Essay Sample on Importance of Managing and Controlling the Business Inventory

Introduction

Managing the inventory of any business to ensure profitability requires the management to balance the demand and supply on one sided and then align such with costs. Carrying too much inventory of a product that has less demand in the market will certainly lead to losses of unprecedented proportions. On the other hand, having lesser volumes of stock than the volumes demanded by the market leads to shortages and the business stands to lose opportunities to sell its products and establish the much-needed customer loyalty (do Rego & de Mesquita, 2015). In this regard, business should apply various methods of inventory control including just in time, economic order quantity, and minimum stock levels necessary for the determination of the company and the consumer needs. Noteworthy is the fact that there is no perfect method of inventory control or management.

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Discussion (Inventory Control)

Basic method of inventory control is essential in controlling the minimum stock levels. This method of stock control dictates that a company should order more stock whenever the stock levels reach certain specified levels. For instance, a company may set the minimum stock at 50 units on an item that sells 100 units in a week. When the inventory reaches the specified 50 units on day five the company should order additional stock. The other method id the just in time inventory control (JIT), which is the popular method of inventory control among the manufacturing companies (Stark, Malone, Weihe & Grow, 2015). The objective of the method id to deliver stock to the production floor within the require time. It delivers only the exact quantities necessary for the production process, meaning that it only supplies the exact amount of stock required. The method depends heavily on the supplier's ability to deliver the inventory on demand.

Safety stock levels method requires a company to have additional amount of stock on top of the normal levels of stock. The additional stock levels act as insurance for uncertainty. Moreover, the additional stock could be a way of mitigating performance problems, dealing with material uncertainty, and safeguarding against long lead times (Rander, 2006). In the contemporary setting, software exists for determining the quantities of safety stocks, which a break from the traditional use of complex formula. The method is not ideal for small businesses or companies that operate on a tight budget since safety stock or additionally stock may have negative financial implications that supersede the intended benefits. The final method is the economic order control (EOQ) that aims at establishing equilibrium between holding too much or too little inventory. Striking the mentioned balance requires the knowledge on the units used annually, the ordering cost per currency per order, the unit cots per order, the carrying cost, and the quantity of the order in specified units.

Evidently, inventory management requires the application of the First-In, First-Out (FIFO) principle. The principle dictates that the company should sell the old stock first before embarking on the sale of the newest stock (Babai, Ladhari & Lajili, 2015). The principle is ideal for perishable stock to prevent losses from spoilages and unsellable stock. In retrospect, it is also a profitable idea to apply the same principle for durable stock. Notably, packaging features and design tend to change with time, meaning that it is imperative for any business to keep up with the changes. Additionally, keeping stock for a long time leads to wear and tear, however durable, which then leads unprecedented losses to the business. The FIFO system requires better organization of the warehouse, which requires the stationing additional stock at the back.

Discussion (Assessment of the Need for Network Analysis of Financial Data for the Most Effective Decisions)

The trading of various financial assets that include foreign exchange and stock happen at the financial market. It is imperative to understand the price changes or the individual asset returns to understand the functioning and the subsequent performance of the financial market structure (Velasquez & Hester, 2013). Such helps the companies and the economy to improve the management of the financial portfolio that then leads to efficient management of risk for the investors and the authorities charged with the responsibility of for ensuring the stability of the macro finance. The changing dynamics in the financial returns is significant to the performance of companies and different business establishments. However, the latter portend static correlation measures (inter-temporal analysis) that often lead to biases. The large of financial assets also cause technicalities, particularly when dealing with correlation network during the grouping of stocks (Shamseer et al., 2015). Therefore, it is important to have a reliable and efficient method for dimensionality reduction for the development of a proper correlation network. The method should provide an analytic framework necessary for the possible grouping of stock returns in a systematic manner.

The network analysis is essential in the determination of the effects that the price of a particular stock has on the price of other stocks and other economic factors. The mentioned influence has a close correlation to the prevailing financial policies, the performance of the industrial sectors, and the national economic growth statistics (Cinelli, Coles & Kirwan, 2014). Moreover, the prospects and the performance of thee future also determine eth the stock price of a given company. High frequency stock returns reveal that that a company's stocks have varying correlation strength during various financial periods. Systematic risks tend to vary during different economic times such as the market crashes or economic depression (Provost & Fawcett, 2013). The network assessment is, therefore, essential in determining the robustness in relation to network fragility and the random fault. Such is quite important in determining the strength of the financial markets and the amount of stress it can take in the face of economic meltdown or financial crush as the one that happened in the years 2001 and 2008.

Conclusion

Managing or controlling the inventory of any business to ensure profitability requires the management to balance the demand and supply on one sided and then align such with costs. Carrying too much inventory of a product that has less demand in the market will certainly lead to losses of unprecedented proportions. The trading of various financial assets that include foreign exchange and stock happen at the financial market. It is imperative to understand the price changes or the individual asset returns to understand the functioning and the subsequent performance of the financial market structure. The globalization of the world economy, led to the connection of the financial markets. The analysis of the indices of the global financial markets presents both slow and fast dynamics. The assessment of the financial networks is important in determining the performance of the markets, particularly during the financial crisis. The influence on the stocks happens almost simultaneously during the times of crisis. The establishment, application of mutual information is a great way to determine the performance of the financial market and the expected potential or the performances of a particular company.

References

Babai, M. Z., Ladhari, T., & Lajili, I. (2015). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290.

Cinelli, M., Coles, S. R., & Kirwan, K. (2014). Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators, 46, 138-148.

do Rego, J. R., & de Mesquita, M. A. (2015). Demand forecasting and inventory control: A simulation study on automotive spare parts. International Journal of Production Economics, 161, 1-16.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision-making. Big Data, 1(1), 51-59.

Render, B. (2006). Quantitative analysis for management. Pearson Education India.

Shamseer, L., Moher, D., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M. & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ, 349, pp 764-7.

Stark, D. W., Malone, R. L., Weihe, J. G., & Grow, R. H. (2015). U.S. Patent No. 9,135,216. Washington, DC: U.S. Patent and Trademark Office.

Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision-making methods. International Journal of Operations Research, 10(2), 56-66.