Artificial intelligence (AI) is well-known for its ability to make data-driven decisions, but there is a lesser-known branch of AI called natural language processing (NLP) that is starting to turn heads.

What is NLP?

The origins of NLP can be traced back to the 1950s, but it had minimal use back then. More widespread use began in the late 1980s and early 1990s. However, it took the introduction of deep learning (DL) techniques and the integration of AI for NLP to become the powerful and sophisticated tool many companies use today. NLP is still not in the mainstream conversation in the way that AI and machine learning (ML) are, but that is quickly changing as more and more businesses and organizations are slowly discovering this technology that promises to alleviate many issues with managing inventory.

NLP enables software to understand and process language in the same way as humans do. It works by combining rule-based human language models with DL, statistical and ML models. This helps to connect large amounts of natural language data, and allows software to understand human language. If you use chatbots, language translators, voice-activated assistants and similar services, then you’ve already used this technology, which is now being increasingly implemented for managing inventory.

How Can NLP Work with Inventory Management?

Using NLP in inventory management enables workers across all departments to engage in their workflows with insights that help them monitor inventory, be proactive when ordering supplies, and understand future needs. The power of NLP helps organizations discover valuable insights from data they already have, continue to produce, or from outside sources. This allows for insightful decision-making, engaged teams, and competitive advantages.

Unlocking Efficiency with NLP in Inventory Management

Workers receive and produce data daily through emails, invoices, customer chats, reports, and in other ways. This large amount of data has tremendous value for every organization if they can only synthesize it. NLP can process and extract critical information from this data for inventory and supply chain management. Using ML and keywords, NLP can give valuable insights related to your inventory management to unlock efficiencies.

NLP uses that data to help engage with suppliers, shippers and the warehouse floor, optimizing the supply chain and workers, who can now ask complex questions to the software, and get answers that provide simple, actionable insights, allowing them to make data-driven decisions.

Automating Inventory Insights: The Role of NLP

NLP can play a pivotal role in automating inventory by processing and interpreting the large amount of unstructured data that businesses have available, such as emails, customer communications, reports, social media, and more. Because it understands human language, NLP can extract useful information that helps with stock levels, supply chain fluctuations, logistics, and workforce issues that might impact inventory. This data helps management and other decision-makers accurately forecast and identify potential issues before they become problems. Now, businesses can quickly adjust to market changes based on data-driven insights.

From Text to Action: Leveraging NLP for Demand Prediction

When forecasting future client demand, NLP analyzes data from market trends, internal unstructured data, historical data, social media, customer reviews, and other sources. Combining NLP with ML can gauge consumer sentiment and the logistics network to help reduce overstocking or other issues while enhancing inventory and production efficiency. By understanding all these distinct but connected data variables, NLP empowers businesses to master demand prediction and have the right supply of inventory levels at the right time.

NLP-Driven Supplier Communication and Risk Mitigation          

NLP can automate supplier interactions, enhancing relationships and promoting understanding. NLP analyzes communications from suppliers, including reports, emails, social media posts, and more, to identify key trends, industry information, and potential interruptions in the supply chain, present or future. Using sentiment analysis and predictive analytics, NLP identifies any issues with suppliers so they can quickly respond to and maintain supply chain continuity. Automated alerts can be programmed in with pre-determined responses to mitigate any challenges. Responses could include finding alternative suppliers, adjusting inventory levels, or telling humans to talk to suppliers. This also helps with risk mitigation and maintaining positive relationships with your vendors.

Inventory Insights at Scale: Scaling NLP Solutions

Scaling NLP solutions requires businesses to expand their data processing and storage capacity, and integrate NLP in the same way they incorporate AI and ML into their existing systems. Expanding data sources and training algorithms with new and more extensive data sets will enable analysis and promote accurate forecasting. Businesses must integrate NLP into their existing ERP (enterprise resources planning), CRMs (customer relationship management), and supply chain management software. Additionally, organizations must automate inventory management, including order processing and supplier outreach, to reduce manual labor costs and to have their NLP manage inventory more effectively.

NLP, along with AI, is a transformative technology that can, with proper human oversight, reduce supply chain challenges even before they occur, thus potentially saving companies substantial time and money.

Nikhil Koranne is an assistant vice president of operations at Chetu.