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Project scheduling inevitably involves uncertainty. The basic inputs (i.e., time, cost, and resources for each activity) are not deterministic and are affected by various sources of uncertainty. Moreover, there is a causal relationship between these uncertainty sources and project parameters; this causality is not modeled in current state-of-the-art project planning techniques (such as simulation techniques). This paper introduces an approach, using Bayesian network modeling, that addresses both uncertainty and causality in project scheduling. Bayesian networks have been widely used in a range of decision-support applications, but the application to project management is novel. The model presented empowers the traditional critical path method (CPM) to handle uncertainty and also provides explanatory analysis to elicit, represent, and manage different sources of uncertainty in project planning.

Abstract:Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR) is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks.Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs) and supply chain operations reference (SCOR) in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some of the performance metrics. Practical implications:Mangers often use simple qualitative metrics for SCRM. However, combining qualitative and quantitative metrics will be more useful. Industries can recognize the important uncertain metrics by predicting supply chain performance and diagnosing possible happenings.Originality/value: This paper proposed a Bayesian method based on SCOR metrics which has the ability to manage supply chain risks and improve supply chain performance. This is the only presented case study for measuring supply chain performance by SCOR metrics.

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Cognitive Reliability and Error Analysis Method (CREAM) is a common Human Reliability Analysis (HRA) method of second generation. In this paper, to improve the capabilities of CREAM, we propose a probabilistic method based on Bayesian Network (BN) to determine control mode and quantify Human Error Probability (HEP). The BN development process is described in a four‐phase methodology including (i) definition of the nodes and their states; (ii) building the graphical structure; (iii) quantification of BN through assessment of the Conditional Probability Tables (CPT) values and (iv) model validation. Intractability of knowledge acquisition of large CPTs is the most significant limitation of existing BN model of CREAM. So, the main contribution of this paper lies in its application of Recursive Noisy‐OR (RN‐OR) gate to treat large CPTs assessment and ease knowledge acquisition. RN‐OR allows combination of dependent Common Performance Conditions (CPCs). Finally, a quantitative HEP analysis is applied to enable more precise estimation of HEP through a probabilistic approach. Copyright © 2016 John Wiley & Sons, Ltd.