In this article we study the Difference between Forecasting and Prediction it is useful  for production planning and control in mechanical engineering so let's start,

Forecasting is scientific, reproducible free from independent bias and error analysis is possible on it.

Prediction is subjective, mainly intuitive, non reproducible and contains independent bias. Only limited error analysis is feasible 

in production.

Difference between forecasting and Prediction

Difference between Forecasting and Prediction

sr.no	Forecasting	Prediction
1	Forecasting involves projection of past into future.	Prediction involves judgement in governance following taking all available information into the account.
2	Forecasting is more scientific.	prediction is more intuitive.
3	It is relatively free from personal bias.	In prediction It is more governed by unique bias and preferences.
4	It is more objective.	It is more subjective.
5	Possible to error analysis.	Does not possible error analysis.
6	Forecasting is reproducible i.e. every time same result would be obtained  by any particular technique	It is non reproducible.
7	Forecast involves estimating the level of demand for a product on basis of factors and previous data that generated the demand in part of months.	Prediction involves anticipated change into the future. it include even new factors that may result in future demand.

We discuss one important question related to this topic,

What is prediction with example?

An example of forecasting and prediction could be predicting the demand for a product in the market. Let's consider a fictional company that manufactures and sells bicycles. Here's how forecasting and prediction could be applied in this scenario:

Data Collection: Gather historical sales data for the bicycles, including sales figures over different time periods (e.g., months or years), as well as data on factors that could influence demand, such as seasonality, marketing campaigns, economic indicators, and competitor activities.

Data Analysis: Analyze the collected data to identify patterns, trends, and correlations. Use statistical methods and data visualization techniques to gain insights into how various factors affect bicycle sales.

Forecasting Model Development: Develop a forecasting model based on the analyzed data to predict future demand for the bicycles. This model may use techniques such as time series analysis, regression analysis, or machine learning algorithms to make predictions.

Demand Prediction: Use the forecasting model to predict the demand for bicycles in the upcoming months or years. This prediction could include estimates of the total number of units to be sold, as well as predictions for different product variants or market segments.

Production Planning: Use the demand predictions to plan production schedules accordingly. Adjust manufacturing capacity, inventory levels, and supply chain logistics to meet anticipated demand while minimizing excess inventory or stockouts.

Marketing and Sales Strategy: Tailor marketing and sales strategies based on the demand predictions. Allocate resources effectively to target specific market segments or promote certain product lines where demand is expected to be highest.

Performance Monitoring: Continuously monitor actual sales figures and compare them to the forecasted demand. Evaluate the accuracy of the predictions and identify any discrepancies or unexpected deviations. Use this feedback to refine the forecasting model and improve future predictions.

By accurately forecasting and predicting demand for their bicycles, the company can optimize production, distribution, and marketing efforts to meet customer needs efficiently, minimize costs, and maximize profitability. This example illustrates how forecasting and prediction techniques can be applied in a business context to make informed decisions and drive success.

So in this article we discussed about Difference between Forecasting and Prediction information is important for production planning and control hope you understand well, thanks for reading it.

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