Description
Introduction
Demand forecasting is one of the significant components of operation management, whereby industries
such as laptop manufacturing require accuracy in terms of demands since the market is always competitive
and the production cycle depends on the demand for products. Forecasting enables an organization to plan
for future requirements, manage the supply chain, and utilize resources to ensure that they do not overstock
or experience long lead times. Choosing the proper forecasting method and having the right forecast time
frame are critical to keep operations running smoothly.
Chosen forecasting technique
In the case of a laptop manufacturing organization, I would prefer to use exponential smoothing as the
forecasting method. Exponential smoothing is effective because it attaches more weight to the latest
information, which is relevant in a highly dynamic and competitive market such as laptop manufacturing.
The method gives flexibility in adapting to changes in demand with less fluctuation, making it a compromise
between flexibility and stability.
The steps in the forecasting process
Forecasting begins with identifying the use of the forecast or what the organization wants to accomplish
from the forecasting process. Secondly, it is important to decide when the forecast should be made in order
to classify it as short-term, middle-term, or long-term. Thirdly, after identifying the application’s goals and
time frame, it is crucial to determine the process of how and which data to collect and clean. This includes
compiling data on previous sales experiences, current markets, or consumer habits and then preprocessing it for outliers or missing data (Stevenson, 2021). Fourthly, once the data has been analyzed,
the organization has to choose the appropriate forecasting technique based on the nature of the data
patterns and organizational needs. Once the technique is settled, the forecast can be produced while the
desired method is used (Stevenson, 2021). Lastly, periodic assessment of forecast errors is crucial to
establishing the accuracy of forecast values relative to actual results and fine-tuning the model for better
results at a given frequency.
Importance of creating a forecast for the correct period
Forecasting for the right period is very noble because it allows the organizational planning systems, such
as production, inventory, and supply chain, to correlate with reality. However, the forecasting covers a very
short period. In that case, the organization may need a proper estimate of the required stock, which leads
to delays in stock and production levels and loss of potential sales (Che-Jung CHANG et al., 2020). On the
other hand, forecasting for an extended period results in producing too much, holding inventory, and
incurring high storage costs. In any of these cases, short or long forecasting periods waste time and money
and dissatisfied customers. According to Fildes et al. (2022), a forecast with the right time frame gives the
organization the right perspectives for decision-making and returns the organizational equilibrium should
the market occur, disturbing the organization’s balance.
Creating a forecast for 3 months when an organization needs an accurate forecast for 6 months
When preparing a forecast of a period that spans 3 months instead of the necessary 6 months, the
organization faces multiple disruptions to its functioning. The short-term forecast is likely to result in an
inaccurate estimate of demand or supply, and production capacity may not meet the needs of consumers
once the initial 3 months have been met. These can lead to delays and stockouts or inhibit the ability to
meet customers’ demands. This means reduced revenue and a loss of the organization’s credibility and
customer loyalty.
Conclusion
Organizations need to be as competitive and adaptive as possible, which means that, in demand forecast
management, they should do as well as they can. When choosing an appropriate forecast, sticking to a
proper plan, and ensuring that the forecast falls into the right temporal category, an organization escapes
operational ineffectiveness, customer dissatisfaction, and resource wastage. A good forecast not only
reduces risks but also enables better planning and, hence, is a valuable tool in managing operations,
especially for firms involved in laptop manufacturing where timing is imperative to success.
References
Che-Jung CHANG, Guiping LI, Jianhong GUO, & Kun-Peng YU. (2020). Data-Driven Forecasting Model
for Small Data Sets. Economic Computation & Economic Cybernetics Studies & Research, 54(4), 217–
229.
Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal
of Forecasting, 38(4), 1283-1318.
Demand forecasting is one of the significant components of operation management, whereby industries
such as laptop manufacturing require accuracy in terms of demands since the market is always competitive
and the production cycle depends on the demand for products. Forecasting enables an organization to plan
for future requirements, manage the supply chain, and utilize resources to ensure that they do not overstock
or experience long lead times. Choosing the proper forecasting method and having the right forecast time
frame are critical to keep operations running smoothly.
Chosen forecasting technique
In the case of a laptop manufacturing organization, I would prefer to use exponential smoothing as the
forecasting method. Exponential smoothing is effective because it attaches more weight to the latest
information, which is relevant in a highly dynamic and competitive market such as laptop manufacturing.
The method gives flexibility in adapting to changes in demand with less fluctuation, making it a compromise
between flexibility and stability.
The steps in the forecasting process
Forecasting begins with identifying the use of the forecast or what the organization wants to accomplish
from the forecasting process. Secondly, it is important to decide when the forecast should be made in order
to classify it as short-term, middle-term, or long-term. Thirdly, after identifying the application’s goals and
time frame, it is crucial to determine the process of how and which data to collect and clean. This includes
compiling data on previous sales experiences, current markets, or consumer habits and then preprocessing it for outliers or missing data (Stevenson, 2021). Fourthly, once the data has been analyzed,
the organization has to choose the appropriate forecasting technique based on the nature of the data
patterns and organizational needs. Once the technique is settled, the forecast can be produced while the
desired method is used (Stevenson, 2021). Lastly, periodic assessment of forecast errors is crucial to
establishing the accuracy of forecast values relative to actual results and fine-tuning the model for better
results at a given frequency.
Importance of creating a forecast for the correct period
Forecasting for the right period is very noble because it allows the organizational planning systems, such
as production, inventory, and supply chain, to correlate with reality. However, the forecasting covers a very
short period. In that case, the organization may need a proper estimate of the required stock, which leads
to delays in stock and production levels and loss of potential sales (Che-Jung CHANG et al., 2020). On the
other hand, forecasting for an extended period results in producing too much, holding inventory, and
incurring high storage costs. In any of these cases, short or long forecasting periods waste time and money
and dissatisfied customers. According to Fildes et al. (2022), a forecast with the right time frame gives the
organization the right perspectives for decision-making and returns the organizational equilibrium should
the market occur, disturbing the organization’s balance.
Creating a forecast for 3 months when an organization needs an accurate forecast for 6 months
When preparing a forecast of a period that spans 3 months instead of the necessary 6 months, the
organization faces multiple disruptions to its functioning. The short-term forecast is likely to result in an
inaccurate estimate of demand or supply, and production capacity may not meet the needs of consumers
once the initial 3 months have been met. These can lead to delays and stockouts or inhibit the ability to
meet customers’ demands. This means reduced revenue and a loss of the organization’s credibility and
customer loyalty.
Conclusion
Organizations need to be as competitive and adaptive as possible, which means that, in demand forecast
management, they should do as well as they can. When choosing an appropriate forecast, sticking to a
proper plan, and ensuring that the forecast falls into the right temporal category, an organization escapes
operational ineffectiveness, customer dissatisfaction, and resource wastage. A good forecast not only
reduces risks but also enables better planning and, hence, is a valuable tool in managing operations,
especially for firms involved in laptop manufacturing where timing is imperative to success.
References
Che-Jung CHANG, Guiping LI, Jianhong GUO, & Kun-Peng YU. (2020). Data-Driven Forecasting Model
for Small Data Sets. Economic Computation & Economic Cybernetics Studies & Research, 54(4), 217–
229.
Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal
of Forecasting, 38(4), 1283-1318.
Stevenson, W. J. (2021). Operations
management.
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