Products' Shared Visual Features Do Not Cancel in Consumer Decisions

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Ping Du ◽  
Erin F. MacDonald
Keyword(s):  
Consumer Preferences ◽  
Product Family ◽  
Image Features ◽  
Visual Features ◽  
Cognitive Responses ◽  
Product Family Design ◽  
Product Complexity ◽  
Product Families ◽  
Mass Customisation ◽  
Model C

Consumers' product purchase decisions typically involve comparing competing products' visual features and functional attributes. Companies strive for “product differentiation” (Liu et al., 2013, “Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization,” ASME J. Mech. Des., 135(8), p. 081007; Thevenot and Simpson, 2009, “A Product Dissection-Based Methodology to Benchmark Product Family Design Alternatives,” ASME J. Mech. Des., 131(4), p. 041002; Kota et al., 2000, “A Metric for Evaluating Design Commonality in Product Families,” ASME J. Mech. Des., 122(4), pp. 403–410; Orfi et al. 2011, “Harnessing Product Complexity: Step 1—Establishing Product Complexity Dimensions and Indicators,” Eng. Econ., 56(1), pp. 59–79; and Shooter et al. 2005, “Toward a Multi-Agent Information Management Infrastructure for Product Family Planning and Mass Customisation,” Int. J. Mass Customisation, 1(1), pp. 134–155), which makes consumers' product comparisons fruitful but also sometimes challenging. Psychologists who study decision-making have created models of choice such as the cancellation-and-focus (C&F) model. C&F explains and predicts how people decide between choice alternatives with both shared and unique attributes: The shared attributes are “canceled” (ignored) while the unique ones have greater weight in decisions. However, this behavior has only been tested with text descriptions of choice alternatives. To be useful to designers, C&F must be tested with product visuals. This study tests C&F under six conditions defined by: The representation mode (text-only, image-only, and image-with-text) and presentation (sequentially or side-by-side) of choice alternatives. For the products tested, C&F holds for only limited situations. Survey and eye-tracking data suggest different cognitive responses to shared text attributes versus shared image features: In text-only, an attribute's repetition cancels its importance in decisions, while in images, repetition of a feature reinforces its importance. Generally, product differences prove to attract more attention than commonalities, demonstrating product differentiation's importance in forming consumer preferences.

Products’ Shared Visual Features Do Not Cancel in Consumer Decisions

2015 ◽  
Author(s):  
Ping Du ◽  
Erin F. MacDonald
Keyword(s):  
Product Differentiation ◽  
Consumer Preferences ◽  
Image Features ◽  
Visual Features ◽  
Cognitive Responses ◽  
Purchase Decisions ◽  
Tracking Data ◽  
Consumer Decisions ◽  
Functional Attributes ◽  
Model C

Consumers’ product purchase decisions typically involve comparing competing products’ visual features and functional attributes. Companies strive for “product differentiation” [1–5], which makes consumers’ product comparisons fruitful but also sometimes challenging. Psychologists that study decision-making have created models of choice such as the cancellation-and-focus (C&F) model. C&F explains and predicts how people decide between choice alternatives with both shared and unique attributes: the shared attributes are “cancelled” (ignored) while the unique ones have greater weight in decisions. However, this behavior has only been tested with text descriptions of choice alternatives. To be useful to designers, C&F must be tested with product visuals. This study tests C&F under six conditions defined by: the representation mode (text-only, image-only, and image-with-text) and presentation (sequentially, or side-by-side) of choice alternatives. For the products tested, C&F holds for only limited situations. Survey and eye-tracking data suggest different cognitive responses to shared text attributes vs. shared image features: in text-only, an attribute’s repetition cancels its importance in decisions, while in images, repetition of a feature reinforces its importance. Generally, product differences prove to attract more attention than commonalities, demonstrating product differentiation’s importance in forming consumer preferences.

Optimal Kinematics Design of an Industrial Robot Family

2008 ◽  
Author(s):  
Johan O¨lvander ◽  
Xiaolong Feng ◽  
Bo Holmgren
Keyword(s):  
Mass Customization ◽  
Industrial Robot ◽  
Product Family ◽  
Industrial Robots ◽  
Mathematical Framework ◽  
Product Family Design ◽  
Product Families ◽  
The Common ◽  
Object Of Study ◽  
Common Platform

Product family design is a well recognized method to address the demands of mass customization. A potential drawback of product families is that the performance of individual members are reduced due to the constraints added by the common platform, i.e. parts and components need to be shared by other family members. This paper presents a formal mathematical framework where the product family design problem is stated as an optimization problem and where optimization is used to find an optimal product family. The object of study is kinematics design of a family of industrial robots. The robot is a serial manipulator where different robots share arms from a common platform. The objective is to show the trade-off between the size of the common platform and the kinematics performance of the robot.

Strategic Decisions in the Early Stages of Product Family Design

2001 ◽  
Author(s):  
Jonathan R. A. Maier ◽  
Georges M. Fadel
Keyword(s):  
Product Family ◽  
Product Family Design ◽  
Design Strategies ◽  
Strategic Decisions ◽  
Product Introduction ◽  
Single Product ◽  
Product Families ◽  
Different Types ◽  
Formal Design

Abstract The realization that designing products in families can and does have significant technological and economic advantages over traditional single product design has motivated increasing interest in recent years in formal design tools and methodologies for product family design. However, currently there is no guidance for designers in the first key strategic decisions of product family design, in particular determining the type of product family to design. Hence in this paper, first a taxonomy of different types of product families is presented which consists of seven types of product families, categorized based on number of products and time of product introduction. Next a methodology is introduced to aid designers in determining which type of product family is appropriate, based upon early knowledge about the nature of the intended product(s) and their intended market(s). From this information it also follows both which manufacturing paradigm and which fundamental design strategies are appropriate for the product family. Finally the proposed methodology is illustrated through a case study examining a family of whitewater kayaks.

A Framework for Ontology-Based Heterogeneous Data Integration for Cost Management in Product Family Design

2008 ◽  
Author(s):  
Xiaomeng Chang ◽  
Janis Terpenny
Keyword(s):  
Product Family ◽  
Heterogeneous Data ◽  
Schema Integration ◽  
Product Family Design ◽  
Related Factors ◽  
Product Families ◽  
Heterogeneous Data Integration ◽  
Related Information ◽  
Semantic Data ◽  
Data Schema

High quality, high impact and economical products and systems are important goals for an enterprise. The usage of product families can be strategic to achieving these goals, yet defining these families can be challenging, requiring the consideration of numerous cost factors. This requires bringing together a great number of heterogeneous data sources of varying formats in a manner that allows the product development team to easily locate and reuse information in a collaborative manner across time and space. To date, our work has focused on the development and use of an Activity-Based Cost ontology (ABC ontology) to guide designers drill down to get at information for product family design. However, this ontology is built in such a way that it can only support information retrieval from the ontology and does not bring together and connect heterogeneous data resources. It does not address the problem of designers who struggle with obtaining relevant details from different departments in an enterprise. While there have been several semantic data schema integration tools for heterogeneous data resources integration, these tools cannot guide users to related information, that would lead to the root cause of the high cost. In this paper, in order to better manage cost in product family design, an ontology-based framework is put forward that builds on our prior work and combines the advantages of ABC ontology and data schema integration tools. The ontology-based framework can guide users to the proper information aspects through querying the central ontology, and give users detailed information about these aspects from heterogeneous data resources with the support of local ontologies. Ultimately, this framework will facilitate designers with better utilization of cost-related factors for product family design from a whole enterprise perspective.

A Product Dissection-Based Methodology to Benchmark Product Family Design Alternatives

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Henri J. Thevenot ◽  
Timothy W. Simpson
Keyword(s):  
Product Family ◽  
Cost Savings ◽  
Production Costs ◽  
Manufacturing Cost ◽  
Successful Implementation ◽  
Product Family Design ◽  
Potential Cost ◽  
Product Families ◽  
Product Platforms ◽  
Design Alternatives

Today’s companies are pressured to develop platform-based product families to increase variety, while keeping production costs low. Determining why a platform works, and alternatively why it does not, is an important step in the successful implementation of product families and product platforms in any industry. Internal and competitive benchmarking is essential to obtain knowledge of how successful product families are implemented, thus avoiding potential pitfalls of a poor product platform design strategy. While the two fields of product family design and benchmarking have been growing rapidly lately, we have found few tools that combine the two for product family benchmarking. To address this emerging need, we introduce the product family benchmarking method (PFbenchmark) to assess product family design alternatives (PFDAs) based on commonality/variety tradeoff and cost analysis. The proposed method is based on product family dissection, and utilizes the Comprehensive Metric for Commonality developed in previous work to assess the level of commonality and variety in each PFDA, as well as the corresponding manufacturing cost. The method compares not only (1) existing PFDAs but also (2) the potential cost savings and commonality/variety improvement after redesign using two plots—the commonality/variety plot and the cost plot—enabling more effective comparisons across PFDAs. An example of benchmarking of two families of valves is presented to demonstrate the proposed method.

A Metric for Evaluating Design Commonality in Product Families

1998 ◽  
Vol 122 (4) ◽  
pp. 403-410 ◽  
Author(s):  
Sridhar Kota ◽  
Kannan Sethuraman ◽  
Raymond Miller
Keyword(s):  
Product Family ◽  
Objective Measure ◽  
Value Added ◽  
New Product ◽  
Product Line ◽  
Product Complexity ◽  
Product Families ◽  
Market Strategy ◽  
Customer Base ◽  
Market Needs

Many companies develop a market strategy built around a family of products. These companies regularly add new product variations to the family in order to meet changing market needs or to attract a broader customer base. Although the core functionality remains essentially unchanged across the products within a family, new functions, feature combinations and technologies are incorporated into each new product. If allowed to grow unchecked, these component variations, commonly referred to as “complexity”, can result in a loss of productivity or quality. The challenge lies in an effective management of product variations in the design studio and on the manufacturing floor. The key is to minimize non-value added variations across models within a product family without limiting customer choices. In this paper we discuss the factors that contribute to product complexity in general, and present an objective measure, called the Product Line Commonality Index, to capture the level of component commonality in a product family. Through our Walkman case study, we present a simple yet powerful method of benchmarking product families1. This method gauges the family’s ability to share parts effectively (modularity) and to reduce the total number of parts (multi-functionality). [S1050-0472(00)02704-5]

Product Family Design With Solution Spaces

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Markus Eichstetter ◽  
Steffen Müller ◽  
Markus Zimmermann
Keyword(s):  
Product Family ◽  
Monte Carlo Sampling ◽  
Optimal Performance ◽  
Design Parameters ◽  
High Dimensional ◽  
Product Family Design ◽  
Product Families ◽  
System Components ◽  
Vehicle Chassis ◽  
Parameter Values

Design for optimal commonality in product families is different from design for optimal performance. While optimal performance may be achieved by the choice of appropriate design parameter values for all system components, optimal commonality requires a particular scheme of sharing components among systems. The number of possibilities to share components can be quantified by Bell's number and becomes large quickly, thus making optimization extremely expensive. This paper presents an approach to identify components that may be shared in order to optimize commonality for a product family of arbitrary high-dimensional nonlinear systems. Solution spaces are computed for each system using iterative Monte Carlo sampling. On these solution spaces, all design goals are reached. They are expressed as sets of permissible intervals for all design parameters. When parameter intervals from different systems overlap, they may assume the same value and components may be shared. The approach is applied to vehicle chassis design. A set of common components is computed for 13 vehicles with ten design parameters each, such that all design goals are satisfied and the number of different component designs is small.

The Commonality Differentiation Index Using Prominence of Visual Information for Medication Package Family Planning

2010 ◽  
Author(s):  
Steven B. Shooter ◽  
Shane W. Cohen
Keyword(s):  
Visual Information ◽  
Product Family ◽  
Quantitative Measure ◽  
Visual Features ◽  
Over The Counter ◽  
Product Families ◽  
Safe Medication ◽  
Strategic Goals ◽  
Differentiation Index ◽  
Safe Medication Practice

Consumable products such as medications rely on the package or label to represent the contents. Package confusion has been widely recognized as a major problem for both over-the-counter and pharmacy-dispensed medications with potentially lethal consequences. It is critical to identify a medication as a member of a product family and differentiate its contributing elements based on visual features on the package or label. Indices that indicate degrees of commonality and differentiation of features for platforms have been shown to benefit development of engineered product families. It is possible to take a similar approach for visual features in packaging such as typography, shape/form, imagery and color. This paper establishes a commonality/differentiation index for prominence of visual features on over-the-counter and pharmacy-dispensed medications based on size and location of features. It provides a quantitative measure to assist package designers in evaluating alternatives to satisfy strategic goals and improve safety. The index is demonstrated with several medications that have been identified by the Institute for Safe Medication Practice as commonly confused.

Recent Advancements in Product Family Design and Platform-Based Product Development: A Literature Review

2011 ◽  
Author(s):  
Zhila Pirmoradi ◽  
G. Gary Wang
Keyword(s):  
Product Development ◽  
Product Family ◽  
Product Variety ◽  
Practical Case ◽  
Product Family Design ◽  
Product Families ◽  
Design And Optimization ◽  
Future Design ◽  
Research Areas ◽  
Research Activities

Increase of demand on product variety has pushed companies to think about offering more and more product variants in order to take more market shares. However, product variation can lead to cost increase for design and production, as well as the lead time for new variants. As a result, a proper tradeoff is required between cost-effectiveness of manufacturing and satisfying diverse demands. Such tradeoff has been shown to be manageable effectively by exploiting product family design (PFD) and platform-based product development. These strategies have been widely studied during the past decades, and a large number of approaches have been proposed for covering different issues and steps related to design and development of product families and platforms. Verification and performance of such approaches have also been traced through practical case studies applied to several industries. This paper focuses on a review of the research in this field and efforts to classify the recent advancements relevant to product family design and platform development issues. A comprehensive review on the state-of-the-art research in this field was done by Jiao et al. in 2007; therefore the main focus of this paper is on the research activities from 2006 to present. Mainly, the effort of this paper is to identify new achievements in regard with different aspects of product family design such as customer involvement in design, market driven studies, new indices and metrics for assessing families and developing the desired platforms, issues relevant to product family optimization (i.e., new algorithms and optimization approaches applied to different PFD problems along with their benefits and limitations in comparison to previously developed approaches), issues relevant to development of platforms (i.e., platform configuration approaches, joint platform design and optimization, and factors effective on forming proper platform types), and issues relevant to knowledge management and modeling of families and platforms for facilitating and supporting future design efforts. Through a comparison with previous research, new achievements are discussed and the remaining challenges and potential new research areas in this field are addressed.

Identification of Product Family Platforms Using Pattern Recognition

2011 ◽  
Author(s):  
Dane Freeman ◽  
Dongwook Lim ◽  
Elena Garcia ◽  
Dimitri Mavris
Keyword(s):  
Pattern Recognition ◽  
A Priori ◽  
Product Family ◽  
Combinatorial Problem ◽  
Equivalence Relations ◽  
Product Family Design ◽  
Product Families ◽  
Cluster Membership ◽  
End Products ◽  
Fuzzy C Means Clustering

In product family design the goal is to generate a set of lowest cost products that target specific market niches. Sharing components, called platforms, between different products can minimize duplication of effort, thereby lowering family costs. However, if the products’ requirements are too dissimilar, sharing components may compromise the end product; such variance will lead to lower end products being overdesigned and/or higher end products being underdesigned. It is important to identify which components are similar enough, so that sharing does not compromise the individual products’ performances. Most existing product family design methods make decisions a priori about platforms; constraining platforms to be used by every product in the family, or not at all. Methods that simultaneously optimize component sharing and design variable settings have the potential to find better families because product subsets may be more similar to each other than to other subsets of products. Allowing components to be shared between any subset of family members leads to a very large combinatorial problem, and considering large product families can be computationally prohibitive. This paper proposes a method to identify possible sets of product family platforms by using the pattern recognition technique of fuzzy c-means clustering on component subspaces. Component subspaces are taken from a database of generated design points for the whole family. If components from different products are similar enough to be grouped into the same cluster, then those components could possibly become the same platform. Fuzzy equivalence relations can be extracted from the cluster membership functions that show the binary relationship from one products’ component to a different products’ component. Ultimately, this method can be used as a platform identification heuristic in a larger product family design methodology. This method is demonstrated by applying it to find possible common components in a family of universal electric motors.

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