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Since the pandemic, logistics businesses have evolved rather quickly. e-Commerce linked logistics demand spiked drastically in the event of COVID-19. These sudden changes put logistics companies in a turmoil, wreaking havoc financially. Therefore, these companies are now making strides in the area of demand forecasting in logistics to prepare for such instances.

What is demand forecasting?
The process of predicting the demand for goods and materials is called demand forecasting. Without a strong demand forecasting system, companies risk overstocking warehouses or face shortage of stock.

What is demand forecasting in logistics?

Demand forecasting in logistics is predicting the future demand for goods, storage, transportation, warehouse logistics and related logistics services. Forecasting is done by evaluating historical data about orders, delivery, rates, and customers during different periods. This is then analysed against market trends and other factors to make informed predictions about consumer demand.

Demand analysis and forecasting helps businesses become more resilient and act with agility in circumstances like COVID-19. This type of predictive analysis helps companies change their shipments and warehousing strategies to accommodate the changing needs.

Importance and objectives of demand forecasting in logistics

The primary goal of demand forecasting in logistics is to identify the full range of possibilities facing a company in terms of their logistics requirements. The need for demand forecasting gains importance in periods like COVID-19 as we have witnessed first-hand the stress placed on logistics chains in these situations.

To understand the significance of demand forecasting you must understand the benefits of demand forecasting:

  • Monitoring the weather and news to find out problems that can cause disruptions in the chain
  • Keeping an eye out for seasonal demand fluctuations to increase/decrease routes and the number of trucks
  • Creating multiple logistics models to estimate the most profitable transportation modes (road, ocean, rails, air) for a shipment.
  • Gathering freight data and market trends to accurately estimate load volumes and capacity
  • Collect data to analyse where it is better to use spot price over contract rates or vice versa to avoid bad investments

This data gathering and analysing is what will help companies accurately predict demand fluctuations and make changes to their business accordingly.

Nowadays, the biggest benefits of demand forecasting are realised using demand forecasting software.  These platforms utilise quantitative analysis techniques and qualitative inputs to increase agility and flexibility of their supply chain. They present actionable and accurate demand forecasts using deep analytics and advanced algorithms to turn real time data gathered by the software.

Note: Logistics companies usually use a combination of models and formulas for predictions e.g., logistics cost analysis, economic order quantity in logistics, and more.

Types of demand forecasting

There are different types of demand forecasting:

  • Short-term forecasting: This is done for a short period of time, generally for six months or less than one year. Short-term forecasting is useful to make tactical decisions.
  • Long-term forecasting: Long-term forecasts are for a longer period, two to five years or more. It gives data for major strategic decisions regarding the logistics network design of the firm’s logistics operations.
  • Macro-level forecasting: Forecasting that involves scrutinising the general economic environment, national income and general level of employment and other things tht are disrupting commerce.
  • Micro-level forecasting: When predictive analysis is done gathering data specific to a particular industry or customer segment then it is called micro-level forecasting.

Methods of demand forecasting

Usually, statistical methods are used to forecast demand in logistics. These methods of demand forecasting utilise historical and cross-sectional data to derive the long-term demand for a product.

The statistical methods include:

  • Trend projection: This technique analysing historical data and identifies patterns.
  • Barometric method: Barometric demand forecasting analyses current events to predict the future. Under this method various economic indicators are analysed and then put into an equation to generate a forecast.
  • Econometric method: The econometric method uses complex mathematical formulations to analyse datasets and variables based on current trends. This is again analysed against historical data to generate a forecast.

Steps in demand forecasting

Before beginning demand forecasting it would be a good idea to know where to start. These are the steps involved in demand forecasting:

Step 1: Identifying the objective and the time period

You should first look at the purpose of doing an in-depth analysis. Are you looking to find out the increase in demand for goods in one segment or multiple segments? Are you planning to increase your delivery area and looking to gather data on demand for products in this area?

There might be multiple other requirements. Pinpoint on the objective and then determine the types of demand forecasting you will be doing.


Step 2: Choosing a method

Once the objective and the time perspective is set the method for performing the forecast must be selected. Determine whether you will be using trend projection, barometric and econometric methods.

Each method varies from one another in terms of the purpose of forecasting, type of data required, availability of data and time frame. Therefore, match your data requirements with the purpose of the forecast.

Step 3: Collection of data and data adjustment

Based on your purpose and time period in question collect the required data for analysis.

Step 4: Estimation and interpretation of results

Once the required data is collected the final step is to estimate the demand for the time period specified. Usually, the estimates are mathematical calculations which are presented in the easy and usable form like bar graphs.

Why should companies adopt demand forecasting in logistics?

Demand forecasting in logistics assists logistics managers in optimising logistics operations and making informed decisions about resources available. Here are some of the most important reasons why demand forecasting in logistics is essential for businesses:

  • Helps companies to make informed and intelligent decisions about production planning, inventory management, and supply chain operations.
  • Helps in calculate profit margins, cash flow, and capital expenditures for managing the logistics chain
  • It helps in demand planning and future capacity planning
  • Assists in better allocation of resources


How does demand forecasting affect logistics?

Demand forecasting predicts the future sales figures thereby allowing businesses to manage inventory. This means that if there is a spike in demand the logistics chain can be optimised to meet customer expectations.

What are the five 5 steps of forecasting in logistics?

Steps in demand forecasting includes:
  1. Identifying the purpose
  2. Specifying the time period
  3. Choosing a method
  4. Collection of data
  5. Evaluating the data

Priyanka Babu

Priyanka is a seasoned content marketing professional with more than 6 years of experience crafting various forms of business and technology sector content. Her insightful writing tackles critical issues faced by small-scale manufacturing businesses. Priyanka’s clear and concise communication empowers businesses to make informed decisions and thrive in today’s dynamic business environment.