AI in Logistics for Demand Forecasting [2022] : Forecasting Benefits & Importance

Daten & Wissen / Nov. 11, 2021
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Many logistics companies have been managing their work just by using pen, phones, and paper for decades but there is also the majority of companies that have started transforming their logistics management using AI solutions.

In the logistics industry, the majority of business growth is dependent on demand and supply combination. The more accurately they predict the demand of a product the more growth they see in their business.

AI is one of the great technologies through which demand prediction or forecasting can be made accurate and fast which leads to business growth and makes it easy to compete with competitors.

Someone has said so true that “if you want to grow your business then your business needs to change & innovate according to market needs”. It also applies to the logistics sector where the majority of tasks are repetitive which could be automated through an Artificial Intelligence solution.

What is Demand Prediction or Forecasting?

Demand forecasting or prediction is the method for companies to correctly predict the demand of the products & shipments all around the supply chain.

Benefits of Demand Forecasting in Logistics

If logistics companies are not able to predict the products demand accurately then there is more chance that they will struggle to fulfill the needs of the market and will create a significant gap in the chain which will lead to revenue loss.

AI technology has great potential to predict the future demand of the products using demand forecasting algorithms which could increase the overall revenue of the logistics companies.

We have listed the major benefits of demand forecasting in the logistics industry.

Increase Employee Efficiency

The task of locating the assets & checking how full they are is a very important but time-consuming task that degrades the efficiency of employees. 

Data analytics algorithms have the ability to create insights that can predict the location of assets and can check the remaining space. After getting the proper data employees can focus on their operations rather than these time-consuming tasks.

If your company wants to compete with competitors then you should integrate advanced AI solution which has the potential to transform the overall business.

Operation Optimization to Reduce Costs

Many logistics companies don’t realize that half-filled trucks and containers can lead to major revenue loss hence it needs to be optimized for reducing the extra costs and to increase the potential revenue.

Time Series Prediction-based model can predict the exact amount of logistics that need to be placed in trucks with every corner filled out which may reduce the extra costs that were getting wasted.

Many logistics companies are leveraging the business transformative power of predictive analytics technology which has resulted in overall productivity improvements which lead to revenue growth.

Dynamic Costs Optimization

Every logistics company wants to sell its products for the right price at the right time to get the best return on its investment based on the current demand, supply, and market status.

Logistics companies can leverage demand forecasting to get the best fit price which may help them to sell their products for the right price.

Predictive algorithms understand the capacity, market trends, and inventory then based on this it forecasts the accurate cost for the particular product and then companies can sell their products at the right price to compete with their competitors.

Ace Fleet Repositioning

There are so many logistics companies that are present in more than 50+ different locations with so many units, hence companies need to achieve a more efficient fleet repositioning schedule.

Major times when transport vehicles reach a particular location to deliver the logistics the importer can demand a bit more logistics which makes it difficult for a logistics company to travel another long-distance to export fewer logistics. This situation could be avoided if they would have predicted this scenario early.

Where the Demand Forecasting solution comes into play, Predictive algorithms have capabilities to extract past data and predict demands with greater accuracy.

By leveraging Demand Forecasting solutions logistics companies can forecast the demand can offer more flexible service to their customers.

Many logistics companies are integrating predictive algorithms in their system to predict the demand for particular products and through this, they can do fleet management with enhanced productivity.

Extra Logistics Assets Selling

There are so many numbers of trucks that come empty during their return journey which causes the underutilization of the logistics assets which may affect the companies revenue in a negative way.

Those companies who are using the proper demand modeling then they can sell the extra units which may reduce the inventory costs.

Predictive algorithms take the historical data of the company and then evaluate the future demand of the logistics with higher accuracy.

Predicting the demand of logistics is not a tough task for AI solutions but it will take more time & money when humans will try to do the same hence to grow your business you should integrate the AI solutions which may boost your business.

The days are gone when companies were spending more time & lots of money to predict the demand of logistics now this is the era of Artificial Intelligence which is greatly impacting the business by providing best-fit solutions.

The main aim of an AI is to solve the most complex problems in less time by achieving great accuracy which may increase the revenue potential of any business.

Importance of Demand Forecasting in Logistics 

Predicting Product Demand

Many logistics companies struggle to forecast the accurate demand of their products so it results in revenue loss.

But forecasting algorithms have great potential to forecast the nearly accurate demand of the product based on historical data.

Reduce Inventory Stockouts

If the company does not know how much and for how long the inventory of the product is to be kept, then the problem of overstock comes due to which the company has to face a huge loss, so it is necessary that the company should keep the stock of its product according to the requirement.

By using the demand forecasting solution, the company can find out how much the demand for the product will be, accordingly, they can keep the inventory stock and the problem of overstock will be solved.

Customers Satisfaction

It is important for any product focus company to fulfill their customer satisfaction and they can do this with the help of demand forecasting.

By using demand forecasting solutions logistics companies can find out which product is going to increase in demand and what customer likes the most, and then according to that the company can manufacture its product.

Process Planning

When the company is well aware of the demand of the product, then they can make a plan according to the need for their product & which results in saving time & cost.

Read Also:

Power of AI in the eCommerce Industry

Let’s Wrap It Up

The overall business growth depends on the overall productivity of the work system. If system work is not productive then it will consume your extra time & money which could stop your business transformation.

We are dedicated to providing the best customized AI solutions which have the potential to transform the growth of logistics industries. To compete with competitors, logistics industries need to integrate AI in logistics for demand prediction which may potentially grow the business.

FAQ

Q: How does AI help in logistics?

A: Logistic companies are using AI to innovate various inventory processes such as data collection, process optimization, demand forecasting, modifying orders, increasing efficiency, etc.

Q: Which company is using artificial intelligence in improving logistics?

A: The main use of AI in Logistics is to automate repetitive processes and save money. Amazon and Google also use artificial intelligence solutions to manage their logistics.

Q: What are the 4 types of forecasting?

A: 4 types of forecasting methods: 1) Time series model 2) Econometric model 3) Judgmental forecasting model 4) The Delphi method.

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Daten & wissen

Daten & Wissen is the team of expert AI engineers to help your business to embark on a transformational journey with the adoption of this futuristic technology.

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