Engineering Change Propagation Research Articles

CGCI: Cross-granularity Causal Inference framework for engineering Change Propagation Analysis

In the dynamic landscape of large-scale and intricate product development, the constant generation and accumulation of configuration data, influenced by factors such as evolving demands and version alterations, exhibit inter-domain and inter-level characteristics. This complexity presents formidable challenges to the management of controlled changes. Central to effective change management is Change Propagation Analysis (CPA), particularly in accurately predicting the potential impacts on affected items. However, conventional CPA methods are insufficient for addressing the challenge of cross-domain, cross-level inference. Therefore, we propose a Cross-granularity Causal Inference Framework (CGCI) tailored for CPA. This framework leverages the diffusion and attenuation of influence, enabling efficient identification of potential configuration items. To assess the feasibility of CGCI, a dataset is constructed using raw industrial configuration data and conducted a comprehensive case study on aircraft configuration change control. The results of our comparative analysis show that CGCI is effective in addressing multi-granularity and multi-hop inference problems, with more comprehensive consideration and less inference overhead in the multi-granularity case.

Operational Context Change Propagation Prediction on Autonomous Vehicles Architectures

Abstract Autonomous vehicles (AV) are designed to operate in a specific operational context (OC), and the adaptability of the vehicle's architecture to its OC is considered a significant success criterion of the design. AV design projects are rarely started from scratch and are often based on reference architectures. As such, the reference architecture must be modified and adapted to the OC. The current literature on engineering change (EC) propagation does not provide a method to identify and anticipate the impact of OC changes on the AV reference architecture. This paper proposes a two-step method for OC change propagation: (1) analyzing the direct impact of OC change and (2) evaluate the probabilities of indirect change propagation. The direct impact is assessed following a propagation path based upon a model mapping between an OC ontology, operational situations, and functional chains (FCs). The effects of functional chain changes on the AV components are analyzed and evaluated by domain experts with types of changes and associated probabilities. A Bayesian network (BN) is proposed to calculate the probabilities of indirect change propagation between component types of changes (ToCs). The method’s applicability and efficiency are validated on a real case design of AV architecture where the probabilities of the system components undergoing types of changes are evaluated.

An integrated ISM-MICMAC approach for modelling and analysing dependencies among engineering parameters in the early design phase

Several studies have proposed methods for modelling and examining the outcomes of engineering change propagation. However, for reasons of practicality, designers need to be supported by simple tools which enable them to model and analyse dependencies among engineering parameters (EPs) in the early design phase. Utilising data gleaned from a prior completed project, this paper proposes a two-stage structural approach to achieve this objective. In the initial stage, interpretive structural modelling (ISM) is utilised to arrange EPs into a simple hierarchical form. In the following stage, cross-impact matrix multiplication (MICMAC) analysis is implemented to classify EPs in terms of their criticality. The results from modelling and analysis show that the proposed approach could be useful to designers opting to use engineering change propagation methods as well as to those who rely only on their experience and knowledge to assess the consequences of changes.

A Bayesian Networks Approach to Estimate Engineering Change Propagation Risk and Duration

An engineering change (EC) is an alteration made to a system that has been released following a system design process. EC propagation is a series of ECs occurring due to dependencies among components of a product. ECs can consume up to 50% of the overall engineering efforts during the development of a complex system. Therefore, EC propagation prediction received considerable attention in past decades as the product development industries started to suffer from the negative impacts of change propagation. This paper evaluates the current approaches to EC propagation prediction and presents a dynamic Bayesian networks (DBNs) approach to estimate change propagation risk (CPR) as well as a novel approach to estimate EC durations. Literature research shows that although some studies have used design structure matrices to estimate CPR and the total redesign duration (TRD) due to change propagation, an approach that allows iteration while accounting for the conjunction of all impacts has not been explored. This paper aims to fill the gaps for calculating CPR using DBN and evaluating change propagation paths from a Split- and task outcome logic, which accounts for the conjunction of all component relationships. This paper compares the proposed method results with the existing CPR and engineering change duration estimation methods using a real-world dataset from a U.S. Navy shipbuilding program. The results indicate that the CPR can be calculated using the proposed method without the shortcomings of the existing method and the accuracy for estimating engineering change durations is increased.

Designing in young organisations: engineering change propagation in a university design project

Engineering change (EC) is an important phenomenon in the design of products and systems, accounting for nearly one-third of the work effort; however, the literature has been focused on mature firms, and few studies have documented the impact of EC beyond them. Hence, we use a case study approach to study EC and its propagation in the context of a university design project as an example of young organisations, and compare it with the existing work done on mature firms. It was found that 33% of the changes that occurred in the case study were planned, and change propagation accounted for 20% of all changes. The propagation of changes was usually one step (67%), and it was concentrated in one independent network (54%). The results were subsequently compared with EC studies done in mature firms, being revealed that EC behaves differently in the context of a university design project; hence, existing change management tools developed to suit mature firms may not be directly suitable for supporting university design projects. The findings from this work can be used as a platform to better understand how EC propagates when designing in young organisations and shape the development of appropriate change management tools.

Associations management and change propagation in the CAD assembly

Engineering changes (ECs) are a kind of changes and/or modifications in forms, fits, functions, materials or dimensions of products and its constituent components. ECs usually stimulate a series of downstream changes. Therefore multiple disciplines and responsibilities are involved in managing those changes. The management of ECs affects the agility of the product development process of the enterprise. But Computer Aided Design (CAD) systems are limited in supporting the multi disciplinary teamwork in engineering change management (especially when they are distributed in terms of location and time). So, in this context of engineering change management we establish our work concerning engineering change propagation from Part to Assembly. This research paper explain the CAD Management Model, which enable us to better manage the Digital Mockup (DMU) and propagate changes to the hole assembly after a modification that affects one of its component, that has been sent to a partner modified an then reinserted. The aim of this model is to easily and automatically propagate this change by creating certain necessary correspondences which aim to precise the targeted entities (that will be affected by the change propagation).

Causes of Engineering Change Propagation: An Analysis During Product Lifecycle

This article identifies the causes of engineering change propagation during the development and production phases of the product lifecycle using a statistical method applied to a large engineering change data set from the locomotive industry. This research identified three key influencing variables that were found to be related to engineering change propagation, namely: the engineering subsystem issuing the change, the subsystem interface complexity, and the designer experience. Predicting key influencing variables can reduce change propagation and provides important insights to practicing engineering managers. In particular, the results of this study can be used to improve engineering change decision-making processes and the utilization of resources. Similar complex systems composed of multiple subsystems can also benefit from the findings of this research.

A study on the Requirements to Support the Accurate Prediction of Engineering Change Propagation

ABSTRACTThis paper builds on previous work in the area of design representation to identify the types of modeling information required to support the accurate prediction of engineering change propagation during the design of complex engineering systems. It uses the Function–Behavior–Structure (FBS) framework as a basis to examine how engineering changes can occur and how they may subsequently propagate. It was revealed that the relationship between change requirements and product components, the functional dependencies between product components, the behavioral dependencies between product components, and the structural dependencies between product components are all essential modeling information that need to be considered to fully describe the propagation of engineering changes. The essential modeling information identified can serve as modeling requirements to support the development of change prediction methods. In this work, three example design methods were discussed to examine the feasibility of using the modeling requirements for method evaluation. It was concluded that the level of accuracy achievable depends on the design context and hence a direct comparison between methods developed for different design contexts may not be meaningful. This suggests that accuracy should not be used as the sole criterion to evaluate change prediction methods.

Well-controlled engineering change propagation via a dynamic inter-feature association map

Currently, engineering companies face the challenge of delivering complex engineering project in a compressed time frame, which warrants concurrent and collaborative engineering. Due to the tedious dependencies embedded in collaborative domains, engineering change propagation is commonly a challenge and the procedure is carried out by engineers manually without systematic consistency check. Design change impacts are difficult to predict. To address the above challenge, an explicit and comprehensive engineering constraint modeling method is investigated according to a feature-based engineering scheme. To represent the network of constraints, a dynamically generated feature parameter association map is developed to analyze the associations among those parameters. Then, a dynamic mechanism of identifying the locally admissible value ranges of those related constraint parameters has been proposed in order to quickly accept design changes or to identify potential design conflicts. This mechanism then determines the admissible domain for the variable arrays in the formulated constraint satisfaction problem (CSP). Consequently, a well-controlled and progressively expanded change propagation method is proposed. This progressively expanded CSP solving approach is dynamic and incremental based on the association map. This proposed method is demonstrated to be effective to assist engineers with a typical intelligent change propagation solution.

Engineering Change Propagation in Collaboration Environment

Engineering change is an important and complex activity for manufacturing enterprises. In order to improve the efficiency of engineering change, we proposed a method of EC propagation in collaboration environment. First, we analyzed the method of creating the knowledge database; then, studied the way knowledge sharing in collaboration; furthermore, proposed a algorithm change propagation in collaboration environment; finally, a created a system based on the method above and the experiment of managing a type of military vehicle engine was employed to validate our method and it showed the correctness of the proposed method.

Engineering Change Propagation Analysis Based on Linkage Model in Complex Product Development

To handle change propagations during complex product development, an analysis framework based on linkage model was proposed. Firstly linkage was defined to encapsulate the relations among product characteristics, and methods of linkage identification from multi-dimensions and linkage model construction were given. Then how to identify change propagation paths step by step in open scene and how to identify change propagation paths with improved mouse maze algorithm in closed scene were discussed. In the following a quantitative evaluation method of change impact risk was advanced. Finally an example of carrier robot moving structure design change was provided to validate this method.

A Multidomain Engineering Change Propagation Model to Support Uncertainty Reduction and Risk Management in Design

Engineering change (EC) is a source of uncertainty. While the number of changes to a design can be optimized, their existence cannot be eliminated. Each change is accompanied by intended and unintended impacts both of which might propagate and cause further knock-on changes. Such change propagation causes uncertainty in design time, cost, and quality and thus needs to be predicted and controlled. Current engineering change propagation models map the product connectivity into a single-domain network and model change propagation as spread within this network. Those models miss out most dependencies from other domains and suffer from “hidden dependencies”. This paper proposes the function-behavior-structure (FBS) linkage model, a multidomain model which combines concepts of both the function-behavior-structure model from Gero and colleagues with the change prediction method (CPM) from Clarkson and colleagues. The FBS linkage model is represented in a network and a corresponding multidomain matrix of structural, behavioral, and functional elements and their links. Change propagation is described as spread in that network using principles of graph theory. The model is applied to a diesel engine. The results show that the FBS linkage model is promising and improves current methods in several ways: The model (1) accounts explicitly for all possible dependencies between product elements, (2) allows capturing and modeling of all relevant change requests, (3) improves the understanding of why and how changes propagate, (4) is scalable to different levels of decomposition, and (5) is flexible to present the results on different levels of abstraction. All these features of the FBS linkage model can help control and counteract change propagation and reduce uncertainty and risk in design.

Research of Engineering Change Analysis

An engineering change is costly and time consuming. Therefore, the research of effective methods and software is highly significant to engineering change analysis. This paper has conducted research on the engineering change analysis method and decision support environment. The characteristics of an engineering change were demonstrated; the data model was set up for supporting engineering change analysis; the method for analyzing engineering change propagation was proposed. The multi-user dynamic analysis of an engineering change and storage of the engineering change scheme with reusability were achieved in this paper. According to the results, the research shows more flexible and efficient to analyze engineering changes and further assure the completeness of changed data.

A method to assess the effects of engineering change propagation

This paper presents a modelling method that seeks to support the prediction and management of undesired engineering change (EC) propagation during the design and development of complex products. The method builds on the house of quality and the change prediction method to model the effects of potential change propagation brought about by different change options. The objective is to better reflect how well each change option can address the product requirements. The method was applied to the design of a jet engine fan during a case study with an aerospace company. The findings suggest that this modelling approach is suitable for assessing the effects of potential EC propagation and can support companies in effective exploration of the design space.

A design exploration method for resolving parameter coupling in engineering change propagation

Product design tasks in the upstream and downstream stages are often interdependent in engineering design processes. When design changes propagate from the upstream to the downstream, or vice versa, design tasks in different stages affect each other. Then solving the relevant design problems has to be repeated if the designer cannot find an acceptable solution to satisfy both downstream and upstream design requirements. In this paper, those design task connections with the interdependent nature or phenomena are referred to as design change propagation couplings and the analysis of the coupling is presented. Two types of coupling morphology named concurrent coupling and sequential coupling are identified. A theoretical method as well as a software system to solve such propagation couplings is developed. A design case of the feeding servo system on a numerical controlled machine tool is used to demonstrate the application of the software.

A framework for managing engineering change propagation

Engineering Changes (ECs) are facts of life for any company developing and introducing new products, despite a commonly held notion that they are distractions from normal operation. Companies can become more innovative by utilising ideas from the ECs or by learning how to handle the ECs. This paper presents a framework to manage the ECs effectively, particularly the issue of EC propagation. An EC seldom confines itself to a single change, but triggers other changes in different components. The framework is designed to identify the affected components automatically, capture the required knowledge during the design phase of the product life cycle, and use it during the Engineering Change Management (ECM) process.

Propagation of engineering changes to multiple product data views using history of product structure changes

The present paper proposes a comprehensive procedure for engineering change propagation in order to maintain consistency between various product data views. A product data model is also proposed for the propagation procedure, which integrates base product definitions for product design, and product data views for other manufacturing or customer support. The product data view in the proposed model enables manufacturing or customer support engineers to define their own product data views, without copying the existing product definition. Integrated with other components, the engineering changes in the data model provide structure-oriented change history, effectivity management for production, and integration with product configurations. Based on the integrated product data model, the proposed procedure propagates engineering changes to product data views using the history of product structure changes. The propagation procedure maintains consistency of product data during collaboration between various design, manufacturing and customer support departments, who have different product data views. Prototype database applications together with an example of illustrative change propagation are also presented.

Engineering Change Propagation System using STEP

Reducing the time for engineering changes can greatly shorten a product's life cycle and improve the productivity of enterprises. This work proposes an approach to engineering change propagation between CAD and product data management (PDM) systems. A system for modeling and triggering changes of models of engineering data, geometries, and features, using STEP AP 214 and AP224, is developed. A change in CAD and PDM systems can propagate along a defined path and trigger a rule by which data of another system are changed. The proposed engineering change propagation (ECP) system provides a flexible, virtually integrated framework to enable engineering change. Affected items and propagation sequences of engineering changes are inferred with the help of an ECP network. Engineering change issued between a STEP-based solid modeling system and a PDM system is implemented and discussed to demonstrate the capability of the ECP system. Change in the feature parameters of a CAD system is propagated and the relevant data, defined in the ECP network, are triggered. Enterprises can use the ECP system to integrate various information systems, such as CAx, PDM, CAPP, ERP, and others, and facilitate collaborative engineering using a virtual framework, to promote competition.

How Do University Inventions Get Into Practice?

Jeannette Colyvas • Michael Crow • Annetine Gelijns • Roberto Mazzoleni Richard R. Nelson • Nathan Rosenberg • Bhaven N. Sampat Institute for International Studies, Stanford University, Stanford, California 94305 Office of the Executive Vice Provost, Columbia University, New York, New York 10027 INCHOIR, Columbia University, New York, New York 10027 Department of Economics and Geography, Hofstra University, Hempstead, New York 11594 School of International and Public Affairs, Columbia University, New York, New York 10027 Department of Economics, Stanford University, Stanford, California 94305 School of Public Policy, Georgia Institute of Technology, Atlanta, Georgia 30332 colyvas@leland.stanford.edu • mc71@columbia.edu • acp10@columbia.edu • ecorzm@mail1.hofstra.edu rrn2@columbia.edu • nate@leland.stanford.edu • bhaven.sampat@pubpolicy.gatech.edu