A 4-step process to demand forecasting

Updated: Apr 11


Forecasting
Photo by petr sidorov on Unsplash

In Supply chain, we say Demand is often a highly volatile parameter and thus it makes demand forecasting both an art and a science.


Demand forecasting forms an essential component of the supply chain process. It’s the driver for almost all supply chain related decisions. While demand forecasting is undeniably important, it’s also one of the most difficult aspects of supply chain planning.


Demand forecasting is important to the supply chain because it is the process by which the strategic and operational strategies are devised. We can think of it as the underlying hypothesis for strategic business activities and the starting point for most supply chain processes, like:

  • Raw material planning

  • Purchasing

  • Inbound logistics

  • Cash flow, and

  • Manufacturing planning

Demand forecasting also facilitates critical business activities, like financial planning & risk assessment. Most importantly, forecast accuracy enables retailers to avoid stock outs and over stocking, improve production lead times, minimize costs, increase operational efficiencies, and improve overall customer experience especially when we talk about build to stock and build to order business models.


Common mistakes in Demand forecasting:

  • Relying on hunches and guesses.

  • Ignoring historical demand/sales pattern

  • Avoiding data cleansing

  • Not using tracking signals i.e. Bias monitoring

  • Avoiding short term forecast accuracy


A guide for starters!


As a matter of fact, there are different approaches being used by companies across the world for their demand forecasting process, however, we will be looking at the basics of what needs to be done and in what sequence to get the maximum benefit.


1. Segmentation:

The very first step in demand forecasting must be segmentation of our products in which we need to define and build segments and group our products based on their importance and forecast volatility; starting from products of low importance and which are easy to forecast.

We end up with the segment that contains products of high importance and those which are hardest to forecast. Most important benefit this process brings in, is that we can customize/optimize processes and analytics for each segment separately, which helps demand planners to focus on critical parts.


2. Data cleansing:

To ensure you are using the best set of historical data for predicting your future demand, it’s very critical to get the dataset clean which might include:

  • Outlier correction

  • Removing promotional impacts

  • Substituting blank values

  • Others

3. Determining pattern in the historical data:

This step involves analysing the historical dataset using time series analysis to identify the data pattern. Based on analysis different patterns could be

Demand Pattern
image source: analyticsvidhya.com

characterised as:

  • Level

  • Trend

  • Seasonal

  • Sporadic









We can use various statistical methods to identify these patterns like:

  • Free hand method

  • Semi average method

  • Moving average method

  • Least square method (Regression)

Based on the identified pattern, next step is to select the most optimum statistical modelling for predicting the future demand.


4. Statistical Modelling:

Once we have the pattern, we choose various statistical models for creating our statistical forecast. Selection of these models depends primarily on the pattern identified in the previous step.


For Level pattern, we can use any of the below statistical models:

  • Simple average

  • Moving average

  • Weighted average

  • Weighted moving average

  • Single exponential smoothing

Similarly, for a “Trend” we can use

  • Double exponential smoothing

  • Auto ARIMA

For “Seasonal” pattern we primarily use:

  • Triple exponential smoothing

  • Auto S-ARIMA

These processes will help us to calculate optimum statistical forecast based on historical demand/sales. We must remember that forecasting is an estimate for the future which can’t be 100% accurate, so as a demand planner our aim should be to minimize the gap between the forecast and actual demand.

High errors in forecast might led either to high inventory at the end of forecasting period or a stock out situation causing low service levels. So, overestimation and underestimation both are not desired.


Forecast error is represented by Et

Et = Dt - Ft


Et = Error during period t

Dt = Demand during period t

Ft = Forecast during period t


We can use various statistical methods to calculate forecast error


  • Average error method

  • Mean absolute deviation (MAD)

  • Mean square error (MSE)

  • Mean absolute percentage error (MAPE)


Along with these methods it is highly advisable to use “Tracking signals” which help us to identify forecast bias. Biasing helps us to understand if a forecast is being consistently underestimated or overestimated or its free from any bias (which is desirable).


We generally use forecast error as our measuring criteria to select the “Best Fit” statistical model.


Impacts of Demand Forecasting on Supply Chain Management


  • Improved supplier relations and purchasing terms

  • Better capacity utilization and allocation of resources

  • Optimization of inventory levels

  • Improved distribution planning and logistics

  • Increase in customer service levels

  • Better product lifecycle management


3 main roles of Demand Forecasting in supply chain management

  • Critical in strategic planning of business

  • It helps to initiate all push processes in SCM

  • It controls all pull process in SCM


Conclusion


To effectively increase profits and mitigate unnecessary costs, every company need to improve demand forecasting and optimize their supply chain. Today, we have various demand forecasting software that uses advanced analytics and distribution metrics to inform business decisions, improve inventory flow and give the resources needed to bring the businesses to the next level.

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