The Impact of Artificial Intelligence (AI) and Its Subset Machine Learning (ML) on Demand Forecasting and Inventory Planning

Impact of Artificial Intelligence (AI) on Demand Forecasting and Inventory Planning

What does it take to increase or supercharge demand for a product? A global pandemic, a gigantic financial crisis, or a famous advertising campaign? The correct answer is that it’s not up to you. The reality is that today, your supply chain can change on a dime. Thanks to the paradigm shift in consumer buying behavior, predicting purchase patterns has become complex.

Post-pandemic, the world has embraced e-commerce, making supply chains busier than ever. Long queues in stores are things of the past; your customers now need products delivered to their doorstep as soon as possible. More than anything else, now the chaos is all about maintaining sufficient inventory and not losing a client because of stockout, or not overordering and then having to manage bloated inventory.

Predicting demand is challenging; you never know which social media trend can boost or lengthen the demand for your product. This is the exact point at which the traditional forms of forecasting become unreliable. Businesses are unsure if they can trust the historical data-based prediction approach or go with whatever their gut suggests.

The quandary has been resolved by an algorithm-based prediction method known as machine learning, a subset of artificial intelligence. AI/ML-enabled demand planning software gives you the most accurate prediction. However, according to a Gartner survey, only 45% of supply chain businesses employ machine learning. 

So, what does AI do differently that makes it so desirable for companies? Well, for starters, AI analyzes the patterns of all your data sets and uses them to predict future occurrences or demand. It also tracks every segment of your supply chain, from when a product sells to how many products are sold in a particular time frame, from identifying when stock will run out to keeping tabs on which products are not performing well and may need a push.

Here are some of the other ways AI will impact demand forecasting and inventory planning:  

    1. Improving automation and reducing the possibility of human error 

The preciseness of your demand forecasting software depends mainly on the data you feed it. There are numerous sources from which businesses collect data and feed it into an ERP. It is a tedious job to consolidate all the data into a spreadsheet and then try to understand the patterns and predict demand. Ultimately, it will be highly error-prone and deviate from actual numbers.  

Using AI ensures proper data consolidation and interpretation. It saves you days that can be further utilized in planning. It also automatically updates the data in the ERP by integrating with your data sources, which reduces the likelihood of errors. 

     2.Automatic update purchase for replenishment 

These days no business can anticipate when and from where a small factor can disrupt the demand for their products and services. During these unpredictable times, it’s essential for companies to adapt to the fluctuations and create ordering plans for products. Using AI will help you respond to ad hoc requirements with proactive planning. Along with its subset machine learning (ML), AI improves your forecasting.

    3. Work towards eliminating understocks and stockouts 

The trick of the trade for demand forecasting is to enhance inventory management. Businesses aim to order just the correct number of products, which fulfills the manufacturing requirements, so they are not running out of stock or spending resources on managing excess inventory.   

AI enables you to analyze demand and supply in real time, ensuring optimal inventory management and an optimal purchasing plan.

    4. No historical data? No problem! 

Many people assume that to get precise technology-based forecasting you need to have piles of past data. Not anymore; with AI-powered demand planning software, you can now achieve accurate forecasting even for newer products. Machine learning (ML) uses forecasting models of similar products to predict, and when new data comes in, the models update automatically.

    5. Mastering demand and supplier unpredictability 

Most of your inventory management depends on your suppliers and their lead times. Uncertainty is the norm now, so a typical lead time of 15 or 20 days (about 3 weeks) can easily lengthen to 40 or 45 days (about 1 and a half months). These changes disrupt supply chain forecasting. AI-enabled software takes into account lead time changes and revises ordering plans accordingly.

In the end 

Demand forecasting and inventory planning are probably the two most essential operations in any supply chain. Still, companies rely on traditional methods of prediction. To gain a competitive edge, you need to bet on today’s technologies. Artificial intelligence is not the talk of the future anymore. It is helping your competitors to provide customers with a pleasing experience. Demand Planning Software powered by AI has become necessary for businesses to make intelligent decisions and devise long-term plans.