# Supplier Risk in Supply Chain Risk Management: An Updated Conceptual Framework
## 3 Theoretical Background
### SCRM from the Supplier Management Approach  
Supplier-related risks constitute one of the most significant sources of risk in a supply chain and must be taken into account when formulating SCRM strategies [36], although it is more appropriate to consider them as \"risk types\" rather than \"risk factors\" [12]. Thus, aiming to deal with this type of risk effectively, [PERSON] [1] shows five main and interrelated issues (or dimensions) that comprise supplier management: network design, supplier selection, order allocation, contract management, and supplier relationships. These five dimensions will be detailed below, in light of the literature on SCRM. Design: This comprises the analysis of different configurations of facilities and resources that can be explored in an integrated manner in the supply chain, such as suppliers, warehouses, production, and transportation planning, among others [1]. For many authors, this is the first step towards efficient disruption management [19; 37] as the supply chain's design must be considered to encompass risks due to supplier complexity and supplier dependence [38]. Due to the density of contemporary chains, one can point out in the literature growing efforts--albeit from beginners and subject to further exploration--to apply programming systems based on artificial intelligence (AI) to supply chain design in order to automate the decision-making process in uncertain environments, such as stochastic programming, robust optimization, fuzzy programming, machine learning, rationalization,data mining, agent-based modeling, and network-based approaches, such as Petri nets, Bayesian networks, Markov networks, and Monte Carlo simulation [14; 16]. Selection: This aims to identify suppliers that best fit the risk criteria determined by the customer, such as risks of stockouts, delivery delays, or low-quality products, generally involving the application of mathematical methods and models [1]. In this case, the model's typical answer is a performance ranking to classify these suppliers, whether based on risk profile, resilience profile, or issues related to sustainability and the environment [17]. Supplier selection is the most crucial decision variable for the success of a supply chain [6], thus representing the majority of articles on SCRM [12]. In this sense, [PERSON] et al. [17] researched articles that present quantitative models in SCRM, where they found that 25% of the works deal with the problem of supplier selection in an uncertain environment. In parallel, in the literature surveyed by this research, applications of several multi-criteria decision aid methods (MCDMs) to the supplier selection problem were identified in the last ten years: AHP [15; 32; 39; 40], ANP [20], GRA [21; 23], TOPSIS [6; 40], DEA [41; 42], MULTIMOORA [43], BWM [44], and VIKOR [8], in addition to simulation models based on fuzzy logic, Bayesian networks, and other programming techniques [13; 45; 46; 47; 48; 49]. Allocation: This concerns understanding the risks related to purchase orders and inventory management caused by uncertainties in demand, inventory, lead time, capacity, and costs [1]. The growing trend of applying lean methodologies, such as the just-in-time concept, causes organizations to practice low inventory levels and reduce safety stocks, which makes them more vulnerable to market fluctuations, which are not unusual incidents [37]. Several authors propose mathematical models to optimize order allocation in this context, thus reducing risks [36; 44; 50]. [PERSON] et al. [51] consider production planning and control (PPC) as a catalytic area for SCRM, which, in turn, is a performance catalytic process. Therefore, they suggest the implementation of improvements related to PPC, such as a focus on the process, reduction in batch size, application of kanban, and use of product tracking systems, via RFID or barcode, to cover the risks arising from suppliers and other external agents of the supply chain.