inventory control

Value stream mapping because of being able to understand process bottlenecks, as one of the most common tools for analyzing, identifying and eliminating various losses in operational and support processes are used. On the other hand, inventory management, precise control entry and exit of goods, accurate and timely information about the inventories status and planning, reduce product maintenance costs, including important issues organizations have a special look to it to consider the whole objectives and strategies, making procedures and policies that have a positive effect on the overall organization’s economy. In this paper, the use of value stream mapping as a lean production technique, in a production process to improve inventory control is discussed. This is a tool seeks to reduce losses, especially losses related to inventory by mining a bearing manufacturer companies. In this study, the literature of value stream mapping considered. The current state and future state of the company drawn in order to improve production process and losses in the company's future status. In addition, after drawing the mapping, the results of the simulation software Arena and the final report suggests that the use of this technique has an impact on reduce losses and increase inventory turnover.

Objective : The Supply chain plays a key role in adapting the organization to variable conditions and an uncertain future. The selection of appropriate suppliers can significantly increase the competitiveness and ability of a business in the market. One of the essential factors in supply chain optimization is controlling and managing inventory cost. This paper aims to simultaneously optimize supplier selection and order allocation while considering inventory control using a fractional programming approach.  Methodology: The methodology integrates quantitative analytical techniques in a multi-phase approach. First, the most frequent supplier selection criteria are identified with a literature review. The Delphi method was used to select the supplier selection criteria. In the next step, fuzzy Shannon entropy determines criterion weights. Then, fuzzy EDAS calculates supplier performance scores. Finally, fractional programming facilitates supplier selection and order allocation. Results : The most frequent supplier selection criteria were extracted from the literature review. In the Delphi technique, experts ultimately agreed on six key criteria: price, quality, delivery, flexibility, responsiveness, and financial stability. The results of the Shannon entropy analysis indicate that flexibility, with a weight of 0.20, holds the highest relative importance among the criteria. The suppliers score obtained from the fuzzy EDAS method is used as one of the parameters of the mathematical model. Conclusion : The proposed hybrid MADM approach and mathematical model have been validated using empirical data obtained from Sirjan Steel Company. The result shows that the hybrid MADM approach and fractional programming have high accuracy in selecting the best supplier.