Demand Forecasting with AI: Avoiding Stockouts and Overstocking

Demand forecasting is a crucial component of any business strategy, as it enables organizations to plan for the future and make informed decisions about their operations.

According to a report the global demand forecasting market size was valued at USD 3.6 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 10.3% from 2022 to 2030.

This growth can be attributed to the increasing adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in demand forecasting. It can also be attributed to the rising need for businesses to optimize their operations in an increasingly competitive market.

What is Demand Forecasting?

Demand forecasting is a game-changer for businesses looking to stay ahead in the tech industry. By analyzing historical sales data, market trends, and other factors, businesses can predict how much inventory they need to produce or order to meet customer demand.

 Avoiding Stockouts and Overstocking

In companies, especially in the mass distribution sector, stock-outs are often a daily fear. Beyond the customer experience that can be inevitably impacted, since they may find their products at a competitor's and not come back, it also implies considerable financial losses for the involved business.

To avoid such a situation and to optimize inventory management, the company must anticipate its sales and foresee the buying behavior of its customers. If the methods of the past were not always accurate, we can now rely on AI, namely artificial intelligence, based on machine learning, a high-precision innovation already adopted by several giants of the mass distribution.

• Granular Demand Prediction: AI can analyze various data points to predict demand not just for your entire inventory, but for specific products at individual store locations. This allows for targeted inventory management, ensuring the right products are in stock at the right places.

• Identifying Early Warning Signs: AI can analyze trends and identify sudden spikes in demand or disruptions in the supply chain that might lead to stockouts. This allows you to take proactive measures, like expediting shipments or sourcing from alternative suppliers.

• Dynamic Safety Stock Levels:  AI can determine optimal safety stock levels for each product based on factors like historical demand variability, lead times, and reorder points. This ensures you have a buffer to handle unexpected demand fluctuations without excessive stockholding.

What is Stock Out 

To understand how the use of artificial intelligence can be of great service to the supply chain, we must first understand what an out-of-stock situation implies and the reasons for it.

An out-of-stock condition means a significant loss of turnover, not only for the company selling the product, but also for the producer of the sold-out product. Indeed, the customer can easily change his choice to a similar article of a competitor. For all these reasons, it is advisable to avoid any problem related to the supply and to take the necessary measures.

What are the reasons for a stock-out?

Demand Forecasting Issues:

• Inaccurate demand forecasts: This is a major culprit. If a business underestimates how much of a product customers will want (poor demand forecasting), they won't order enough inventory to meet demand. This can happen due to not considering historical data properly, failing to account for seasonal trends, or not being aware of upcoming promotions that might increase demand.

• Unexpected spikes in demand: Sometimes, even with good forecasting, demand can surge unexpectedly due to factors like positive publicity, social media trends, or stockouts at competitor stores.

Supply Chain Issues:

• Supply chain disruptions: Delays in shipments, raw material shortages, or production problems at any point in the supply chain can lead to stock-outs. These disruptions can be caused by events like natural disasters, labor strikes, or political instability.

• Supplier issues: A supplier might be unable to deliver on time, send faulty products, or go out of business entirely. This can leave a business scrambling to find alternative sources for the needed inventory.

• Poor inventory management: Even if a business forecasts demand well, they can still experience stock-outs if they don't have good inventory management practices in place. This could involve not having clear reorder points (when to order more stock), not considering safety stock levels (extra inventory to buffer against unexpected demand), or simply having inefficiencies in warehouse operations.

Why choose AI to prevent stock-outs

Ensuring the availability of its products must be a priority for the company, which implies that it takes a minimum of measures. While the market can sometimes be very uncertain, as was the case with the retail sector during the health crisis, new solutions now make it possible to avoid supply-related worries. One of these solutions is machine learning, an innovation that uses artificial intelligence to analyze data and make highly accurate forecasts.

This solution is all the more effective because effective inventory management is based on a defined strategy, which includes both the characteristics of the business and those of the external environment. Market trends, seasonality, product popularity, carrier strikes, etc. - a multitude of parameters can come into play throughout the supply chain and negatively impact supply.

In order to put all the chances on its side, a company must therefore have control over its supply threshold, but also over the volume of orders and the quantities needed to avoid any risk of shortage or overstock. This requires an increased knowledge of market trends, as well as a mastery of customer behavior and supply chain specificities.

How to prevent out-of-stock situations with AI?

To prevent a stock-out, it is necessary to set up a well-functioning process, not only by anticipating the needs and volumes of stock studied, but also by an effective communication, as much with the actors of the supply chain as with the final customer, and this, to minimize the consequences of a potential error. In all cases, stock-outs and overstocking are events that can be avoided thanks to artificial intelligence. This explains why online retail giants have already embraced this solution, such as Amazon, which has invested heavily and bases a large part of its business on these algorithms. However, even with machine learning, it is necessary to follow a few essential steps to make this innovation an effective tool.

Gathering Data for AI

For AI to truly shine in preventing stock-outs, it needs a healthy diet of data. This data can come from various sources like sales history, social media trends, and even weather forecasts. The more comprehensive this data is, the more accurate AI's forecasts become, allowing for targeted inventory management and a significant reduction in stock-outs.

Analyzing AI predictions

Once artificial intelligence has been able to provide its predictions, it still needs to be understood and analyzed in order to draw the appropriate conclusions. This implies using real experts and specialists, as well as tools specifically designed to visualize the results and help interpret them. In addition, it remains to adapt this data to the expectations of the company and to the issues that led it to implement this solution.

As you can see, this step cannot be improvised. On the contrary, it requires advanced skills and knowledge, both in terms of data and forecast analysis.

Conclusion : 

In conclusion, traditional demand forecasting methods often fall short in today's dynamic market.  AI, powered by machine learning, offers a powerful alternative for businesses to gain a competitive edge. By analyzing vast amounts of data and identifying hidden patterns, AI can predict demand with greater precision, preventing costly stock-outs and minimizing excess inventory. This translates to happier customers, higher sales, and a more efficient supply chain.  However, to harness the full potential of AI, remember to provide it with high-quality data and keep your AI models up-to-date.  Ultimately, AI is a valuable tool, but human expertise remains essential for interpreting its insights and making informed decisions for optimal inventory management.

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