Reply to Forecasting Applications Discussion 1
Q – Please read the discussion below and prepare a Reply to this discussion post with comments that further and advance the discussion topic.
Please provide the references you used.
Ensure zero plagiarism.
Word limit: 200 words
Discussion
Forecasting Applications
Highline Financial Services Ltd. offers three classes of services (A, B, and C), the demand of which has fluctuated over the past two years. One key decision-making challenge the company faces is forecasting the demand for the following year. Therefore, its history helps forecast the demand that is likely to occur in the future to aid in the proper allocation of resources.
Demand forecasts for the next four quarters for all three products
Service
Year
Quarter
A
B
C
3
1
94.29
60.46
99.25
2
98.57
56.35
99.92
3
102.86
52.23
100.58
4
107.14
48.11
101.25
Forecasting method
Linear trend forecast will be used on all three services to predict demand for the next four quarters. According to a study by Stevenson (2021), the method is applicable when comparing different periods, and as such, linear trend forecast is best suited to the scenario because Highline’s demand data shows that despite volatility, the trends are linear. Thus, linear trend forecast can describe each service’s general demand trend and give acceptable predictions for subsequent quarters.
The demand for Service A is unstable, although it depicts an upward trend over the eight quarters (from 60 to 112). The demand will likely increase steadily for one year through linear regression analysis. Conversely, the trend of Service B is characterized by relative fluctuations, a steady downward trend, and occasional upturns. Linear regression is suitable for extending and suggesting a potential deterioration if appropriate measures are not implemented. Finally, the demand for Service C remains more or less constant and fluctuates within a narrow range with no sign of consistent growth or decline. Although this trend may not be perfectly linear and might not capture this pattern accurately, it can provide a fundamental forecast for relatively stable services. The method gives a clearer picture of the pattern and any shifts in the trend that require intervention.
Rationale for Selecting Forecasting Method
Despite using the same forecasting method, the rationale is different. Service A has an upward trend, which makes the linear regression model the right choice. The same can also be said about Service B’s decline, thereby supporting the use of this method to forecast further declines. Although the demand for Service C is more stable than Service A’s, linear regression can be used to make reasonable forecasts because we are not looking at day-to-day changes but rather at changes in the long run. Sharma et al. (2020) argue that selecting a suitable method of forecasting that correlates with the data attributes indicates that forecasting should be adjusted according to all services’ demand patterns.
Benefits of a Formalized Forecasting Approach
Linear trend forecast is a more structured and less subjective approach to forecasting than other formalized approaches. It provides precision and reliability by eliminating bias from estimates developed on self-perceived or instinct, thus providing better predictions (Stevenson, 2021). It also justifies the reason behind the forecasts to the stakeholders. Using history as a basis for making its forecasts, Highline can avoid misallocating resources in processes like recruitment, development of marketing strategies, and overall financial planning.
In conclusion, it is recommended that Highline Financial Services use the Linear trend forecast to predict the service demand in the next year. Although the demand patterns differ slightly between services A, B, and C, linear trend forecast gives insights required to make informed decisions. Using formalized forecasting techniques will give the company a stable base to build a strategy. This will improve the business’s planning aspect, especially where Freddie Mack will need to consider staffing and resource allocation issues.
References
Sharma, H. K., Kumari, K., & Kar, S. (2020). A rough set approach for forecasting models. Decision Making: Applications in Management and Engineering, 3(1), 1-21.
Stevenson, W. J. (2021). “Forecasting” In Operations Management. 14 Ed, McGraw-Hill