Role of Demand Forecasting to Predict Future Demand of Products

As we know, we are living in a satellite era and everything is based on technology these days. In an organization, most of the concepts are based on technology. An organization faces many internal and external risks, such as high competition, failure of technology, inflation, recession, and change in government laws. Risk and Uncertainty are the two factors on which business decisions are made. By determining the demand and sale of the products in future, effects of risk can be lower down. Thus, Demand forecasting is a logical process that includes the future demand for the product and facilities of a group in future under a set of uncontrollable and competitive forces.

A Prediction or forecasting system is a statement about the uncertain event, based on the knowledge or experience. The term “prediction” is used to refer an “opinion or guess” which is informed by person’s Inductive, Adductive, Deductive reasoning and previous experience that may be useful. Demand forecasting helps an organization to take business decisions such as production process planning, raw material purchasing, funds management and decide product price.

Demand plays a crucial role in managing any business. Its main aim is to lower down the risk factors and make profitable business decisions. Apart from this, demand forecasting provides an insight into the organization’s capital investment and expansion decisions. There are various machine learning algorithms which are used for demand forecasting.

A forecast is becoming the sign of survival and the language of business. All necessities of the business segment want the technique of accurate and practical readings into the future. Management needs predicting information when making a wide range of decisions.

Following areas are notified where demand forecasting is used:

• Scheduling and planning the production and obtaining the efforts accordingly.
• Making the provisions for finances.
• Expressing a pricing approach.
• Preparation of advertisement and implementing.

Mainly demand forecast is used in the large-scale production and holds significance in business. Since the large-scale production needs a long gestation period, a good deal of onward planning should be done. Also, the possible future demand should be projected to evade the conditions of overproduction and underproduction. Most often, the firms face a question of what would be the future demand for their product as they have to acquire the input. It is possible with the help of machine learning techniques only.

It is also true, that objective of the demand forecasting can be obtained only when forecasting is done systematically and scientifically. Thus, the following steps in demand forecasting are followed to enable a systematic estimation of future demand for product:

• Requiring the Objective
• Defining the Time Perspective
• Method selection for Demand Forecasting
• Data collection and Data Adjustment
• Estimation and Interpretation of Results

It is becoming progressively significant and essential for the business to forecast their future scenarios in terms of sales, cost, and profit. The value of future sales is vital as it affects cost revenues, so the prediction of upcoming sales is the normal starting point for all business development. A forecast is a prediction or approximation of future situation. It is an objective assessment of upcoming course of action. Since future is undefined, no estimation can be percent correct. Forecasts can be both physical as well as financial in nature. More accurate the predictions, more real decisions can be made. Prediction is based upon time-series methods with time-series data. Various statically methods used for forecasts comprises linear regression, logistic regression, and vector auto-regression. These models are comprised in Machine learning which can be used for commercial usage. 

In the nutshell, we can say that our technology allows building fully automated predictive behavior modeling system which acquires data, make decisions, and execute transactions based on those decisions. Data Science helps us to build customer behavior predictive models of markets and behavior of financial instruments traded in markets. It is helpful to predict future profit in the business.