Modeling uncertainty management strategies in the supply chain: Causal cognitive mapping approach

Introduction: Uncertainty about possible future events is one of the most important factors affecting supply chain decisions. This factor is sometimes considered as one of the objective dimensions of the external environment and sometimes as an interpretation of the perception process that helps decision makers in conceptualizing the environment. Many researchers are looking for uncertainty management solutions in order to make effective decisions. The correct management of the factors that cause uncertainty takes place when when strategies are designed for this purpose and communication and interaction between them are taken into consideration. But the review of the studies done on uncertainty management shows that these studies lack a coherent structure to understand the conditions affecting the optimal management of uncertainty. The proposed models often pointed to separate activities and different tasks in the chain, and different researchers discussed the uncertainty of the supply chain according to their research field. This issue causes industrial managers to have problems in understanding how to implement and use uncertainty management strategies, and after spending a lot of money, they cannot achieve the expected results from uncertainty management. Especially in the food industry, due to the wide variety of products and continuous changes in customers' tastes, there is intense competition in this industry. For success in this competition, it is necessary to manage the uncertainties governing this industry well. Therefore, the aim of this research is to identify the factors that cause uncertainty in the supply chain of the food industry, as well as to identify uncertainty management strategies and to examine the cause and effect relationships between these strategies in order to prioritize the necessary activities in order to improve the performance of the supply chain and policy making in this field.
Methodology: This research is applied in terms of purpose, because the purpose of this research is to apply and test theoretical concepts in the situations of real issues of uncertainty management in the supply chain of the food industry so that a solution can be found to strengthen the infrastructure of uncertainty management. In addition, in terms of data obtaining method, it is a descriptive research because in this research, the conditions and relationships between different factors in the field of supply chain uncertainty management have been described and interpreted. In the framework of mixed research methodology, the process of modeling uncertainty management strategies in this article is done in three stages. In the first stage, based on the subject literature and analysis of semi-structured interviews with experts, the initial conceptual framework of uncertainty management strategies of the food industry supply chain is extracted. In the second stage, the mental model of food industry experts is extracted based on semi-structured interviews and the results are analyzed based on causal mapping method. In the third stage, the relevant integration map is extracted and after performing the necessary analysis, a model is presented for examining uncertainty management strategies in the food industry. At this stage, causal mapping technique has been used for analysis and modeling. The target statistical population of the research consists of production and factory managers as well as CEOs of the food industry who have a higher than bachelor's degree and have sufficient experience in their field of work.
Results and discussion: In this research, after identifying the sources of uncertainty and their management strategies through the review of various researches, the relationship and interaction between the 21 identified strategies was studied through interviews with 9 experts from the food industry. Then, by completing the matrix of relationships between factors based on the opinion of experts, the data obtained from these matrices were analyzed and explained through the method of causal mapping. According to the findings of the research, these 21 factors can be classified into five groups or clusters, one of the clusters was removed after path analysis, and four clusters containing 18 factors remained. These four clusters have a high interaction with each other and the positive synergy between them improves the performance of companies and supply chains in the field of uncertainty management. These four clusters are: improving systems and methods, improving decisions, product management and process management. Each of these factors, from a unique angle, helps the supply chain in the management of uncertainties, and if the relationships between them are properly adjusted, we can witness the growth of supply chain performance.