Management of Inventory

Management of inventory is another important aspect of a business. The goal here is to maintain an optimal level of inventory. If the inventory levels are too low, it can lead to a loss of sales because of stock-outs. At the same time, if there is too much inventory, it indicates that the firm's excess capital is tied up in inventory, which could have otherwise been used to invest in short-term securities, or to clear debt.

To evaluate inventory management, the manager can observe the average days of inventory, or inventory turnover ratio. If the average days of inventory is increasing (turnover ratio decreasing), this indicates a very large inventory and the manager should take steps to bring it down to a reasonable level.

The average days of inventory and inventory turnover can also be compared to peers in the industry and with the company's past performance to see the trends. Comparing these ratios with companies in other industries can provide false signals, as each industry will have a different benchmark. For example, retail stores will have a high turnover ratio compared to automobile show rooms.

Companies also employ innovative techniques and technology to bring their levels of inventory down. One such approach is the economic-order quantity-reorder point, under which the company forecasts demand. These forecasts help the company determine the levels at which the new inventory is ordered. Another method is the just-in-time method, which is based on the philosophy of producing and delivering finished goods just in time for selling.

The key to effectively managing inventory levels is to strike a balance between the risks and returns from overinvestment and underinvestment in inventory.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.