Price Forecasting

What is Price Forecast?

Price forecasting is a process by which product/commodity price can be predicted , surmised or foretold. This is done by previewing different factors like:

• Demand of the commodity
• Makeup, features or characteristics of
  the commodity
• Seasonal trend
• Prices
• Offers made from numerous suppliers
  etc.

Entrepreneurs may need to describe an optimal time to buy a commodity in order to adjust prices of products or services and or evaluate the invest.

Why is price forecasting needed?

The need for price forecasting cannot be over-emphasized. It is necessary and important to forecast the price of a product; this is because in keeping the right amount of stock (not having less or over stock) is very essential in the line of business.
When goods stock are less or little, it means running out at inopportune times; this will lead to customers and potential customers to buy elsewhere causing you to lose your customers. Then, when you over stock (having goods in excess), may lead to unnecessary high cost for storage and inventory management.
Then how can one reliably find a middle ground between these two undesirable condition? In supply chain management, it is achieve by forecasting.

This is to say that forecasting can help an entrepreneur be able to know the desired amount of stock to keep.

In supply forecasting data about suppliers whether they provide completed products or parts that are assembled further down the supply chain – and uses it to project how much product they will have available and when. This helps determine the amount of products that can be ordered and delivered in a specific period of time. The data important to supply forecasting isn’t limited to production or delivery capacity; but also economics, technology and even weather all play a role.
Demand forecasting analyzes how much product a customer is willing to have at a particular day, week month or quarter.
The data helps the organisation in order to keep right stock which will be enough to satisfy the customer orders, but not in excess where time, money and effort might be wasted.
likely to want during a specific week, month or quarter. This data allows organizations to keep a suitable volume in stock – enough to fill customer orders, but not so much that time, mobey and effort might be wasted. Demand forecasting or planning is largely about predicting customer behavior, but it goes beyond simply anticipating wants and needs. Even the consumers confidence, cultural trends, and seasonality are considered.
a price cut.
Effective price forecasting helps businesses predict when it is necessary for price to increase or decrease . This liable to affect customer demand.
Price forecasting examines data related to supply and demand to project how each factor will affect prices.
A bad hurricane season along the Gulf Coast can cause fuel prices to spike, raising transportation costs throughout the supply chain. That expense may be passed along to customers in the form of higher-priced products. Additionally, shifting cultural trends could make a fashion accessory suddenly popular, allowing for a price hike. Or, increase in the rate of unemployment can make that same accessory seem frivolous, forcing a price cut.

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The Best Methods For Price Prediction/Forecasting.

There are different methods of predicting price and they include but not limited to:

• Moving Average
  This is a trend produced by changes in
  time series. It is simple and
  mathematically less reliable.
  It is applied in inventory management.

• Analytical Trend
  This type of forecast describe trend with
  some mathematical function. It is
  simple, clear and well represented
  graphicallyy. The problem with this
  method of forecast is that past
  development CA be over estimated.
  It is applied in the analysis of technology
  development and this is done in a short
  term interval.

• Exponential Smoothing
  It has greater weight which are given to
  those data that have bigger significance.
  It has an advantage of professional
  expertise building in the weight.

• Harmonic Weighted Partial Trend
  It gives different weight of partial trends
  within an examined period of time.
  It is reliable in short period of time. It is
  applicable in employment technological
  development.

• Box Jenkins
  It has a complex management of time
  series.
  The predominant effect are treated
  together; but it has a disadvantage of
  intensive labour.

• Dynamic Factor Analysis
  This combines time series analytical
  methods with factor analysis.

The dynamic Factor Analysis is one of the best method for forecasting, this is because of its dynamic nature of combining time series with analytical method of analysis.

Crypto Asset Chart Graph

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