Product Design Decisions Research Articles

The evolution, challenges, and future of knowledge representation in product design systems

Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today’s world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future.

An investigation on the impact of product modularity level on supply chain performance metrics: an industrial case study

The benefit of integrating product design decisions and supply chain design decisions has been recognized by researchers. Such integration can facilitate better communication between design teams and operations groups. Consequently, potential supply chain risks can be highlighted and addressed before the launch of a new product. Modularization is one of the most critical elements for both product design and supply chain design decisions as it impacts the assembly sequence and hence the selection of component and module suppliers. However, the impact of modularity level on supply chain performance is still unclear, and thus is the focus of this study. The proposed analytical method incorporates both product design and supply chain design functions, and hence, enables simultaneous consideration of these decisions. The supply chain performances of all two-module and three-module design concepts are fully investigated in an effort to explore the impact of modularity level on supply chain performance. Results show that increased modularity is advantageous for the time-based performance of a supply chain network, whereas decreased modularity yields superiority in terms of cost performance.

Product Design Generation and Decision Making Through Strategic Integration of Evolutionary Grammars and Kano Model

The ability for a product developer to successfully launch useful products is tied to the company’s product design strategies. Due to the complexity of perception and expectations on new product design from customers and the diverse perspectives of the product developer, any approaches without systematically analyzing these complex criteria to assess decision making on product design strategies are therefore deemed as inappropriate. A systematic approach to determine appropriate product design strategies based on the attributes of product design and customer expectations should thus be considered. This research focuses on the investigation of shape formulation process using evolutionary grammars (EG) and non-linear product design analysis with Kano model to refine product design strategies. Through the strategic integration of evolutionary grammars and Kano model, the complex effects on the evolutionary design process and product design strategies are revealed. In order to demonstrate the viability of the approach, experiments are described and a comparison analysis on three EG-Kano reference models is performed. Upon determining the appropriate EG-Kano reference model, the product developer can refine the product design strategies to suit the targeted market.

OEM product design in a price competition with remanufactured product

An original equipment manufacturer (OEM) produces new products and often faces a dilemma when determining the level of interchangeability in its product design. The interchangeability is considered as a degree to which the product can be disassembled without force, and thus an increasing degree of interchangeability would decrease the OEM's production cost, but it would also lower a remanufacturer's cost in cannibalizing used items. Decreasing the level of interchangeability to deter the remanufacturer, on the other hand, would simultaneously increase the production costs of the OEM. We thus formulate a two-period supply chain model consisting of two chain members, an OEM and a remanufacturer, to investigate the product design decision of the OEM and both chain members' competitive pricing strategies. We then characterize the equilibrium decisions and profits with regard to costs and consumers' preference for the remanufactured product. We also evaluate a strategic game in which the OEM chooses the degree of interchangeability, and the remanufacturer determines its collection strategy. We find that the product-design strategy is effective for the OEM in competing with the remanufacturer, but it is not necessarily harmful to the remanufacturer.

Assessment of a Product Range Model concept to support design reuse using rule based systems and case based reasoning

Knowledge reuse is recognised as a key element to support agile and effective decision-making processes during product development. The provision of information and knowledge for reuse relies on defined information structures and requires computational techniques that enable the reuse process within an integrated environment. Although, representing, saving, and sharing information is relatively well known for product modelling, it is accepted that effective and agile product development also requires knowledge sharing. For this reason, extensions of the Product Model structure are required to support specific product development process phases. This work argues that a product information model combined with extended knowledge models can provide decision support throughout the product development phases. This paper presents the results of an investigation into a specific extended knowledge model concept, named a Product Range Model (PRM), which combines both rule based systems and case based reasoning to provide product design decision support. Two product development scenarios, injection moulding and friction materials, have been selected in order to evaluate the ideas presented. For both applications the relevance of the models to support the capture and reuse of information and knowledge is stressed.

Understanding Indians Purchase Behaviour Process: Luxury Apparel Sector

This study aims to understand the buying habits and decision behaviours of Indian luxury consumers' in luxury apparel industry through uncovering the factors that influence purchase choices and preferences. Qualitative research was conducted through structured observations and structured in-depth interview with luxury consumers and store managers in Mumbai city. The sample of respondents was based on demographic criteria- gender and age. Data analysis was carried on the basis of the transcripts, notes and observation. The contributions of this research are threefold. First, the combined key drivers of luxury among both gender segments are fit, comfort, quality, exclusive, and sophistication, look good and powerful/influential. Second, consumers' possess different in-store shopping characteristics. Third, the proposed comprehensive model describes the entire consumers' consumption procedure. The paper even theorizes and empirically investigates how intrinsic and extrinsic clues influence consumers' choices and decisions. It also illuminates eight facets of consumers buying tendency. The results provide insights for both luxury apparel firms and store owners. Brand managers can build their product strategy and design decision according to the prevailing gender differences. The study also suggests advantageous findings for the store owners, to amplify consumers shopping experience. The in-store service could be such that it provides consumers' with customized hospitality to gain mutual benefit.

Noncompensatory Dyadic Choices

Whereas literature in marketing shows that individuals often use noncompensatory decision rules, existing research on dyadic choice is based on compensatory models. In this paper we present a dyadic consider-then-choose model that investigates both compensatory and noncompensatory aspects of the joint decision process. The intersection of individual consideration sets at the dyad level gives rise to dyadic decision processes (DDPs) where dyad members are in concordance or discordance about alternatives to consider. We empirically investigate the implications of different DDPs on outcomes such as decision efficiency and dyadic welfare. The methodological approach merges choice experiments with Bayesian statistical models to uncover nuances of the dyadic choice process. Data were collected using a multiphase nationwide study of 265 husband-and-wife dyads. Results across three categories indicate that both concordant and discordant dyads exist. Among concordant dyads, the noncompensatory dyads make quicker decisions that result in higher dyadic welfare. Among discordant dyads, those that restrict their consideration set make quicker decisions that result in higher welfare than those that expand their consideration set. These findings have important implications for buyers looking to maximize dyadic welfare when making joint choices and for sellers making pricing and new product design decisions.

A Bayesian Method for Predicting Future Customer Need Distributions

This article presents a Bayesian method to predict future customer need distributions based on forecasts of external factors and forecast accuracies. It is critically important to accurately predict future customer needs when strategically deciding what product to develop. However, prediction is challenging because customer preference changes as external factors that influence customer preference change over time. Consequently, customer needs at the time of product design decisions and at the time of new product market introductions may be significantly different. While external factors can be forecasted, forecasts are not necessarily accurate. This paper outlines a Bayesian approach to incorporate accuracies of forecasts in predicting future customer need distributions. An illustrative example demonstrates impacts of forecast accuracies on customer need distributions and on a strategic design decision. In this example, the customer need is ‘fuel-efficient vehicle’, the external factor is the gasoline price index (defined as gasoline price divided by consumer price index), and the strategic design decision is the selection of the next vehicle from gasoline-powered, hybrid, and electric. Sensitivity analysis is performed in order to study how a strategic design decision changes with forecasts and forecast accuracies.

Systematic causal knowledge acquisition using FCM Constructor for product design decision support

Despite its usefulness, design knowledge is not often captured or documented, and is therefore lost or damaged after a product design is completed. As a way to address this issue, two major formalisms can be used for modeling, representing, and reasoning about causal design knowledge: fuzzy cognitive map (FCM) and Bayesian belief network (BBN). Although FCM has been used extensively in knowledge engineering, few methodologies exist for systematically constructing it. In this paper, we present a methodology and application—FCM Constructor—to systematically acquire design knowledge from domain experts, and to construct a corresponding BBN. To show the system’s usability, we use three realistic product design cases to compare BBNs that are directly generated by domain experts, with BBNs that are generated using the FCM Constructor. We find that the BBN constructed through the FCM Constructor is similar, based on reasoning results, to the BBN constructed directly by specifying conditional probability tables of BBNs.

Strategic Design Decisions for Uncertain Market Systems Using an Agent Based Approach

Market players, such as competing manufacturing firms and retail channels, can significantly influence the demand and profit of a new product. Existing methods in design for market systems use game theoretic models that can maximize a firm’s profit with respect to the product design and price variables given the Nash equilibrium of the market system. However, in the design for uncertain market systems, there is seldom equilibrium with players having fixed strategies in a given time period. In this paper, we propose an agent based approach for design for market systems that accounts for learning behaviors of the market players under uncertainty. By learning behaviors we mean that market players gradually, over time, learn to play with better strategies based on action–reaction behaviors of other players. We model a market system with agents representing competing manufacturers and retailers who possess learning capabilities and based on some prespecified rules are able to react and make decisions on the product design and pricing. The proposed agent based approach provides strategic design and pricing decisions for a manufacturing firm in response to possible reactions from market players in the short and long term horizons. Our example results show that the proposed approach can produce competitive strategies for the firm by simulating market players’ learning behaviors when they react only by setting prices, as compared to a game theoretic approach. Furthermore, it can yield profitable product design decisions and competitive strategies when competing firms react by changing design variables in the short term—case for which no previous method in design for market systems has been reported.

The Research and Realization of the Product Design and Process Decision System Based on the Semantic Technology

After several decades of research, the traditional modeling methods, such as geometric model，feature model and integrated product model, can’t fully meet the integrated product information and the related knowledge that required. Meanwhile, lack of semantics, is not conducive to the using and sharing of knowledge. Semantic-based product design and process decision can shorten the product development cycle, reduce costs and allow the computer to participate in the design. Based on building of complex electronic products ontology and already been built OWL ontology file introduced into database, this paper combines Ontology model of complex electronic products with the semantic Web, studies a network design model based on the semantic Web and develops a product design and process decision Web platform based on semantic. The platform can realize product design, semantic search of the resources and decision of parts machining process, thus, to improve the productivity and enhance the efficiency and competitiveness of the company by the system.

Sensitivity-Based Product Portfolio and Design Integration

In this study we present a novel integration between product portfolio management evaluation criteria and product design decisions involving the design of chemical-based configured consumer products. We consider the variance contribution made by the product’s quality characteristics to the product’s economic performance that is measured via its net present value (NPV). The sensitivity-based time-constrained selection problem (STCSP) is modeled as a static deterministic problem, solved with the objective of mean cost minimization. The STCSP model utilizes a hybrid approach involving the application of Monte Carlo simulation, heuristics, and algorithmic processing to optimize the product design planning process. In this approach product design activities dependencies are modeled as linear inequality constraints. An industry-based case is used to illustrate the proposed approach.

An Integrative Methodology for Product and Supply Chain Design Decisions at the Product Design Stage

Supplier selection is one of the key decisions in supply chain management. Companies need not only to make the “make” or “buy” decisions but also differentiate across potential suppliers in order to improve operational performance. Product design is an engineering based activity that realizes the customer requirements into functions of a new product. Many studies have pointed out that the integration of product and supply chain is a key factor for profitability and efficiency. However, most studies address supply chain performance after freezing the design of the product; only a few studies discuss when and how to incorporate supply chain decisions during product design. This paper presents a graph theory based optimization methodology to tackle this problem. The supplier selection issue is considered by evaluating its impact on both internal (e.g., ease of assembly) and external (e.g., transportation time) enterprise performances, which are aggregated as supply chain performance at the conceptual design stage. A case study in the bicycle industry demonstrates the advantages of this methodology. The presented mathematical programming formulation enables simultaneous optimization of both product design and supply chain design during the early design stages.

Integrated Product Architecture and Pricing for Managing Sequential Innovation

Science and technology advances drive firms to continually enhance their product's performance and launch sequentially improving offerings. Firms face challenges in marketing such improving products to well-informed, forward-looking consumers who anticipate product improvements and seek to delay their purchase timing. Product design, specifically a modular upgradable architecture in which improving and stable subsystems of a product are separated and selectively upgraded, can be a valuable approach for marketers to alleviate consumer concerns about product obsolescence. However, such an architecture-based approach can present new challenges as well, and dealing with them requires carefully coordinated cross-functional decision making by the firm. In this paper, we identify and formalize the notion of design inconsistency, which refers to the monopolist firm's inability to commit to future product design architectures. We find that firms experience design inconsistency even when they are able to commit to future prices, and design inconsistency lowers firm profits as well as consumer surplus. We then derive a joint product architecture and pricing approach to solve this problem; this enables an innovating firm to optimally and in a time-consistent manner launch modular upgradable products. The modeling and analysis in the paper lends insight into types of markets and products for which modular upgradability is most appropriate and offers guidelines on making pricing and product design decisions jointly for managing sequential innovation. This paper was accepted by Preyas Desai, marketing.

Integrated Product Architecture and Pricing for Managing Sequential Innovation

Science and technology advances drive firms to continually enhance their product’s performance and launch sequentially improving offerings. Firms face challenges in marketing such improving products to well informed, forward-looking consumers who anticipate product improvements and seek to delay their purchase timing. Product design, specifically a modular upgradable architecture in which improving and stable subsystems of a product are separated and selectively upgraded, can be a valuable approach for marketers to alleviate consumer concerns about product obsolescence. However, such an architecture-based approach can present new challenges as well, dealing with which requires carefully coordinated cross-functional decision making by the firm. In this paper, we identify and formalize the notion of design inconsistency, which refers to the monopolist firm’s inability to commit to future product design architectures. We find that firms experience design inconsistency even when they are able to commit to future prices, and design inconsistency lowers firm profits as well as consumer surplus. We then derive a joint product architecture and pricing approach to solve this problem; this enables an innovating firm to optimally and in a time consistent manner launch modular upgradable products. The modeling and analysis in the paper lends insight into types of markets and products for which modular upgradability is most appropriate, and offers guidelines on making pricing and product design decisions jointly for managing sequential innovation.

Development of a Simultaneous Design for Supply Chain Process for the Optimization of the Product Design and Supply Chain Configuration Problem

:In the complex business environment of today, it is well recognized that paying insufficient attention to product design and the supply chain might result in financial losses due to both demand generation and profitability problems. This article aims to investigate and quantify the potential benefits of a process called Design for Supply Chain, which tackles optimization of product and supply chain design decisions simultaneously via mathematical modeling and optimization methods. The results, based on the optimization with illustrative data, suggest that this concurrent optimization provides better visibility and higher profits by achieving a delicate balance between selecting a better product design and constructing a cost efficient supply chain.

Management Insights

Management Insights

Comparative Assessment of Life Cycle Assessment Methods Used for Personal Computers

This article begins with a summary of findings from commonly cited life cycle assessments (LCA) of Information and Communication Technology (ICT) products. While differing conclusions regarding environmental impact are expected across product segments (mobile phones, personal computers, servers, etc.) significant variation and conflicting conclusions are observed even within product segments such as the desktop Personal Computer (PC). This lack of consistent conclusions and accurate data limits the effectiveness of LCA to influence policy and product design decisions. From 1997 to 2010, the majority of published studies focused on the PC concluded that the use phase contributes most to the life cycle energy demand of PC products with a handful of studies suggesting that manufacturing phase of the PC has the largest impact. The purpose of this article is to critically review these studies in order to analyze sources of uncertainty, including factors that extend beyond data quality to the models and assumptions used. These findings suggest existing methods to combine process-based LCA data with product price data and remaining value adjustments are not reliable in conducting life cycle assessments for PC products. Recommendations are provided to assist future LCA work.

The application of codesign in new bra product innovations

PurposeThe purpose of this paper is to discuss how brassiere manufacturers develop new designs for bra products, suitable for individual consumers, through consumer codesign.Design/methodology/approachNew product design that relies on conjoint analysis algorithms can depict multidimensional attribute profiles, such that consumers' choice behavior reflects their preferences and overall judgment of the profiles. This statistical technique provides a means to codesign and customize bra products and thereby enhance the overall bra design process.FindingsBra products codesign suggests goals such as attractive appearance, shoulder strap style, vivid/mild color, elegance/sexy lace, comfort/practicality, fabric, lining, comfort/attractive appearance, neckline design, comfort/excellent function cut, sewn cups, and generous quantities. The most preferred combination of attributes for all respondents is a cotton/cotton blend fabric, seamless bra that offers a detachable shoulder strap, lavender color, a two‐strap style, lace details, and a low‐cut plunge neckline. The paper illustrates consumers' bra awareness attributes, codesign approach, and individual optimum individualized bra designs.Practical implicationsThe results provide a useful source of information for product managers, who should consider the use of codesign to design the best products for individual consumers and decrease the risk of design failure, as well as promote consumer loyalty and satisfaction toward the product.Originality/valueThe paper provides a unique method to understand the new product codesign structure and make bra product design decisions that integrate optimum individualized design.

Models for concurrent product and process design

We propose procedures to address product design and manufacturing process configurations concurrently in environments characterized by large degrees of product proliferation. Exploiting the intrinsic flexibility of product and process design, we present two approaches that synchronize production flows through the manufacturing system. These approaches integrate product and manufacturing system design decisions with operational concerns and provide powerful means for managing production in environments characterized by a proliferation of products. Experimental results show that the proposed methods can substantially reduce manufacturing lead times, work in process (WIP), and overall system complexity.