|Type of paper:||Essay|
|Categories:||Planning Budgeting Financial management|
The fundamental application of a forecast in business is to strategize and plan to maintain and surpass the current financial status of an organization. Inevitably, every business is subject to the dynamic business environment which results to fierce competition, demanding customers, management and operations, and even height demand for quality goods and services (Petropoulos et al., 2014). These forces essentially require a business to have a forward-looking perspective or risk facing out in future when they eventually occur. A correct forecast allows the entity to access the future uncertainties realistically as possible and therefore create feasible strategic decisions to achieve the set future successes.
In the fashion industry, for example, proper inventory planning, which is a critical aspect of retail operation, requires a consistent balance in supply and demand. The management of such inventory heavily relies on the accuracy of the future demand forecast. With a good forecast, the retailer is efficiently able to manage due dates, pricing, production planning and ultimately achieve high customer satisfaction (Liu et al., 2013). Thus, the significance of a skill in forecasting cannot be overemphasized in the present and future management of a business.
To successfully carry out a forecast there are several approaches employed. Among the commonly used forecasting method, apart from the renowned time series approaches, are the explanatory models' methods. These quantitative models, which consists of linear regression, nonparametric additive, and distributed lag regression models, fundamentally assume that the variables have an explanatory relationship which can be used to predict the future values of these variables (Hyndman & Athanasopoulos, 2018). This is different from the time series methods which forecasts based the information about that particular variable with attempts to discover the factors influencing its behaviours.
However, while various forecasting models are available, the accuracy and performance of a model are influenced by how well the model can accommodate new data omitted during the fitting of the model. It is therefore important to use part of the available data for testing and use the rest for fitting the model. Additionally, it is important to check the accuracy using the prescribed approaches such as percentage errors, scaled errors and scale-dependent error to fully ascertain its optimal performance for the variable under forecast (Hyndman & Athanasopoulos, 2018).
Adherence to the above mentioned factors will ultimately produce a reliable forecast ,which is instrumental in developing a body of knowledge to assist relevant strategists and planners in their task of establishing an effectual foresight about the future.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
Liu, N., Ren, S., Choi, T. M., Hui, C. L., & Ng, S. F. (2013). Sales forecasting for fashion retailing service industry: a review. Mathematical Problems in Engineering, 2013.
Petropoulos, F., Makridakis, S., Assimakopoulos, V., & Nikolopoulos, K. (2014). 'Horses for Courses' in demand forecasting. European Journal of Operational Research, 237(1), 152-163.
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