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Real-time Insights: The Role of AI in Inventory Visibility


Accurate and Real-time visibility into inventory is critical to the operations of any business that resells, assembles or manufactures any product. According to recent statistics, the average level of inventory accuracy in US retail operations stands at a modest 63 percent*. In this article, we explore the most critical & common inventory management challenges faced by businesses and the role of Artificial Intelligence (AI) in addressing these challenges.


1. Inaccurate Product / Material Data

One of the challenges that organizations often face is inaccurate data, stemming from errors in data entry, barcode scanning, or system glitches.  Inaccuracies in product quantities, descriptions, or other essential details can lead to misinformed decision-making, delays in order fulfillment and operational inefficiency.


Solution - AI-Powered Data Validation

AI systems can perform data analysis on historical inventory data, identifying patterns and anomalies to understand common errors in the inventory management process. The models are trained using a diverse dataset and can, for example, identify duplicate entries by comparing incoming data with the existing database, detecting patterns associated with missing information, and flagging potential issues for further investigation, whether it's incomplete product details or absent barcode scans. In real-time, as data flows into the system, AI systems can continuously assess and validate, triggering immediate alerts or corrections to ensure that product / material data remains accurate and current.


Use Case - Walmart, being one of the world’s largest retailers, has a vast inventory to manage across its thousands of stores. To handle this efficiently, Walmart uses AI-powered data cleansing techniques to identify and rectify errors and duplicates in their inventory data. Timely identification and rectification of irregularities in inventory data has helped Walmart prevent costly errors such as overstocking or stockouts.


2. Unreliable Stock Numbers

One of the most common challenges faced by businesses is the lack of accurate inventory data or differences between recorded inventory numbers and physical inventory. Manual tracking, external factors like theft or misplacement, and operational gaps contribute to inaccuracies. These challenges can cause stockouts, overstock situations, and hinder data-driven decision-making.


Solution - Computer Vision and Machine Learning for Automated Recognition

Implementing computer vision technologies can automate the tracking process by visually identifying and counting items in the warehouse. Cameras placed in the warehouse capture images of inventory, which are then processed through computer vision algorithms to recognize objects, shapes, and patterns. The system identifies and classifies each item, associating it with the inventory database. Real-time counting and tracking eliminate the need for manual counting, minimizing errors and automatically updating inventory levels.


Use Case - Amazon's automated warehouses use a robotics solution with integrated computer vision called Sparrow to track inventory. Sparrow robots can “see” and “learn” from their environment. They can identify items, understand their location within the warehouse, and make decisions based on this information.  Amazon utilizes around 45,000 robots across 20 warehouses, cutting warehousing costs by 20%. It plans to expand robotics in fulfillment centers, potentially saving $22 million per automated warehouse.


3. Warehouse Inefficiency

The inventory challenge of Warehouse Inefficiency refers to the issue of suboptimal management and utilization of warehouse resources. This challenge encompasses various factors such as inaccurate inventory tracking, poor layout design, inefficient picking and packing processes, inadequate storage space utilization, and ineffective inventory replenishment strategies. These inefficiencies can result in stockouts, excess inventory, increased labor costs, and decreased productivity. 


Solution - Dynamic Slotting 

Machine learning algorithms analyze historical sales to identify patterns and consider seasonal variations, leading to strategic item placement closer to the packing area for high-demand products. AI algorithms can also optimize storage space by grouping items with similar storage conditions, ensuring continuous real-time adaptability to changes in demand and order patterns. This dynamic approach enhances overall efficiency and responsiveness in inventory management.


Use Case - Siemens Healthineers uses AI and SAP Integrated Business Planning (IBP) to optimize the allocation of their medical devices. AI predicts demand at different locations, allowing strategic positioning of stock. This ensures timely availability of devices, reduces holding costs, and improves operational efficiency in healthcare.**


4. Dynamic Demand 

Markets are subject to constant changes influenced by factors such as economic conditions, consumer preferences, and global events. Businesses can struggle to adapt quickly to these shifts, leading to mismatches between inventory levels and actual demand.


Solution - Demand Forecasting using Predictive Analytics

Predictive analytics can play a pivotal role in demand sensing by analyzing historical and real-time data using statistical models or machine learning algorithms. It involves pre-processing historical data to identify patterns, selecting suitable predictive models, integrating real-time data sources, and engineering relevant features for improved forecasting accuracy. The process includes dynamic replenishment strategies and inventory optimization based on anticipated demand, allowing for seamless adjustments to changing market conditions. Predictive analytics enables scenario analysis and sensitivity testing, which can help organizations simulate the impact of various factors on demand.


Use Case - Zara leverages demand sensing to optimize inventory and dynamically adjusts stock levels to align with consumer demand and market trends. This results in reduced waste and increased sales.


Conclusion

The challenges faced by SMBs in inventory management are significant but not unbeatable. With the adoption of AI, businesses now have powerful tools at their disposal to transform their inventory management capabilities.


References

*Nikolaus Hilgenfeldt, June 14, 2023 - 20 Inventory Management Statistics You Should Know in 2023

**Joseph Quinlivan, Vice President Fulfillment Technologies and Robotics at Amazon, June 26, 2023 - How Amazon deploys collaborative robots in its operations to benefit employees and customers


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