Publication detail

Spatially distributed production data for supply chain models - Forecasting with hazardous waste

PAVLAS, M. ŠOMPLÁK, R. SMEJKALOVÁ, V. NEVRLÝ, V. SZÁSZIOVÁ, L. KŮDELA, J. POPELA, P.

Original Title

Spatially distributed production data for supply chain models - Forecasting with hazardous waste

English Title

Spatially distributed production data for supply chain models - Forecasting with hazardous waste

Type

journal article

Language

en

Original Abstract

This paper introduces a novel approach to forecasting future commodity production in hundreds of nodes, which represents a key input for many applications of supply-chain models. A mathematical model was proposed to handle the problem of forecasting with spatially distributed and uncertain data. It is derived from the principle of regression analysis and extended by a data reconciliation technique. Additional areal constraints guarantee mass conservation in a tree-like structure, which reflects the organisational arrangement of an investigated region. The proposed model was tested through a case study, where future production of hazardous waste suitable for thermal treatment was forecasted in 206 base-nodes, 14 superior nodes and one apex. Based on an extensive investigation of historical data, it was revealed that extrapolations carried out at different levels of the hierarchical organisational structure lead to inconsistent forecasts. The differences between forecasts reached up to 50%. In addition to this, mass conservation was violated. Significant corrections were performed by computations utilising the formulated model. The corrections ranged from between 0% and 12% for 90% of nodes. There were 17 nodes, where massive adjustments of up to 30% were inevitable.

English abstract

This paper introduces a novel approach to forecasting future commodity production in hundreds of nodes, which represents a key input for many applications of supply-chain models. A mathematical model was proposed to handle the problem of forecasting with spatially distributed and uncertain data. It is derived from the principle of regression analysis and extended by a data reconciliation technique. Additional areal constraints guarantee mass conservation in a tree-like structure, which reflects the organisational arrangement of an investigated region. The proposed model was tested through a case study, where future production of hazardous waste suitable for thermal treatment was forecasted in 206 base-nodes, 14 superior nodes and one apex. Based on an extensive investigation of historical data, it was revealed that extrapolations carried out at different levels of the hierarchical organisational structure lead to inconsistent forecasts. The differences between forecasts reached up to 50%. In addition to this, mass conservation was violated. Significant corrections were performed by computations utilising the formulated model. The corrections ranged from between 0% and 12% for 90% of nodes. There were 17 nodes, where massive adjustments of up to 30% were inevitable.

Keywords

Supply chain; forecasting; extrapolation; short time series; hazardous waste; thermal treatment

Released

14.07.2017

Publisher

Elsevier Ltd

Location

UK

Pages from

1317

Pages to

1328

Pages count

11