Cognitive Solutions with Artificial Intelligence In Inventory Management

Inventory management is crucial for effective supply chain automation. Automated vehicles, drones, and robots are automating, almost, the entire process of inventory management whereas Artificial Intelligence and self-learning algorithms help in optimizing inventory management from the data intelligence obtained.

With its thinking capacity, Artificial Intelligence is enabling the supply chain industry with cognitive solutions to effectively manage their inventory and understand the multitude of real-time dynamics that influence the inventory levels. The AI-powered inventory management system will predict situations, recommend solutions, and take actions – either with human intervention or autonomously.

The supply chain and inventory management generate vast pools of data that needs to be analyzed to draw strategies on the basis of real-time data intelligence to maximize business profits. Artificial Intelligence and Machine Learning technologies are now capable to make suggestions and recommendations on reducing working capital on surplus stock, optimization of stocks, alerts on the expiring stocks, etc.

How Artificial Intelligence is Optimizing Inventory Management?

Artificial Intelligence, Machine Learning, and a variety of predictive and prescriptive techniques go far beyond conventional tools and help provide unlimited data intelligence. Let’s see the various applications of Artificial Intelligence in inventory management.

  • Automation

AI enables automation in monitoring the inventory levels, eliminating the need to manually check inventory. This will enhance employee productivity and keeps track of stocks in real-time, thereby, optimizing the inventory. Tracking inventory with AI also reduces the common and major risk of human errors, thus, saving time and operational costs.

  • Customer Satisfaction with Robot Automation

AI automates robots to check and refill the inventory with the help of sensors and the system that is set by the algorithms that guide them. Autonomous robots create real-time data intelligence by using computer vision and machine learning to scan inventory and look for patterns. Voice recognition, laser-based sensors, etc help increase the customer experience (especially in retail) and improve the efficiency of the bot.

  • Predictive Analytics

Demand and supply are the major factors that affect inventory management as product availability is crucial to supply chains. Big data and AI-powered technology can help optimize inventory at all levels of demand and supply by predicting the demand and identifying the supply anomalies. This will enable to take appropriate measures and accurate decisions in optimizing the inventory. Predictive analytics enhances the effective management of expiring inventory. AI induced technology can predict with the data that is continuously updated in real-time.

  • Data Mining

Before the technological advancements, customer relationships were the major source of collecting data. It is now possible to predict and recommend products based on the intelligence gathered through AI and machine learning algorithms. .Data mining analyzes and mines the data to gather insights on demand of products and then optimize their inventory accordingly.

Automated inventory management intelligence helps in cutting down costs, maximizing the sell-throughs, and ensure that the right amount of inventory is maintained in the warehouse. But, while choosing an AI-powered inventory management solution into a pre-existing system, it is important to ensure that the right fit is made out of the business’ information so that a clear understanding can be attained on the predictability of the inventory.

With the integration of inventory management with the latest developments of AI, companies can ensure sustained growth. But with changing patterns in demand, supply, and other data related to inventory, customers, etc., the AI systems tend to learn these changes and repeat the actions. Sometimes, this is time-consuming as AI systems cannot adjust as quickly as humans.

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