Demand forecasting is the process of anticipating the demand for a product or a service. It helps businesses produce and deliver more efficiently and cater to the needs of their customers. In the supply chain planning of a business, demand forecasting is considered a vital step. It is referred to as a field of anticipatory data analytics that tries to get insight and anticipate customer demands. Based on the predictions, there is an optimization of supply decisions made by business management and supply chain operations.
Types of demand forecasting
There are numerous types of demand predictive analysis.
Short-term
This demand forecast is carried out for a shorter period, ranging from three to twelve months. This type of forecasting is based on the seasonal pattern of demand, and effective strategic decisions on customer needs and requests are considered.
Medium to long-term
Medium to long-term demand forecasting is conducted for 12 months to 24 months and 36 months to 48 months in certain business cases. Long-term data predictive analysis facilitates sales and marketing planning, financial planning, business strategy planning, capital expenses, and capacity planning.
External macro-level
It is a type of demand forecasting that involves extensive market movement and relies on the macroeconomic environment. External data demand forecasting is conveyed for assessing the tactical goals of a business, which include expansion of the product portfolio, risk mitigation tactics, technological challenges, entering new customer categories, changing consumer behavior, and so on.
Internal business level
The anticipation process that involves the internal operations of businesses is referred to as “internal business-level demand forecasting.” It includes the financial category, product category, sales division, manufacturing team, etc. The interior company also has a yearly sales forecast, cash flow, net profit margin, COGS anticipation, etc.
Passive and active forecasting
Passive demand forecasting is conducted for stable business enterprises with a traditional growth plan. Simple calculations of historical data are implemented with the minor assumption approach. On the other hand, active demand forecasting is executed for branching out and estimating a business with a fast-paced growth plan concerning growth in the product portfolio, marketing activities, external economic ambiance, and activities of competitors.
Importance of forecasting in data analysis
Forecasting in data analytics and data science is a unique technique that can be utilized to anticipate the future by determining different data sets. Businesses from other sectors use demand forecasting to predict any future event or trend, helping define the future demands of business products and services.
When strategic decisions are made in a business, it is necessary to select a suitable forecasting method that fits the demands of the current industry. Demand forecasting can help analyze historical and analytical data and produce the most precise anticipations or predictions.
Conclusion
Demand forecasting is key to different business processes around which the operational and strategic plans of a business are conceived. Based on demand forecasting, long-term strategic plans for a business are devised. Factors like capacity planning, risk evaluation, risk mitigation, sales, marketing, financial planning, and others can be efficiently formulated thanks to demand predictions.
