Using Vision AI to resolve critical challenges in inventory management
Inventory management is crucial in the supply chain. It facilitates inventory tracking of the company’s stock mix (raw material and finished goods) while ordering, storing, using, and selling it.
Poor inventory management, however, can hurt a company’s bottom line due to delayed delivery orders, increased costs, missed sales opportunities, unbalanced inventory, and so on. Identifying and eliminating the root cause of a given error is essential for business revenue and customer experience.
To understand this further, one must understand the critical challenges the inventory management team faces while managing inventory.
Key challenges for inventory management
Inventory visibility is knowing what inventory you have, its status, and its locations. Poor inventory visibility may lead to stockouts, delivery delays, and needless expenses resulting in lost or reduced sales. Businesses must maintain visibility into inventory levels so they can promptly complete client orders, reduce stockouts, shorten shipment turnaround times, and keep an eye on oversells and missed sales.
Recognize obsolete inventory
Obsolete inventory is stock that a company still has after the company should have sold it. According to manufacturing.net, the average firm always has between 20-30% of obsolete inventory.
Stock can become obsolete due to various circumstances, including incorrect predictions of consumer demand, subpar production or marketing, rapid industry changes, and antiquated or insufficient inventory systems.
Addressing this challenge is crucial as it costs your business a lot of money and deposit of unsold products which are now of no use to an organization.
Product demand can be erratic. Multiple variables can influence it, such as changing needs, seasonal demands, product type, vendors and manufacturers, lead time, and more. Other factors, such as natural disasters and significant events like COVID-19, can significantly impact customer behavior and demand for items. According to the US Federal Reserve, demand for consumer products increased, but industrial production could not react rapidly enough to satisfy the sudden spike in demand.
Demand forecasting is necessary to avoid such imbalances between demand and available supply.
How can these challenges be resolved?
In collaboration with Microsoft and Intel, Neal Analytics has developed a vision AI-driven inventory management solution, StockView for Inventory.
StockView uses the strength and adaptability of Microsoft’s Azure Stack Edge devices and the Azure platform to help fill the gaps in the inventory management process.
StockView for Inventory offers an easy-to-use interface to track how and where the stock moves across sources (warehouses). This equips employees with the data and visibility to better manage their inventory, whether they’re in the office or on the warehouse floor.
Leveraging Vision AI for inventory management
StockView can track and manage inventory levels by leveraging warehouse cameras in conjunction with Vision AI running at the edge. Vision AI models can detect empty spaces and gaps in warehouse shelves. The solution will then create alerts informing employees that they either have too much inventory or that inventory levels are low and need to reorder soon.
With adequate model training, the solution can leverage a vision AI model to recognize the various products and SKUs in the inventory. Tracking the unique SKUs in an organization’s inventory, StockView for Inventory, can help discover and flag obsolete inventory. Automating the process of obsolete stock discovery can help reduce the effort and time spent on manual tracking, identifying, and tagging obsolete inventory, allowing warehouse employees to focus on other important tasks.
Activating StockView insights
StockView for Inventory offers comprehensive analytics and dashboarding that can help enhance inventory management on a larger scale.
StockView for Inventory can be integrated with modern, machine learning-based solutions like reporting and forecasting to provide additional data points that can help improve model accuracy.
Curious about StockView? Please visit our StockView for retail blog to learn about our other StockView solutions.