Smart Product-Service Systems Solution Design via Hybrid Crowd Sensing Approach

The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively.

[1]  Angappa Gunasekaran,et al.  IoT powered servitization of manufacturing – an exploratory case study , 2017 .

[2]  J. Wincent,et al.  Mastering the Transition to Product-Service Provision: Insights into Business Models, Learning Activities, and Capabilities , 2014 .

[3]  Lei Shu,et al.  When Mobile Crowd Sensing Meets Traditional Industry , 2017, IEEE Access.

[4]  Xing Xie,et al.  TaskMe: Toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing , 2017, Int. J. Hum. Comput. Stud..

[5]  Chen-Khong Tham,et al.  Fairness and social welfare in incentivizing participatory sensing , 2012, 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[6]  Min-Jun Kim,et al.  Design of informatics-based services in manufacturing industries: case studies using large vehicle-related databases , 2015, Journal of Intelligent Manufacturing.

[7]  Mirco Musolesi,et al.  Urban Sensing : Opportunistic or Participatory ? , 2007 .

[8]  Baik Hoh,et al.  Dynamic pricing incentive for participatory sensing , 2010, Pervasive Mob. Comput..

[9]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[10]  Andrew Raij,et al.  A Survey of Incentive Techniques for Mobile Crowd Sensing , 2015, IEEE Internet of Things Journal.

[11]  Jian Ma,et al.  Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities , 2018, IEEE Transactions on Industrial Informatics.

[12]  Heide-Rose Vatterrott,et al.  Identification of structural landmarks in a park using movement data collected in a location-based game , 2013, COMP '13.

[13]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[14]  Kim-Kwang Raymond Choo,et al.  Mobile crowd sensing of human-like intelligence using social sensors: A survey , 2017, Neurocomputing.

[15]  Stoyan Tanev,et al.  A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services , 2015, Expert Syst. Appl..

[16]  Ray Y. Zhong,et al.  User-experience Based Product Development for Mass Personalization: A Case Study , 2017 .

[17]  Oksana Mont,et al.  Clarifying the Concept of Product-Service System , 2002 .

[18]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[19]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

[20]  Chun-Hsien Chen,et al.  Towards an automatic engineering change management in smart product-service systems - A DSM-based learning approach , 2019, Adv. Eng. Informatics.

[21]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[22]  F. Tao,et al.  Long / short ‐ term utility aware optimal selection of manufacturing service composition towards Industrial , 2019 .

[23]  Zhu Wang,et al.  Mobile Crowd Sensing and Computing , 2015, ACM Comput. Surv..

[24]  Kehua Guo,et al.  CSF: Crowdsourcing semantic fusion for heterogeneous media big data in the internet of things , 2017, Inf. Fusion.

[25]  Ken Fukuda,et al.  Enhancing products and services using smart appliance networks , 2016 .

[26]  Yunhao Liu,et al.  Incentives for Mobile Crowd Sensing: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[27]  Yan Yu,et al.  A Real-Time Big Data Gathering Algorithm Based on Indoor Wireless Sensor Networks for Risk Analysis of Industrial Operations , 2016, IEEE Transactions on Industrial Informatics.

[28]  Huadong Ma,et al.  Opportunities in mobile crowd sensing , 2014, IEEE Communications Magazine.

[29]  Boi Faltings,et al.  Incentive Mechanisms for Community Sensing , 2014, IEEE Transactions on Computers.

[30]  Fan Min,et al.  Three-way recommender systems based on random forests , 2016, Knowl. Based Syst..

[31]  Chao Liu,et al.  Personalized product configuration framework in an adaptable open architecture product platform , 2017 .

[32]  Bin Guo,et al.  From participatory sensing to Mobile Crowd Sensing , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[33]  Zhiwen Liu,et al.  A framework integrating interval-valued hesitant fuzzy DEMATEL method to capture and evaluate co-creative value propositions for smart PSS , 2019, Journal of Cleaner Production.

[34]  Chun-Hsien Chen,et al.  Industrial smart product-service systems solution design via hybrid concerns , 2019, Procedia CIRP.

[35]  Michele Germani,et al.  From PSS to CPS Design: A Real Industrial Use Case Toward Industry 4.0 , 2017 .

[36]  Christopher L. Magee,et al.  Bringing Service Design to manufacturing companies: Integrating PSS and Service Design approaches , 2017 .

[37]  Mianxiong Dong,et al.  QUOIN: Incentive Mechanisms for Crowd Sensing Networks , 2018, IEEE Network.

[38]  M. Porter,et al.  How Smart, Connected Products Are Transforming Competition , 2014 .

[39]  M. Taisch,et al.  The value of Big Data in servitization , 2015 .

[40]  Y YenNeil,et al.  Mobile Crowd Sensing and Computing , 2015 .

[41]  Ming Li,et al.  Electric Vehicle Route Selection and Charging Navigation Strategy Based on Crowd Sensing , 2017, IEEE Transactions on Industrial Informatics.

[42]  Xun Xu,et al.  A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment , 2018, Journal of Intelligent Manufacturing.

[43]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[44]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[45]  Dimitris Mourtzis,et al.  A Lean PSS design and evaluation framework supported by KPI monitoring and context sensitivity tools , 2018 .

[46]  Pai Zheng,et al.  A systematic design approach for service innovation of smart product-service systems , 2018, Journal of Cleaner Production.

[47]  Erik Jan Hultink,et al.  How Today’s Consumers Perceive Tomorrow’s Smart Products , 2007 .

[48]  A. Tukker,et al.  Product-services as a research field: past, present and future. Reflections from a decade of research , 2006 .

[49]  Oksana Mont.  Product-Service Systems: Panacea or Myth? , 2008 .

[50]  Xun Xu,et al.  A system framework for OKP product planning in a cloud-based design environment , 2017 .

[51]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[52]  Kevin Ashton,et al.  That ‘Internet of Things’ Thing , 1999 .

[53]  Ruth Mugge,et al.  The Design of Smart Product-Service Systems (PSSs): An Exploration of Design Characteristics , 2015 .

[54]  Athanasios V. Vasilakos,et al.  A Manufacturing Big Data Solution for Active Preventive Maintenance , 2017, IEEE Transactions on Industrial Informatics.

[55]  Yuhua Qian,et al.  Three-way decisions with reflexive probabilistic rough fuzzy sets , 2018, Granular Computing.