Purpose: In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.Design/methodology/approach: A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.Findings: The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.Originality/value: Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

Globally, power projects are prone to cost overrun projects. Within the body of knowledge, previous studies have paid less attention to predicting the cost overruns to assist contingency cost planning. Particularly, in thermal power plant projects (TPPPs), the enormous risks involved in their delivery undermine the accuracy of cost overrun prediction. To prevent cost overrun in thermal power plant projects, these risks need to be accounted for by employing sophisticated cost overrun prediction techniques. This study aims to develop a hybrid predictive-probabilistic-based model (HPPM) that integrates a genetic programming technique with Monte Carlo simulation (MCS). The HPPM was proposed based on the data collected from TPPPs in Bangladesh. Also, the sensitivity of the HPPM was examined to identify the critical risks in cost overruns simulation. The simulation outcomes show that 40.48% of a project’s initial estimated budget was the most probable to cost overrun, while the maximum cost overrun will not exceed 75% with 90% confidence. Practically, the analysis will sensitize project managers to emphasize thermal plants’ budget accuracy not only at the initial project delivery phase but throughout the project life cycle. Theoretically, the HPPM could be employed for cost overrun prediction in other types of power plant projects.