• Artificial Intelligence

How AI Technology Can Contribute to Inventory Management System in Fashion Retail

10 Jul 2022
How AI Technology Can Contribute to Inventory Management System in Fashion Retail

AI-based tools can expand the capabilities of inventory management systems in many ways. Companies successfully employ visual recognition, demand forecasting, and smart recommendations. The use of such techniques reduces expenses, increases sales, and enhances customer experience.

Shops like GAP, Levis, and Old Navy use visual recognition AI. It allows people to view and try on clothes virtually. Digital tools can also send personalized, smart recommendations to users. This feature simplifies the search process for shoppers.

The manufacturing side of fashion benefits from AI innovations as well as customers do. AI solutions can help retail and eCommerce companies optimize supply chain management — typically, by arranging product deliveries.

Moreover, analysis of sales data done by machine learning (ML) algorithms can be used to forecast demand. Also, AI-based software can build optimal delivery routes more effectively than regular programs. 

Finally, AI can enhance the quality of products. Software solutions monitor production processes and alert about any discrepancy with the quality standards.

Basically, ML has the potential to upgrade every stage of the production process. But let’s take a look at the issues of inventory management and the positive influence of ML tools on the area.

Inventory Challenges in Fashion

New fashion trends appear every season. Brands have to produce new designs regularly to stay in business. The main issue is that inventory management in fashion is still mostly labor-based. Here are other difficulties of the fashion industry when it comes to inventory:

  • Excessive waste 

It’s estimated that about 100 billion garments are being produced yearly. Globally, over 30% of all fashion items are thrown away within the first year. It amounts to 92 million tonnes of discarded cloth. Improved ML inventory management aims to prevent overstocking that results in excessive waste. 

  • Delivery routes planning 

Manual delivery routes planning is ineffective. That is why managers use digital technology to facilitate the process. But utility tools do not perform well in the changing business environment. That is why AI-based solutions are needed to optimize delivery routes.

  • Changing trends

Fast fashion trends make most of the brands’ inventories obsolete within months. These fast-flow changes make forecasting and inventory management in the fashion industry increasingly complicated. In this situation, manual analysis is unhelpful.  

  • Channel-specific trends 

Nowadays, brands usually run online stores and several small retail shops. It’s challenging for experts to predict in which particular location inventory will be sold out first. At the same time, knowing this information is vital for allocating the right number of items. 

But is AI capable of solving these issues? Let’s see how inventory management can benefit from artificial intelligence and machine learning.

How AI Is Reshaping Inventory Management System

AI clothing inventory management systems are not autonomous — personnel supervision is still required. However, artificial intelligence can significantly reduce the time and effort needed for inventory system maintenance. Here are some of the chief advantages of AI-enhanced inventories:

  • Reduced stock discrepancies. Understocking or overstocking is no longer an issue. AI can correlate inventory data and consumer demand effectively. Smart software systems can order items and provide shipment details promptly.
  • Stock control. It is another way how AI ML for inventory management helps make the process more efficient. AI-based smart shelves can notify staff about out-of-stock items and request ordering them. 
  • Demand prediction. AI can analyze external data like seasonal trends, preferences of customers, and availability of stocks. The analyzed data can be used to forecast demand, optimize stock inventory, and schedule orders. 
  • Fulfillment procedures. The system can store profiles of the clients that the company has already worked with. It creates opportunities for faster shipping, reordering, and responding to the customers’ needs.

Postindustria uses ML regularization techniques to develop digital solutions that deliver better performance results to our clients. 

Learn how we can help you build custom fashion inventory management software.

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    Amazon Inventory Management System Insights

    Amazon is the largest eCommerce company that provides inventory management services. The company employs AI-driven robots to comply with increasing workloads. 

    Additionally, Amazon implements various AI tools and techniques. They are needed for better warehousing, delivery, and demand forecasting. Here are several examples:

    • Pegasus. A system for item categorization needed to reduce incorrect product sorting. 
    • Xanthus. A modular drive system for warehouse automation enabling employees to configure robotic machines. It is needed to quickly adjust the capabilities of robots to carry different types of cargo. 
    • Distance minimization. The use of AI algorithms to optimize the distance that robots travel to a required destination. 
    • Delivery optimization. The use of machine learning to make AI-based digital solutions plan delivery routes effectively. 

    AI-driven robots and inventory management techniques help Amazon to address inventory issues. 

    Improving Inventory Management Using AI Demand Forecasting

    AI can have a direct impact on inventory management in fashion retail. However, machine learning and big data analytics can also improve inventory processes. And they do so through demand forecasting.

    This feature integrates with AI inventory management software to find out customer preferences. It can also analyze external sources to predict trends and help organize stock replenishment. 

    There are several benefits of AI in demand forecasting:

    • Increased accuracy in predicting demand. AI-powered systems can reduce the demand forecasting error of regular software solutions up to 50%.
    • Personalization. Knowing the audience’s buying patterns can help form special promotion programs. It is done by AI-driven sales data analysis.
    • Marketing and promotional impact prediction. The rise in demand after a marketing campaign impacts inventory management. So, another way AI can improve inventory management is by forecasting the influence of advertising. 
    • Strategic planning optimization. Demand irregularities are difficult to predict using regular software solutions for predicting demand. The use of AI-driven techniques can enhance strategic planning with more plausible demand forecasting. 

    Fashion companies can reduce expenses from improper warehousing, forecasting, and goods delivery. All they need is to employ AI-based systems in fashion retail inventory management. 

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