4 Methods to Forecast Demand

# 4 Methods to Forecast Demand

Accurate demand forecasting is a vital procedure for manufacturing in today's industrial world. With operational efficiencies and automation at an all-time high, advanced demand planning can be an extreme competitive advantage.

## What is Demand Forecasting & Why is it Important?

Broadly, demand forecasting is the prediction of customer demand at X time based on historical sales. The accuracy of this prediction can be influenced by seasonality, trends, and relevant influencing factors.

Accurate and insightful predictions are more important than ever to improve:

• Inventory Turnover
• Cash Flow
• Replenishment Optimization
• Operation Efficiency

#### Moving Averages

The moving averages approach creates a series of averages over a period of time. The trend these averages create can be used for future predictions. Moving averages are best used when limited data is available, and influencing factors are unknown or non-existent.

To use a moving average to calculate your next month's demand (P4) you should have the total demand for the previous 3 months (P1, P2, P3) at a minimum.

Formula:

$P_{4}=\frac{P_{1}+P_{2}+P_{3}}{3}$

Example:

Actual Sales
JanFebMar
225190230

$April=\frac{225+190+230}{3}=215$

#### Weighted Moving Average

Weighted moving average is similar to moving averages, except that it places a weight consideration on recent or influenced data. This difference is helpful when considering fluctuating data over a greater period of time. The emphasis on data is determined by a weighting scale.

Weighting Scale:

Flat scale: A variety of scales can be used for this formula, if there is not a known influencing factor on a specific period (sale, seasonality, etc) then a flat sale can be used. Your scale will be the # of time periods being used to calculate the weighted moving average. With the highest value being the most recent data point. If June, July, & August were the time periods used, August would be 3, July 2, and June 1.

Variable scale: If an influencing factor on a time period needs to be considered, the weight cannot be linear. You must decide on a weight multiplier. In this case, a fraction of 1 could be used. In this instance, June and August had normal sales demand, however, in July there was a blow-out sale which tripled sales volume. Less emphasis should be placed on July. In this example, the following weights could be used for June, July, and August; .5, .15, .5.

Formula:

W represents the weight, T represents the time period, and D represents the demand.

$\text{Weighted Moving Average}=WT_{1}(D_{T})+WT_{2}(D_{T-1})+WT_{3}(D_{T-2})$

Variable Scale Example:

MonthActual SalesInfluencing FactorWeight Factor
1190None.4
2610July Sale.1
3170None.4
440Unforeseen supply shortage, limited sales.4

$\text{Weighted Moving Average}=.4(190)+.1(610)+.4(170)+.1(40)=209$

*If your conclusions are vastly out of range with historical data you may need to adjust your weights. Try using historical data without influencing factors to determine a weighting scale that is best for you.

#### Exponential Smoothing

Exponential smoothing is a slightly more advanced and popular method for demand forecasting. This method places an exponentially decreasing weight on older data points. A major limitation to this is that the formula cannot account for influencing real-world factors such as supply shortages, seasonal changes, or promotions.

Formula:

Forecasted sales

α Smoothing constant

Actual sales from the previous period

Forecasted sales from the previous period

$F=\alpha A+(1- \alpha)B$

Example:

This formula is best shown through excel.

1. You will need historical data for actual sales and a smoothing factor.
1. You will need an estimated sales forecast for your first data entry. In this case month 1. As this data isn't always available you can start by entering an exact or similar estimate to your actual sales.
1. Next you will need to add a formula to calculate the demand forecast for your second data entry, in this case, month 2.

$F=\alpha A+(1- \alpha)B$

or

$=(B9*B2)+((1-B9)*C2)$
1. Lastly, drag your formula down to the last cell or current month to find your forecasted demand.

The above time-series forecasting methods can be quickly exercised in your business with access to historical data and a bit of excel work. However, they do not account for the real-life variables that constantly manipulate the manufacturing industry and can require dozens of hours to prepare reports for each SKU.

For these reasons, Flexfab implemented an AI-driven Demand Forecasting System. Rather than running a report for each formula, this software uses a mixture of 14 leading demand planning algorithms and automatically optimizes the predicting algorithm for each SKU! Additionally, it accounts for external variables such as weather, stock markets, supply chain data, etc. We've been able to exponentially increase the accuracy of our forecasting predictions, connect our findings to specific demand driving variables, and find a number of new opportunities.

And it's easy to use. The software was specifically designed so existing systems don't need to be reconfigured, it can connect to any existing custom or out-of-the-box program. Implementation can be as fast as 2 weeks!

### Streamlined Workflow

Reduce connected spreadsheets and feed data directly to a cloud or offline dashboard.

### Fast Implementation

Once data has been received, standard implementation can take less than 2 weeks!

### Low-Velocity Forecasting

SKU-Level optimization is designed to accurately forecast low sales volume SKUs that are still critical to operations.