Using Shape Grammars to Identify Salient Features in Support of Product Family Design

2014 ◽  
Author(s):  
Timothy D. Culbertson ◽  
Timothy W. Simpson
Keyword(s):  
Product Family ◽  
Consumer Preference ◽  
Fractional Factorial ◽  
Factorial Experiment ◽  
Ultrasound Transducers ◽  
Shape Grammars ◽  
Product Family Design ◽  
Baseline Design ◽  
The Impact ◽  
Full Factorial

Product form and aesthetics play a major role in consumer preference and product differentiation. During product family design, it is important to differentiate products in the family yet similarities among some stylistic features may connote a more coherent design strategy. Shape grammars offer a method for producing designs with a coherent style along with the ability to control the variation of the output shapes. In this paper, we investigate the use of shape grammars to support product family design, namely, identification of features that shape the perceptions of similarity within a family. A survey-based approach is implemented wherein the impact of a shape parameter on product style is evaluated by comparing design variants to a baseline design. Respondents are asked to rate the style similarities on a Likert-like scale, and candidate shape parameters are screened for aesthetic significance using a fractional factorial experiment. The approach is demonstrated using a family of medical ultrasound transducers, and our screening is validated using a full factorial experiment with practicing ultrasound transducer designers and engineers.

Exploring the Role of Interaction Effects in Visual Conjoint Analysis

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Brian Sylcott ◽  
Jeremy J. Michalek ◽  
Jonathan Cagan
Keyword(s):  
Conjoint Analysis ◽  
Consumer Preferences ◽  
Consumer Preference ◽  
Interaction Effects ◽  
Fractional Factorial ◽  
Main Effects ◽  
Full Factorial ◽  
Biased Estimates ◽  
Data Requirements

In conjoint analysis, interaction effects characterize how preference for the level of one product attribute is dependent on the level of another attribute. When interaction effects are negligible, a main effects fractional factorial experimental design can be used to reduce data requirements and survey cost. This is particularly important when the presence of many parameters or levels makes full factorial designs intractable. However, if interaction effects are relevant, main effects design can create biased estimates and lead to erroneous conclusions. This work investigates consumer preference interactions in the nontraditional context of visual choice-based conjoint analysis, where the conjoint attributes are parameters that define a product's shape. Although many conjoint studies assume interaction effects to be negligible, they may play a larger role for shape parameters. The role of interaction effects is explored in two visual conjoint case studies. The results suggest that interactions can be either negligible or dominant in visual conjoint, depending on consumer preferences. Generally, we suggest using randomized designs to avoid any bias resulting from the presence of interaction effects.

Assembly Hierarchy Generation for Assembly System Design for a Product Family

2016 ◽  
Author(s):  
Zhengqian Jiang ◽  
Hui Wang
Keyword(s):  
Modular Design ◽  
Recursive Algorithm ◽  
Product Family ◽  
Graph Model ◽  
Product Variety ◽  
Assembly System ◽  
Product Family Design ◽  
Flexible Assembly ◽  
The Impact ◽  
Hierarchy Generation

Increased demand on product variety entails a flexible assembly system for product families which can be quickly configured and reconfigured in a responsive manner to deal with various product designs. Development of such a responsive assembly system requires an in-depth understanding of the impact of product family design on assembly system performance. In this paper, the linkage between the product family design and assembly systems is characterized by an assembly hierarchy model, which reflects a hierarchical relationship among all possible sub-assemblies and components, assembly tasks, and material flow among the tasks. Our prior research developed a recursive algorithm to generate all assembly hierarchy candidates for one single product based on its liaison graph without redundancy. These generated assembly hierarchies provide a structure to help efficiently explore optimal assembly system designs with reduced computational load. In this paper, the application of the assembly hierarchy generation algorithm will be extended to a product family by developing joint liaison graph model. Taking the advantage of the modular design of the product family, we proposed a concept of multi-level joint liaison graphs to overcome the computational challenge brought by assembly hierarchy generation for joint liaisons. Two case studies were conducted to demonstrate the algorithm.

Generation of Two-Level Cost Optimal Fractional Factorials

1978 ◽  
Vol 22 (1) ◽  
pp. 599-599
Author(s):  
Joseph J. Pignatiello
Keyword(s):  
Mathematical Programming ◽  
Programming Problem ◽  
Minimum Cost ◽  
Fractional Factorial ◽  
Factorial Experiment ◽  
Mathematical Programming Problem ◽  
Fractional Factorials ◽  
Number Of Factors ◽  
Main Effects ◽  
Full Factorial

It is assumed that, in a 2k factorial experiment, there are different costs per observation at each of the factor combinations. When the number of factors, k, increases, the total number of observations in the full factorial increases rapidly as does the expense of observing all observations in the full factorial. If the experimenter can assume certain classes of higher-order interactions are negligible, then advantage may be taken by observing measurements from an orthogonal fractional factorial. For any “1/2p” fraction of the full factorial, a 2k-p experiment, there are 2p feasible orthogonal fractions that could be selected at random. This paper develops an algorithm for generating the minimum cost such fraction in an efficient way. The problem is formulated as a mathematical programming problem subject to a resolution III constraint (main effects unconfounded). Computational experience is presented.

scholarly journals An Investigation of the Impact of Assembly Sequencing on the Product Family Design Outcomes

2007 ◽  
Author(s):  
Shafin Tauhid ◽  
Hakan U. Artar ◽  
Saraj Gupta ◽  
Gu¨l Okudan
Keyword(s):  
Decision Making ◽  
Simulation Study ◽  
Product Family ◽  
Shop Floor ◽  
Design Decision ◽  
Product Family Design ◽  
Floor Level ◽  
And Performance ◽  
The Impact

While many approaches have been proposed to optimize the product family design for measures of cost, revenue and performance, many of these approaches fail to incorporate the complexity of the manufacturing issues into family design decision-making. One of these issues is assembly sequencing. This paper presents a simulation study by which the impact of assembly sequencing on the product family design outcomes is investigated. Overall, the results indicate that when the product family design takes into account the assembly sequencing decisions, the outcomes at the shop floor level improve. The results have implications for companies that are looking into increasing their revenue without increasing their investment in the shop floor.

An Investigation of Impact of the Product Family Design on the Shop Floor Performance

2008 ◽  
Author(s):  
Hakan U. Artar ◽  
Gu¨l Okudan
Keyword(s):  
Decision Making ◽  
Real Life ◽  
Product Family ◽  
Shop Floor ◽  
Design Decision ◽  
Product Family Design ◽  
Computer Simulation Study ◽  
Family Case ◽  
And Performance ◽  
The Impact

While many approaches have been proposed to optimize the product family design for measures of cost, revenue and performance, many of these approaches fail to incorporate the complexity of the manufacturing issues into family design decision-making. One of these issues is different approaches for assembly sequencing. This paper presents a computer simulation study by which the impact of two postponement strategies is investigated for a real-life product family case under various demand conditions. Overall, the results indicate that when the product family design takes into account the assembly sequencing decisions, the outcomes at the shop floor level improve. The results have implications for companies that are looking into increasing their revenue without increasing their investment in the shop floor.

Identification of Platform Variables in Product Family Design Using Sensitivity Analysis

2012 ◽  
Author(s):  
Chad Hume ◽  
David W. Rosen
Keyword(s):  
Sensitivity Analysis ◽  
Effective Means ◽  
Product Family ◽  
Product Variety ◽  
Product Performance ◽  
Constructal Theory ◽  
Product Platform ◽  
Product Family Design ◽  
The Impact ◽  
Selection Of

Product family design strategies based on a common core platform have emerged as an efficient and effective means of providing product variety. The main goal in product platform design is to maximize internal commonality within the family while managing the inherent loss in product performance. Therefore, identification and selection of platform variables is a key aspect when designing a family of products. Based on previous research, the Product Platform Constructal Theory Method (PPCTM) provides a systematic approach for developing customizable products, while allowing for multiple levels of commonality, multiple product specifications, and balancing the tradeoffs between commonality and performance. However, selection of platform variables and the modes for managing product variety are not guided by a systematic process in this method. When developing a platform with more than a few variables, a quantitative method is needed for selecting the optimal platform variable hierarchy. In this paper we present an augmented PPCTM which includes sensitivity analysis of platform variables, such that hierarchical rank is conducted based on the impact of the variables on the product performance. This method is applied to the design of a line of customizable finger pumps.

Optimal Design Procedure for Two-Level Fractional Factorial Experiments Given Partial Prior Information about Parameters

1978 ◽  
Vol 22 (1) ◽  
pp. 598-598
Author(s):  
Steven M. Sidik ◽  
Arthur G. Holms
Keyword(s):  
Optimal Design ◽  
Prior Knowledge ◽  
Prior Information ◽  
Design Procedure ◽  
Fractional Factorial ◽  
Factorial Experiment ◽  
Factorial Experiments ◽  
Full Factorial Experiment ◽  
Main Effects ◽  
Full Factorial

In many cases in practice an experimenter has some prior knowledge of indefinite validity concerning the main effects and interactions which would be estimable from a two-level full factorial experiment. Such information should be incorporated into the design of the experiment.

scholarly journals Patient preferences for use of virtual consultations in an orthopaedic rehabilitation setting: Results from a discrete choice experiment

2021 ◽  
pp. 135581962110354
Author(s):  
Anthony W Gilbert ◽  
Emmanouil Mentzakis ◽  
Carl R May ◽  
Maria Stokes ◽  
Jeremy Jones
Keyword(s):  
Conceptual Model ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Patient Preferences ◽  
Choice Experiment ◽  
Best Practice ◽  
Time Of Day ◽  
Problem Area ◽  
Fractional Factorial ◽  
The Impact

Objective Virtual Consultations may reduce the need for face-to-face outpatient appointments, thereby potentially reducing the cost and time involved in delivering health care. This study reports a discrete choice experiment (DCE) that identifies factors that influence patient preferences for virtual consultations in an orthopaedic rehabilitation setting. Methods Previous research from the CONNECT (Care in Orthopaedics, burdeN of treatmeNt and the Effect of Communication Technology) Project and best practice guidance informed the development of our DCE. An efficient fractional factorial design with 16 choice scenarios was created that identified all main effects and partial two-way interactions. The design was divided into two blocks of eight scenarios each, to reduce the impact of cognitive fatigue. Data analysis were conducted using binary logit regression models. Results Sixty-one paired response sets (122 subjects) were available for analysis. DCE factors (whether the therapist is known to the patient, duration of appointment, time of day) and demographic factors (patient qualifications, access to equipment, difficulty with activities, multiple health issues, travel costs) were significant predictors of preference. We estimate that a patient is less than 1% likely to prefer a virtual consultation if the patient has a degree, is without access to the equipment and software to undertake a virtual consultation, does not have difficulties with day-to-day activities, is undergoing rehabilitation for one problem area, has to pay less than £5 to travel, is having a consultation with a therapist not known to them, in 1 weeks’ time, lasting 60 minutes, at 2 pm. We have developed a simple conceptual model to explain how these factors interact to inform preference, including patients’ access to resources, context for the consultation and the requirements of the consultation. Conclusions This conceptual model provides the framework to focus attention towards factors that might influence patient preference for virtual consultations. Our model can inform the development of future technologies, trials, and qualitative work to further explore the mechanisms that influence preference.

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