INTEGRATED SMART INDUSTRY 4.0-BASED DECISION SUPPORT FOR OPTIMIZING TACTICAL-OPERATIONAL DECISIONS: CASE STUDY

The paper considers a model for an intelligent sales-distribution network that makes decisions related to marketing, inventory control, and pricing in the entire network. Considering the importance of staying in the competitive world, using machine learning tools is of great importance. A decision support system has been used to forecast the demand. Actual sales data is inserted into the decision support system, and then, using available evidence and past performance, a decision is made to estimate the demand. Then, an intelligent algorithm for solving the mathematical model is raised, which performs the model real-time using the data received from the decision support system and provides updated results. Sensitivity analysis was performed for validation. According to the obtained results, intelligent has a great impact on the objective function values and the use of real data helps in the network performance leading to cost reduction and lost sales. Two innovations used have not been investigated simultaneously in any of the previous studies.