Decision-based Design Research Articles

Product Design Incorporating Competition Relations: A Network-Based Design Framework Considering Local Dependencies

Abstract System design has been facing the challenges of incorporating complex dependencies between individual entities into design formulations. For example, while the decision-based design framework successfully integrated customer preference modeling into optimal design, the problem was formulated from a single entity’s perspective, and the competition between multiple enterprises was not considered in the formulation. Network science has offered several solutions for studying interdependencies in various system contexts. However, efforts have primarily focused on analysis (i.e., the forward problem). The inverse problem still remains: How can we achieve the desired system-level performance by promoting the formation of targeted relations among local entities? In this study, we answer this question by developing a network-based design framework. This framework uses network representations to characterize and capture dependencies and relations between individual entities in complex systems and integrate these representations into design formulations to find optimal decisions for the desired performance of a system. To demonstrate its utility, we applied this framework to the design for market systems with a case study on vacuum cleaners. The objective is to increase the sales of a vacuum cleaner or its market share by optimizing its design attributes, such as suction power and weight, with the consideration of market competition relations, such as inter-brand triadic competition involving three products from different brands. We solve this problem by integrating an exponential random graph model (ERGM) with a genetic algorithm. The results indicate that the new designs, which consider market competition, can effectively increase the purchase frequency of specific vacuum cleaner models and the proposed network-based design method outperforms traditional design optimization.

A knowledge graph-aided decision guidance method for product conceptual design

In the fast-paced world of product design, businesses seek a competitive edge by swiftly addressing user requirements and developing precise solutions. Depending on diverse knowledge, conceptual design makes enterprises invest significantly in knowledge management. Following the philosophy of decision-based design, a knowledge graph-aided decision guidance method is proposed to streamline and enhance knowledge utilisation in product conceptual design. Firstly, the decision-making process in product conceptual design is modelled using the Concept-Decision-Knowledge (CDK) model, yielding the meta-model of CDK (mCDK). A data-augmented BERT-BiLSTM-CRF model is adopted to extract information from diverse data sources, forming a CDK knowledge graph (CDK-KG). A decision case for employing problem-solving knowledge configuration is generated to resolve specific design decision problems when new requirements arise. Validation of the method is demonstrated through a case study on the launch vehicle conceptual design of the first and second-stage separation system. The results indicate that the proposed method can automatically extract information from data resources to construct a knowledge graph. Furthermore, it can provide decision cases for design requirements through knowledge configuration, supporting decision-making.

A network-based discrete choice model for decision-based design

Abstract Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.

Decision-based design in development of Suwarno farm poultry farming agro-industry

Most of the food demand in Indonesia come from animal protein, and the poultry industry meets most of the demand for meat. Livestock is one of the agricultural sectors, and poultry farming is a part of the agro-industry that plays a vital role in increasing protein consumption in Indonesia. Sukabumi is a very potential area for developing poultry farming agro-industry as it has a relatively complete livestock ecosystem. Therefore, this study will discuss a poultry farm in Sukabumi called Suwarno farm. This farm is an independent medium-to-small scale poultry farming agro-industry located in Warnajati Village, Cibadak District, Sukabumi. They cope with difficulty to develop their business due to a large number of competitors, and therefore a development design is needed. This study uses three methods, i.e. AHP, Fuzzy TOPSIS, and ANN. The AHP method was used to determine prioritized criteria most affecting the selection of primary commodities; the Fuzzy TOPSIS method was used to determine a selected alternative most potential to develop. The ANN method was used to predict profits and sales of the selected products alternative in the next period. Based on the results of data processing obtained, it can be concluded that there were three prioritized criteria, i.e. price stability, demands, and market opportunities, resulting in broiler ducks as the primary commodity with the highest profit increase prediction of IDR19,494,929 and the highest increase in sales, IDR57,584,210. There are several strategies to achieve this, i.e. increasing the population with a partnership system, creating alternative feeds, and expanding the market via e-commerce.

Evaluating Mine Design Alternatives for Social Risks Using Discrete Choice Analysis

As with other engineering design tasks, mine design involves setting design objectives and constraints (the feasible solution space) and finding the optimal design alternative. Mine engineers often struggle to incorporate the preferences of local community members into their evaluation of mine design alternatives because the mining literature lacks tools to quantify such risks during mine planning. This paper presents an approach to evaluate community acceptance (i.e., community preferences for the alternatives) using discrete choice models and decision-based design during mine planning. Using discrete choice models and a rigorous framework, engineers can estimate the cost of social risks as a function of the probability that individuals in the host community will prefer a particular design alternative. They can then estimate the overall utility of a particular design alternative to the project proponents. This paper illustrates the proposed approach with a strategic mine planning exercise for a gold mine. The framework can be a useful tool for designing mines for sustainability, if combined with effective community engagement and management’s commitment to creating shared value.

Exergy approach in decision-based design of absorption refrigeration system using artificial intelligence and simulink

Exergy approach is essential for the design and possible operation of the absorption system with energy loss minimization. Operational limitations of absorption refrigeration system are due to Gibbs phase rules-base. Optimized operational decision rules are extracted from machine learning algorithm C4.5 which is suitable for different exergy evaluation methods and reference conditions. This study investigates proposed and investigated classification models for the same which are based on different thermodynamic features and operational design data. ANN model is used to predict the thermodynamic properties of the working fluids using improved thermodynamics properties of data patterns. Mathematical expressions are formulated from validated artificial neural network for predicting specific enthalpy and entropy of water–lithium bromide solution. Exergy design data are estimated on 356 thermodynamic design data pattern of absorption refrigeration system by MATLAB Simulators. Pearson’s correlation heatmap is used for extracting 14 thermodynamic features. 94.38% is the highest performance observed with 12 feature class six classification model.

Information Exchange Patterns in Digital Engineering: An Observational Study Using Web-Based Virtual Design Studio

Abstract This paper performs an observational human subjects study to investigate how design teams use an information system to exchange, store, and synthesize information in an engineering design task. Framed through the lens of decision-based design, a surrogate design task models an aircraft design problem with 12 design parameters across four roles and six system-level functional requirements. A virtual design studio provides a browser-based interface for four participants in a 30-min design session. Data collected across 10 design sessions provide process factors about communication patterns and outcome factors about the resulting design. Correlation analysis shows a positive relationship between design iteration and outcome performance but a negative relationship between chat messages and outcome performance. Discussion explains how advances in information exchange, storage, and synthesis can support future design activities.

Teaching Market-Driven Engineering Design with an Agent-Based Simulation Tool

In product design, there is often a disconnect between the engineers creating the product and the marketing team determining the best characteristics for the product. The research areas of “design for market systems” and “decision-based design” seek to bridge that disconnect through quantitative approaches that facilitate simultaneous technical design and marketing decisions. However, these market driven design frameworks have been primarily evaluated in the context of industry case studies, with limited integration into engineering education. This article presents the development and implementation of a simulation tool for teaching market-driven design in undergraduate engineering design courses. This classroom tool demonstrates the basic relationships between product design decisions, pricing and marketing choices, and predicted market success, by simulating the interactions among producers and consumers in a market system. The product attributes influence the cost and consumer utility of the product, which, along with price, affect market dynamics. The simulator was implemented in a third-year undergraduate design course to introduce the concept of market-driven product design, allowing student design teams to assess the impacts of different design variables on the market success of their design projects. Surveys and written reflections by the students were used to evaluate the simulator’s value in contributing to self-reported learning. The results showed that a majority of students expressed that the simulator provides a meaningful and engaging way to learn about market-driven design concepts.

Assessing Lifecycle Value Using Object-Based Modeling by Incorporating Excess and Changeability

Abstract Prior research suggests that excess (purposeful inclusion of margin beyond what is required for known system uncertainties) can limit change propagation and reduce system modifications. Reducing change costs increases system flexibility, permitting adaptions that satisfy uncertain future requirements. The benefits of excess, however, must be traded against higher costs of the initial system and likely performance decreases. Assessing the benefits and costs of excess requires evaluating what forms, locations, and magnitudes of excess inclusion are optimal. This paper improves the state-of-the-art in two ways. First, prior research has generally assessed excess in system-level properties (aggregating component properties into a single metric). The approach presented in this paper extends excess assessment to the component level so that the effects of excess on change propagation may be explicitly captured. Second, this approach holistically assesses the value of excess by evaluating both its costs and benefits. The approach borrows from Decision-Based Design and Model Based System Engineering (MBSE) in creating a generic modeling method capable of excess valuation. A desktop computer example is used for demonstrating how excess is valued in a system and the potential gains associated with excess inclusion when mining cryptocurrency. A single component optimization of the power supply capacity for the desktop is assessed to be 750 W, which balances the initial cost against the future flexibility. A system-level optimization then demonstrates the identification of critical change propagation pathways and illuminates both where and how excess may be included to inhibit change propagation. This key component was identified as the motherboard-central processing unit (CPU) slot in the tested systems.

A Product Development Approach Advisor for Navigating Common Design Methods, Processes, and Environments

Many different product development approaches are taught and used in engineering and management disciplines. These formalized design methods, processes, and environments differ in the types of projects for which they are relevant, the project components they include, and the support they provide users. This paper details a review of sixteen well-established product development approaches, the development of a decision support system to help designers and managers navigate these approaches, and the administration of a survey to gather subjective assessments and feedback from design experts. The included approaches—design thinking, systems thinking, total quality management, agile development, waterfall process, engineering design, spiral model, vee model, axiomatic design, value-driven design, decision-based design, lean manufacturing, six sigma, theory of constraints, scrum, and extreme programming—are categorized based on six criteria: complexity, guidance, phase, hardware or software applicability, values, and users. A decision support system referred to as the Product Development Approach Advisor (PD Advisor) is developed to aid designers in navigating these approaches and selecting an appropriate approach based on specific project needs. Next, a survey is conducted with design experts to gather feedback on the support system and the categorization of approaches and criteria. The survey results are compared to the original classification of approaches by the authors to validate and provide feedback on the PD Advisor. The findings highlight the value and limitations of the PD Advisor for product development practice and education, as well as the opportunities for future work.

Design for the Marketing Mix: The Past, Present, and Future of Market-Driven Engineering Design

AbstractThe four Ps of the marketing mix (Product, Price, Place, and Promotion) serve as a framework for characterizing the marketing decisions made during the product development process. In this paper, we describe how the last 40 years of engineering design research has increasingly incorporated representations of preference as a means of addressing the decisions that come with each “P.” We argue that this incorporation began with problem formulations based on Product only, with surrogates of preference posed as objectives (such as minimizing weight, minimizing part count) representing a firm's desire for offering a mix of products while reducing cost and maximizing profit. As the complexity of problem formulations progressed, researchers began representing preferences of the designer (using decision theory techniques) and of the customer (often in the form of random utility models). The Design for Market Systems special session was created specifically in the Design Automation Conference for advancing our understanding of design in the content of a market, extending from the decision-based design framework introduced by Hazelrigg. Since then, researchers have explored the engineering design problem formulation challenges associated with the marketing decisions of Price, Place, and Promotion. This paper highlights the advancements of the design community in each of the Ps and shows how the marketing decisions of Place and Promotion extend from the central hub of considering Price in an engineering design problem. We also highlight the exciting research opportunities that exist as the community considers more complicated, and interconnected, problem formulations that encompass the entirety of the Marketing Mix.

An Ontology for Representing Knowledge of Decision Interactions in Decision-Based Design

Design processes for complex engineered systems are inherently complex due to the dependencies among subsystems at the same level and between levels of a partitioned hierarchy. In decision-based design, this results in interactions among sets of design decisions. Representing and capturing the knowledge related to the decision interactions is critical for designing decision-based design processes. Two key challenges in modeling the interactions are:•there are different types of decisions, and•these decisions are made at different levels.To address these challenges, in this paper we identify nine basic interaction patterns among decisions and propose an ontology to define the knowledge associated with these interaction patterns. Key advantages of the ontology include that we can capture both the vertical and horizontal interactions between decisions in a decision-based design process, and we can design flexible, reusable, and executable decision workflows for designing complex systems using the ontology. The utility of the ontology is illustrated via a one[HYPHEN]stage reduction gearbox design example, a hot rod rolling process design example and a composite structure design example.

Template-based configuration and execution of decision workflows in design of complex engineered systems

The research reported in the paper is from a decision-based design perspective wherein the principal role (but not only) role of a designer is to make decisions. Decision workflows are the processes by which the solutions pertaining to the design of complex systems are generated. Decision workflows are core to design processes, in which a set of decisions are connected (or interconnected) to generate shared and desired design outputs. Careful configuration of decision workflows is very important to ensure the generation of designs using available resources. Configuration of decision workflows is a process that requires a designer to use the basic elements to compose feasible workflows and then select an appropriate one for implementation in designing a product or a system. In this paper, we propose a template-based method for the design and execution of decision workflows associated with designing engineered systems. The value of the method is anchored in that it facilitates designers rapidly planning the processes, namely, the decision workflows, for designing products or systems. Moreover, due to the fact that these decision workflows are modeled in a computational manner, designers are able to execute decision workflows to explore the solution space and identify satisficing design solutions in early design stages. A gearbox with connected gears and shafts is a typical complex engineered system that can be partitioned into multiple levels of interacting subsystems. We illustrate the method and the decision workflows using a gear and shaft (within a gearbox) design example.

Ethics and Strategy in Decision-Based Design Frameworks: Problems and Solutions

AbstractEngineering Design decisions impact customers, the environment and society at large in ways that have profound ethical and strategic implications for designers. Previous research in decision-based design has proposed the decisions should be made on the basis of maximizing the expected utility of the design to the designer. This paper discusses ethical and strategic challenges for these frameworks across five levels: the axioms that underlie utility, the definition of utility, the consideration of multiple stakeholders, the modeling scope, and resulting design framework implementation. Based on these problems, solutions are suggested to account for each in the development of improved, ethically- informed frameworks. Challenges presented here do not prohibit the prudent use of decision-based design frameworks per se, but instead point to cases that must addressed in practice while providing grounds for further research towards the development of decision-based design frameworks that are ethical by design.

A decision approach with multiple interactive qualitative objectives for product conceptual schemes based on noncooperative-cooperative game theory

Product development based on a morphological matrix involves the process of decision-based design. Although the decision process can generate conceptual schemes under the guidance of qualitative decision objectives, analysis of the interactions among the qualitative objectives is seldom considered, which can lead to unreliable optimal solutions by combining conflicting principle solutions. In addition, due to the ambiguity of the constraints among the qualitative objectives, multiple feasible schemes with equilibrium states are not considered in the concept decision stage. To solve these problems, a decision approach with multiple interactive qualitative objectives is developed for conceptual schemes based on noncooperative-cooperative game theory to consider the tradeoffs among objectives (e.g., cost, quality and operability) using discrete principle solution evaluation data. First, the morphological analysis method can obtain feasible schemes and determine the principle solutions for each subfunction. Second, the principle solutions are quantified using linguistic terms. Then, the subfunctions are categorized through cluster analysis to determine the suitable principle solution. Third, based on the clustering results, a noncooperative game decision model is constructed to identify multiple Nash equilibrium solutions that satisfy the constraints among the objectives. Fourth, a cooperative game decision model is constructed to obtain the optimal scheme as screened by the noncooperative game model. The case study proves that this approach can choose a relatively superior scheme under the existing technical conditions, thereby preventing inconsistency with the actual design expectations.

An Inverse, Decision-Based Design Method for Integrated Design Exploration of Materials, Products, and Manufacturing Processes

A material's design revolution is underway with a focus to design the material microstructure and processing paths to achieve certain performance requirements of products. A host of manufacturing processes are involved in producing a product. The processing carried out in each process influences its final properties. To couple the material processing-structure-property-performance (PSPP) spaces, models of specific manufacturing processes must be enhanced and integrated using multiscale modeling techniques (vertical integration) and then the input and output of the various manufacturing processes must be integrated to facilitate the flow of information from one process to another (horizontal integration). Together vertical and horizontal integration allows for the decision-based design exploration of the manufacturing process chain in an inverse manner to realize the end product. In this paper, we present an inverse method to achieve the integrated design exploration of materials, products, and manufacturing processes through the vertical and horizontal integration of models. The method is supported by the concept exploration framework (CEF) to systematically explore design alternatives and generate satisficing design solutions. The efficacy of the method is illustrated for a hot rod rolling (HRR) and cooling process chain problem by exploring the processing paths and microstructure in an inverse manner to produce a rod with specific mechanical properties. The proposed method and the exploration framework are generic and support the integrated decision-based design exploration of a process chain to realize an end product by tailoring material microstructures and processing paths.

Systematic design space exploration using a template-based ontological method

The realization of complex engineered systems using models that are typically incomplete, inaccurate and not of equal fidelity requires the understanding and prediction of process behavior in design. This necessitates the need for extending designer’s abilities in making design decisions that are robust, flexible and modifiable particularly in the early stages of design. To address this requirement, we propose in this paper, an ontology for design space exploration and a template-based ontological method that supports systematic design space exploration ensuring the determination of the right combination of design information that meets the different goals and requirements set for a process chain. Using the proposed method, a designer is able to (1) systematically adjust the design space in due time to manage the risks of errors accumulating and propagating during the design of different stages of the process chain, (2) improve the ability to communicate and understand the interactions between design information in the process chain. We achieve the said through (1) procedure for design space exploration is identified to determine the sequence of activities needed for the systematic exploration of design space under uncertainty; (2) the decision-based design information flow is archived using the design space exploration process template and represented by utilizing frame-based ontology to facilitate the management of re-usable information. We demonstrate the efficacy of this template-based ontological method for design space exploration by carrying out the design of a multi-stage hot rod rolling system in steel manufacturing process chain.

Ontology-Based Representation of Design Decision Hierarchies

The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.

Design Case Representation Under Decision-based Design Theory

Current case representation methods are inappropriate to support case retrieval and case-based reasoning process under decision-based design environment. Therefore, a case representation approach of mechanical product based on decision based design theory is proposed. The design unit concept and its classification and expression methods are proposed with knowledge components and decision-based design theory. Furthermore, a product design case model are presented. This study provides a new idea for the case representation method, and explores the integration of design reuse technology with case representation under the decision-based environment.

Do design decisions depend on "dictators"?

Design decisions often require input from multiple stakeholders or require balancing multiple design requirements. However, leading axiomatic approaches to decision-based design suggest that combining preferences across these elements is virtually guaranteed to result in irrational outcomes. This has led some to conclude that a single "dictator" is required to make design decisions. In contrast, proponents of heuristic approaches observe that aggregate decisions are frequently made in practice, and argue that this widespread usage justifies the value of these heuristics to the engineering design community. This paper demonstrates that these approaches need not be mutually exclusive. Axiomatic approaches can be informed by empirically motivated restrictions on the way that individuals can order their preferences. These restrictions are represented using "anigrafs"-structured relationships between alternatives that are represented using a graph-theoretic formalism. This formalism allows for a computational assessment of the likelihood of irrational outcomes. Simulation results show that even minimal amounts of structure can vastly reduce the likelihood of irrational outcomes at the level of the group, and that slightly stronger restrictions yield probabilities of irrational preferences that never exceed 5%. Next, an empirical case study demonstrates how anigrafs may be extracted from survey data, and a model selection technique is introduced to examine the goodness-of-fit of these anigrafs to preference data. Taken together, these results show how axiomatic consistency can be combined with empirical correspondence to determine the circumstances under which "dictators" are necessary in design decisions.