Cost-Benefit Model for Smart Items in the Supply Chain

  • Christian Decker
  • Martin Berchtold
  • Leonardo Weiss F. Chaves
  • Michael Beigl
  • Daniel Roehr
  • Till Riedel
  • Monty Beuster
  • Thomas Herzog
  • Daniel Herzig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4952)


The Internet of Things aims to connect networked information systems and real-world business processes. Technologies, such as barcodes, radio transponders (RFID) and wireless sensor networks, which are directly attached to physical items and assets transform objects into Smart Items. These Smart Items deliver the data to realize the accurate real-time representation of ’things’ within the information systems. In particular for supply chain applications this allows monitoring and control throughout the entire process involving suppliers, customers and shippers. However, the problem remains what Smart Item technology should be favored in a concrete application in order to implement the Internet of Things most suitable. This paper analyzes different types of Smart Item technology within a typical logistics scenario. We develop a quantification cost model for Smart Items in order to evaluate the different views of the supplier, customer and shipper. Finally, we conclude a criterion, which supports decision makers to estimate the benefit of the Smart Items. Our approach is justified using performance numbers from a supply chain case with perishable goods. Further, we investigate the model through a selection of model parameters, e.g. the technology price, fix costs and utility, and illustrate them in a second use case. We also provide guidelines how to estimate parameters for use in our cost formula to ensure practical applicability of the model. The overall results reveal that the model is highly adaptable to various use cases and practical.


Supply Chain Sensor Node Wireless Sensor Network Cost Model Enterprise Resource Planning System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christian Decker
    • 1
  • Martin Berchtold
    • 1
  • Leonardo Weiss F. Chaves
    • 2
  • Michael Beigl
    • 3
  • Daniel Roehr
    • 3
  • Till Riedel
    • 1
  • Monty Beuster
    • 3
  • Thomas Herzog
    • 4
  • Daniel Herzig
    • 4
  1. 1.Telecooperation Office (TecO)University of Karlsruhe 
  2. 2.SAP Research CEC Karlsruhe 
  3. 3.DUSLabTechnical University of Braunschweig 
  4. 4.University of Karlsruhe 

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