Design Analytics in Consumer Product Design: A Simulated Study

2013 ◽  
Author(s):  
Kemper Lewis ◽  
Dave Van Horn
Keyword(s):  
Design Process ◽  
Data Driven ◽  
Next Generation ◽  
Product Usage ◽  
Customer Needs ◽  
Customer Preferences ◽  
Use Of Data ◽  
Data Driven Decisions ◽  
Behavior Profiles ◽  
Usage Data

A growing area of research in the engineering community is the use of data and analytics for transforming information into knowledge to design better systems, products, and processes. Data-driven decisions can be made in the early, middle, and late stages in a design process where customer needs are identified and understood, a final concept for a design is chosen, and usage data from the deployed product is captured, respectively. Design Analytics (DA) is a paradigm for improving the core information-to-knowledge transformations in these stages of a design process resulting in better performing and functioning products that reflect both explicit and implicit customer needs. In this paper, a simulator is used to model usage of a hypothetical refrigerator and generate artificial data driven by four different customer behavior profiles with variation. The population of customers is randomly divided among the four behavior profiles so that the underlying customer preferences are unknown to the experimenter prior to data analysis. The purpose of the simulation is to illustrate the use of DA in the late stage of a design process to improve the transition from an existing product to the next generation product. Metrics are developed to analyze the product usage data, and both prevailing and subtle usage trends are identified. After conclusions are made, the study proceeds to the early and middle stages of a subsequent design process where a hypothetical next-generation refrigerator is conceptualized.

scholarly journals Data driven parametric design

2011 ◽  
Vol 2 (7) ◽  
pp. 49
Author(s):  
Ferdinand Facklam ◽  
Felipe Pecegueiro do Amaral Curado
Keyword(s):  
Design Process ◽  
Parametric Design ◽  
Digital Fabrication ◽  
Geometric Shape ◽  
Data Driven ◽  
Systematic Procedure ◽  
Use Of Data ◽  
Sensory Process

The focus of this paper is that we want to give a brief introduction about the idea of Parametric Design (PD) and the use of data to inform the design process. The digital fabrication is not covered in detail in this document. In the case study “Live Building” explains a sensory process. The project shows how to collect data, transformed and transported into a shape. Innovation is not only the approach of the draft, but the systematic procedure and the resulting diversity of solutions. The search for the geometric shape and the key to the concept will be answered in detail.

Consumer Information for Data-Driven Decision Making: Teaching Socially Responsible Use of Data

2018 ◽  
Vol 41 (2) ◽  
pp. 109-126 ◽  
Author(s):  
Kristen L. Walker ◽  
Nora Moran
Keyword(s):  
Decision Making ◽  
Critical Role ◽  
Consumer Information ◽  
Data Driven ◽  
Socially Responsible ◽  
Privacy And Security ◽  
Data Usage ◽  
Use Of Data ◽  
Resource Requirements ◽  
Data Driven Decisions

The marketing field is undergoing dramatic shifts in the digital age. The increasing reliance on, collection, and use of data enabled by technological innovations requires teaching the responsible use of data for personalization, and marketing educators play a critical role. Students, universities, accrediting agencies, and employers demand curriculum that equips students with appropriate knowledge, skills, and abilities to make data-driven decisions. We explore the curricular advantages of a unique marketing course that applies a social science lens to frame the emerging issue of socially responsible data usage. This type of curriculum fulfills students’ needs for current and relevant courses; provides key knowledge, skills, and abilities for prospective employers; meets department curriculum and resource requirements, all while addressing existing and newer AACSB guidelines for “Technology Agility” with a focus on “evidence-based decision making that integrates current and emerging technologies, . . . [the] ethical use and dissemination of data, including privacy and security of data.”

scholarly journals How Operation Fistula uses data visualization to help women with obstetric fistula

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Naledi Hollbrügge
Keyword(s):  
Obstetric Fistula ◽  
Data Driven ◽  
Full Time ◽  
Data Visualisation ◽  
New Developments ◽  
The Public ◽  
Staff Members ◽  
Complex Processes ◽  
Use Of Data ◽  
Data Driven Decisions

Aims: To enable all parts of Operation Fistula to make data-driven decisions in their work and to use data visualisation to simplify complex processes. Moreover, to effectively use data visualisation to educate the public about Obstetric Fistula and other issues of gender inequality. Methods: An experienced visual design analyst was hired full time to lead the analytics setup at the organisation and provide training for the team in the use of Tableau Desktop software. Results: Using a visual representation of data has spread information about Obstetric Fistula in an entirely new way to new audiences. One of our analyses has received over 10,000 views at this time. The use of data visualisation has created powerful partnerships for the organisation that have allowed Operation Fistula to raise significant funds and improve visibility of the cause. Staff members are now empowered to use data in their everyday work to make data-driven decisions.   Conclusions: In a world that is driven by data, it is important that evidence-driven actions are implemented not only in the treatment of obstetric fistula but that we make data central to the everyday operation of our workplaces. Data visualisation is a powerful technique to make unfamiliar topics accessible, gain clarity on complex processes, and garner interest from otherwise unengaged actors. With new developments in commercial tools, data visualisation has become more accessible to every level of the business and is no longer exclusively the realm of data analysts.

A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Kangjie Li ◽  
Yicong Gao ◽  
Hao Zheng ◽  
Jianrongg Tan
Keyword(s):  
Evaluation Method ◽  
Tolerance Analysis ◽  
Tolerance Allocation ◽  
Data Driven ◽  
Angular Deviation ◽  
Mechanical Assembly ◽  
Difference Matrix ◽  
Product Usage ◽  
Tolerance Method ◽  
Usage Data

Abstract Industry 4.0, the fourth industrial revolution, puts forward new requirements for the sustainable service of products. With the recent advances in measurement technologies, global and local deformations in inaccessible areas can be monitored. Product usage data such as geometric deviation, position deviation, and angular deviation that lead to product functional performance degradation can be continuously collected during the product usage stage. These technologies provide opportunities to improve tolerance design by improving tolerance allocation using product usage data. The challenge lies in how to assess these deviations for identifying relevant field factors and reallocate the tolerance value. In this paper, a data-driven methodology based on the deviation for tolerance analysis is proposed to improve the tolerance allocation. A feature graph of a mechanical assembly is established based on the assembly relationship. The node representation in the feature graph is defined based on the unified Jacobian-torsor model and the node label is calculated by a synthetic evaluation method. A novel hierarchical graph attention networks (HGAT) is proposed to investigate hidden relations between nodes in the feature graph and calculate labels of all nodes. A modification necessity index (MNI) is defined for each tolerance between two nodes based on their labels. An identification of the to-be-modified tolerance method is proposed to specify the tolerance analysis target. A deviation difference matrix is constructed to calculate the MNI of each tolerance for identifying the to-be-modified tolerance value with high priorities for product improvement. The effectiveness of the proposed methodology is demonstrated through a case study for improving tolerance allocation of a press machine.

Data-Driven Decisions in Prototyping and Product Development: A Framework for Uncertainty and Decision-Making

2019 ◽  
Author(s):  
Hadi Ali ◽  
Micah Lande
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Product Development ◽  
Design Process ◽  
Design Thinking ◽  
Data Driven ◽  
Product Platforms ◽  
Creative Solutions ◽  
Data Driven Decisions

Abstract We propose a framework, established in the literature on uncertainty and decision-making, that guides the development of effective prototypes that inform the design of products and systems in a way where digital, big-data become central to the design process. Ultimately, we seek to move design thinking to the new domain of digitization of organizations and product platforms so that the gap between data-driven decisions and creative solutions is bridged through design thinking.

scholarly journals COVID-19 Brings Data Equity Challenges to the Fore

2021 ◽  
Author(s):  
H.V. Jagadish ◽  
Julia Stoyanovich ◽  
Bill Howe
Keyword(s):  
Quality Control ◽  
Government Policy ◽  
Data Driven ◽  
Contact Tracing ◽  
Control Mechanisms ◽  
Sources Of Information ◽  
Decision Systems ◽  
Physical Resources ◽  
Multiple Levels ◽  
Data Driven Decisions

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: they can inform local, state and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise, and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

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