Advertisement

Big Bang Based Decision Automation

On the Implementation of Innovative Methods, Discovered by Top-Level Research, for Automatized Decisions in Replenishment, Price Optimization, and Campaign Management
  • Mareike Clasen
  • Michael Milnik
Chapter

Abstract

In the following contribution, an innovative artificial intelligence technology will be introduced. It was developed by international top-level researchers and has found successful application in retail for years now. Future events in complex business environments can be recognized and precisely assessed by this artificial intelligence, and the insights can serve as basis for founded decisions. Further, a high degree of automatization can serve to reduce strain on business operations, while simultaneously creating more efficient processes.

This contribution highlights the implementation of statistical models and procedures that take all influencing factors and their interdependencies into account to calculate forecasts. In addition, the economic advantage by including variable costs and business targets into the optimization will be portrayed in particular. The area of application, the replenishment sector, as well as many other examples will be introduced.

This contribution aims at an audience consisting replenishment, purchase, marketing, and sales experts and responsible persons, as well as all those that are interested in an individual assessment of their customers, but do not have the necessary experience or methods to process the data at their disposal.

Keywords

Artificial intelligence Automatization Forecast calculation Machine learning Neuronal networks Predictive analytics 

References

  1. Baehr S et al (2015) Online-analysis of hits in the Belle-II pixel detector for separation of slow pions from background. J Phys Conf Ser 664:092001.  https://doi.org/10.1088/1742-6596/664/9/092001 CrossRefGoogle Scholar
  2. Beyer M (2011) Das neue Business Intelligence. BI wird schlauer, mobiler und vorausschauender, www.cio.de. Accessed 18 Oct 2017
  3. Blue Yonder (2017) Sechs Gründe, warum Lebensmittelhändler schnellere Entscheidungen treffen müssen. https://www.blue-yonder.com/de/Studie-Sechs-Gruende-warum-Lebensmittelhaendler-schnellere-Entscheidungen-treffen-muessen. Accessed 16 Oct 2017
  4. FAO (2015) Food loss and waste facts. http://www.fao.org/resources/infographics/infographics-details/en/c/317265/. Accessed 16 Oct 2017
  5. Feindt M et al (2011) A hierarchical NeuroBayes-based algorithm for full reconstruction of B mesons at B factories. http://arxiv.org/abs/1102.3876v2. Accessed 16 Oct 2017
  6. Gartner (2012) Analytic value escalator. https://www.flickr.com/photos/27772229@N07/8267855748/. Accessed 16 Oct 2017
  7. Manta C (2009) Hintergrund BI. Besserer ROI durch predictive analytics. www.cio.de. Accessed 16 Oct 2017
  8. Morris HD (2003) Predictive analytics and ROI: lessons from IDC’s financial impact study, IDCGoogle Scholar
  9. Noleppa S et al (2015) WWF Studie Das Grosse Wegschmeissen. http://www.wwf.de/fileadmin/fm-wwf/Publikationen-PDF/WWF_Studie_Das_grosse_Wegschmeissen.pdf. Accessed 16 Oct 2017
  10. O.V. (o.j.) Cost of hard drive storage space. http://ns1758.ca/winch/winchest.html. Accessed 18 Oct 2017
  11. Spies R (2011) Es wächst zusammen was zusammen gehört. Das Verhältnis von strukturierten und unstrukturierten Daten, IBM Management Forum. http://www-05.ibm.com/de/events/im-forum/. Accessed 18 Oct 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mareike Clasen
    • 1
  • Michael Milnik
    • 1
  1. 1.Blue YonderKarlsruheGermany

Personalised recommendations