The first characteristic is,

Forecasts have to include both expected value and forecast errors.

Let us take examples of paint companies X and Y and their forecast for the Vizag city. The company X forecasted 1.2 lakh litres and one 17.8 lakhs litres of paint per annum. The company Y forecasted 12 lakhs and 8 lakhs litres of paint per annum. Both companies have 10 lakh litres per annum as the average demand. However, the company X faces more uncertainty for getting better forecasts. This uncertainty is popularly known as a forecast error.

Second forecasting characteristics is

The accuracy of the forecast is better for shorter duration.

Let me illustrate this with an Indian airline company, Indigo Airlines. Indigo Airlines keep updating their travel plans by getting weather forecasting Currently, Indigo Airlines is tied up with Indian Meteorological department for supplying the weather data This will help the company in the capacity expansion on a quarterly basis. The Ajatus software (https://www.ajatus.in/) that Indigo uses brought fruitful results to the company. It improved Fleet assignment and customized pricing on ancillaries such as leg rooms and meals

The artificial intelligence Used by Indigo Airlines enhanced their revenue. Indigo's forecasting included increasing aircraft seat availability and route planning.

Third characteristics of the forecasting is

aggregate forecasts are easier than Disaggregate forecasts.

Though, many companies have their forecasting system in place, When they transform their data with others in the supply chain find forecast errors due to non collaboration This has made many companies formulate their block chain with supply chain members. In this Block Chain, the data is integrated across the supply chain. That improves the quality of the data. The Ledger sharing and data security also improve the forecast across the block chain Similarly, the forecasting of per capita income of India is much easier than forecasting a Market Basket value of a customer coming to a retail store.

Fourth characteristics of forecasting in supply chain is

Distortion in customer information effects company forecasting in the supply chain.

Retailers who are very close to the customers forecasts better But as one moved up in a supply chain to the distributor level, C&F level, manufacturers level and a supplier level, the forecast error also increases This is commonly known as the bullwhip effect. After the implementation of CPFR and block chain technology in the supply chain, companies have experienced reduction in the forecast errors