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KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association INSTITUTE FOR INFORMATION MANAGEMENT IN ENGINEERING.

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Presentation on theme: "KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association INSTITUTE FOR INFORMATION MANAGEMENT IN ENGINEERING."— Presentation transcript:

1 KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association INSTITUTE FOR INFORMATION MANAGEMENT IN ENGINEERING (IMI) Configuration and Change Process Maturity Improvement Masoud Niknam reported by Pierre Bonnal

2 2 Information Management in Engineering Engineering Change Management - Essential for continued development of companies Triggers for Engineering ChangeImpact of Change Process Emergent Changes  Error Correction  Product Safety  Change of Function  Product Quality  Additional Cost  Scheduling  Supply Chain  Product Performance  Reliability  Availability  Maintenance  Safety  etc. Initiated Changes  Customer Requirement  Supplier Issues  Product Support Enhancement Efficient and effective Engineering Change Management is a prerequisite for success in an ever more dynamic competitive environment Definition of Engineering Change (EC): An engineering change is an alteration made to parts, drawings or software that have already been released to production. The change can be of any size or type; the change can involve any number of people and take any length of time (Jarratt et.al. 2004). Configuration and Change Process Maturity Improvement

3 3 Information Management in Engineering Cross-industry Configuration Management Maturity Assessment utilizing the developed CM Maturity Model Dimensions of Configuration Management Maturity Model (CM3)Configuration Management Maturity Levels Evaluation of more than 67 organizations from various industry sectors such as Automotive and Aerospace Configuration and Change Process Maturity Improvement

4 4 Information Management in Engineering High Engineering Change Process Maturity and low IT Tool Maturity indicate need for action (Jarratt et.al. 2004) EC request raised Identification of solutions Risk / impact assessment Selection and approval Implementation of solution Review of change process Change Trigger Break PointIteration loop Engineering Change process is well understood and implemented in many organizations Competitive environment requires timely and high quality decisions With the growing complexity of products, production, organizations and value chains, fast and well informed decision making is getting increasingly difficult Support of risk / impact assessment needed to close gap between process and IT tool maturity Engineering Change Process Maturity Maturity of IT tools supporting Engineering Change Process (Nikham et. al. 2013) Configuration and Change Process Maturity Improvement

5 5 Information Management in Engineering Engineering Change Mgmt. Decision Making not sufficiently supported by current tools Problems: Mostly focused on physical propagation Non-functional requirements not considered (safety, reliability, etc.) Tacit knowledge from experts not considered Proposal: Utilizing knowledge from previous cases to estimate impact of proposed change Support of Decision Makers IT System Focus PLM systems Supporting the workflow and automation of the ECM processes Computer Aided Design (CAD) Analysis of the impact and mismatch of design changes based on geometrical values Redesign IT [Ollinger & Stahovich 2001] Generates proposals for redesign plans using a product model with relevant physical quantities and causal relationships Change FAvourable Representation (C-FAR) Examination of attributes and interactions between core elements of various entities in product models to predict propagation Change Prediction Method [Clarkson et al. 2001] Change Prediction Method (CPM) [Clarkson, Simons & Eckert 2001] ADVICE [Kocar & Akgunduz 2010] Virtual environment for ECM with the prioritization agents and propagation prediction techniques Hamraz et al [2012] Multidomain EC Propagation model utilizing DSM and Multidomain Matrices (MDM) for predicting the change propagation Configuration and Change Process Maturity Improvement

6 6 Information Management in Engineering Proposed ECM Process Backup Configuration and Change Process Maturity Improvement

7 7 Information Management in Engineering Proposed Information Flow Configuration and Change Process Maturity Improvement

8 8 Information Management in Engineering Knowledge-Driven Decision Support System proposed to assist decision makers (Jarratt et.al. 2004) EC request raised Identification of solutions Risk / impact assessment Selection and approval Implementation of solution Review of change process Change Trigger Break PointIteration loop Determine Engineering Change Properties Find similar previous cases Predict impacts based on existing knowledge from the case base Knowledge-Driven Decision Support Systems: attempt to understand and formalize human and implicit knowledge in computer systems and are used for data analysis in structured decision- making (Wiig, 1994) emphasize the usage and creation of knowledge and data mining to find associations between data points Configuration and Change Process Maturity Improvement

9 9 Information Management in Engineering Case Based Reasoning identifies and adjusts similar cases to estimate implications on case Input Identify similar cases Adjust cases via rulesEstimate Impact  Calculation of similarity measure based on ontological description of case features  Use of input values only  Adjustment of cases to match input case  Rules induced from case base with impact on output variables  Combine Values of similar cases to achieve high information level  Include case similarity in impact weighing Input Case Problem Product Component Process Type Priority … Perf. SUV Axle Pilot Adj. Urg.S.... Case C i Input Problem Product Component Process Type Priority … Output Cost Lead time Safety issue Process iss. … Perf. SUV Axle Pilot Adj. Urg.S S1,S4 P2,P8 … Case C i Input Problem Product Component Process Type Priority … Output Cost Lead time Safety issue Process iss. … Perf. SUV Axle Pilot Adj. Urg.S S1,S4 P2,P8 … IF Problem=Perf AND Comp=Axle AND Process Pilot  Prototype THEN Cost  -80% IF Problem=Perf AND Comp=Axle AND Process Pilot  Prototype THEN Cost  -80% IF Problem=Perf AND Comp=Axle AND Process Pilot  Prototype THEN Cost  -80% Case C i Input Problem Product Component Process Type Priority … Output Cost Lead time Safety issue Process iss. … Perf. SUV Axle Proto. Adj. Urg.S S1,S4 P2,P8 … Input Case Input Problem Product Component Process Type Priority … Output Est. Cost Lead time Safety issue Process iss. … Perf. SUV Axle Proto. Adj. Urg.S S1,S3 P2,P5 … Configuration and Change Process Maturity Improvement

10 10 Information Management in Engineering Similarity assessment uses Ontology based representation of cases 1) Weights determined with Analytic Hierarchy Process Problem Component ProductProcess Ontology based assessment with higher performance Automatic calculation of concept similarity from previous cases in case base Consideration and weighing of different ontologies and concept similarities Flexibility for adjustment of ontologies High effort for Analytic Hierarchy Process Similarity assessment for each ontology  Sim(C 1,C 2 ) Adjustment for weight 1) (w) and depth (d) of ontology Use previous cases with Sim(C 1,C 2 ) > Threshold Sim.

11 11 Information Management in Engineering Adjustment rules automatically drawn from existing case database Automatic generation of adjustment rules by using information contained within the case base Discovery of rules through machine learning algorithms Candidate methods include: Multilayer Perceptron Multiclass Support Vector Machine Application of rules that meets high required support and confidence levels Support (AB) = P (A U B) Confidence (AB) = P (B | A) IF Problem=Perf AND Comp=Axle AND Process Pilot  Prototype THEN Cost  -80% IF Problem=Perf AND Comp=Axle AND Process=Pilot THEN Cost=200 IF Problem=Perf AND Comp=Axle AND Process=Pilot THEN Cost=200 IF Problem=Perf AND Comp=Axle AND Process=Pilot THEN Cost=40 IF Problem=Perf AND Comp=Axle AND Process=Pilot THEN Cost=40 Configuration and Change Process Maturity Improvement

12 12 Information Management in Engineering Combination of several previous cases enhances Information quality Several estimations of Engineering Change Impact Combination based on similarity and confidence High quality information basis for EC Decision Maker Output Case Input Problem Product Component Process Type Priority … Output Est. Cost Lead time Safety issue Process iss. … Perf. SUV Axle Proto. Adj. Urg.S S1,S3 P2,P5 … Output Case Input Problem Product Component Process Type Priority … Output Est. Cost Lead time Safety issue Process iss. … Perf. SUV Axle Proto. Adj. Urg.S S1,S3 P2,P5 … Output Case Perf. SUV Axle Proto. Adj. Urg.S S1,S3 P2,P5 … Input Problem Product Component Process Type Priority … Output Est. Cost Lead time Safety issue Process iss. … Output Estimation Cost Lead time Safety issues Process issues Deny EC request Demand reiteration Accept EC request Weighing of output estimates based on similarity measure and rule confidence Completed Cases are reviewed and serve as input for machine learning Decision Support System allows for faster and higher quality decisions Configuration and Change Process Maturity Improvement

13 13 Information Management in Engineering Improved Decision Speed and Quality justify additional Effort for DSS Implementation Consideration of input from previous cases allows for reuse of (tacit) knowledge Constantly expanding case base allows for dynamic update of knowledge Applicability of system to various fields Accuracy of predicted impacts highly dependent on data quality and used algorithms Comparatively high effort for implementation and information gathering Development of Knowledge-based Decision Support System strongly supports more efficient and effective Engineering Change Management Further Study Evaluation of suitable algorithms Identification of data sources and formats Practical implementation and validation Configuration and Change Process Maturity Improvement

14 14 Information Management in Engineering Configuration and Change Process Maturity Improvement

15 15 Information Management in Engineering Sources Ahmad, N, Wynn, DC & Clarkson, PJ 2011, 'Information models used to manage engineering change: a review of the literature ', International Conference on Engineering Design, Technical University of Denmark. BBC News, Volkswagen recalls 2.6 million cars, viewed on 15.Novemver 2013http://www.bbc.co.uk/news/business Clarkson, PJ, Simons, C & Eckert, CM 2001, 'Predicting change propagation in complex design', Proceeding of ASME design engineering technical conferences, Pittsburgh, USA, CD-ROM, paper no. DETC2001/DTM Jarratt, TAW, Eckert, CM & Clarkson, PJ 2004, 'Development of a product model to support engineering change management', Proceedings of TMCE 2004, Lausanne, Switzerland. Kocar V., Agunduz A. 2010, “ADVICE: a virtual environment for engineering change management. Comput Ind 61(1): Ollinger, GA & Stahovich, TF 2001, 'ReDesignIT - A constraint-based tool for managing design changes', Proceedings of ASME design engineering technical conferences, Pittsburgh, USA, CD- ROM, paper no. DETC2001/DTM Sajja, PS & Akerkar, R 2010, 'Advanced Knowledge Based Systems: Model, Application & Research', vol.1, pp Wiig, KM 1994, Knowledge Management. The central management focus for intelligent acting organization, Schema Press, Arlington. Etc. Configuration and Change Process Maturity Improvement


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