12 Sep 2022

Overview Of Demand Forecasting Techniques and Impact Of AI/ML On It

A brief on Demand Forecasting and How AI/ML is changing traditional methods.

Overview Of Demand Forecasting Techniques and Impact Of AI/ML On It

Forecasting demand is a critical function for any business, and the accuracy of your forecast directly impacts your ability to meet customer needs. It is an essential process to optimize your inventory levels. Getting it right can be challenging, but with a solid understanding of the basics and best practices, you can develop an effective demand forecasting process for your business.

By the end of this blog, you should understand what goes into a demand forecast and how to get one for your business. Let's get started.

What is Demand Forecasting?

Demand Forecasting assesses and evaluates future demand for a product or service. Businesses use demand forecasts to make decisions about production levels, inventory management, marketing campaigns, and other strategic initiatives.

Demand Forecasting Process

Why is Demand Forecasting required?

Getting accurate demand planning is essential for businesses of all sizes. You'll be stuck with excess inventory that ties up cash and space if you produce too much product. On the other hand, if you don't produce enough, you'll miss out on sales and damage your reputation with customers.

In order to forecast demand accurately in a supply chain, businesses need to consider various factors, including historical sales data, seasonality, customer trends, and macroeconomic indicators. People used traditional methods to forecast demands in the past, but these methods are becoming obsolete each day. Let's have a look at these traditional methods:

Traditional Demand Forecasting Techniques

One common approach to demand forecasting is to use historical sales data. This data can be used to check and identify trends and patterns that can be extrapolated into the future. This approach works well in stable environments with little change from one period to the next. However, it can break down when there are sudden changes in demand, such as new product launches or economic recessions.

Some traditional forecasting methods used in the supply chain industry are:

Traditional Demand Forecasting Methods

  • Moving Averages
    A moving average is a technique that smoothes historical data to identify trends better. This method can be used with weekly, monthly, or quarterly data. Choose the time period you want to use to calculate a moving average. For example, you could use a 3-month moving average, which would involve taking the average of the past three months of data points.
  • Weighted Moving Averages
    A weighted moving average is like a regular moving average, but each data point is given a different priority based on its importance in the chain. For example, you may give more weight to recent data points because they are more likely indicative of future demand.
  • Exponential Smoothing
    Exponential smoothing is a technique that gives more weight to recent data points while still considering historical data. This method is useful for forecasting data with much randomness or noise.
  • Regression Analysis
    Regression analysis is a statistical technique that can be used to find relationships between different variables. This technique can be used to identify trends in historical data and then predict future demand.
  • Bottom-Up Forecasting
    Bottom-up forecasting is a demand forecasting method that starts with planning the demand for individual products or SKUs and then aggregates this data to get an overall picture of demand. This approach is often used by businesses that have many SKUs.
  • Top-Down Forecasting
    Top-down forecasting is the opposite of bottom-up forecasting. This approach starts with estimating the overall demand for a product category and then breaks this down into individual SKUs. This method is much more commonly used by businesses that don't have a lot of historical data for their products.

Choosing the most suitable forecasting method will depend on the type of data you have available and the nature of your business. You may need to tinker and try different techniques to find the one that works best for you.

Problems with Traditional Demand Forecasting Methods

While traditional demand forecasting methods can be helpful, they also have some limitations. Some of the major methods are the following:

  • One problem is that they often rely on a single data source, such as historical sales data, making them less accurate in environments with sudden changes in demand.
  • Another problem is that these methods can be time-consuming and resource-intensive for any supply chain. This is because they require businesses to gather and clean historical data and then use this data to create forecasts. It can be challenging for companies that don't have a lot of data or have data spread across different departments and systems.
  • Finally, traditional demand forecasting methods can be challenging in real-time environments. This is because they often require businesses to make assumptions about the future, such as the shape of a demand curve, which can make it hard to respond quickly to changes in demand.

The good news is that new demand planning methods address these problems. These methods are often more accurate and easier to use than traditional methods.

New Demand Forecasting Techniques

In recent years, several substitute approaches have been developed to address some of the limitations of traditional demand forecasting methods. One of these approaches is called machine learning.

Machine learning is artificial intelligence that allows computers to learn from data. Businesses can use machine learning to create models that can automatically improve over time as they are exposed to more data.

This differs from traditional demand forecasting methods, requiring businesses to create models manually. Machine learning can help businesses save time and resources by automatically creating forecasts.

Another advantage of machine learning is that it can handle big data relatively quicker than traditional methods. This means that businesses can use machine learning to create real-time forecasts that are based on data from multiple sources.

AI-driven Intelligent Demand Forecasting Method

How Can 3SC Help Your Business in Automatically Forecasting Demands?

3SC can help your business automatically forecast demands by providing a platform that uses machine learning to create forecasts. Our platform is designed and developed to be easy to use and require minimal setup, and this means that businesses can get started quickly and without the need for expensive data or software. That's how 3SC's advanced forecasting methods are helping businesses worldwide achieve their full potential.

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