Intelligent Prototyping System for Developing Consumer Smart Products
The Intelligent Prototyping System (IPS) is proposed for developing consumer smart products. It is a context-aware system that can collect, recognize and integrate data from products, environments and design participants in order to drive Data Enabled Design. It provides participants an intelligent platform to do collaborative innovation related to the product under development.
Start
About the Author
Index
Archtecture of Intelligent Prototyping System
What are smart products
How to develop a smart product currently
Framework of Data Processing
Implications of Intelligent Prototyping System
Current Challenges for developing a smart product
Why the Intelligent Prototyping System
Reference
What are Smart Products
Philips Hue is a collection of smart lighting products including light bulbs being able to be controlled by the smart hub and wireless dimmer switches. Users can work on their smart phone App to control the lights and set their favorite lighting environment in the house.
Although the term of "Smart Products" can refer to a physical devices, a software or a service in diffrent sections, it most often refers to tangible products. The defines the smart products as an autonomous object with capbilities of self-orgnized, interaction with human, proactively approching the user, connection with other devices and context-awareness in different environments in the course of its life-cycle. Aligning with the definition, Maass, W & Janzen, S (2007) summarized Smart Products into 6 characteristics:
1. Situatedness: recognition of situational and community contexts; 2. Personalisation: tailoring of products according to buyer's and consumer's needs and affects; 3. Adaptiveness: change product behaviour according to buyer's and consumer's responses and tasks; 4. Pro-activity: anticipation of user's plans and intentions; 5. Business-awareness: consideration of business and legal constraints; 6. Network capability: ability to communicate and bundle with other products situatedness.
SmartProducts consortium [1]
https://www.philips-hue.com/en-ca/explore-hue/philips-hue-benefits
How to develop a smart product currently
Iterative Process of Design
User Experience Research
Smart Product Development is an iterative process of action research that aims at the design of a novel product or upgrade of an existing product. During the process, the participators with different knowledge backgrounds, such as user experience researchers, market researchers, industrial designers and technical supporters, etc., conduct collaborative learning and creative activities to find solutions for a variety of problems in the development.
Even though the diversity of product categories, markets, end-users, etc., causes the product development to vary greatly in strategies, design schemes and technical implementation, etc., it is nevertheless possible to refine the process of developing a smart product into 7 general steps.
Concept Generation
User Study Market Analysis
Function Definition
Design Scheme
Industrial design Interface design Technical implement
Fedility Prototype
Test Optimize
Current challenges for developing a smart product
In recent years, smart products, such as wearables, smart appliances, smart furniture, smart accessories... have become the hot spot of market and technology pursuit. However, many existing so-called smart connected products have not been integrated into our daily life as much as we expected. Some even appears to be designed for the sake of using a particular technology.
Read more
Complexity of product composition
Product Composition
Complexity of end-users' context
Usage Context
Compared to the rapid development of AI and IoT technologies in the past decades, novel smart products have not been emerging as quickly as imagined. Currently, the development of smart products is facing challenges from the aspects of product innovation, design procedure and data usage and so on.
Complexity of collaboration
Collaboration
Ever-growing data from every-day use
Growing Data
Why the Intelligent Prototyping System
Generally speaking, making a workable prototype is a common step in product design to test and optimize the product under development. IPS is proposed firstly for building a prototype but not limited in testing and optimizing it. IPS is a context-aware system that drives , as well as provides design participants an intelligent platform to do collaborative creation related to the product under development.
Data Enabled Design
Conduct Data Enabled Design for dynamic development of smart products
Strengthen collaborative innovation on mobile platforms
Support group problem-solving by machine Learning
info
info
info
Architecture of Intelligent Prototyping System
AI Consultant System
Sensors data processor
Database
Adaptive Application
Cloud
Marketer
Designer
Engineer
Smart Prototype
User
Environment
Participants
Framework of data processing in IPS
IPS is a dynamic data processing system aiming at supporting group problem solving in product innovation. Referenced the general framework for AI supporting group decision that was proposed by Shaw, M. J. & Fox, M. S, (1993) [8], IPS opts to include 5 functional compositions.
Goal Identification
Distribution of Knowledge
Orgnization
Innovation
Coordination
Learning schemes
Implications of Intelligent Prototyping System
Design Education
Industry
Machine Learning
The emerging smart products have been changing methodologies and procedures of product design...
As a subfield of AI, ML needs to be trained in various disciplines and circumstances to create new algorithms improving...
In the tide of Industry 4.0, more and more companies are facing challenges of developing smart products to meet market requirements...
info
info
info
Reference
1. Sabou, M., Kantorovitch, J., Nikolov, A., Tokmakoff, A., Zhou, X., & Motta, E. (2009). Position paper on realizing smart products: Challenges for semantic web technologies. In CEUR workshop proceedings (Vol. 522, pp. 135-147).2. Maass, W. and Janzen, S. (2007): Dynamic Product Interfaces: A Key Element for Ambient Shopping Environments. 20th Bled eConference "eMergence: Merging and Emerging Technologies, Processes, and Institutions". - Bled, Slovenia. 3. Funk, M., Eggen, J. H., & Hsu, J. Y. (2018). Designing for systems of smart things. International Journal of Design, 12(1), 1-5. 4. Drain, A., & Sanders, E. B. -. (2019). A collaboration system model for planning and evaluating participatory design projects. International Journal of Design, 13(3), 39-52. Retrieved from https://ezproxy.library.ubc.ca/login?url=https://www-proquest-com.ezproxy.library.ubc.ca/scholarly-journals/collaboration-system-model-planning-evaluating/docview/2340481536/se-2?accountid=14656 5. Ryan, A. (2014). A framework for systemic design. Formakademisk, 7(4)https://doi.org/10.7577/formakademisk.787 6. Data Enabled design, retieved March 30, 2021, from https://www.tue.nl/en/our-university/departments/industrial-design/innovation/projects/systemic-change/data-enabled-design/#top, 7. Bogers, S.,Frens, J., Kollenburg, J., Deckers, E. and Hummels, C.,(2016) Connected Baby Bottle: A Design Case Study Towards a Framework for Data-Enabled Design, DIS '16: Proceedings of the 2016 ACM Conference on Designing Interactive Systems, pp301-311,https://doi-org.ezproxy.library.ubc.ca/10.1145/2901790.2901855 8. Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard business review, 93(10), 96-114. 9. Shaw, M. J., & Fox, M. S. (1993). Distributed artificial intelligence for group decision support: integration of problem solving, coordination, and learning. Decision Support Systems, 9(4), 349-367. 10. Nunes, M. L., Pereira, A. C., & Alves, A. C. (2017). Smart products development approaches for Industry 4.0. Procedia manufacturing, 13, 1215-1222.
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Intelligent Prototyping System for Developing Consumer Smart Products
The Intelligent Prototyping System (IPS) is proposed for developing consumer smart products. It is a context-aware system that can collect, recognize and integrate data from products, environments and design participants in order to drive Data Enabled Design. It provides participants an intelligent platform to do collaborative innovation related to the product under development.
Start
About the Author
Index
Archtecture of Intelligent Prototyping System
What are smart products
How to develop a smart product currently
Framework of Data Processing
Implications of Intelligent Prototyping System
Current Challenges for developing a smart product
Why the Intelligent Prototyping System
Reference
What are Smart Products
Philips Hue is a collection of smart lighting products including light bulbs being able to be controlled by the smart hub and wireless dimmer switches. Users can work on their smart phone App to control the lights and set their favorite lighting environment in the house.
Although the term of "Smart Products" can refer to a physical devices, a software or a service in diffrent sections, it most often refers to tangible products. The defines the smart products as an autonomous object with capbilities of self-orgnized, interaction with human, proactively approching the user, connection with other devices and context-awareness in different environments in the course of its life-cycle. Aligning with the definition, Maass, W & Janzen, S (2007) summarized Smart Products into 6 characteristics: 1. Situatedness: recognition of situational and community contexts; 2. Personalisation: tailoring of products according to buyer's and consumer's needs and affects; 3. Adaptiveness: change product behaviour according to buyer's and consumer's responses and tasks; 4. Pro-activity: anticipation of user's plans and intentions; 5. Business-awareness: consideration of business and legal constraints; 6. Network capability: ability to communicate and bundle with other products situatedness.
SmartProducts consortium [1]
https://www.philips-hue.com/en-ca/explore-hue/philips-hue-benefits
How to develop a smart product currently
Iterative Process of Design
User Experience Research
Smart Product Development is an iterative process of action research that aims at the design of a novel product or upgrade of an existing product. During the process, the participators with different knowledge backgrounds, such as user experience researchers, market researchers, industrial designers and technical supporters, etc., conduct collaborative learning and creative activities to find solutions for a variety of problems in the development. Even though the diversity of product categories, markets, end-users, etc., causes the product development to vary greatly in strategies, design schemes and technical implementation, etc., it is nevertheless possible to refine the process of developing a smart product into 7 general steps.
Concept Generation
User Study Market Analysis
Function Definition
Design Scheme
Industrial design Interface design Technical implement
Fedility Prototype
Test Optimize
Current challenges for developing a smart product
In recent years, smart products, such as wearables, smart appliances, smart furniture, smart accessories... have become the hot spot of market and technology pursuit. However, many existing so-called smart connected products have not been integrated into our daily life as much as we expected. Some even appears to be designed for the sake of using a particular technology.
Read more
Complexity of product composition
Product Composition
Complexity of end-users' context
Usage Context
Compared to the rapid development of AI and IoT technologies in the past decades, novel smart products have not been emerging as quickly as imagined. Currently, the development of smart products is facing challenges from the aspects of product innovation, design procedure and data usage and so on.
Complexity of collaboration
Collaboration
Ever-growing data from every-day use
Growing Data
Why the Intelligent Prototyping System
Generally speaking, making a workable prototype is a common step in product design to test and optimize the product under development. IPS is proposed firstly for building a prototype but not limited in testing and optimizing it. IPS is a context-aware system that drives , as well as provides design participants an intelligent platform to do collaborative creation related to the product under development.
Data Enabled Design
Conduct Data Enabled Design for dynamic development of smart products
Strengthen collaborative innovation on mobile platforms
Support group problem-solving by machine Learning
info
info
info
Architecture of Intelligent Prototyping System
AI Consultant System
Sensors data processor
Database
Adaptive Application
Cloud
Marketer
Designer
Engineer
Smart Prototype
User
Environment
Participants
Framework of data processing in IPS
IPS is a dynamic data processing system aiming at supporting group problem solving in product innovation. Referenced the general framework for AI supporting group decision that was proposed by Shaw, M. J. & Fox, M. S, (1993) [8], IPS opts to include 5 functional compositions.
Goal Identification
Distribution of Knowledge
Orgnization
Innovation
Coordination
Learning schemes
Implications of Intelligent Prototyping System
Design Education
Industry
Machine Learning
The emerging smart products have been changing methodologies and procedures of product design...
As a subfield of AI, ML needs to be trained in various disciplines and circumstances to create new algorithms improving...
In the tide of Industry 4.0, more and more companies are facing challenges of developing smart products to meet market requirements...
info
info
info
Reference
1. Sabou, M., Kantorovitch, J., Nikolov, A., Tokmakoff, A., Zhou, X., & Motta, E. (2009). Position paper on realizing smart products: Challenges for semantic web technologies. In CEUR workshop proceedings (Vol. 522, pp. 135-147).2. Maass, W. and Janzen, S. (2007): Dynamic Product Interfaces: A Key Element for Ambient Shopping Environments. 20th Bled eConference "eMergence: Merging and Emerging Technologies, Processes, and Institutions". - Bled, Slovenia. 3. Funk, M., Eggen, J. H., & Hsu, J. Y. (2018). Designing for systems of smart things. International Journal of Design, 12(1), 1-5. 4. Drain, A., & Sanders, E. B. -. (2019). A collaboration system model for planning and evaluating participatory design projects. International Journal of Design, 13(3), 39-52. Retrieved from https://ezproxy.library.ubc.ca/login?url=https://www-proquest-com.ezproxy.library.ubc.ca/scholarly-journals/collaboration-system-model-planning-evaluating/docview/2340481536/se-2?accountid=14656 5. Ryan, A. (2014). A framework for systemic design. Formakademisk, 7(4)https://doi.org/10.7577/formakademisk.787 6. Data Enabled design, retieved March 30, 2021, from https://www.tue.nl/en/our-university/departments/industrial-design/innovation/projects/systemic-change/data-enabled-design/#top, 7. Bogers, S.,Frens, J., Kollenburg, J., Deckers, E. and Hummels, C.,(2016) Connected Baby Bottle: A Design Case Study Towards a Framework for Data-Enabled Design, DIS '16: Proceedings of the 2016 ACM Conference on Designing Interactive Systems, pp301-311,https://doi-org.ezproxy.library.ubc.ca/10.1145/2901790.2901855 8. Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard business review, 93(10), 96-114. 9. Shaw, M. J., & Fox, M. S. (1993). Distributed artificial intelligence for group decision support: integration of problem solving, coordination, and learning. Decision Support Systems, 9(4), 349-367. 10. Nunes, M. L., Pereira, A. C., & Alves, A. C. (2017). Smart products development approaches for Industry 4.0. Procedia manufacturing, 13, 1215-1222.