A New Information Infrastructure Approach for End-To-End Supply Chain Management

  1. Simić, Dragan
  2. Calvo-Rolle, José Luis
  3. Villar, José R. 1
  4. Ilin, Vladimir
  5. Simić, Svetislav D.
  6. Simić, Svetlana
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Actas:
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)

ISSN: 2194-5357,  2194-5365

ISBN: 9783030878689,  9783030878696

Año de publicación: 2021

Páginas: 314-323

Tipo: Aportación congreso

DOI: 10.1007/978-3-030-87869-6_30 GOOGLE SCHOLAR lock_openAcceso abierto editor

Objetivos de desarrollo sostenible

Resumen

The hypercompetitive aspects in the contemporary business environment, such as technological progress, changing customers’ demands, economic cycles, and globalization, have the important influence in business organisation and supply chain in multinational companies and large enterprises. Supply chain occupies the enterprises with different information systems. The information infrastructure (II) presents a set of entirely new challenges regarding design and use compared to traditional information systems. This paper mainly investigates the II in organising agility end-to-end supply chain management (SCM). The proposed new II approach is based on good features in data warehouse environment. The proposed approach is not a final solution in great challenges in SCM; nevertheless, its application can easily improve agility and business performance.

Referencias bibliográficas

  • Chu, C.-H., Hayya, J.C.: Buffering decisions under MRP environment: a review. Omega 16(4), 325–331 (1988). https://doi.org/10.1016/0305-0483(88)90069-2
  • Fabbe-Costes, N., Roussat, C., Taylor, M.: Sustainable supply chains: a framework for environmental scanning practices. Int. J. Oper. Prod. Manag. 34(5), 664–694 (2014). https://doi.org/10.1108/IJOPM-10-2012-0446
  • Bangemann, M.: Europe and the Global Information Society – Recommendations to the European Council. Publications Office of the EU, Brussels, European Commission (1994)
  • Hughes, T.P.: Networks of Power. Electrification in Western Society 1880–1930. The Johns Hopkins University Press (1983)
  • Star, S.L., Ruhleder, K.: Steps toward an ecology of infrastructure: design and access for large Information spaces. Inf. Syst. Res. 7(1), 111–134 (1996). https://doi.org/10.1287/isre.7.1.111
  • Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. Wiley, USA (2002)
  • Ciborra, C.U.: From Control to Drift: The Dynamics of Corporate Information Infrastructures. Oxford University Press (2000)
  • DeSanctis, G., Fulk, J.: Shaping Organization Form Communication, Connection, and Community. SAGE Publications Inc, Newbury Park, CA, USA (1999)
  • Groth, L.: Future Organizational Design: The Scope for the IT-based Enterprise. Wiley, Wiley, New Jersey, USA (1999)
  • Imhoff, C., Galemmo, N., Geiger, J.G.: Mastering Data Warehouse Design: Relational and Dimensional Techniques. Wiley, New Jersey, USA (2003)
  • Torlone, R.: Interoperability in data warehouses, 2nd edn. In: Liu, L., Ozsu, M.T. (eds.) Encyclopaedia of Database Systems. Springer, Boston (2018). https://doi.org/10.1007/978-0-387-49616-0
  • An Oracle White Paper: Best Practices for Real-Time Data Warehousing. Oracle (2014)
  • Simić, S., Milutinović, D., Sekulić, S., Simić, D., Simić, S.D., Đorđević, J.: A hybrid case-based reasoning approach to detecting the optimal solution in nurse scheduling problem. Logic J. IGPL 28(2), 226–238 (2020). https://doi.org/10.1093/jigpal/jzy047
  • Simić, D., Kurbalija, V., Budimac, Z.: An application of case-based reasoning in multidimensional database architecture. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 66–75. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45228-7_8
  • Simić, D., Budimac, Z., Kurbalija, V., Ivanović, M.: Case-based reasoning for financial prediction. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 839–841. Springer, Heidelberg (2005). https://doi.org/10.1007/11504894_114
  • Simić, D., Simić, S.: An approach to efficient business intelligent system for financial prediction. Soft Comput. 11(12), 1185–1192 (2007). https://doi.org/10.1007/s00500-007-0156-0
  • Simić, D., Svirčević, V., Corchado, E., Calvo-Rolle, J.L., Simić, S.D., Simić, S.: Modelling material flow using the Milk run and Kanban systems in the automotive industry. Expert Syst. 38(1), e12546 (2021). https://doi.org/10.1111/exsy.12546
  • Simić, D., Svirčević, V., Ilin, V., Simić, S.D., Simić, S.: Particle swarm optimization and pure adaptive search in finish goods’ inventory management. Cybern. Syst. 50(1), 58–77 (2019). https://doi.org/10.1080/01969722.2018.1558014
  • Simić, D., Ilin, V., Svirčević, V., Simić, S.: A hybrid clustering and ranking method for best positioned logistics distribution centre in Balkan Peninsula. Logic J. IGPL 25(6), 991–1005 (2017). https://doi.org/10.1093/jigpal/jzx047
  • Salaheldin, S., Francis, A.: A study on MRP practices in Egyptian manufacturing companies. Int. J. Oper. Prod. Manag. 18(6), 588–611 (1998) https://doi.org/10.1108/01443579810209557
  • Straube, F.: E-Logistik: Ganzheitliches Logistikmanagement. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-642-18599-1
  • Moniruzzaman, M., Kurnia, S., Parkes, A., Maynard, S.B.: Business intelligence and supply chain agility. In: Australasian Conference on Information Systems, Adelaide, South Australia (2015)
  • Sahay, B.S., Ranjan, J.: Real time business intelligence in supply chain analytics. Inf. Manag. Comput. Secur. 16(1), 28–48 (2008). https://doi.org/10.1108/09685220810862733