FACTORS CONTROLLING/INFLUENCING FORECASTING OF DEMAND

 


FACTORS CONTROLLING/INFLUENCING FORECASTING OF DEMAND

1. Goods Types: Goods types have a greater impact on demand forecasting than other factors. Products can be manufactured products, services or goods for consumers. In addition, established and innovative commodities are also possible. dependable products are those that are already available on the market, whereas new products are ones that have not yet been created. released on the market. Information on the amount of competition, substitutes, and demand for goods is only known if dependable products. However, it is challenging to predict the demand for the novel products. Therefore, Various sorts of goods require different forecasting techniques.

2. Competition Level: The level of competition affects demand forecasting. The demand for products in a market with lots of competitors is also influenced by how many competitors there are. Furthermore, there is always a chance of new entrants in a market that is extremely competitive. Demand forecasting becomes complex and difficult in such a situation.

3. Goods Price: The cost of the product has a significant impact on demand forecasting. Organizations' demand projections are significantly impacted by changes to their pricing strategies. It is challenging to determine the precise product demand in such a situation.

4. Technology Level: Obtaining accurate demand estimates depends on a number of factors, including the level of technology. Rapid technological advancements can make current items and technologies obsolete. For instance, the need for floppy discs has significantly decreased since the advent of compact discs (CDs) and pen drives for computer data storage. It is challenging to predict future demand for currently available products in such a situation.

5. Economic Viewpoint: In order to establish demand projections, an economic viewpoint is essential. For instance, demand projections of organizations would be optimistic if there is a favorable trend in the economy, such as globalization and high levels of investment.

6. Forecasting Time Period: The forecasting time period has a significant impact on demand forecasting. The length of the forecasting period affects how accurate it is.

Short-term projections are those that are made for a period of one year or less and are based on the expertise of the employees. The production policy, price policy, credit policy, and distribution policy of the organization are all determined by short-term projections.

Long-term forecasts are those that are made over a period of five to ten years and are supported by statistical analysis. The projections assist in making decisions regarding the launch of a new product, business expansion, or need for additional funding.

7. Forecast Level: Demand forecasting is more heavily influenced by forecast level. There are three levels at which a demand forecast can be made: macro, industry, and firm. Forecasts for broad economic conditions, such as industrial production and the distribution of national income, are made at the macro level. Forecasts are created at the industry level by trade organizations using statistical data. 

Forecasts also include goods whose sales are affected by a particular industry's industry-level strategy. Conversely, forecasts are prepared at the firm level to ascertain the demand for those goods whose sales are reliant on a certain firm's policy. A corporation considers a range of factors, including as changes in income, consumer tastes and preferences, technology, and rival strategies, when projecting the demand for its products.

8. Forecast nature: Forecast nature is a significant aspect that influences demand forecasting. A forecast may be detailed or broad. A particular forecast offers insight into the business environment in which an organisation operates, whereas a general forecast paints a picture of the overall business climate. Organizations typically choose to utilise both forecasts together since overgeneralization makes it difficult to estimate demand accurately and too-specific data offers a poor foundation for planning and execution.

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