Inventory management

This research investigates the impact of inventory management on company performance, specifically focusing on Return on Assets (ROA) andReturn on Equity (ROE) across different life cycle stages. Utilizing panel data from 192 firms listed on the Tehran Stock Exchange over the period from 2019 to 2023, the study employs regression analysis to explore these relationships. The findings reveal significant influences of inventory management on financial performance metrics. Notably, while effective inventory management enhances ROE, its relationship with ROA is more complex, indicating potential inefficiencies in larger firms. Specifically, a one-unit increase in inventory management correlates with a decrease in ROA but positively affects ROE. The analysis further shows that during growth and maturity phases, there is a statistically significant negative relationship between inventory management and performance metrics, whereas a positive relationship is observed during the decline phase. Additionally, control variables such as financial leverage consistently demonstrate negative correlations with both ROA and ROE. This study contributes valuable insights into the intricate dynamics between inventory management and company performance across various life cycle stages

Efficient management of liquidity and inventory is a critical challenge in production planning, especially for organizations aiming to optimize financial performance while mitigating risks. This study introduces a novel mathematical model that integrates accounts receivable financing into production planning. Unlike traditional models, our approach explicitly incorporates parameters such as cash inflows, advance payments, and bank discount rates, while addressing constraints related to liquidity, inventory balance, and credit limits. The objective function is designed to maximize liquidity at the end of the planning horizon, ensuring financial stability and operational efficiency. The model employs advanced optimization techniques, including linear and nonlinear programming, to provide actionable insights for decision-makers. Sensitivity analyses highlight its adaptability to dynamic market conditions, such as demand fluctuations and interest rate changes. The results demonstrate the model’s potential to reduce financial risks, improve cash flow, and optimize resource allocation across periods. This work serves as a practical framework for managers in diverse industries, offering a comprehensive solution to production planning challenges. Limitations and future research opportunities are also discussed to inspire further advancements