J 2023

Modelling and Coordination of Supply Networks with Dynamic Analytic Network Process

FIALA, Petr a Renata MAJOVSKÁ

Základní údaje

Originální název

Modelling and Coordination of Supply Networks with Dynamic Analytic Network Process

Název česky

Modelování a koordinace dodavatelských sítí pomocí dynamické verze metody ANP

Autoři

FIALA, Petr (203 Česká republika, garant) a Renata MAJOVSKÁ (203 Česká republika, domácí)

Vydání

LOGOS POLYTECHNIKOS, Jihlava, Vysoká škola polytechnická Jihlava, 2023, 2464-7551

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

50202 Applied Economics, Econometrics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Celý text článku

Kód RIV

RIV/04274644:_____/23:#0001073

Organizační jednotka

Vysoká škola finanční a správní

Klíčová slova česky

Supply networks; Analytic Network Process; time dependent priorities; compositional data; hybrid procedure

Klíčová slova anglicky

Supply networks; Analytic Network Process; time dependent priorities; compositional data; hybrid procedure

Štítky

AR 2023-2024, odmeny_2024, RIV_2024, xJ5

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 13. 2. 2024 09:18, Mgr. Jitka Štruncová

Anotace

ORIG CZ

V originále

Supply chain management is more and more affected by the network and dynamic business environment. There are inefficiencies in supply network behaviour. Coordination and cooperation can significantly improve the efficiency of supply networks. There are some approaches to modelling and analysing the supply dynamics. Important features of this environment are presented in the proposed approach. The combination of network structure modelling and simulating the dynamic behaviour of units in supply network can be a powerful instrument for coordination in dynamic supply networks. The AHP (Analytic Hierarchy Process) method is modified with respect to network structures and the dynamics of the analysed systems. The ANP (Analytic Network Process) method is appropriate for setting priorities in network systems where there are different types of dependencies between evaluation criteria and system elements. However, with time-varying environments in network systems, time-dependent priorities play an increasingly important role. Long-term priorities can be based on time-dependent comparisons of criteria and system elements. For short-term prediction, exponential smoothing of compositional data can be used. The paper proposes a hybrid procedure DNAP (Dynamic Analytic Network Process) that combines and enriches advantages and benefits of both approaches by analysing network systems.

Česky

Supply chain management is more and more affected by the network and dynamic business environment. There are inefficiencies in supply network behaviour. Coordination and cooperation can significantly improve the efficiency of supply networks. There are some approaches to modelling and analysing the supply dynamics. Important features of this environment are presented in the proposed approach. The combination of network structure modelling and simulating the dynamic behaviour of units in supply network can be a powerful instrument for coordination in dynamic supply networks. The AHP (Analytic Hierarchy Process) method is modified with respect to network structures and the dynamics of the analysed systems. The ANP (Analytic Network Process) method is appropriate for setting priorities in network systems where there are different types of dependencies between evaluation criteria and system elements. However, with time-varying environments in network systems, time-dependent priorities play an increasingly important role. Long-term priorities can be based on time-dependent comparisons of criteria and system elements. For short-term prediction, exponential smoothing of compositional data can be used. The paper proposes a hybrid procedure DNAP (Dynamic Analytic Network Process) that combines and enriches advantages and benefits of both approaches by analysing network systems.
Zobrazeno: 30. 10. 2024 04:57