Evaluation of Variance-Covariance-Matrix Application for Identifying Temporary Demand Shifts in a Forecast Evolution System.

Fabian Brockmann, Klaus Altendorfer

Research output: Contribution to journalConference articlepeer-review

Abstract

Customer provided demand forecasts are in many production systems the basis for production planning and significantly influence the performance of the respective system. To improve the forecast accuracy, forecast correction methods can be applied to mitigate the effects of systematic customer behavior when providing their forecasts. For applying such forecast correction methods, it is important to identify different kinds of systematic customer behavior in generating their forecasts. In this paper we introduce a Variance-Covariance-Matrix analysis, which is based on forecast evolution literature, to identify demand shifting between periods. On the one hand we derive analytically the systematic effect of demand shifts within forecasts on parts of the Variance-Covariance-Matrix. On the other hand, we provide a Monte Carlo simulation to identify the stochastic disturbance effects on the Variance-Covariance-Matrix if stochastic forecast updates are realized. The numerical study shows that this analysis is a promising tool to identify such demand shifting between periods, however, there is still a lot of further research necessary.

Original languageEnglish
Pages (from-to)793-802
Number of pages10
JournalProcedia Computer Science
Volume200
DOIs
Publication statusPublished - 2022
Event3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 - Linz, Austria
Duration: 19 Nov 202121 Nov 2021

Keywords

  • Forecast
  • Forecast Evolution
  • Production Planning
  • Simulation

Fingerprint

Dive into the research topics of 'Evaluation of Variance-Covariance-Matrix Application for Identifying Temporary Demand Shifts in a Forecast Evolution System.'. Together they form a unique fingerprint.

Cite this