Overall, artificial intelligence in retail or anywhere, is a branch of computer science that aims to enhance software with the ability to analyze its environment using either predetermined rules and algorithms, or pattern recognizing machine learning models, and then make decisions based on those analyses.
Solutions such as automated inventory management, demand forecasting, customer assistance, predictive analytics and more to enhance retail operations.
Machine learning helps forecast inventory, demand and supply in that predictions are not based solely on historic data.
Rather, the technology predicts what will sell, driving enhanced forecasts based on real-time data using demographics, weather, performance of similar items and even online reviews and social media.
Predictions can be made by store, SKU, size, color and other criteria.
The discipline of category management has always played an important role within retailers as well as their CPG manufacturer suppliers.
While the eight steps within the category management process (category definition, category role, category assessment, category scorecard, category strategies, category tactics, category implementation, and category review) have remained the same, with digitalization the discipline is undergoing a massive transformation, and the approach to the process is getting disrupted through the availability of huge volumes of transactional data, customer loyalty data; advancement in hardware technology through better scanners, image recognition devices, sensors and IoT devices and machine learning, and artificial intelligence.
Let’s look at some essential processes and operations carried out by category managers working at retail chains.
They are different in each specific case, but most often CatMans are responsible for negotiations with suppliers and internal communications with analysts and other managers.
CatMans also deal with market and competition analysis, procurement management, demand planning, and pricing.
The list shows how diverse is the scope of responsibilities and processes run by category managers.
But let’s return to our question: Can all these processes be fully operated by artificial intelligence in retail?
Try to imagine an AI-powered solution capable of defining the category goals, analyze the market performance, and then negotiate with suppliers to get the required products on the best terms.
You must admit that any kind of machine could hardly do all these things at the same time with the efficiency of a human being.
But every category manager knows that the strategic processes and tasks outlined above are just one part of the job.
Behind this one, managers have to gather, process, and analyze large volumes of data and get involved in numerous routine tasks related to the strategy’s execution.
And that’s where AI comes into play.
When it comes to data analysis, operations automation, and analytics-based decision making, AI can do the job better than human beings.
And this is good news because AI can finally allow category managers to focus on genuinely important strategic goals.
WORKSHOP – AUTOMATED CATEGORY MANAGEMENT
- The sense of automated category management
- How does automated category management work?
- The role of machine learning in category management
- What are the basic requirements for automated category management?
- Highly dynamic category management with DMSAI
- State-of-the-art technologies: Tangible and concrete
- Developing solution strategies, practical examples and use cases
There are many examples of the use of AI or artificial intelligence in retail. Here are a few examples:
- Recommender systems: Many online retail websites use AI-powered recommender systems to suggest products to customers based on their previous purchases, browsing history, and search queries.
- Personalization: Retail companies are using AI to personalize the shopping experience for their customers. For example, AI can be used to create personalized product recommendations, targeted marketing campaigns, and customized search results.
- Inventory management: AI can help retailers manage their inventory more efficiently by predicting demand for specific products and automatically reordering inventory when necessary. This can help retailers avoid overstocking and out-of-stock situations.
- Fraud detection: Retail companies are using AI to detect fraudulent transactions and protect their customers’ payment information. AI algorithms can analyze large amounts of data to identify unusual patterns and flag potential fraud.
- Customer service: AI-powered chatbots and virtual assistants are being used by many retailers to provide quick and efficient customer service. These AI systems can handle a wide range of customer inquiries, freeing up human customer service agents to handle more complex tasks.
Here are some ways that AI can be used in retail inventory management:
- Demand prediction: AI algorithms can analyze sales data, customer behavior, and other factors to predict the demand for specific products. This can help retailers ensure that they have the right amount of inventory on hand to meet customer demand without overstocking or running out of stock.
- Inventory optimization: AI can help retailers optimize their inventory levels by considering factors such as sales data, demand prediction, and supplier lead times. This can help retailers minimize excess inventory, reduce stock-outs, and maximize profits.
- Automatic reordering: AI can be used to automatically reorder inventory when it reaches a certain threshold. This can help retailers avoid running out of stock and ensure that they always have the products that their customers want.
- Inventory tracking: AI can be used to track inventory in real-time, providing accurate and up-to-date information on the location and quantity of each product. This can help retailers avoid lost or misplaced inventory, and ensure that they have accurate inventory counts.
Overall, using artificial intelligence in retail inventory management can help retailers improve the accuracy and efficiency of their inventory management processes, leading to better customer service and higher profits.
Here are some ways that AI can be used for retail inventory tracking:
- Real-time tracking: AI-powered systems can track inventory in real-time, providing accurate and up-to-date information on the location and quantity of each product. This can help retailers avoid lost or misplaced inventory, and ensure that they have accurate inventory counts.
- Automated tracking: AI can be used to automate the process of tracking inventory, reducing the need for manual input and data entry. This can help retailers save time and labor costs, and reduce the risk of errors.
- Predictive tracking: AI algorithms can analyze historical sales data and other factors to predict future demand for specific products. This can help retailers anticipate future inventory needs and adjust their tracking systems accordingly.
- Inventory visibility: AI can provide retailers with visibility into their entire inventory, from the time a product is received from a supplier to the time it is sold to a customer. This can help retailers identify bottlenecks and inefficiencies in their inventory management processes and take steps to improve them.
Overall, using AI for retail inventory tracking can help retailers improve the accuracy and efficiency of their inventory management processes, leading to better customer service and higher profits.
There are many tools and technologies that AI can use to track retail inventory.
One common approach is to use computer vision, which involves using cameras and specialized algorithms to automatically recognize and track items in a retail environment.
This can be combined with machine learning techniques to continuously improve the system’s accuracy and efficiency over time.
Other technologies that can be used for inventory tracking include RFID (radio-frequency identification) and barcode scanners, which can be used to quickly and accurately identify and track items as they move through the supply chain.
AI can be used in a variety of ways in retail operations. Some common applications include:
- Inventory management: AI can be used to track inventory levels and automatically reorder products when they reach a certain threshold. This can help retailers avoid stock-outs and overstocking, which can save money and improve customer satisfaction.
- Customer service: AI can be used to provide personalized recommendations and support to customers, such as through chatbots or virtual assistants. This can improve the customer experience and help retailers provide more efficient and effective support.
- Pricing optimization: AI can be used to analyze market data and customer behavior to determine the optimal pricing for products and services. This can help retailers maximize their profits and remain competitive in the market.
- Fraud detection: AI can be used to identify fraudulent transactions and prevent losses from fraudulent activity. This can be done through the use of machine learning algorithms that can analyze transaction data and identify patterns that are indicative of fraud.
Overall, the use of AI in retail operations can help retailers improve their efficiency, profitability, and customer satisfaction.