## Thursday 3 January 2019

A moving Average or in the language of Indonesia is call the moving average is one of the simple business of forecasting methods and is often used to estimate the conditions in the future by using the collection of data of the past (historical data). In operations management and production, data collection here may be the sales volume of the historical company.

The period of data collection can be either annual, monthly, weekly even Daily. Forecasting method of Moving Average is often used in forecasting business as market demand forecasting (demand forecasting), technical analysis of stocks and Forex movements and predict business trends in the future come.

## How to calculate moving average

Basically, the sense of Moving Average or moving average forecasting is a method that calculates a value average order time and then used to estimate the value of the next period. The Moving Average or moving average obtained via summation and average rating search from a certain period, and then remove the value the longest and add a new value.

Moving Average method is better used to calculate the data are stable or data that do not fluctuate with the sharp (the data changes very drastic rise and fall). This is because the data at each period-given equal weight and therefore cannot represent certain periods or specific data which is the last period which is usually rated as the best in data describe conditions up to date.

Therefore, the come up methods of the Moving Average of the others to try to overcome it, a method of moving average that another such method of Weighted Moving Average (Weighted moving average) or abbreviated with WMA and Exponential Smoothing Method (method of multilevel Refinement). Whereas the method of Moving Average that simple is often referred to with the Simple Moving averages or shortened with the high school.

## The formula of the moving average

The Moving Average formulas or moving average are as follows:

MA = ΣX/Number of Periods

Description:

MA = Moving Average
ΣX = the overall Summation of all data time period that counts
the number of Periods = total Period moving average or it can be written:

MA = (n1 + n2 + n3 + ...)/n

Description:

MA = Moving Average
n1 = first period data
n2 = second period data
n3 = third period onward data
n = The number of the period moving average

### Examples of cases and how to calculate Moving averages

Xyz company engaged in the manufacturing of mobile phones like to predict sales for April and may using data monthly, beginning from the month of January. The average period is 3 months back. Following are the results of the methods and calculation.

 Moon Sales (units) Estimate (unit) January 22,500 – February 37,500 – March 30,000 – April ? May ?

### The solution:

Estimated Sales for the month of April is:

(1) MA April = (22,500 + 37,750 + 30,000)/3
(2) MA April = 90,000/3
(3) MA April = 30,000

So the estimated phone sales in April is around 30,000 units.

We can continue again for the month of May using the calculated estimate of the data or by waiting for the actual results in the month concerned. For example, the actual data in April obtained is 35,000 units, then the calculations are as follows:

(1) MA Mei = (37,500 + 30,000 + 35,000)/3
(2) MA Mel = 102,500/3
(3) MA May = 34,167

With the obtained calculations that estimated phone sales for May is about 34,167 units.

Note: for the calculation of the month of May, sales in January were removed and replaced with sales results in April. This is because the calculation of the Moving Average or moving average is 3 monthly.

We can create a table of sales forecasting with a table like this:

 Moon Sales (units) Estimate (unit) January 22,500 – February 37,500 – March 30,000 – April 35.000 30,000 May ? 34.167

We can continue this table after getting the actual charted data. The following are examples of tables and charts forecasting calculations or estimates sales along with actual sales data.

#### 1 comment:

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