Smart manufacturing enterprises rely on adapting to rapid engineering changes while minimizing the generated risk. Making informed decisions related to engineering changes and managing risks against unexpected costs requires more information to be extracted from limited data. However, limited information in early-stage design can come in many forms, namely text and images. The development of innovative design tools and processes to link multisource data together is essential to assist designers in building model-based engineering (MBE) systems. However, the formal computational linking of multisource data is yet to be realized in MBE. We propose a framework to implement transfer learning and integrate domain specific knowledge to bridge this information gap. A synthetic dataset is created using web scraping techniques based on keywords extracted from the requirements. Requirement-image pairs are used to fine tune a contrastive language-image pretraining model to acquire domain knowledge. The results demonstrate how the content of images can be used to indicate all affected requirements for tracing engineering changes in a complex system.

Engineering changes are necessary to stay competitive, unavoidable and occur more frequently with increased product complexity. Currently, scheduling of engineering changes into production and supply chain is a manual process. With new possibilities in the field of artificial intelligence, this publication presents the vision of a flexible multi-agent system with four agents and a single shared database. By autonomously scheduling changes and predicting KPI impacts of implementation dates, the agent-system provides additional capacity and decision-making support to the organisation.

Engineering change management is a central part of the product development process. This paper investigates how variations from the PGE - Product Generation Engineering can improve the evaluation of engineering changes from the wiring harness. Engineering changes that occur in an automotive wiring harness development process are analysed in a case study, evaluated in expert interviews with regard to the risk and effort connected to the implementation and compared to the types of variations. Additional influencing factors are discussed. The variations provide an indication on risk and effort.

As an outcome of the multi-criteria evaluation of different alternatives, product developers receive information on whether the evaluation alternatives meet the demanded property profile. If not, product characteristic and property changes are required, which can have desired and undesired effects. This contribution presents an MBSE-based approach, which extends the relational requirement model by value functions and product properties. Its novelty can be found in the integration of multi-criteria decision models, which are used to improve alternatives based on property change impact analyses.

Each year, automotive OEMs implement a variety of Engineering Changes (ECs) in their production. In the timing of ECs, different KPIs are often in conflict with one another or even unknown to the OEMs. Therefore, OEMs struggle to identify the optimal date to implement an EC. This paper presents a method to determine the cost-optimal implementation date for each EC, considering time, cost, and quality KPIs based on a new EC classification rule-set. To evaluate the presented method, case-studies at a German automotive OEM were performed, two of which are discussed.

Predicting the impact of changes in a design can be challenging, especially for complex designs. Margins are often built into the designs which can absorb the knock-on effect of such changes, erroneously allocating which can however, lead to propagation. A method for localising and sizing margins in an incremental design context is the Margin Value Method. This paper adapts MVM in the context of uncertainty in input specifications. It discusses possible ways to allocate them in a design such that undesirable effects of margins are minimised while preventing change propagation.

Engineering changes (ECs) and engineering change management (ECM) are crucial for successful product design processes (PDP). Due to the increasing complexity of today's products (like vehicles) and the interaction of different engineering domains (mechanics, electric/electronics, software) involved in the PDP, cross-domain EC impact assessments as well as processes are required. To better support engineers in assessing change propagation across domains and products, existing approaches for ECM product models are analyzed in this paper and an enhanced product model is derived using MBSE.

When developing new systems, there is always some kind of reference to existing systems. Various approaches aim at describing qualitatively different characteristics of such connections, often depicted as some form of variation. Among other things, this is done with regard to innovation potential and development risk. In this paper, we investigate the extent to which established methods of risk management refer to modelling approaches for variations by means as mentionend above. After a litertaure search 11 methods and method clusters are analyzed more in detail within a method benchmark.

The model of PGE describes the emergence of new systems based on reference by the activities carryover, embodiment and principle variation - qualitatively different manifestations of a transfer process. We investigate indicators which constitute these different manifestations measurably for different types of systems. We propose generalized variation operators to describe system development with respect to different product elements and system types. We use case studies from automotive, production systems and simulation models.

Overspecification or excess margin in a design can enhance its ability to absorb changes and uncertainty, but also deteriorates performance criteria such as weight and cost. This paper shows how the Margin Value Method (article in review) can be applied in conjunction with CAE tools such as FEA to quantify excess margin where a design is too complex for algebraic analysis. This new application context for the MVM is illustrated using a case study of a flange coupling design, in which topology optimisation is used within the MVM to identify opportunities for design improvement.

Complex products and shorter development cycles lead to an increasing number of engineering changes. In order to be able to process these changes more effectively and efficiently, this paper develops a description model as a first step towards a data driven approach of processing engineering change requests. The description model is systematically derived from literature using text mining and natural language processing techniques. An example of the application is given by an automated classification based on similarity calculations between new and historic engineering change requests.

The problem at hand is that vast amount of data on industrial changes is captured and stored; yet the present challenge is to systematically retrieve and use them in a purposeful way. This paper presents an industrial case study where complex product development processes are modeled using the design structure matrix (DSM) to analyze engineering change requests sequences. Engineering change requests are documents used to initiate a change process to enhance a product. Due to the amount of changes made in different projects, engineers want to be able to analyze these change processes to identify patterns and propose the best practices. The previous work has not specifically explored modeling engineering change requests in a DSM to holistically analyze sequences. This case study analyzes engineering change request sequences from four recent industrial product development projects and compares patterns among them. In the end, this research can help to identify and guide process improvement work within projects.