Essay on Exploring Quantitative Analysis: Business Forecasting Methods and Real-world Applications

Introduction

The textbook defines quantitative analysis as using the scientific method to analyze a business in an executive decision-making capacity (Render, Stair & Hanna, 2012). The context identifies the need for raw data sourced from an organization and its operations. Analysis of the data enables the formulation of mathematical assessments, which provides insight into the business's validity. One such method is Scientific Analysis, which borrows several concepts from scientists' protocols in their experiments. The steps involved in the latter roles include observation, following up with questions, formulation of a hypothesis, and experimenting. The Scientific method also commences with observation, which is a critical attribute of a good quantitative analysis model. Such an approach enables the parties involved to develop facts about the issue at hand. The comprehension of the same also influences the understanding of the various factors necessary to include in the analysis.

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Understanding quantitative analysis as a scientific approach to making executive decisions highlights the need for the manipulation and processing of raw data to provide meaningful information. The raw data should also provide irrevocable accuracy in the definition of a problem. The context creates a significant challenge since the parties involved need corporate goals when forming the priority of elements. Additionally, it is prudent to dig deeper to find the probable root cause. The second attribute of a good quantitative analysis model is the acquisition of accurate input data. The context mitigates the probability of providing information that is not actionable. The situation identifies Askira Gelman's GIGO rule stipulating the conclusion arrived at is subject to the data collected (2012).

Quantitative analysis fosters conditions enabling the understanding of executive parties of behaviors within the business. The executive uses complex mathematical and statistical models to achieve the same. The models are subject to research and development, each of which requires measuring and assigning numerical values to the variables collected. Accuracy in the raw data provides actionable information, which provides the foundation for formulating mathematical and statistical models capable of evaluating the validity of elements (Sullivan & Sargeant, 2011). The reality of the values resulting from formulation through the models provides another significant attribute of a good quantitative analysis model. The context enables the organizations to measure, evaluate, and place value on their finances against their business environment dynamics.

It is prudent that businesses' statistical and mathematical models are capable of executing the functions from the raw data and manipulating the processes such that the results align with the objective of the organizations. Before processing the information, the best approach lies in testing the data to eliminate all elements that may hinder the acquisition of the most appropriate solution. The models and processing functions used in the manipulation and assessment of data provide an additional attribute.

Another consideration for the models is the simplicity of use and understanding by the personnel in charge. Such contexts identify the need for users to comprehend the model's functions with ease and adapt it to make predictions and evaluations. The requirement for ease of use should not compromise the need for the models to be comprehensive and adequately detailed. The two factors enable the models to provide insight to users about the probable complete picture of expected results. The models' design should also demonstrate flexibility and portability, such that the users can manipulate and process the data in any working environment without physical restrictions. The flexibility also allows the model to process various forms of data sets. The above considerations are attributes that instill uniqueness in the quantitative analysis model and make their application diverse in real-world scenarios.

Business Forecasting

Business forecasting presents a critical tool useful in planning for budgets, profits, and projections, all of which determine the organization's future. The methods adopted currently face the challenge of accuracy, but they still prove influential in projecting future business activities. An additional challenge includes instability from economic fluctuations, natural disasters, or epidemics such as the COVID-19 pandemic. Therefore, it is prudent for businesses to have backup plans but not solely depend on the projections.

Uses of Business Forecasting

Business forecasting is a useful tool for organizations in projecting future business situations. The business gains the ability to perceive potential trends for products and services. The insight from such processing allows the organizations to structure their activities to reap significant success in the potential markets highlighted. Such analysis benefits are both for the business and its targeted consumers (Montgomery, Jennings, & Kulahci, 2015). Additionally, business forecasting also borrows insight from the past of the organization or industry. The knowledge gathered involves challenges encountered and potential strategies to adopt that could result in success. Such understanding enables the business to prepare to mitigate the recurrence of challenges, efficiently avoiding losses and failures. The organization's anticipation from previous encounters requires a reactive and adaptive business structure, which is more responsive to known challenges than earlier models.

Forecasting also has relevance in providing actionable information about the status of future markets. Such information proves valuable to the organization in its planning for inventory. An adequate stock enables savings for the organization in terms of transportation costs, warehousing, and obsolescence. The insight of future markets also proves influential in structuring the workforce. A potential drop in the market demand presents an opportunity to retrench some non-permanent staff and freelancers. Such a structure eliminates the presence of ghost workers in the organization. Additionally, the labor implications could also prompt an increase in hires or temporary contracts from a forecast of increased demand.

Business Forecast Methods

Executive leaders in an organization rely on various methods of business forecasting to provide actionable projections. The insight still faces significant challenges from uncertainties. However, forecasting methods are still influential and beneficial to business practices and activities (Axsäter, Schneeweiss & Silver, 2012).

Delphi Method

The method employs qualitative research to source insight from potential consumers and experts. The information gathered includes the experience that the target population has on the product from the business. The same data also allows for the grouping of consumers who prefer the product under consideration. Combining target consumers and experts to collect data enables the organization to arrive at a much more comfortable consensus and improve the data collected.

The Trend Projections Method

The trend projections approach in forecasting relies on time constraints and identified indicators over the stipulated period. A combination of the data sets highlights potential shifts in sales and identifies a change duration projection. It is possible to incorporate statistical and scientific techniques to account for all variables that significantly impact the highlighted indicators. The statistics involved include frequency, statistical inference, and probability calculations. Business administrators can employ the approach and assemble various data sets that impact business activities (Montgomery, Jennings & Kulahci, 2015). such insight has a significant influence on forecasting the potential success of organizations.

Real-World Scenario

The government is also a significant player in forecasting techniques as the source of insight for the future performance of the nation's economy. On such national application is the calculation of GDP by the United States and the economy's measure over time. The following highlights the use of both methods in the analysis of GDP for the nation.

Delphi Method

Using the Delphi method, the forecasting strategy involves including various personnel and experts on the team. Deliberation will first take each member's opinion privately before engaging in discussions to foster each member's reflection. The process goes on similar procedures until the team arrives at a consensus. The GDP data above highlights several considerations. Possible arguments involve the lack of negative percentages from 2009 and continuously fluctuating rates. The team's potential agreement would be a drop in the rate due to the pandemic's influence.

Trend Projections Method

The trend forecasting method uses statistical models, and in this case, we will use frequency. The term identifies the value with more appearance in comparison to others within a given set of data. The table has a value of 2.9 appearing twice, while the other values do not repeat. Frequency presents the possibility that the GDP rate for the year 2020 will be 2.9.

References

Askira Gelman, I. (2012). An Empirical Study of the GIGO Axiom in Satisficing Decisions.

Axsäter, S., Schneeweiss, C., & Silver, E. (Eds.). (, 2012). Multi-stage production planning and inventory control (Vol. 266). Springer Science & Business Media.

Duffin, E. (2020, February 3). US – Real GDP Growth by the Year 1990 – 2019. Retrieved from Statista (2020, September 30). https://www.statista.com/statistics/188165/annual-gdp-growth-of-the-united-states-since-1990/

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.

Render, B., Stair, R. M., & Hanna, M. E. (2012). Quantitative Analysis For Management, Eleventh E.

Sullivan, G., and Sargeant, J. (2011). Qualities of Qualitative Research: Part I. Journal of Graduate Medical Education, 3(4), pp.449-452.