Highline Financial Services Inc.’s Forecasting

Introduction

Forecasting can be defined as an attempt to predict the future value of a variable of interest. Forecasts are extremely important in business, as they help to plan resources and make necessary corrections to the current strategy. Price and demand are the most common types of forecasts utilized by businesspeople. The present paper examines a case study of Highline Financial Services that provides three types of services. The purpose of the present paper is to make a forecast of demand for these services during the next four quarters using the data for the past two years. The paper argues that utilization of the moving average method is the most appropriate in the situation with the limited data provided.

Selecting the Forecasting Method

The selection of the most appropriate forecasting method requires analysis of available data. The dataset for the case study is presented in Table 1 below. There are four common methods used for forecasting demand: straight-line method, moving average, simple linear regression, and multiple linear regression. The straight-line method supposes that the demand is expected to grow at a constant rate over a defined period. The rate is usually determined by market research. Moving average is a smoothing technique that estimates future values based on the underlying pattern of the provided data (Lestari et al., 2017). The method estimates the next value as an average of a set of previous values. Simple and multiple linear regressions are statistical methods that predict the dependent variable by fluctuations in one or several independent variables (Ciulla & D’Amico, 2019). Not all of these methods are applicable in the situation with Highline Financial Services.

Table 1. Historical data for service demands

Year Quarter Service A Service B Service C
1 1 60 95 93
2 45 85 90
3 100 92 110
4 75 65 90
2 1 72 85 102
2 51 75 75
3 112 85 110
4 85 50 100

Considering the type of provided data, the most appropriate forecasting method in the situation is the moving average. The straight-line approach is inappropriate, as the results of market research for the services are unavailable. While predictions can be made based on seasonal differences in demand, there is not enough data to make reliable predictions. It was considered to regress the demand against time using simple regression analysis. However, such an approach would be inappropriate, as it would not take into consideration seasonal fluctuations in demand, which are obvious after simple observations. Multiple regression analysis could have resolved the problem by treating year and quarter as independent variables. However, the case states it clearly that there is not enough data to make seasonal predictions. Thus, the only option left is the moving average.

The provided data is appropriate for forecasting using the moving average approach. According to Lestari et al. (2017), the moving average technique is appropriate if it is expected that the demand will continue to change at a stable pace. However, it may be challenging to select an appropriate period to acquire reliable results (Lestari et al., 2017). For the present case study, it was decided to make forecasts by calculating the average values of four quarters, as it is a minimal number of values that would include the demand for the same quarter in the previous year. The same method will be utilized for forecasting the demand for all services, as the provided data shares common characteristics. In particular, the data for the demand is available only for the past two years, the data represents quarterly values, and there are obvious seasonal fluctuations in demand of all services.

Forecasts

Year 3 demand forecasts are presented in Table 2 below.

Table 2. Forecasted demand for Year 3

Year Quarter Service A Service B Service C
3 1 80 74 97
2 82 71 95
3 90 70 101
4 84 66 98

The results demonstrate that the moving average was an appropriate method, as it predicted seasonal changes in the demand for services. Conclusions can be drawn after juxtaposing the forecasted values with previous years’ demand. The results reveal that Service A is expected to experience a significant rise, as average quarterly demand will increase from 80 in Year 2 to 84 in Year 3. In other words, 5% growth is expected. The demand for Service B is expected to decrease from a quarterly average of 74 in Year 2 to a quarterly average of 70 in Year 3. This implies that a 5% decrease in demand is expected. Finally, Service C will be stagnant, as forecasts demonstrate no significant changes in the average quarterly demand during Year 3 in comparison with Year 2. Thus, Freddie Mack, the managing partner, should ensure that the management team realizes the expected fluctuations in demand in Year 3.

Formalized Approach to Forecasting

A formalized approach to forecasting demand is associated with numerous benefits. In general, forecasting demand leads to improved management and data-driven decisions (Song et al., 2019). In particular, a formalized approach to demand forecasting leads to improved supply chain management, effective staffing, accurate budgeting, and enhanced performance evaluation (Song et al., 2019). When applied to Highline Financial Services, after receiving the results of formalized forecasting, the company can make necessary changes to improve its performance. First, the company can reduce the number of employees for Service B and increase the number of employees for Service C to attain the changes in demand. Additionally, the company can use the forecasted data to adjust the vacation schedules of employees.

Second, the company can run a formal investigation to understand if the change in demand for Service B is associated with factors that the company can influence. For instance, if the decreased demand for the service is associated with decreased quality, the authorities can use benchmarking to compare the current process to the best practices and make necessary corrections. Third, the company can utilize the data to adjust the budget by decreasing the expected revenues from Service B and increasing the expected revenues for Service A. This will help to plan the costs accordingly to avoid discrepancies. Finally, a formalized approach to forecasting is associated with improved accuracy, as it is driven by objective data rather than subjective experts’ opinions.

Conclusion

Demand forecasting is a crucial procedure that helps adjust companies’ plans based on the analysis of data. When selecting a forecasting method, the peculiarities of data must be assessed. In the case of Highline Financial Services, the most appropriate approach is the moving average. The application of the approach revealed that Service A would experience a 5% increase in demand on average, Service B will experience a 5% decrease, and the demand for Service C will not change significantly. Thus, the company should make appropriate alterations in planning, considering the acquired results.

References

Ciulla, G., & D’Amico, A. (2019). Building energy performance forecasting: A multiple linear regression approach. Applied Energy, 253, 113500.

Lestari, F., Anwar, U., Nugraha, N., & Azwar, B. (2017). Forecasting demand in blood supply chain (case study on blood transfusion unit). In Proceedings of the World Congress on Engineering (Vol. 2). Web.

Song, H., Qiu, R. T., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362.