Managing Stock with AI: Small Steps, Big Impact

Many business operators consider increased sales as the main measure of business success. In fact, business growth is not only determined by how many products are sold, but also by how the business manages its resources. One of the problems often faced by micro, small, and medium enterprises (MSMEs) is stock management. Inventory issues seem simple, but their impact on profits is enormous. Too much stock can cause capital to be tied up and products to spoil. Conversely, too little stock can disappoint customers because goods are unavailable when needed. In today’s digital era, artificial intelligence (AI) is beginning to offer new solutions. If AI has been more commonly known for creating content, assisting digital marketing, or answering customer questions, this technology actually also has great potential in helping MSMEs manage inventory more intelligently. AI enables business operators to make decisions based on data, not just on estimates or experience alone. With simple steps, this technology can help MSMEs reduce waste, increase efficiency, and build a more sustainable business. In running a business, inventory is a very important part. However, many MSMEs still manage stock simply. Raw material purchases are often made based on the business owner’s intuition, without being supported by accurate analysis of demand patterns. In food and beverage businesses, for example, excess raw material stock can cause losses because products have a limited shelf life. Vegetables, meat, milk, or other food ingredients can spoil if not used on time. Meanwhile, in retail businesses, too much stock can cause business capital to be tied up in unsold goods. Conversely, a shortage of stock can cause lost sales opportunities, especially when a product is in demand by customers. These issues show that stock management is not only an operational problem, but is also directly related to business sustainability. One of AI’s main capabilities is analysing data to find certain patterns. In inventory management, this capability can be used to estimate future stock needs. For example, a home catering business often has difficulty determining the amount of food to produce each day. On a normal day, the number of orders might only be 50 portions, but on weekends or during certain seasons, demand can increase significantly. With the help of AI, previous sales data can be analysed to determine demand patterns. The system can help estimate which menu items are most ordered, when demand increases, and what the more ideal production quantity is. One of the biggest benefits of AI-based stock management is reducing waste. In the culinary business, for instance, unused raw materials are often a source of loss. A small coffee shop might buy too much milk, food ingredients, or other supporting materials because it estimates the number of customers will increase. However, when the number of customers does not meet expectations, some of these materials must be discarded. AI can help analyse material usage patterns and provide more precise purchasing recommendations. The system can show how much material is usually used in a certain period and provide a warning when stock becomes excessive. This step not only reduces costs but also supports more environmentally friendly business practices because the amount of waste can be reduced. Besides preventing excess stock, AI also helps MSMEs avoid stock shortages. An example can be seen in the online fashion business. A small clothing store sells products through social media and marketplaces. One of its products suddenly becomes popular after being promoted by a content creator. In a short time, demand increases, but the available stock is insufficient. With an AI-based system, business operators can know which products are experiencing an increase in demand. The system can provide a warning when stock levels start to decrease or when purchasing patterns show an increasing trend. With this information, the business owner can procure goods more quickly so that sales opportunities are not lost. Not all products contribute equally to business profits. However, many MSMEs do not yet have enough information to determine which products should be prioritised. For example, an MSME producing snacks has 20 types of products. After analysis using sales data, it turns out that only five products contribute the largest profit. Meanwhile, other products have low sales and require many resources. Through AI-based analysis, business operators can understand: the most popular products, the products that provide the largest profit, customer purchasing patterns, and the best time to increase production. There is still an assumption that AI can only be used by large companies with advanced technology. In fact, MSMEs can start from simple steps. The use of AI can begin with building a habit of digital recording. Sales data, raw material purchases, and stock quantities become the basis for the system to provide better analysis. MSMEs also do not need to immediately use complex systems. Various digital applications currently available have provided features that can be used in stages.