It seems like managing a company's inventory would be easy when there are only a handful of goods available in the business. However, having thousands of items in store, ever-changing demands of the customers, and varying conditions in the market make this task quite challenging.
To satisfy customer demand, a business needs to keep sufficient inventory at hand. However, keeping too many products in stock could tie the money up, cause extra expenses, and finally result in dead stock.
That's why using AI in demand forecasting is becoming more relevant nowadays.
While traditionally businesses only relied on historical sales figures or forecasts made manually, now, using artificial intelligence helps them analyze larger amounts of information and discover various patterns that might go unnoticed by humans. In this way, companies get a great opportunity to make informed decisions related to stocking goods and re-ordering.
Thus, when a business wants to cut unnecessary inventory costs but still ensure the availability of products, it may rely on AI demand forecasting.
AI Demand Forecasting: An Overview
AI demand forecasting is defined as the application of artificial intelligence and machine learning technologies to predict future customer demands on products or services.
Traditionally, demand forecasting is based mostly on previous sales data and static assumptions. For instance, a company can analyze last year's sales and assume that the same goods will work just as well this year.
This method can work in a stable environment. But modern businesses have to deal with much more complex conditions.
AI is capable of analyzing several data points at once such as:
- Previous sales and order data
- Seasonality of the demand
- Customer behavior
- Pricing changes
- Discounts and promotions
- Market conditions
- Product performance
- Regional demands
- External factors that affect purchasing behavior
With all this data in hand, AI can give businesses more flexible forecasts and help them adapt quickly to changes in the demand.
Why Is It Difficult To Manage Inventory?
Inventory is an important asset of any business. But inventory management also has a downside as well.
When a business buys more products than it sells, it does not mean that these goods stay in the warehouse without creating any expenses. The company has to pay for warehousing, insurance, handling, transportation, maintenance, and labor.
Also, there is a risk that the inventory becomes outdated, gets damaged or loses attractiveness.
But having too little inventory is not a good thing either. If the customers cannot buy the product they need, they'll turn to the competition.
So, there is a dilemma:
- Excessive inventory = more inventory costs
- Inadequate inventory = lost sales and dissatisfied customers
AI forecasting can help to find the optimal point between these two extremes.
How AI Demand Forecasting Can Help to Reduce Inventory Costs
There are several ways how using artificial intelligence in demand forecasting helps to cut down the costs associated with the inventory management.
1. Helps To Avoid Excess Stocks
The first advantage of using AI demand forecasting is the possibility to avoid over-ordering products.
The artificial intelligence model analyzes the previous demand together with the current one and predicts how much inventory would be needed in the future.
For example, a company running an online fashion boutique is preparing for a new season. But instead of ordering the same quantity of goods as they were ordered last year regardless of how well they sold, the company can use AI to find out which categories of goods are growing in popularity and which are losing popularity.
It would allow to cut unnecessary stocks and eliminate related costs.
2. Minimizes Stockouts
Reducing the inventory doesn't mean simply reducing the stocks.
A business should make sure that the popular products remain available for customers.
With AI inventory forecasting, businesses will be able to identify those goods that are going to experience increased demand and will need to reorder them in advance before inventory level drops too low.
This feature is especially important for the products that have seasonal demand, fast-changing customer preferences or unpredictable demand pattern.
Better forecasting allows keeping sufficient stock level and avoid unnecessary safety stock.
3. Improves Reordering Decisions
Knowing the time when products need to be reordered is important as well.
Traditionally, manual inventory management is based on the predefined reorder point. But the demand can change even before this point is reached.
Using AI allows continuously analyzing sales and inventory data and make reordering recommendations depending on current conditions.
For example, when a product begins to sell faster than it was initially estimated, AI will identify this trend and allow the company to adjust their replenishment strategy.
This makes inventory management more reactive than proactive.
4. Simplifies Seasonal Planning
Seasonality is a common feature of most of the businesses. Retailers have peaks during holidays. The travel industry experiences peaks as well. The agricultural business is affected by harvesting cycles and the weather. Even the technology industry has peaks when new product hits the market.
Using artificial intelligence allows to analyze past seasonal patterns and combine them with the current behavior of the market.
It provides the company with more solid ground for inventory planning before the peak season.
Instead of responding to the increased demand after it happens, companies can get ready in advance.
The Functionality of the Demand Forecasting Software
Enterprises having huge product catalogs may face problems with manual forecasting.
In such cases, they need demand forecasting software.
Nowadays, forecasting tools are capable of integrating with existing enterprise systems and consolidating sales, inventory, customer and operational data into one place.
Based on the software, enterprises are able to:
- Evaluate demand trends
- Make future sales predictions
- Identify products with fluctuating demand
- Plan inventory replenishment
- Check the inventory status
- Compare forecasted demand with the actual demand
- Make reporting and creating dashboards
- Help with making purchasing decisions
The most significant benefit is that forecasting will be a continuous process rather than a spreadsheet work done occasionally every several weeks or months.
The Ways Predictive Analytics Enables Better Inventory Decisions
Another important technology behind forecasting solutions is the predictive analytics.
Predictive analytics analyzes historical and current data to reveal patterns and predict what may happen in the future.
Regarding inventory management, it may give answers to the following questions:
- Which products are going to be sold more next month?
- Which products may have decreasing demand?
- When it is time to make additional orders?
- Which locations may require additional inventory?
- What impact will a promotion have on product demand?
- Which products have risks of becoming excess inventory?
If the predictive analytics is combined with machine learning, forecasting tools will adapt to the new data and will enable businesses to improve their forecasts constantly and not depend on a single prediction.
Example of How the AI-Based Inventory Forecasting Works
For example, let us take a grocery retailer selling thousands of products.
During summer, there will be a growing demand for certain beverages. Besides, during this period, there may appear products with lower demand due to the change of purchasing behavior of customers.
A traditional system will be based primarily on sales numbers of the previous year.
An AI-based forecasting system will analyze past sales, current purchasing behavior, seasonal changes, promotions, regional demand and other related data.
This system will determine that there is a possibility that there will be increasing demand for a particular beverage in certain locations during the next several weeks.
The retailer can allocate its inventory based on the results of the forecast.
The outcome may be less unnecessary products in low-demand locations and no risk of lack of popular products.
Benefits besides Lowering the Cost of Inventory
Although lowering the cost of inventory is a primary advantage of the AI forecasting, there are other fields of a business which may benefit from this technology as well.
Improved Customer Experience
Customers are more likely to find products which they need if there is proper inventory allocation.
Better Cash Flow
Lowering the amount of excess inventory means that there will be less funds invested in products stored in warehouses.
Improved Planning for Suppliers
Companies can give suppliers more exact expectations regarding inventory needs.
Effective Utilization of Warehouse Space
Proper inventory predictions will help to utilize warehouse space more efficiently.
Faster Business Decisions
Rather than waiting for a report made manually, decision-makers will be able to make decisions based on the updated forecasting.
Is AI Demand Forecasting a Solution for All Companies?
AI demand forecasting can be especially useful for businesses with high inventory, many locations, seasonal demand and volatile customer behavior.
However, implementation of the AI is not just buying of the forecasting software.
There should be high-quality data to generate a good forecast.
Also, there should be a proper forecasting model that matches the needs of a business.
That is why, a company should evaluate its existing systems, data quality, business goals and forecasting requirements prior to choosing a forecasting solution.
Conclusion
Ultimately, inventory management is balancing. Enterprises need enough products to satisfy their customers but not to invest in excessive amount of inventory sitting in warehouses for months.
AI demand forecasting helps to do it smarter.
Analyzing historical information, current trends, customer behavior and other data, AI can help businesses to make better inventory decisions. From prevention of overstocking and lowering the rate of stockouts to better replenishment and seasonal planning, the technology impacts operations directly.
As companies generate more data, manual forecasting methods will become increasingly difficult. The combination of demand forecasting software, inventory forecasting AI and predictive analytics will allow organizations to have the necessary insights to make quicker and more informed decisions.
The aim is not only to lower inventory but to have right amount of inventory at the right place and at the right time.
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