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Toward Dynamic Expiration Dates: An Architectural Study

  • Åse Jevinger
  • Paul Davidsson
Conference paper
Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

The durability of perishable food varies due to different storage and handling conditions during the supply chain as well as final consumer activities. If the durability of the individual products can be estimated, dynamic expiry dates may be developed and used to prevent food waste, ensure quality, and improve supply chain activities etc. Depending on the system architecture used for such a service, different qualities can be obtained in terms of usability, accuracy, security etc. This paper presents a novel approach for how to identify and select the most suitable system architectures of a dynamic expiry date service. The approach is illustrated by focusing on one of the potential user groups, the supply chain managers. The approach consists of three steps: (i) identify the potential architectures, (ii) filter out the least relevant candidates by applying a specified set of principles, and (iii) perform an analytic hierarchy process (AHP) based on a set of quality attributes.

Keywords

Dynamic expiry date Perishables Architecture AHP Supply chain management 

Notes

Acknowledgment

The work presented in this paper is a part of an interdisciplinary project called “Minimized food waste with dynamic expiry dates,” funded by the TvärLivs programme managed by VINNOVA (Swedish Governmental Agency for Innovation Systems).

References

  1. Bartels PV, Tromp SO, Rijgersberg H, Kreft F (2010) Improvement of sustainability in the perishable food supply chain by using communicative packaging devices. In: Towards effective food chains: models and applications. Academic Publishers, WageningenGoogle Scholar
  2. Bhushan N, Gummaraju K (2002) A petri net based simulation approach for evaluating benefits of time temperature indicator and wireless technologies in perishable goods retail management. In: FOODSIM’2002 the second international conference on simulation and modeling in the food and bio-industryGoogle Scholar
  3. Clements P, Kazman R, Klein M (2001) Evaluating software architectures. Addison-Wesley, ReadingGoogle Scholar
  4. Dobon A, Cordero P, Kreft F, Østergaard SR, Robertsson M, Smolander M, Hortal M (2011) The sustainability of communicative packaging concepts in the food supply chain. A case study: part 1. Life cycle assessment. Int J Life Cycle Assess 16:168–177CrossRefGoogle Scholar
  5. Gustavsson J, Cederberg C, Sonesson U, van Otterdijk R, Meybeck A (2011) Global food losses and food waste. Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  6. Jedermann R, Lang W (2008) The benefits of embedded intelligence -tasks and applications for ubiquitous computing in logistics. The internet of things. Springer, Berlin, pp 105–122Google Scholar
  7. Jedermann R, Ruiz-Garcia L, Lang W (2009) Spatial temperature profiling by semi-passive RFID loggers for perishable food transportation. Comput Electron Agric 65:145–154CrossRefGoogle Scholar
  8. Jevinger Å, Davidsson P, Persson JA (2011) A framework for agent-based modeling of intelligent goods. Agents in principle, agents in practice. Springer, Berlin, Heidelberg, pp 97–112Google Scholar
  9. Jol S, Kassianenko A, Wszol K, Oggel J (2006) Issues in time and temperature abuse of refrigerated foods. Food Saf 11(6):30–32Google Scholar
  10. Larsson M (2004) Predicting quality attributes in component-based software systems. Dissertation, Mälardalen University, VästeråsGoogle Scholar
  11. Likar K, Jevšnik M (2006) Cold chain maintaining in food trade. Food Control 17:108–113CrossRefGoogle Scholar
  12. López TS, Ranasinghe DC, Patkai B, McFarlane D (2011) Taxonomy, technology and applications of smart objects. Inf Syst Front 13:281–300CrossRefGoogle Scholar
  13. Meyer GG, Främling K, Holmström J (2009) Intelligent products—a survey. Comput Ind 60:137–148CrossRefGoogle Scholar
  14. Moureh J, Flick D (2004) Airflow pattern and temperature distribution in a typical refrigerated truck configuration loaded with pallets. Int J Refrig 27:464–474CrossRefGoogle Scholar
  15. O’Brien L, Merson P, Bass L (2007) Quality attributes for service-oriented architectures. In: International workshop on systems development in SOA environments, IEEE Computer SocietyGoogle Scholar
  16. Ruiz-Garcia L, Barreiro P, Rodríguez-Bermejo J, Robla J (2007) Review monitoring the intermodal, refrigerated transport of fruit using sensor networks. Span J Agric Res 5:142–156CrossRefGoogle Scholar
  17. Saaty TL (1980) The analytic hierarchy process. McGraw Hill, New YorkzbMATHGoogle Scholar
  18. Sahin E, Babaï MZ, Dallery Y, Vaillant R (2007) Ensuring supply chain safety through time temperature integrators. Int J Logist Manag 18:102–124CrossRefGoogle Scholar
  19. Svahnberg M, Wohlin C, Lundberg L, Mattsson M (2003) A quality-driven decision-support method for identifying software architecture candidates. Int J Softw Eng Knowl Eng 13:547–573CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Malmö UniversityMalmöSweden

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