drive pattern, road characteristics and traffic characteristics. Drive cycles are typically used to assess vehicle performance parameters, perform system sizing and perform accelerated testing on a test bed or a virtual test environment, hence reducing the expenses on road tests. This study is an attempt to design a relatively robust process to generate a real world drive cycle. It is based on a Six Sigma design approach which utilizes data acquired from real world road trials. It explicitly describes the process of generating a drive cycle which closely represents the real world road drive scenario. The study also focuses on validation of the process by simulation and statistical analysis.
The automotive Air Induction System (AIS) is an important part of the engine systems which delivers the air to the engine. A well-designed AIS should have low flow restriction and radiates a good quality sound at the snorkel. The GT-Power simulation tool has been widely utilized to evaluate the snorkel noise in industry. In Fiat Chrysler Automobiles, the simulation method enhanced with Design For Six Sigma (DFSS) approach has been developed and implemented in AIS development to meet the functional requirements. The development work included different types of DFSS projects such as identifying new concept, robust optimization and robust assessment etc. In this paper, the work of a robust optimization project is presented on developing an AIS parametric model to achieve optimized snorkel noise performance for a V8 engine. First, the theory of AIS acoustic modeling using GT-power and DFSS robust optimization using Taguchi’s parameter design method are described. Secondly, the effects of several AIS design control factors on the AIS sound attenuation and snorkel performance are studied. Finally, the eight steps of Taguchi’s parameter design method are presented on developing a parametric AIS model for a V8 engine. Based on the results from the verification step, an optimized AIS parametric model to this specified V8 engine is suggested. The optimized model is more robust against temperature variation and has better snorkel noise performance. The lessons learned from this project and future work are discussed in the conclusion section.
Motivation - Ambiguous product targets, a global market, innovation pressure, changing process requirements and limited resources describe the situation for engineering management in the most R&D organizations. Achieving complex objective with limited resources is a question of performance. Performance in engineering departments is highly correlated to the existing capability of the engineering staff. When the reduction of engineering effort in development projects becomes additional goal for the management, an increase of engineering productivity is required. International engineering sites are established globally to push the capacity limits and to increase the productivity by the accessing big employment markets of engineering talents. By solving the conflict of limited resources and complex engineering goals, a need organizational challenge occurs - global co-engineering. Co-engineering is the extension of simultaneous engineering by the distribution of tasks and responsibilities in a global organization. Different to other global enterprise functions, like sales, the individual engineering staff takes over global responsibilities independent of their own localization. Systems are designed, constructed, implemented or tested in one region for the product release in a different region. Contribution - This technical report analyzes the challenges of engineering management in a global co-engineering environment. The relevance and value of a transparent organization overview is described and derived. The Organization Architecture (OA) is explained as tool to achieve the required transparency of globally distributed roles and responsibilities. The relevance of the organization structure is distinguished from the process structure required for product quality (acc. Six Sigma) or process maturity (acc. SPICE). The method to introduce, maintain and use of the Organization Architecture is described, including the nomenclature for the organizational elements. The benefits of the OA along those phases are evaluated in an industry use-case. The typical organizational optimizations - identified by the OA - are introduced and explained. The limitations of the OA for optimization of engineering organizations are explained. The possible combination of the OA with other management methods for R&D are discussed.
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