The role of Quantitative Analysis and Modeling within Cross-Functional Decision-Making

Organizations use different quantitative analyses to meet different operational objectives. The main objectives of organizations are to maximize profits, minimize costs, and predict the trend in business operations. Some of the techniques used in the analysis include the use of time series modeling and linear programming. During the Capism Ops competition, a study of three articles helped me to understand the role of the different techniques used in the quantitative analysis by organizations. As such, an elaboration of the key competency learned is essential to distinguish the different dimensions of quantitative analysis.

Competency 1- The role of Quantitative Analysis and Modeling within Cross-Functional Decision-Making

Modeling of past and present data help in using time series as a forecasting tool to aid in decision making. I learned of the different types of forecasting models such as the AR, MA, and ARIMA models in time series analysis. Moreover, the study of the article by Mustafa and Nejat (2016) has also enabled me to differentiate how each forecasting model can be used. However, I still find predicting future trends using the models mentioned a little confusing, as opposed to the normal technique of exponential smoothing. Time series analysis can be used to predict trends in a business organization. Some of the trends that significantly impact business performances include; trends in sales, operational costs, and consumer preferences. For instance, Mustafa and Nejat (2016) focused on the use of time series analysis in the natural gas market. Additionally, Moving average (MA) is a common forecasting technique used by forex traders in predicting the market trends in the foreign exchange market.

The use of time series modeling may significantly influence the decision-making of a business. Nonetheless, majority of businesses do not use such techniques due to their complex calculations. Others avoid incorporating the time series model because of ignorance. As such, businesses should aim to train employees on the importance of such techniques in order to gain a competitive advantage by predetermining trends in sales and consumers’ preferences.

 

Competency 2- Applications of Quantitative Decision making in Business II: Transportation, Transshipment, Assignment, or Shortest-route Problem

Tackling the transportation and assignment problem enabled me to understand how businesses operate in minimizing costs. Furthermore, a look into the article by Ahmed et al. (2016) helped me to get a wider scope on the different scenarios where the concept of assignment problem can be applied. I learned to use two approaches in solving the short-route problem, or the assignment problem. The two methods are the use of the Northwest corner rule and the Least Cost Method. Per Vidhya (2017), the Northwest corner rule involves beginning allocations from the top-left cell of the solution box. On the other hand, the Least Cost Method begins supply allocations from the cell with the least demand of units. Hence, the least Cost method is more accurate in providing the optimal solution to minimizing transportation costs by suppliers.

The application of linear programming is rampant in the day to day activities. However, not many people have a deep understanding on how the concepts of linear programming or operational research apply to their business operations. The study made me realize that the transportation problem is a basic way to explain the use of linear programming to people who might not have knowledge or competency on how to apply the theoretical supply allocations to their businesses. Additionally, the church services can also be used to explain how resource allocations can be done to achieve an optimal outcome. Therefore, the assignment helped me to realize we unknowingly apply quantitative analysis on a day to day basis

Competency 3- Applications of Quantitative Decision Making in Business III: Forecasting problem

Linear programming as used by most businesses aims to provide the optimal solution to either maximizing profit or minimizing cost by taking into consideration the different constraints faced by a business in achieving profits. Indeed the use of linear programming requires the incorporation of wisdom to determine the optimal combination of inputs that minimizes costs during the production process. Additionally, the concept requires wisdom to determine the optimal prices in maximizing profits.

During the Capism Ops competition, my perspective of how decision-making by businesses changed. I used to view businesses as profit-making entities that solely relied on their return on investments (ROI) to determine future stock levels. Moreover, I used to think that inventory levels were used to calculate the optimal selling prices of products. However, the perspective I developed while interviewing the business leader from the nutritional consultant services is that Linear programming, though not a widely known concept, is used by businesses to determine input combinations and prices of products. Indeed, businesses apply mathematics to solve operational problems.

The church has also incorporated the use of mathematics and linear programming to determine the optimal outcome of its service allocations. Since wisdom plays a crucial role in such decisions, the incorporation of linear programming during decision making augers with the attributes of a Christian believer, I do agree with Paugh (2019) that Jesus Christ showed he was an effective problem solver by the solutions he provided to the challenges faced by his followers. Additionally, Bezalel was chosen in the Old Testament was knowledgeable in craftsmanship, but he was chosen to make artistic designs for the sanctuary because he was wise. Therefore, just as wisdom was essential in the olden days to solve problems, so is wisdom still necessary in tackling problems of today

Quantitative analysis may be seen as a complex technique that should be left for experts. However tackling different dimensions of quantitative analysis has not only made me to understand the different techniques used in quantitative analysis better, but also how they are applied in decision making by businesses to maximize profit, and minimize costs.

 

 

References

Ahmed, M. M., Khan, A. R., Uddin, M. S., & Ahmed, F. (2016). A new approach to solve transportation problems. Open Journal of Optimization5(1), 22-30.

Mustafa, A., & Nejat, Y., (2016). Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods. MDPI energies article. Retrieved from: file:///C:/Users/user/Downloads/energies-09-00727%20 (1).pdf

Paugh, B. (2019). Words of Wisdom and Instruction from God’s Word: Word’s of Truth, Conviction, and Instruction for the Godly Order of Our Lives (Vol. 1). Christian Publishing House.

Vidhya, A. (2017). An introductory guide to linear programming for (aspiring) data scientists. Retrieved from https://www.analyticsvidhya.com/blpg/2017/02/introductory-guide-on-linear-programming-explained-in-simple-english.