Coordination in project supply chain based on uncertainty theory

Abstract

In this paper, we investigate channel coordination of project supply chain based on uncertainty theory. The project is conducted by one manufacturer, whose materials are provided by different suppliers. To complete the project before its deadline, the manufacturer needs to consider two elements-the on-site task duration and the material delivery lead time. Due to the lack of historical data, these two elements are assumed as uncertain variables. Through modeling and analyzing, the time-based contract is proved to achieve channel coordination if the manufacturer can decide the following two terms: the targeted material delivery date, and the fraction and the timing of the delayed payment. The manufacturer and his suppliers can have a win-win outcome by contract design. The impacts of variables' uncertainty degrees are discussed as well. Generally speaking, uncertainty degrees' increases bring more risks to the project's completion. However, it is proved that the manufacturer can still keep his profit by changing the bonus and penalty even if the on-site duration and material delivery lead time's uncertainty degrees increase.

A decade of engineering-to-order (2010–2020): Progress and emerging themes

“…The review also underlined a need for a better understanding of the engineering dimension and its interface with the production dimension, as per the twodimensional customer order decoupling point (CODP) conceptualised by Wikner and Rudberg (2005). 13 (Adrodegari et al, 2015;Denicol et al, 2020;Gosling et al, 2013Gosling et al, , 2015aGuillaume et al, 2013;Jansson et al, 2015;Li et al, 2017;Lian and Ke, 2018;Mello et al, 2015aMello et al, , 2017Pacagnella et al, 2019;Pal et al, 2017;Reid et al, 2019) 2 (Birkie et al, 2017;Birkie and Trucco, 2016) 16…”

“…Studies that covered the 'uncertainty/risk' theme aimed at defining methods and developing instruments to identify uncertainty, measure risk and react to them (Ferreira et al, 2018;Gosling et al, 2013bGosling et al, , 2013aGuillaume et al, 2013;Hernadewita and Saleh, 2020;Kayis and Karningsih, 2012;Li et al, 2017;Lian and Ke, 2018;Radke and Tseng, 2012;Reid et al, 2019;Shen et al, 2011;Shurrab et al, 2020aShurrab et al, , 2020bSylla et al, 2020). While the majority of papers in this theme were analytical and theoretical papers, the empirical evidence presented is primarily concerned with illustrating and characterising causes and categories of risk and uncertainty (e.g.…”

“…(Ferreira et al, 2018;Gosling et al, 2013bGosling et al, , 2013aGuillaume et al, 2013;Hernadewita and Saleh, 2020; Ishii et al, 2011;Kayis and Karningsih, 2012;Li et al, 2017;Lian and Ke, 2018;Radke and Tseng, …”

The Use of an Artificial Neural Network in the Process of Choosing the Project Supply Chain by the Principal Contractor

“…All in all, such a chain is inseparably connected with the risk, the largest part of which the value cannot be precisely determined, is borne by the principal contractor. The latest research already includes the analysis of the risk of this chain, but most researchers examine it based on a demand assessment, or take into account its uncertain environment [Lian, Ke 2018]. However, there is a lack of research related to supporting the risk management process that would include assistance in the assessment of specific project supply chains.…”

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“…The Use of an Artificial Neural Network in the Process … 191 chain, but most researchers examine it based on a demand assessment, or take into account its uncertain environment [Lian, Ke 2018]. However, there is a lack of research related to supporting the risk management process that would include assistance in the assessment of specific project supply chains.…”