TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Kiefer, Daniel
A1  - Ulmer, Annette
ED  - Kloos, Uwe
ED  - Martínez Madrid, Natividad
ED  - Tullius, Gabriela
T1  - Application of artificial intelligence to optimize forecasting capability in procurement
T2  - Wissenschaftliche Vertiefungskonferenz : Informatik-Konferenz an der Hochschule Reutlingen, 27. November 2019
N2  - The aim of this paper is to show to what extent Artificial Intelligence can be used to optimize forecasting capability in procurement as well as to compare AI with traditional statistic methods. At the same time this article presents the status quo of the research project ANIMATE. The project applies Artificial Intelligence to forecast customer orders in medium-sized companies. 

Precise forecasts are essential for companies. For planning, decision making and controlling. Forecasts are applied, e.g. in the  areas of supply chain, production or purchasing. Medium-sized companies have major challenges in using suitable methods to improve their forecasting ability.

Companies often use proven methods such as classical statistics as the ARIMA algorithm. However, simple statistics often fail while applied for complex non-linear predictions. 

Initial results show that even a simple MLP ANN produces better results than traditional statistic methods. Furthermore, a baseline (Implicit Sales Expectation) of the company was used to compare the performance. This comparison also shows that the proposed AI method is superior. 

Until the developed method becomes part of corporate practice, it must be further  optimized. The model has difficulties with strong declines, for example due to holidays. The authors are certain that the model can be further improved. For example, through more advanced methods, such as a FilterNet, but also through more data, such as external data on holiday periods.
KW  - Artificial Intelligence
KW  - machine learning
KW  - deep learning
KW  - artificial neural network
KW  - forecasting
KW  - time series prediction
Y1  - 2019
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-38706
SN  - 978-3-00-064236-4
SB  - 978-3-00-064236-4
U6  - https://doi.org/10.34645/opus-3870
DO  - https://doi.org/10.34645/opus-3870
SP  - 69
EP  - 80
S1  - 12
PB  - Hochschule Reutlingen
CY  - Reutlingen
ER  -