Description
Reply to Case Study: Highline Financial Services, Ltd Discussion 2
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
Highline Financial Services: Demand Forecasting for the Coming Year
Highline Financial Services, a company offering financial and personnel hiring services, needs help to develop demand forecasts for the services it will offer in the next year in the three major service categories. The demand patterns for the company’s services have fluctuated for the past eight quarters; hence, there is a need to conduct a historical analysis of the demand patterns and choose the right forecasting method for each service category. This discussion delves into the demand forecasts for the next four quarters, justifies the reason for selecting the forecasting methods, and outlines the advantages of using a formalized approach to demand forecasting.
Service A has a relatively stable demand pattern, with the demand ranging from 45 to 100 units per quarter and no clear trend or seasonal pattern. Based on this pattern, it is recommended to have a simple moving average forecasting technique. A 4-quarter moving average will be useful because it will filter out the short-term fluctuations and give an accurate forecast of future demand. The forecasts for Service A are as follows:
– Q1 Year 2: (60+45+100+75)/4 = 70
– Q2 Year 2: (45+100+75+70)/4 = 73
– Q3 Year 2: (100+75+70+73)/4 = 80
– Q4 Year 2: (75+70+73+80)/4 = 75
In contrast, the demand for Service B has been more volatile from one quarter to another. Thus, to address this issue while prioritizing the most recent data, exponential smoothing with a smoothing factor of 0. 3 is recommended (Meade, 2020). This method assigns more weight to recent observations; hence, the forecast is flexible and adapts to changing demand patterns. The exponential smoothing forecasts for Service B are:
– Q1 Year 2: 0.3(95) + 0.7(85) = 88
– Q2 Year 2: 0.3(85) + 0.7(88) = 87
– Q3 Year 2: 0.3(92) + 0.7(87) = 88
– Q4 Year 2: 0.3(65) + 0.7(88) = 80
In detail, it has been found that Service C’s number of units in demand has risen from 90 to 110 in the past four quarters. Hence, to capture this growth, the best model that can be used is the linear trend forecast model (Sharma et al., 2020). From the historical data, the trend line that will be fitted will show the increase in demand in the next year, as shown in the forecast. The linear trend forecasts for Service C are as follows:
– Q1 Year 2: 83.4 + 6.4(5) = 115
– Q2 Year 2: 83.4 + 6.4(6) = 122
– Q3 Year 2: 83.4 + 6.4(7) = 128
– Q4 Year 2: 83.4 + 6.4(8) = 134
Given these differences in the demand patterns identified for the three services, the use of different forecasting methods for the three services is justified. Service A has a relatively stable demand pattern for which moving averages are applicable, while Service B has a variable demand pattern for which the exponential smoothing technique is applicable. Service C, however, has a growth trend, which makes the trend-line approach best applicable. Therefore, according to Ar?o?lu et al. (2021), by applying these methods, depending on each type of service, Highline Financial Services can effectively predict the future and adapt to it.
There are several major benefits that Highline Financial Services can accrue from the application of a structured forecasting technique. First, it eliminates the use of either random or biased means rather than the use of competent and systematic approaches. Further, when demand forecasting is more quantitatively complex, resource managers allocate resources better since demand expectations are quantified. Similarly, it is helpful for the company to identify forecast errors that can be used to assess forecast accuracy and identify improvements that can be made. Finally, the formalized forecasts serve as a benchmark that can be adjusted with the help of management and other stakeholders.
In summary, the demand forecasts for Highline Financial Services’ three service categories have been calculated using the most suitable methods that reflect their past trends. The forecast for Service A through the moving average should be combined with the exponential smoothing for Service B and the linear trend for Service C to provide the company with an accurate demand forecast. Ultimately, Highline Financial Services has the opportunity to enhance the decision-making, resource management as well as organizational performance in the subsequent year if it applies more formal and quantitative methods of forecasting.
Ltd Discussion 2
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
Highline Financial Services: Demand Forecasting for the Coming Year
Highline Financial Services, a company offering financial and personnel hiring services, needs help to
develop demand forecasts for the services it will offer in the next year in the three major service
categories. The demand patterns for the company’s services have fluctuated for the past eight
quarters; hence, there is a need to conduct a historical analysis of the demand patterns and choose
the right forecasting method for each service category. This discussion delves into the demand
forecasts for the next four quarters, justifies the reason for selecting the forecasting methods, and
outlines the advantages of using a formalized approach to demand forecasting.
Service A has a relatively stable demand pattern, with the demand ranging from 45 to 100 units per
quarter and no clear trend or seasonal pattern. Based on this pattern, it is recommended to have a
simple moving average forecasting technique. A 4-quarter moving average will be useful because it
will filter out the short-term fluctuations and give an accurate forecast of future demand. The
forecasts for Service A are as follows:
– Q1 Year 2: (60+45+100+75)/4 = 70
– Q2 Year 2: (45+100+75+70)/4 = 73
– Q3 Year 2: (100+75+70+73)/4 = 80
– Q4 Year 2: (75+70+73+80)/4 = 75
In contrast, the demand for Service B has been more volatile from one quarter to another. Thus, to
address this issue while prioritizing the most recent data, exponential smoothing with a smoothing
factor of 0. 3 is recommended (Meade, 2020). This method assigns more weight to recent
observations; hence, the forecast is flexible and adapts to changing demand patterns. The
exponential smoothing forecasts for Service B are:
– Q1 Year 2: 0.3(95) + 0.7(85) = 88
– Q2 Year 2: 0.3(85) + 0.7(88) = 87
– Q3 Year 2: 0.3(92) + 0.7(87) = 88
– Q4 Year 2: 0.3(65) + 0.7(88) = 80
In detail, it has been found that Service C’s number of units in demand has risen from 90 to 110 in
the past four quarters. Hence, to capture this growth, the best model that can be used is the linear
trend forecast model (Sharma et al., 2020). From the historical data, the trend line that will be fitted
will show the increase in demand in the next year, as shown in the forecast. The linear trend
forecasts for Service C are as follows:
– Q1 Year 2: 83.4 + 6.4(5) = 115
– Q2 Year 2: 83.4 + 6.4(6) = 122
– Q3 Year 2: 83.4 + 6.4(7) = 128
– Q4 Year 2: 83.4 + 6.4(8) = 134
Given these differences in the demand patterns identified for the three services, the use of different
forecasting methods for the three services is justified. Service A has a relatively stable demand
pattern for which moving averages are applicable, while Service B has a variable demand pattern for
which the exponential smoothing technique is applicable. Service C, however, has a growth trend,
which makes the trend-line approach best applicable. Therefore, according to Arıoğlu et al. (2021),
by applying these methods, depending on each type of service, Highline Financial Services can
effectively predict the future and adapt to it.
There are several major benefits that Highline Financial Services can accrue from the application of a
structured forecasting technique. First, it eliminates the use of either random or biased means
rather than the use of competent and systematic approaches. Further, when demand forecasting is
more quantitatively complex, resource managers allocate resources better since demand
expectations are quantified. Similarly, it is helpful for the company to identify forecast errors that
can be used to assess forecast accuracy and identify improvements that can be made. Finally, the
formalized forecasts serve as a benchmark that can be adjusted with the help of management and
other stakeholders.
In summary, the demand forecasts for Highline Financial Services’ three service categories have been
calculated using the most suitable methods that reflect their past trends. The forecast for Service A
through the moving average should be combined with the exponential smoothing for Service B and
the linear trend for Service C to provide the company with an accurate demand forecast. Ultimately,
Highline Financial Services has the opportunity to enhance the decision-making, resource
management as well as organizational performance in the subsequent year if it applies more formal
and quantitative methods of forecasting.
References
Arıoğlu, M. Ö., Sarkis, J., & Dhavale, D. G. (2021). Selection of suppliers using Bayesian estimators: a
case of concrete ring suppliers to Eurasia Tunnel of Turkey. International Journal of Production
Research, 59(18), 5678–5689.
Meade, N. (2020). Evidence for the Selection of Forecasting Methods. Journal of Forecasting, 19(6),
515–535.
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.
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