Internet of connected ServGoods: Considerations, consequences and concerns

Abstract

In an earlier paper (Tien 2012), the author augurs that, in contrast to the first and second industrial revolutions which respectively focused on the development and the mass production of goods, the next – or third – industrial revolution is focused on the integration of services and/or goods; it began in this second decade of the 21st Century. The Third Industrial Revolution (TIR) is underpinned by the integration or mass customization of services and/or goods. The benefits of real-time mass customization cannot be over-stated as goods and services become indistinguishable and are co-produced as “ServGoods”, resulting in an overwhelming economic advantage to the industrialized countries where the consuming customers are at the same time the co-producing producers. Adding sensors to these ServGoods and letting them connect or communicate among themselves or with other ServGoods can result in an Internet of Things (i.e., connected ServGoods). A number of considerations, consequences and concerns relating to such an Internet of Connected ServGoods are discussed herein.

Keywords

Goods / services / ServGoods / sensors / Big Data / decision analytics / adaptive services / digital manufacturing / value chain / supply chain / demand chain / mass production / mass customization / industrial revolution / Internet of Things

References

[1] Barbot S., Lapusta N., Avouac J.-P.. Under the hood of the earthquake machine: toward predictive modeling of the seismic cycle. Science, 2012, 336: 707-710.

[2] Bifet A.. Mining big data in real time. Informatica, 2013, 37: 15-20.

[3] Carr N.. The Shallows: What the Internet Is Doing to Our Brains, 2010, New York, NY: Norton

[4] Davis S.M.. Future Perfect, 1987, MA: Addison-Wesley Publishing

[5] Delen D., Demirkan H.. Data, information and analytics as services. Decision Support Systems, 2013, 55: 359-363.

[6] Dombrowski U., Wagner T.. Mental strain as field of action in the 4th industrial revolution. Proceedings of the 47th CIRP Conference on Manufacturing Systems, 2014, 17: 100-105.

[7] Hattori H., Nakajima Y., Ishida T.. Learning from humans: agent modeling with individual human behaviors. IEEE Transactions on Systems, Man and Cybernetics — Part A, 2011, 41(1): 1-9.

[8] Hipel K.W., Jamshidi M.M., Tien J.M., White C.C.. The future of systems, man and cybernetics: application domains and research methods. IEEE Transactions on Systems, Man, and Cybernetics Part C, 2007, 30(2): 213-218.

[9] Lavalle S., Lesser E., Shockley R., Hopkins M.S., Kruschwitz N.. Big data, analytics and the path from insights to value. MIT Sloan Management Review, 2011, 52(2): 21-31.

[10] Lazer D., Kennedy R., King G., Vespignani A.. The parable of Google flu: traps in big data analysis. Science, 2013, 343: 1203-1204.

[11] Manyika J., Chui M., Bughin J., Brown B., Dobbs R., Roxbury C., Byers A. H.. Big Data: The Next Frontier for Innovation, Competition, and Productivity, 2011, New York, NY: McKinsey Global Institute

[12] MIT The 50 smartest companies in 2014, 2014

[13] Mlodinow L.. Subliminal: How Your Unconscious Mind Rules Your Behavior, 2012, New York, NY: Pantheon Books

[14] Olhager J.. Strategic positioning of the order penetration point. International Journal of Production Economics, 2003, 85(3): 319-329.

[15] Porter M. E., Heppelmann J. E.. How smart, connected products are transforming competition. Harvard Business Review, 2014, 3: 1-23.

[16] Rifkin J.. The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World, 2011, New York, NY: Palgrave Macmillan

[17] Rubinstein I. S.. Big data: the end of privacy or a new beginning, 2013

[18] Segall P.. Understanding earthquakes. Science, 2012, 336: 676-710.

[19] Siegel E.. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, 2013, New York, NY: John Wiley and Sons

[20] Shi Y.. Big data: history, current status, and challenges going forward. The Bridge, 2014, 44(4): 6-11.

[21] Taleb N.N.. The Black Swan, 2010, 2nd New York, NY: Random House, Inc.

[22] Tien J.M.. Individual-centered education: an any one, any time, any where approach to engineering education. IEEE Transactions on Systems, Man, and Cybernetics Part C: Special Issue on Systems Engineering Education, 2000, 30(2): 1-6.

[23] Tien J.M.. Toward a decision informatics paradigm: a real-time information based approach to decision making. IEEE Transactions on Systems, Man and Cybernetics, Part C: Special Issue, 2003, 33(1): 102-113.

[24] Tien J.M.. On integration and adaptation in complex service systems. Journal of Systems Science and Systems Engineering, 2008, 17(2): 1-31.

[25] Tien J.M.. Manufacturing and services: from mass production to mass customization. Journal of Systems Science and Systems Engineering, 2011, 20(2): 129-154.

[26] Tien J.M.. The next industrial revolution: integrated services and goods. Journal of Systems Science and Systems Engineering, 2012, 21(3): 257-296.

[27] Tien J.M.. Big Data: unleashing information. Journal of Systems Science and Systems Engineering, 2013, 22(3): 127-151.

[28] Tien J.M.. Overview of big data: a US perspective. The Bridge, 2014, 44(4): 12-19.

[29] Tien J.M., Berg D.. A case for service systems engineering. International Journal of Systems Engineering, 2003, 12(1): 13-39.

[30] Tien J.M., Cahn M.F.. An Evaluation of the Wilmington Management of Demand Program, 1981, Washington, DC: National Institute of Justice

[31] Tien J.M., Krishnamurthy A., Yasar A.. Towards real time management of supply and demand chains. Journal of Systems Science and Systems Engineering, 2004, 13(3): 257-278.

[32] Yasar A.. Real-Time and Simultaneous Management of Supply and Demand Chains, 2005, Troy, NY: Rensselaer Polytechnic Institute Ph.D. Thesis

[33] Zikopoulos P.C., Eaton C., DeRoos D., Deutsch T., Lapis G.. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, 2012, New York, NY: The McGraw-Hill Companies