What is Supply Chain Forecasting?

Supply chain forecasting refers to the process of predicting demand, supply or pricing for a product or a range of products in a particular industry. It looks at suppliers’ data, whether the supplier provides completed products or parts that are further down the supply chain, and uses it to project how much stock the supplier will have available and when.

Advanced supply chain forecasting uses AI to save time and money, improve accuracy, and help enterprises react to data in real-time. And AI-powered supply chain platforms can assimilate large volumes of forecasting data and provide effective insights to ensure an agile and flexible supply chain operation.

Why Is Supply Chain Forecasting Important?

Supply chain forecasting is essential in e-commerce and a major component of supply chain management. Without forecasting abilities and predictions on future demand, pricing trends, and supply availability, it’s hard for organizations to make informed decisions about tactical, operational, and strategic activities. 

Forecasting enables brands to move forward based on both data and research, from conducting a competitive analysis to predicting future demand based on historical order data, trends, and patterns.

Supply Chain Forecasting Methods: A Closer Look At Demand Forecasting Within Supply Chains

The two different types of forecasting methods in the supply chain are:

• 1.

  Quantitative forecasting: This method uses historical data to determine the future and make sales projections. Based on the assumption that the future will largely mimic the past, it involves the use of formulas to calculate a predetermined forecasting measurement. This information is especially useful if steady growth is anticipated with few operational changes. The disadvantage is that it does not take new developments into account such as market trends or increased competition. It could also be skewed by unusual circumstances such as the COVID-19 pandemic. 

• 2.

  Qualitative forecasting: This data is often used for new product lines or when a business first launches. Common types of qualitative data include surveys and interviews, industry benchmarks, competitive analyses, and more. Industry publications frequently provide information on upcoming developments, market trends, and consumer sentiment changes. And all of these factors should be considered when making financial projections.

5 Quantitative Forecasting Methods

There are several quantitative forecasting methods to use in e-commerce logistics. Here is an overview of the most common methods, how to use them, and when.

1. Exponential smoothing

Exponential smoothing is a sophisticated approach to supply chain forecasting. It uses weighted averages with the assumption that past trends and events will mirror the future.

When compared to other quantitative methods, it makes it easier to come up with data-driven predictions without the need to analyze multiple data sets. 

With the right tools, the exponential smoothing method can be easy to use and is ideal for short-term forecasting.

2. Adaptive smoothing

The adaptive smoothing approach delves deep into understanding the fluctuations between different time periods and identifies intricate patterns within the data.

This methodology empowers businesses to pinpoint specific variables and make more precise decisions. 

To implement adaptive smoothing effectively, automation tools play a pivotal role. These tools are designed to seamlessly capture, compile, and update data in real time.

3. Moving average

The moving average is one of the simplest methods for supply chain forecasting. It examines data points by creating an average series of subsets from complete data. The average is used to predict the upcoming time period and is then recalculated every month, quarter, or year.  

For instance, if you started your business at the beginning of Q1 and want to make a sales prediction for Q4, you can take the sales average of the prior three quarters combined to determine the next quarter’s sales projections. 

It’s important to remember, however, that the moving average method doesn’t take into account that recent data may be a better indicator of the future and should be given more weight. It also doesn’t allow for seasonality or trends. As a result, this supply chain forecasting method is best for inventory control for low order volume. 

4. Regression analysis

Regression analysis works by examining the relationship between two or more specific variables. While there are variations in how a regression analysis is conducted, they all examine the influence of one or more independent variables on a dependent variable.

This is a simple supply chain forecasting method used to measure some determinations using existing assumptions such as seasonality. When compared to other methods, it offers a fast and easy way to make predictions. 

5. Life cycle modeling

Life-cycle modeling is a supply chain forecasting method that analyzes the growth and development of a new product. It requires data across different market groups such as creators, early and late adopters, and the early and late majority. 

The data then determines the future performance and demand of a specific product across multiple markets, which helps brands determine how to distribute and market products, and how long the product will be in demand.

4 Qualitative Forecasting Methods

In many cases, e-commerce brands use a combination of both quantitative and qualitative forecasting methods to get as close to accurate predictions as possible. Qualitative forecasting methods also come in handy when there is a lack of data. available 

Here are the most common qualitative forecasting methods used in e-commerce supply chain forecasting.

1. Market research

Market research can be used to determine whether or not there is strong demand for a product that will support profit goals. 

Market research can be executed internally by marketing or sales experts, or businesses can hire a third party that specializes in market research.

There are different tactics used, including developing stakeholder surveys, conducting a thorough competitive analysis, or interviewing experts in a specific field or industry. 

2. Delphi method

The Delphi method consists of market orientation and judgments within a small group of experts or advisors, which is then sorted, grouped, and analyzed by third-party experts. 

The opinions of the experts are gathered individually to avoid the influence of others’ options which differs from a panel discussion or focus group. The gathering of opinions is outsourced to a third party that analyzes the opinions and information shared.

Once reviewed closely, the information is then summarized with an emphasis on different patterns or trends before handing the findings over to the business for review. 

This method has proved effective and dependable for long-term forecasting.

3. Historical analysis

Historical analysis assesses the sales history of a product in parallel with a present product to predict future sales.

It can be utilized to predict the market’s response to a new product or product line and it can also be collected by looking at your competition’s high-selling products and comparing similar products in your line to determine demand when possible. 

4. Panel consensus

The panel consensus method brings together members of a business across all levels to establish its forecast. It is an open process that allows all the participants to express their opinions and predictions based on what they know.

Characteristics of Forecasting in Supply Chain

• 1.

  All forecasts have inherent errors due to assumptions and hence are always inaccurate.
  Forecasts thus need to include the expected value of forecast, a range specifying the minimum and maximum forecast, and a measure of forecast errors.

• 2.

  Short-term forecasts are generally more accurate than long-term forecasts.
  The forecasting process includes consideration of factors that can influence future demand. Hence, the short-term factors are more predictable than long-term ones.

What are the Challenges of Supply Chain Forecasting?

Demand forecasting helps businesses anticipate customer needs, optimize inventory levels, and plan production and distribution processes accordingly. However, when demand forecasting accuracy falls short, it can lead to significant supply chain problems. Here are the main associated challenges:

1. Excessive inventory or Stockouts:

One of the most common supply chain problems arising from inaccurate demand forecasting is excessive inventory or stockouts. If a business overestimates demand, it may end up with surplus inventory, tying up valuable capital and storage space. On the other hand, underestimating demand can result in stockouts, leading to dissatisfied customers and missed sales opportunities.

Solution: Implementing agile manufacturing practices can help alleviate these challenges. By adopting flexible production systems and responsive supply chains, businesses can quickly adjust their operations based on changing demand signals. This agility enables efficient production planning, reduced lead times, and improved responsiveness to market fluctuations. Additionally, leveraging demand sensing technologies, such as real-time sales data and social media sentiment analysis, can provide valuable insights for more accurate production and procurement decision-making.

2. Increased Costs:

Inaccurate demand forecasting can also lead to increased costs throughout the supply chain. Overestimating demand may result in excess carrying costs, including storage, handling, and obsolescence expenses. Conversely, underestimating demand can lead to expedited shipping, premium freight charges, and lost sales due to stockouts.

Solution: Employing demand-driven supply chain strategies can help mitigate cost-related issues. By adopting a demand-driven approach, businesses can align their entire supply chain with customer demand signals. This entails establishing strong collaboration between different supply chain partners, implementing real-time demand monitoring systems, and adopting lean inventory management practices. This customer-centric approach reduces carrying costs, minimizes stockouts, and optimizes the use of transportation resources, ultimately lowering overall supply chain costs.

3. Customer Dissatisfaction and Diminished Loyalty:

Inaccurate demand forecasting can have a direct impact on customer satisfaction and loyalty. Experiencing stock-outs or delayed deliveries due to forecast inaccuracies can result in frustrated customers who may switch to competitors for more reliable service.

Solution: To ensure customer satisfaction and loyalty, businesses should focus on enhancing their responsiveness and service levels. Implementing a robust order management system can help optimize order fulfillment processes, reduce lead times, and improve delivery accuracy. Additionally, leveraging customer data and feedback can provide valuable insights for refining demand forecasting models and better understanding customer preferences and behavior.

Supply Chain Forecasting Best Practices by o9 Solutions

o9 is trusted by the world's leading companies. Our Supply Chain forecasting software approach excels in providing businesses with actionable insights, allows easy collaboration, and leverages built-in models.

Take the first step to achieve insights into your supply chain forecasts. Make better decisions based on both data and research.

Building a superior forecasting model with AI/ML

AI/ML-based forecasting models have become essential. Learn how to build the right planning solutions now.

Work Email*

I agree to the privacy policy.

Download guide

Learn more

I agree to the privacy policy.

About the author

Simon Joiner

Product Manager of Demand Planning

Simon Joiner is a Product Manager of Demand Planning at o9 Solutions. He has over 20 years of experience in transforming Demand Planning Systems, Resources and Processes in such diverse sectors such as Pharmaceutical, Building Supplies, Agriculture, Chemical, Medical, Food & Drink, Electronics, Clothing and Telecoms. Simon lives in Hemel Hempstead in the UK with his wife and two (grown up) children and in his spare time likes to play guitar, research family history, walk the dogs and keep fit with running.