Systematic design of a Maturity Model for the Development of New
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Systematic design of a Maturity Model for the Development of New
Gökhan Akkasoglu* Albert Weckenmann** Systematic design of a Maturity Model for the Development of New Forming Processes Introduction The development of new forming processes aims primarily for increasing process robustness and shortening process chain. The latter reduces also process time and increases cost-effectiveness. Sheet metal forming processes are in the focus of current developments, since they enable the increase of workpiece complexity by functional integration and meet the aspects of lightweight construction. Within the framework of the Transregional Collaborative Research Centre 73 different forming processes are analyzed, designed and optimized to unite the advantages and design possibilities of sheet and bulk metal forming to the novel SheetBulk Metal Forming (SBMF) using semifinished sheet workpieces. The SBMF is defined as “the plastic changing of the shape of a plain semifinished product not changing the specimen integrity. During the forming process both two- and three-axial strain- and stress-conditions as well as changes in shape occur simultaneously or shortly after each other” [Merklein, 2010]. The design and analysis of the SBMF is complex due to low previous knowledge and distinct creative phases, which complicates decisionmaking due to an intransparent development status. Especially in early development stages a low level of knowledge exists and the number of recognizable and thus quantitatively assessable characteristics of the manufacturing process is small [Reithmeier, 2011; Giapoulis, 2000] (Figure 1, left). Paradoxically, in these early stages the number of influenceable features is to be classified as high. Towards the end of development the number of recognizable features raises, but the number of influenceable Dipl.-Ing., Chair Quality Management and Manufacturing Metrology, University Erlanen-Nuremberg, [email protected] ** Prof. Dr.-Ing. Prof. h.c. Dr.-Ing. E.h. Dr. h.c. mult., Chair Quality Management and Manufacturing Metrology, University Erlanen-Nuremberg, [email protected] * 192 Gökhan Akkasoglu, Albert Weckenmann parameters decreases together with the change effort. That relates to the increasing modification effort according to the rule-of-ten, which indicates that the (financial) effort for changes is increasing tenfold with each development stage (Figure 1, right) [Ross, 1999]. Exemplary it is very costintensive to make major modifications on a forming tool which is already built up, whereas it is easy to perform such a change during tool design in a CAD-Model. Recognizable features (Financial) change effort Information uncertainty Number of product features Information uncertainty Figure 1. Uncertainty in development phases (left) and Rule-of-Ten (right) 1000,- 100,1,- Development phases 10,Development phases Source: [Reithmeier, 2011; Giapoulis, 2000; Ross, 1999]. An early characterization of the maturity before serial production enables in accordance to the rule-of-ten cost-effective decisions and modifications with reduced effort. A maturity represents the current development status of a considered process, product or system to a specific time and can be assessed by comparison with relevant indicators and their staged requirements within maturity levels [Weckenmann, 2011; Ahlemann, 2005]. Maturity models summarize these indicators and staged requirements. Thus maturity models provide best-practices for a subject matter to be evaluated by comparison. With determining the current status of the considered processes, products or systems specific improvement possibilities can be detected. In addition maturity models enable the capturing of lessons learned by adjusting the up to now documented requirements. Typically maturity models consist of four to six maturity levels. Currently existing maturity models like CMMI [SEI 2010] or ISO 15504 [ISO 20003] have primarily a strategic focus and are inappropriate for the evaluation of new forming processes due to their focus on software development processes [Berg 2001]. There exist several other maturity models for the evaluation of forming processes such as the Manufacturing Readiness Level of the United States Department of Defense [DoD 2009], the Process Survey Tool for Manufacturing Process Management of the European Foundation for Quality Management [EFQM 2004] or the Ma- Systematic design of a Maturity Model for the Development of New… 193 turity Level Assurance for new Parts of the German Association of the Automotive Industry [VDA 2007]. But these maturity models do not consider either typical phases of simulation and experimentation in the development of the SBMF or manufacturing relevant aspects, why they do not provide an adequate basis for the evaluation and assessment of the development status of the SBMF to be developed. They have a reduced relevance to the demands of an assessment for new forming processes. Thus, provided improvement measures on the basis of the determined maturity have low adaptability to the specific concern. Hence, a maturity model for assessing new forming processes has to be designed systematically for the novel SBMF to enhance the adequacy of derived improvement actions and also their acceptance within organizations. 1. Systematic design of a Maturity Model for Sheet-Bulk Metal Forming A customized maturity model creates the possibility for adequate assessments and identifies appropriate improvement possibilities. Approaches for the development of maturity models are discussed in different literature [Bruin, 2005; Becker, 2009], which have in common, that they do not provide methodic support in designing maturity models. Although creating a maturity model with methodical support would enhance the efficiency and usability of the procedure. For the methodic design of a maturity model initially a reference model of the considered object is to be created, which can comprise its structure or the elementary activities of a general process for a specific aim (Figure 2). A reference model summarizes the considered object in a generalized way and defines the focus of the investigation. On the basis of the reference model relevant maturity indicators are to be deduced by brainstorming and categorized to superordinate indicators, for example using the quality management technique of affinity diagrams. The selected indicators have to be weighted with the help of a pairwise comparison. The so called Maturity Level Matrix captures the defined indicators and their weightings as well as the hierarchically staged requirements on the indicators. The requirements are assigned to maturity levels which in turn are related to certain percentage intervals. The highest maturity value of 100% represents the best-practice for the specific indicator. The maturity can be determined by evaluating the indicators in percent with comparison of the current status of a considered object and the fulfillment of the set require- 194 Gökhan Akkasoglu, Albert Weckenmann ments on the specific indicators with a subsequent calculation of the weighted arithmetic mean. Figure 2. Systematic approach for the design of a maturity model Reference Model Sustainable Maturity indicators Comprehensive Level 4 Level 3 Appropriate Ad hoc/ unstructured Indicator weighting Indi Indi … Indn … ... … ... … Level 2 Wt.’s Indn … Level 1 0% - 15% 15% - 50% 50% - 85% 85% - 100% Maturity M Maturity Level Matrix Indi wi Maturity Levels … … Staged ... ... ...requirements ... ... ... Evaluation n 1 M(t) = n wi ⋅ Ind i ( t ) wi i =1 ∑ ∑ i =1 Source: Own work. The methodic procedure for the design of maturity models has been adopted to create an assessment basis for the development status of the novel sheet-bulk metal forming. Maturity relevant indicators are derived on the basis of a phase model that classifies the elementary development steps. Afterwards the weighted indicators are assigned to specific phases. For maturity determination all indicators assigned to the current development phase as well as those in the previous phases are to be used. The development of new forming processes takes place at the level of digital models and experimental investigations [Brenner, 2010a], in order to estimate the structure and behavior of cost-intensive components previously and to validate and improve them afterwards. The phase model for the analysis and design of forming processes categorizes these significant development periods and gathers the basic activities of the respective Systematic design of a Maturity Model for the Development of New… 195 phases, whereby a generalized procedure model is provided (Figure 3). In addition, preparatory phases such as “system analysis” and “system design” are considered, which can influence significantly the fulfillment of aims in simulative and experimental phases. Milestones (Mi) and Quality Gates (Qi) at the end of each phase indicate points of time to evaluate the maturity. In contrast to Quality Gates, Milestones can be passed without meeting all requirements. Still, determined improvement measures have to be applied until the next Milestone. 2. Maturity model for new forming processes The indicators for the assessment of the development status are derived on the basis of specific activities of the phase model for new forming processes (Figure 3). Figure 4 illustrates the elicited indicators with their sub-indicators and their weightings determined by a pair-wise comparison. The usefulness of defined aims and requirements is captured with the indicator of the same name. It is also considered to what extent the state of the art and market developments were implicated. Furthermore it is asked about the implementation of a development timeline and a feasibility study. The maturity indicator of “System analysis and design” assesses the extent of the system definition, as well as the relevance and significance of the acquired system parameters. The indicators “Modeling” captures the model reduction (idealization degree of reality), the model equations used and accuracy of the model geometry. The “Model verification” deals with the extent of the discretization and numerical errors [Oberkampf, 2007]. The indicator “Simulation” asks for the type of simulation used and the plausibility of the simulation results. The indicators on “Concept and design”, “Experiments”, “Cause and effect relationships and interactions”, “Evaluation and validation” and “Optimization” have a generic nature and are both used in the simulation phase as well as in the experimental phase to assess the development status. The experimental environment and the extent of concept analysis as well as the inclusion of experimental data uncertainties, such as process variations are surveyed. Furthermore, the significance and the type of the determined causal relationships (e.g. analytic or empiric) are assessed, so that the acquired knowledge on the new forming process is appraised. Moreover, the designed and implemented system elements are evaluated in terms of their functional performance, reliability and robustness. The type of criteria (e.g. quantitative or qualitative) used for evaluating the forming process provides information about the decision Possible influencing and target quantities as well as cause and effect relationships gathered (2.2; 8.1). Concepts of forming tools, machine components and workpieces designed (1.2; 6.4). Measurement Causal relations requirements and determined and tasks defined (1.2). transferred to the Model (3.2). State of the art and market development of forming tools, machine components and workpieces analyzed (1.1). Source: Own work. Manufacturing and quality objectives defined (1.2). Timeline of development established (1.3). Simulation D Feasibility studies (Existing) Model geometry performed (1.4). Measurement designed (3.3). systems evaluated (12.1; 12.2). M4 E Virtual optimization Simulation validated (5). Lubrication purchased and evaluated (9.3; 9.4; 11.1; 11.2). Forming tools and machine components as well as measurement system integrated (9.3; 9.4; 12.3). H Experimental validation Forming tools, machine components and workpieces evaluated (9). Measurement system designed and evaluated (12.1; 12.2). Selected concepts Measurement of forming tools and system validated machine com(12.1; 12.2). ponents produced and launched (9.3; 9.4). I Experimental optimization Q2 Parameter of measurement system optimized (10). Cause and effect relationships of process parameters dimensioned robust (10). Parameter of the forming machine components dimensioned optimally (10). Tool-lubricantworkpiece-system parameterized optimally (10). Manufacturing process evaluated and improvement possibilities identified (9). M7 Cause and effect relationships and interactions analyzed and validated (8). Experimental concept analysis performed on the whole system (7; 6.1; 6.2). M6 Selected concepts Lubrication Selected concepts Simulation model of formed selected (9.1; 9.2). of forming tools and validated und workpieces machine comcalibrated (9.2). designed (6.4). ponents purchased (9.1; 9.2; 11.1; 11.2; 9.3; 9.4). Cause and effect Process relationships and parameterized interactions optimally (10.1). identified (8.1). Simulation based concept analysis of forming tools, machine components and workpieces performed (6.1; 6.2; 7.1). Selected concepts of forming tools and machine components designed (6.4). Concepts of Make-or-Buy formed workpieces decision made revised, evaluated (9.1; 9.2). and selected (6.3). Concepts of forming tools and machine components revised, evaluated and selected (6.3). G Experimental preparation Aims and requirements on experiments defined and approach specified (1.2; 1.3). Q1 System design 2 F Additional model experiments performed (7; 9.1; 9.2; 9.3) M5 Model verified (4). Cause and effect relationships and interactions characterized (8.2). M3 Characteristic Approach for values determined simulation tests (7.1; 7.2; 12.2), defined (7.1). model parameterized (3.1). Effort and feasibility of simulations and experiments compared (1.4). Aims and requirements on simulation models defined (1.2). Modeling C System structure and procedure model created (2.1). M2 Current state of forming tools , machine components and workpieces analyzed (1.1). B System design 1 M1 System analysis A 196 Gökhan Akkasoglu, Albert Weckenmann Figure 3. Phase model for the development of new forming processes Systematic design of a Maturity Model for the Development of New… 197 Figure 4. Maturity indicators and sub-indicators for the development of new forming processes 1 Aims and requirements (11%) 1.1State of the art and market development (2.75%) 1.2 Aims and requirements (4.4%) 1.3 Timeline (2.75%) 1.4 Feasibility studies (1.1%) 6 Concept and design (11%) 6.1 Investigation surroundings f or concepts (2.2%) 6.2 Concept analysis (5.5%) 6.3 Concept evaluation and selection (1.1%) 6.4 Design (2.2%) 2 System analysis and design (7%) 2.1 System limitation and modeling (2.4%) 2.2 System parameters (4.6%) 3 Modeling (5%) 3.1 Model reduction / Idealization (2.5%) 3.2 Causal relations of parameters (1.5%) 3.3 Model geometry (1%) 4 Model verification (3%) 4.1 Approach (1.2%) 4.2 Discretization error (0.9%) 4.3 Numerical error (0.6%) 4.4 Benchmarking (0.3%) 5 Simulation (4%) 5.1 Type of simulation (0.8%) 5.2 Results (3.2%) 10 Optimization (7%) 10.1 Target value optimization (5.3%) 10.2 Ef fectiveness of optimization (1.7%) 11 Supplier management (2%) 11.1 Requirements specif ication (1.3%) 11.2 Supplier monitoring (0.7%) 7 Experiments (7%) 7.1 Approach (4.9%) 7.2 Data uncertainty (2.1%) 12 Measurement system (7%) 12.1 Analysis of measurement system (2.8%) 12.2 Measurement conditions (2.1%) 12.3 Process integration (2.1%) 8 Cause and effect relationships and interactions (11%) 8.1 Identif ication of causal relations (5.5%) 8.2 Characterization of causal relations (5.5%) 13 Employee (7%) 13.1 Further education, motivation (3.5%) 13.2 Communication (3.5%) 9 Evaluation and validation (11%) 9.1 State of the art, requirements (1.7%) 9.2 Evaluation criteria and decision (2.1%) 9.3 Environmental conditions (1.7%) 9.4 Function f ulf illment, reliability and robustness (5.5%) 14 Information management (7%) 14.1 Inf ormation system (3.5%) 14.2 Inf ormation content (3.5%) Source: Own work. reliability. The time-critical collaboration with suppliers is surveyed by validating the requirements specification and the supplier monitoring. The measurement system is evaluated in terms of degree of analysis (taking measurement uncertainty into account), the consideration of surrounding conditions and the degree of process integration. The cross-phase factor of “Employee”, who is identified as the main pillar of any development project, is considered with regard to education and training, motivation and communication. The handling of the large amounts of data and information acquired during the development of a forming process is evaluated by the further cross-phase indicator “Information management” in terms of used information systems and the information content stored therein. These indicators reflect the capability of the development for a high maturity. Figure 5. Excerpt of the Maturity-Level-Matrix Sub-indicator "6 Concept and design" Maturity levels Wt. Level 1 Level 2 Level 3 Level 4 (0% - 15%) (15% - 50%) (50% - 85%) (85% - 100%) 6.1 Investigation surroundings for concepts 2.2% Concept investigations are performed as a sketch. Simulation based concept investigations. Concepts with real model experiments investigated under laboratory conditions. Concept investigations are performed within a production environment with comparable influencing and noise quantities . 6.2 Concept analysis 5.5% No concept analysis and selection. Concepts are defined unstructured and designed with low variation of parameters. Significant parameters of concepts are varied (according to the requirements) sufficiently. Concept analysis is performed according to a reproducible approach by extensive and appropriate variation of significant parameters. Source: Own work. 198 Gökhan Akkasoglu, Albert Weckenmann The staged requirements on each sub-indicator are assigned to four maturity levels within a Maturity Level Matrix (Figure 5). The percentage intervals of the maturity levels comprise level 1 covering 0% to 15%, level 2 covering 15% to 50%, level 3 covering 50% to 85% and level 4 covering 85% to 100%. The complete Maturity Level Matrix with all indicators is called maturity model. 3. Exemplary appliance of the maturity model. The validation of the established maturity model is carried out by an instructed self-assessment within the development of the Sheet-Bulk Metal Forming. The current development time is localized within the phase of “Experimental preparation”. All assigned indicators up to that phase are used to assess the maturity. The assessment was performed independently by experts for deep drawing processes, forming machines and measurement systems. The maturity is determined in dependence of the phases and the system elements. By these two perspectives on the maturity improvement opportunities can be identified specifically. The maturities are calculated using a weighted arithmetic mean [Brenner, 2010b] and are shown in A maturity value of 100% corresponds to the full compliance of the current development status with the requirements for this phase. Similarly, the weighted maturity deviations are calculated and prioritized [Brenner, 2010b] to demonstrate the aim-oriented improvement possibilities. Specific acronyms were used to indicate, to which field the evaluated generic maturity sub-indicators refer. Based on the determined phase-dependent maturities distinctive improvement opportunities result in the development stage of “Virtual optimization”. Simulative optimization enables concept studies with many variations in the run-up to real and cost-intensive experiments and provides a wide base of information and knowledge about the process behavior. This knowledge can be used early in the design of forming tools, for example to avoid costly changes at the already produced tool. For the current phase of “Experimental preparation” the systematic functional evaluation of the purchased tool for the deep drawing process (indicator “9.4/TZ-P-WZ-B”) according to the defined criteria of the system model and the elimination of the determined noise quantities for increasing the robustness can be recommended to optimize the development status. This improvement possibility can be seen also in the maturity perspective related to the defined system elements. In addition, this perspective shows improvement opportunities in the selection of the “Lubrication”. The evalu- Source: Own work. 95 % 92 % 90 % Workpiece Tool, workpiece and process meas. technology Forming machine TZ UM WS WZ 275 303 275 413 Deep Drawing Forming Machine Workpiece Tool 110 98 7.1 / UM 42 91 6.2 / UM 9.4 / UM-H 12.3 / UM-P-MT 9.3 / WZ-MT 9.4 / WZ-MT 44 1.2 / UM-WS 55 49 6.2 / UM-WS 7.1 / UM-WS 55 9.2 / UM-S 9.3 / UM-S 83 138 9.4 / TZ-P-WZ-H 9.4 / UM-S 138 83 131 138 9.4 / UM-WZ 9.4 / TZ-P-WZ-B 8.2 / UM-Sim 10.1 / TZ-P-Sim 8.1 / UM Weighted maturity deviation Measurement Technique Process Lubrication Simulation 59 % Lubrication MT P S Sim 84 % Tool Used acronyms B Purchase H Design KW Characteristic values MS Measuring System 90 % Maturity Process design System element Maturity of system elements Experimental preparation System design 2 Virtual Optimization Simulation Modeling System design 1 System analysis Phase Maturity 80 % 95 % 85 % 93 % 92 % 94 % 87 % 55 55 53 6.4 / TZ-P-WZ 12.3 / UM-P-MT 303 275 9.4 / WZ-MT 9.4 / UM-H 9.4 / TZ-P-WZ-B 55 9.2 / UM-S 79 6.3 / TZ-P-WZ 10.1 / UM-Sim 131 83 8.2 / UM-Sim 10.1 / TZ-P-Sim 110 98 7.1 / UM 42 74 6.2 / UM 7.2 / UM-KW 7.1 / UM-KW 413 219 138 75 46 2.2 / UM 3.2 / TZ-P 48 55 105 2.1 / WZ-MT 8.1 / UM 1.3 / TZ-P 13.2 13.1 Weighted maturity deviation Maturity of phases Systematic design of a Maturity Model for the Development of New… 199 Figure 6. Maturity evaluation subject to phases and system elements 200 Gökhan Akkasoglu, Albert Weckenmann ation of the lubricant should ideally be based on defined, quantitative and multi-dimensional characteristics within a production environment (with comparable influencing and noise quantities), where the functional performance is examined over a defined period. Conclusion A quality-oriented development of new forming processes requires determination and managing of the development status based on reproducible evaluations to identify improvement opportunities in early development phases for enabling necessary changes at low costs. For the assessment of the development status and for the identification of improvement possibilities a maturity model for new forming processes has been created. The indicators are deduced on the basis of a combined reference model (consisting of a system and a phase model) and extended to a maturity model with specific maturity levels. The maturity assessment was carried out independently from one another by members of the development team for the Sheet-Bulk Metal Forming. The quantified development status gives information about improvement possibilities to achieve the set aims. Literature 1. Merklein M. et al. (2010), Manufacturing of Complex Functional Components with variants by using a new Metal Forming Process - Sheet-Bulk Metal Forming, „Intl. Journal of Material Forming” no. 1, s. 347-350. 2. Reithmeier E., Weckenmann A., Behrens B.-A. et al. (2011), Qualitätsorientierte Entwicklung der Blechmassivumformung, [w:] Tagungsband zum 1. Erlanger Workshop Blechmassivumformung 2011, Merklein M. (red.), Meisenbach, Bamberg, s. 97-118. 3. Giapoulis A. (2000), Einsatz von Methoden zur Produktentwicklung in der industriellen Praxis, [w:] VDI Berichte 1558, VDI (red.), Düsseldorf, s. 1-9. 4. Ross J. E., Perry S. (1999), Total quality management: text, cases, and readings, St. Lucie Press, Florida. 5. Weckenmann A., Akkasoglu G. (2011), Maturity Method for the Development of Metal Forming Processes considering Fuzzy Input Parameters, [w:] 6th Intl. Conf. on Design and Production of Machines and Dies/Molds, Akkök M. et al. (red.), Atilim University, Ankara, s. 9-15. 6. Ahlemann F. et al. (2005), Kompetenz- und Reifegradmodelle für das Projektmanagement. Grundlagen, Vergleich und Einsatz, [w:] ISPRI-Work Report, Ahlemann F. et al. (red.), University of Osnabrück. Systematic design of a Maturity Model for the Development of New… 201 7. SEI (2010), CMMI for Development v1.3., Carnegie Mellon University, Pittsburgh. 8. ISO 15504-2, Information technology - Process assessment – Part 2: Performing an assessment, Geneva, 2003. 9. Berg P. et al. (2001), Assessment of quality and maturity level of Research and Development, Intl. Conf. on Management of Engineering and Technology PICMET, Portland. 10. Department of Defense (2009), Manufacturing Readiness Assessment (MRA) Deskbook - Version 7.1., Department of Defense, Washington. 11. EFQM-Philips (2004), Process Survey Tool For Manufacturing Process Management, EFQM, Brussels. 12. VDA (2007), Das gemeinsame Qualitätsmanagement in der Lieferkette: Produktentstehung; Reifegradabsicherung für Neuteile, VDA, Oberursel. 13. de Bruin T. et al. (2005), Understanding the Main Phases of Developing a Maturity Assessment Model, 16th Australasian Conference on Information Systems, Sydney. 14. Becker J. et al. (2009), Developing Maturity Models for IT Management, „Wirtschaftsinformatik” no. 3, s. 249-260. 15. Brenner P.-F., Weckenmann A., Akkasoglu G. (2010), Reifegradmethode für neue Fertigungsverfahren, „Qualität und Zuverlässigkeit” no. 6, s. 52-55. 16. Oberkampf W. L., Pilch M., Trucano T. G. (2007), Predictive Capability Maturity Model for Computational Modeling and Simulation, Sandia National Laboratories, Washington. 17. Brenner P.-F., Akkasoglu G., Weckenmann A. (2010), Referenzmodellgestützte Reifegradabsicherung bei der Entwicklung neuer umformtechnischer Verfahren, [w:] Unternehmerisches Qualitätsmanagement, Schmitt R. (red.), Apprimus, Aachen, s. 123-144. Acknowledgement The authors thank the German Research Foundation (DFG) for funding this research within the framework of the Transregional Collaborative Research Centre (Transregio) TR 73. Summary Development of new forming processes aims for overcoming the current process limitations so that function extended components with a wider range of application and lower manufacturing costs are producible. But the comprehensive and complex investigations of cause-effect relationships and interdepend- 202 Gökhan Akkasoglu, Albert Weckenmann encies in the development lead to an intransparent development status, whose assessment is often based on undefined and not reproducible criteria. This can result in wrong decisions with vain modifications that require subsequent changes associated with an increased effort. A valid characterization of the development status is needed to identify improvement potentials in early development phases and thus to apply the effective measures with reduced efforts. Maturity models provide essential indicators and assign their values to maturity levels, whereby a uniform assessment base is created to identify the development status reproducibly. Currently there is neither a maturity model for the assessment of new forming processes in development available nor a method-based procedure for the design of specifically needed maturity models. Therefore, a systematic approach has been designed to be able to determine maturity-related indicators on the basis of a combined reference model. The indicators are to be subsumed, weigthed and provided in a maturity-levelmatrix. This approach has been applied within the development of the novel Sheet-Bulk Metal Forming which aims for uniting the advantages of sheet and bulk metal forming processes. The designed maturity model for new forming processes facilitates the assessment of the development status with referencebased indicators and provides more transparent results in comparison to subjective evaluations. Keywords Maturity model, Sheet-Bulk Metal Forming, development process, information uncertainty