What is Forecasting?
A forecast is a statement about the future. It is a prediction, estimate, or a determination
of what will occur in the future based on certain set of factors. Forecast can help managers by
reducing some of the uncertainties that cloud the planning horizon, making it difficult for a
manager to plan effectively. A forecast may not always be accurate, but it is critically important in
business since decisions are made every day based on available information.
Forecasts are also used to predict profits, revenues, costs, productivity changes, prices
and availability of energy and raw materials, as well as other variables. The factors in which a
forecast is based may be on any of the following: past data, opinion, or judgment, company data
or perceived pattern related to time.
Major Categories of Forecasting Time Horizons
1. Short-term forecast – it is used for one day to one year, usually used in short-run controls
such as purchasing, schedules, sales and production rates.
2. Intermediate-term forecast – it is used for periods ranging from one season to one to
two years such as revenues, cash flows and budget planning.
3. Long-term forecast – it is used for periods from two to five years such as market trends,
technology, and facilities expansion.
Forecasting Techniques
1. Qualitative Techniques – these are based on qualitative data such as consumer survey,
opinion of sales force, and judgment of executives that are used to forecast the future.
2. Time Series Analysis – it is a statistical technique that is based on historical data
accumulated over a period.
3. Causal Method – is a method used to define relationships among independent and
dependent variables in a system of related equations.
Forecasting Methods or Time Series Methods
1. Simple Moving Average
2. Weighted Moving Average
3. Simple Exponential Smoothing
4. Adjusted Exponential Smoothing
5. Forecast Reliability
, Below example will serve as a reference for solving the different forecasting methods.
Example: Alpha automobile dealer in Region IV wants to accurately forecast the demand for the
Alpha special edition car for the upcoming month. Their distributor is from Korea, it will be difficult
for them to reorder or send back cars if the proper number of cars is not ordered a month ahead.
The dealer has accumulated the following data for the past months from their sales record.
Month Motorcycles
Demanded
January 60
February 70
March 50
April 90
May 10
June 80
July 150
August 70
September 10
October 150
November 130
A. Simple Moving Average
Simple Moving Average is the un-weighted average of a consecutive number of data
points. It can be used as a forecast seasonal adjustment of the data.
𝛴(𝑚𝑜𝑠𝑡 𝑟𝑒𝑐𝑒𝑛𝑡 𝑛 𝑑𝑎𝑡𝑎 𝑣𝑎𝑙𝑢𝑒𝑠)
Simple Moving Average =
𝑛
Example 1: Compute for the December forecast using the three-month moving average.
Before you can compute for the December forecast, you must compute for the previous
months first. Since you are computing for the three-month period, you must start with the April
forecast. First, you must get the first three recent values on the table which is the data for January,
February and March. 𝑛 stands for the total number of data values, in this case 𝑛 = 3 as we are
computing for the three-month moving average.
Note: In moving average, as each new actual value becomes available, the forecast is updated
by adding the newest value and dropping the oldest value before recomputing the average.
A forecast is a statement about the future. It is a prediction, estimate, or a determination
of what will occur in the future based on certain set of factors. Forecast can help managers by
reducing some of the uncertainties that cloud the planning horizon, making it difficult for a
manager to plan effectively. A forecast may not always be accurate, but it is critically important in
business since decisions are made every day based on available information.
Forecasts are also used to predict profits, revenues, costs, productivity changes, prices
and availability of energy and raw materials, as well as other variables. The factors in which a
forecast is based may be on any of the following: past data, opinion, or judgment, company data
or perceived pattern related to time.
Major Categories of Forecasting Time Horizons
1. Short-term forecast – it is used for one day to one year, usually used in short-run controls
such as purchasing, schedules, sales and production rates.
2. Intermediate-term forecast – it is used for periods ranging from one season to one to
two years such as revenues, cash flows and budget planning.
3. Long-term forecast – it is used for periods from two to five years such as market trends,
technology, and facilities expansion.
Forecasting Techniques
1. Qualitative Techniques – these are based on qualitative data such as consumer survey,
opinion of sales force, and judgment of executives that are used to forecast the future.
2. Time Series Analysis – it is a statistical technique that is based on historical data
accumulated over a period.
3. Causal Method – is a method used to define relationships among independent and
dependent variables in a system of related equations.
Forecasting Methods or Time Series Methods
1. Simple Moving Average
2. Weighted Moving Average
3. Simple Exponential Smoothing
4. Adjusted Exponential Smoothing
5. Forecast Reliability
, Below example will serve as a reference for solving the different forecasting methods.
Example: Alpha automobile dealer in Region IV wants to accurately forecast the demand for the
Alpha special edition car for the upcoming month. Their distributor is from Korea, it will be difficult
for them to reorder or send back cars if the proper number of cars is not ordered a month ahead.
The dealer has accumulated the following data for the past months from their sales record.
Month Motorcycles
Demanded
January 60
February 70
March 50
April 90
May 10
June 80
July 150
August 70
September 10
October 150
November 130
A. Simple Moving Average
Simple Moving Average is the un-weighted average of a consecutive number of data
points. It can be used as a forecast seasonal adjustment of the data.
𝛴(𝑚𝑜𝑠𝑡 𝑟𝑒𝑐𝑒𝑛𝑡 𝑛 𝑑𝑎𝑡𝑎 𝑣𝑎𝑙𝑢𝑒𝑠)
Simple Moving Average =
𝑛
Example 1: Compute for the December forecast using the three-month moving average.
Before you can compute for the December forecast, you must compute for the previous
months first. Since you are computing for the three-month period, you must start with the April
forecast. First, you must get the first three recent values on the table which is the data for January,
February and March. 𝑛 stands for the total number of data values, in this case 𝑛 = 3 as we are
computing for the three-month moving average.
Note: In moving average, as each new actual value becomes available, the forecast is updated
by adding the newest value and dropping the oldest value before recomputing the average.
