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Summary

In business, linear programming models have the potential to be used in profit optimization. As evidenced in the article by Maurya, Misra, Anderson, and Shukla (2015), linear programming can be used to facilitate the integration of effective quantitative techniques to predict the need for profit optimization. Using the example of an Ethiopian chemical company, the researchers found that the use of the method could be helpful in determining the limitations in the capabilities of organizations. In order to use the model in business, for example, the e-commerce industry, it is essential to identify its needs and unique factors.

The need of the industry is capturing as many potential customers as possible and ensuring that they buy a certain product that a company offers. A unique factor of the industry is the fact that sales take place online. The linear model can be used for profit optimization of an e-commerce company through identifying the number of products in stock and the frequency of their purchasing as combined with marketing efforts to get a maximum daily profit. The linear model addresses the challenge of forecasting the capacity of an e-commerce company to sell the maximum number of units possible.

Managers of organizations are encouraged to use linear programming methods to find the most beneficial arrangement of finance, determine the most appropriate times for starting and ending projects, as well as choose those tasks that would minimize the costs and increase profits (Anderson et al., 2016). Linear programming thus is intended for optimizing a dependent variable subject to independent variables in a linear relationship (Maurya et al., 2016, p. 52).

In financial contexts, dependent variables are predominantly set as objective functions for such economic concepts as sales, cost, income, production, and others. Independent variables in these contexts are variables of unknown value, which means that decision-makers should calculate their value through solving a problem. Thus, the linear modeling tool is important to consider because it offers a degree of objectivity and accuracy that other methods lack. Unfortunately, few companies use the method and rely predominantly on intuition and trial and error solutions.

Reaction

In the exploration of linear modeling, it is important to mention the study by Maurya et al. (2015) that studied profit optimization with the use of linear programming by providing an example of an Ethiopian chemical company. The authors focused on underlining the need for companies to begin using quantitative techniques more frequently in order to be more effective in their forecasting methods. Still, the use of such programming tools has their peculiarities, which is why it is essential to use quantitative techniques to determine obstacles as well as become more profitable in the long-run.

The research is significant for understanding the most effective ways to make strategic decisions with the help of quantitative models. The study was intended to provide an in-depth understanding and insight into the application of methods of linear programming within industries as well as how they could be applied in real-life contexts. Important recommendations developed by the researchers were related to encouraging companies to use linear programming methods in order to determine their process limitations and solve them accordingly. More studies on the topic are needed because the information available at this time is limited to a few industries and problems that are relevant in those contexts.

References

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. L., Fry, M. J., & Ohlmann, J. W. (2016). Quantitative methods for business with CengageNOW (13th ed.). Boston, MA: Cengage Learning.

Maurya, V., Misra, R., Anderson, P., & Shukla, K. (2016). Profit optimization using linear programming model: A case study of Ethiopian chemical company. American Journal of Biological and Environmental Statistics, 1(2), 51-57.

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