Using AI for Demand Forecasting in B2B Supply Chains

Using AI for Demand Forecasting in B2B Supply Chains
Note: AI for demand forecasting in B2B supply chains
Using AI for Demand Forecasting in B2B Supply Chains

Aditi Tripathi

Content Writer

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What is it? An era of global commerce. What’s evident? The market conditions are changing at a relentless pace. What’s it causing? Supply chains are becoming increasingly complex and interconnected. What’s the challenge? Businesses are facing the unbearable pain of inaccurate forecasts leading to overstocking or understocking, missed opportunities, and a failure to meet customer demand.

So, in a world where margins are too thin, businesses need a plan that not only helps them ease the pain but also unlocks the full potential of supply chain management. Enter AI-powered demand forecasting!

According to a Gartner survey, 45% of companies are already using this technology, and 43% plan to adopt AI-powered demand forecasting within the next two years.

But what about traditional supply chain forecasting methods? Well, they rely heavily upon historical data which often falls short in providing accurate and real-time insights into market trends and customer demand.

Sit back and read on to find out how you can reduce your overall operational costs and gain a competitive edge by using AI for demand forecasting in B2B supply chains!

What is B2B Supply Chain Forecasting?

B2B supply chain forecasting is a critical aspect of supply chain management, especially in the B2B context. It helps businesses anticipate what’s coming down the pipeline and keep tabs on their inventory levels by analysing historical data, market trends and current product & service insights. Why is it necessary? Because it is still a nightmare for businesses to be caught off guard with too much or too little stock!

However, forecasting demand accurately can be challenging, especially in a global economy where market trends and customer preferences can change rapidly. 

AI technology offers a solution by providing real-time data analysis and predictive analytics, enabling supply chain managers to make informed decisions and optimize their inventory levels efficiently.

Benefits of Demand Forecasting in B2B Supply Chains

Did you know? Businesses with optimised supply chains experience 15% lower supply chain costs, hold less than half the inventory, and achieve cash-to-cash cycles that are three times faster.

In B2B supply chains, demand forecasting is crucial because:

  • It maintains a seamless flow of goods and services between businesses. It involves analysing demand signals, such as customer orders, market trends, and historical data, to predict future demand accurately. 
  • It helps B2B companies to ensure they have the right products or services available at the right time while avoiding excess inventory or stockouts.
  • It helps in evaluating supplier performance. By understanding customer demand patterns, B2B companies can assess their suppliers' ability to meet demand and make informed decisions regarding sourcing and inventory management. 
  • Lastly, demand forecasting allows B2B companies to optimise their supply chain operations, reduce costs, and improve customer satisfaction.

The Problem with Traditional Supply Chain Forecasting

Traditional supply chain forecasting methods refer to historical data resulting in understocking or overstocking of products, leading to lost sales or increased carrying costs.

Moreover, traditional forecasting methods cannot often analyse real-time data and adapt to dynamic market conditions. This can lead to inaccurate demand forecasts and suboptimal inventory levels. 

How Inaccuracies in Demand Forecasting Affect Business Operations

Inaccurate demand forecasting can have significant implications for business operations:

  • When demand is underestimated, businesses experience stockouts, which leads to missed sales opportunities & dissatisfied customers. 
  • On the other hand, overestimating demand can result in excess inventory, tying up capital and increasing carrying costs.
  • Higher operational costs may occur due to rush orders, expedited shipping, and inefficient production planning. 
  • Service levels may suffer as businesses struggle to fulfil customer orders on time. Business processes may also be affected, as inaccurate forecasts can disrupt supply chain workflows and lead to inefficiencies. 

Introduction to AI-Based Forecasting in B2B Supply Chains

  • Demand forecasting is crucial for supply chain operations as it helps businesses anticipate customer demand and optimise inventory levels.
  • Traditional supply chain forecasting methods based on historical data often lead to missed opportunities and inefficiencies.
  • By leveraging AI for demand forecasting, B2B companies can gain a competitive advantage, reduce operational costs, and enhance customer satisfaction.

AI-based demand forecasting in B2B supply chains involves the use of AI systems and predictive analytics to optimize demand forecasting and inventory management. These AI systems leverage advanced algorithms and machine learning to analyse large datasets and generate accurate forecasts. 

As per a report, over 80% of logistics companies are inclined towards using AI and machine learning by 2030.

The use of AI-based forecasting can result in significant cost savings for B2B companies. By accurately forecasting demand, businesses can optimize their inventory levels, reducing carrying costs and minimising the risk of stockouts or excess inventory.
AI systems can also provide real-time insights into market trends and customer preferences, enabling businesses to make data-driven decisions and improve their overall supply chain efficiency.

Why B2B Companies Should Leverage AI for Demand Forecasting

AI demand forecasting stats

B2B companies should leverage AI for demand forecasting because:

  1. AI technology enables real-time data analysis and predictive analytics, allowing businesses to have a clear understanding of market trends and customer demand. 
  2. By leveraging AI, B2B companies can make more accurate forecasts and optimize their supply chain operations.
    AI-driven demand forecasting also enables B2B companies to scale their operations more effectively. 
  3. By analysing vast amounts of data, AI systems can identify patterns and trends that may not be apparent to humans, allowing businesses to make data-driven decisions and optimize their inventory levels. This, in turn, can result in cost savings and improved overall supply chain efficiency.

Enhancing Accuracy with AI Algorithms

AI algorithms play a crucial role in enhancing the accuracy of demand forecasting in supply chains. These algorithms leverage machine learning techniques to analyse vast amounts of data and identify patterns and trends that can help generate more accurate forecasts.

Improved accuracy results in a 65% reduction in lost sales from out-of-stock inventory situations and a decrease in warehousing costs by approximately 10% to 40%, as per McKinsey Digital.

By leveraging predictive analytics and machine learning, AI algorithms can detect hidden patterns in the data and allow businesses to make more accurate forecasts and optimize their inventory levels. 

By maintaining optimal inventory levels, businesses can reduce carrying costs, minimize the risk of stockouts or excess inventory, and improve overall supply chain efficiency.

Real-time Data Analysis and Its Impact on Demand Forecasting

Real-time data analysis is a key component of AI-driven demand forecasting in supply chains. By analysing vast amounts of data in real time, businesses can gain valuable insights into market trends, customer preferences, and demand patterns.

It allows businesses to respond quickly to changes in customer demand and market conditions, ensuring that they can optimize their inventory levels and meet customer expectations. 

For example, by analysing real-time data on traffic conditions, businesses can adjust their delivery routes and schedules to minimize delays and improve overall delivery efficiency.

Understanding Predictive Analytics & ML For Demand Forecasting

Predictive analytics plays a crucial role in AI-driven demand forecasting. By analysing historical data and identifying patterns and trends, predictive analytics can generate accurate predictions of future demand.

AI technology uses predictive analytics to analyse vast amounts of data and make forecasts based on market trends and customer preferences. 

Predictive analytics in AI also enables businesses to respond proactively to changes in market conditions. By identifying emerging market trends, businesses can adjust their supply chain strategies and make informed decisions to stay ahead of the competition.

By understanding patterns and relationships within the data, generative AI models can provide valuable insights for supply chain managers.

With generative AI, supply chain organizations can forecast demand more accurately, reduce inventory costs, and improve customer satisfaction. By leveraging the power of AI technology, businesses can stay ahead of market trends, optimize production planning, and ensure timely delivery of products to customers.


This is a must-read! → Artificial Intelligence in eCommerce: Unveiling AI's 8-Ball Answers!

Steps to Make Your B2B Supply Chain AI-Ready

To make your supply chain AI-ready, businesses should follow a few key steps:

#1 Evaluate your existing systems and determine the areas where AI can add value. Identify AI systems that align with your business goals and requirements.

#2 Establish clear contingency plans to address any potential risks or challenges that may arise during AI integration. This may include training employees to work alongside AI systems and creating backup plans to ensure uninterrupted operations.

#3 Collaborate with supply chain partners to ensure seamless integration of AI technologies. 

According to a report, artificial intelligence is projected to be valued at approximately US$17.5 billion in the global supply chain management (SCM) software market by 2028.

By working together, businesses can leverage AI to improve the entire supply chain ecosystem and drive better outcomes. 


Say goodbye to outdated techniques and missed opportunities by empowering your supply chain demand forecasting with AI! 

Embrace the power of AI algorithms and real-time data analysis to revolutionize your operations. By integrating AI seamlessly into your supply chain, you're not just reducing costs but also setting the stage for a future-ready business. 

So, gear up your supply chain for an AI-driven transformation, and get ready to witness the magic of predictive analytics and machine learning in action. 

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