Advances in supply chains together with more turbulence in today's business environments, increase the vulnerability of supply chains to disruption. Various cases of supply chain disruptions in recent decades have revealed that besides focusing on efficiency, improving resilience is also crucial for a supply chain to be competitive in the marketplace. The first step in improving supply chain resilience, or in general, improving supply chain risk management, is to identify and assess risks. The approaches toward assessing risk mostly depend on the availability of information about the disruptive risks. These approaches assume a known probability of occurrence of a disruptive risk and its magnitude, or the methods provide ways to estimate these risk parameters. Although probability‐based approaches toward risk modeling work fine for common supply chain disturbances or operational risks, they cannot deal with less frequent or unknown disruptions. In this research, we suggest a consequence-based approach toward the supply chain risk analysis. Instead of focusing on identifying the root cause of risk and determining its characteristics, such as the probability of occurrence and intensity, one concentrates on analyzing the negative impact of the disruption on supply chain performance. A big challenge for performing an efficient consequence-based risk analysis of a supply chain is analyzing a comprehensive set of disruption scenarios that the supply chain may encounter in the future. Also, resilience practices for overcoming supply chain disruptions must be effective under as many disruption scenarios in the future as possible. To address these challenges, we draw on the paradigm of decision making under deep uncertainty (DMDU). This paradigm emerged in the context of climate change adaptation in response to limits to predictability. A foundational idea in this paradigm is to apply models for exploring rather than for predicting the future. Robust decision making (RDM) is one of the approaches in DMDU that focuses on identifying planning strategies that result in satisfactory outcomes across a large set of scenarios regarding the future. We investigate the potential of using RDM for supporting consequence-based risk analysis of a supply chain.
Network-level stress testing of a supply chain plays an important role in resilience practices. However, the common graphical perspective on the structure of a supply chain network, where different network structures are represented by a configuration of nodes (facilities) and links (transport), may ignore key operational aspects of the supply chain that can affect resilience. We proposed an approach in which we compared the resilience of several supply chain structures that differ in three operational aspects including product structure, sourcing strategy, and production strategy.
A successful supply chain resilience practice depends on reliable stress-testing of the supply chain. A reliable stress test of a supply chain depends on the availability of information about the supply chain structure, where relations among different actors of the supply chain are clear. However, the complexity and globalization of today's supply chains make it difficult for decision makers to access data and information, especially from actors more than a few tiers upstream or downstream. Also, as a dynamic system, a supply chain is confronted with changes in structure and configuration over time. Here, there is a need for an approach for stress testing a supply chain that can deal with a lack of information about the structure of the supply chain. We proposed ensemble modeling as a solution. Rather than focusing on a single supply chain structure, this approach involves generating and analyzing an ensemble of structures to identify vulnerability sources requiring resilience practices.

This paper presents a new method for measuring and improving production time and cost performance by applying project management concepts and methods. In the context of production and manufacturing industries, we demonstrate how to improve production performance by considering production as a project with limited time and budget in order to track the production progress at any given point of time. The proposed approach is capable of monitoring cost and time of production implementation in an adaptive and real-time fashion. According to the fact that in the production and manufacturing environment, cost and time of fulfilling customer demand can be considered as a measure of production performance, this research applied Earned Value Analysis from project management and integrated it with Gain Scheduling Fuzzy Control to design an adaptive monitoring system to support real-time control of production cost and time. Gain Scheduling Fuzzy Control was used to adapt the monitoring system with different conditions of the production environment. To the best of authors’ knowledge, this research is a new application of Fuzzy Adaptive Control in the literature of production and project cost-time performance monitoring. The proposed model in this paper is capable of online monitoring of cost and time performance for different products, at different production periods, and machine centers. The proposed method was implemented successfully in a case study. The results indicated a substantial improvement in production time and cost performance.