Data-Driven Inventory Audits

Hospitals are making less money because of these mistakes! In healthcare, managing inventory to align with real demand is a constant challenge. With items billed to in-patients, out-patients, or not billed at all, the risk of overstock or stockouts can be high. Consider the impact of one hospital’s approach: This issue affects cost, resource allocation, and patient care. But what if healthcare facilities could analyze consumption patterns and align supply with actual demand? Here’s how leading hospitals are using data-driven strategies to reduce waste, ensure fulfillment, and cut costs. Many hospitals stock up to avoid shortages. The first step? Analyzing usage across the board. Track demand through metrics like bed days, duration of stay, department, and care provider, hospitals gain a complete view of supply needs, item by item. With this data, they can build statistical models that accurately forecast inventory levels, applying correction factors based on operational changes. Here’s how this data-driven model is transforming inventory management: 1) Demand-driven forecasting: Tracking metrics such as patient stay duration and care provider needs enables precise demand planning. 2) Item-level alignment: Each department and provider receives supplies matched to actual usage, reducing waste and unnecessary stock. 3) Correction factors: By adjusting for seasonal or operational changes, hospitals avoid costly overstocks and stockouts. 4) Financial impact: Reduced inventory costs mean more resources for direct patient care. The outcome? A supply chain where inventory is optimized, every item accounted for, and every dollar maximized. In this way hospitals save time and money to work effectively across all the channels.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐃𝐨𝐦𝐚𝐢𝐧 (𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 & 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬) --- > 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐒𝐞𝐫𝐢𝐞𝐬  Tackling Pilferage in Q-Commerce Dark Stores and Warehouses through Smart Auditing In Q-commerce warehouses (or dark stores), customers sometimes place orders only to receive a message that the product is out of stock. One common reason for this is pilferage—someone may have stolen the item from the warehouse, and no one realizes it’s missing until an order is placed. If there’s only one unit left, the system may still show it as available, but when the picker goes to retrieve it, the item is gone. Many might think that regular audits are the solution. However, with 8,000 to 10,000 SKUs in inventory and around 100 online orders per hour, it’s not practical to count every single item daily, especially for fast-moving products like snacks or ice cream. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐰𝐨𝐮𝐥𝐝 𝐬𝐮𝐠𝐠𝐞𝐬𝐭 𝐚𝐬 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭: Introduce a KPI: Cycle Count or Audit Count ( matching system count with physical count Rather than doing full audits, we can break down the inventory into categories and introduce a cycle counting system. This will allow us to audit a small portion of the inventory regularly and detect discrepancies earlier. Inventory Categories: Highly Expensive Items: These items don’t sell often but represent significant losses if stolen. They should be audited every 1-2 days to ensure the physical inventory matches the system records. Any loss here would be a big hit to the company. Highly Selling Items: These products move quickly. Based on available resources, we can audit 2,000 to 2,500 SKUs weekly. The goal is to track the difference between physical stock and system inventory and identify discrepancies. Types of Discrepancies: Inventory Higher than System Count: For example, someone may have sent a 50 Rupee Lay's packet instead of a 20 Rupee one. The customer might not complain, so the error goes unnoticed until the next audit. But if we send a 20 Rupee packet in place of a 50 Rupee one, the customer will definitely raise a complaint. This kind of variation often happens with fast-moving items that have multiple variants. For these, we need to set some acceptance criteria for minor differences. Inventory Lower than System Count: For expensive items like chocolates, we can’t afford such discrepancies. We need a stricter tracking process here. Auditing and Tracking Discrepancies: After an audit, if discrepancies are found, we can track how many orders have been placed for the item since the last audit. We can then check who picked the orders and review the CCTV footage from the location where the stock is stored. This will help determine whether extra items were mistakenly given or if theft occurred. Reducing Pilferage: The visibility and accountability created by audits and data tracking discourage potential theft.

In warehousing, the timing of information is crucial. Shrinkage, misplacements, and stockouts are ongoing challenges, but the real issue lies in discovering them too late. The implications of delayed awareness are becoming increasingly costly. Embracing warehouse intelligence powered by real-time, autonomous data yields transformative outcomes: - Labor for cycle counting slashed by 90% - Transition to weekly full-warehouse scans from sporadic or monthly audits - Achieving a remarkable location accuracy of up to 99.89% - Enhancing picking accuracy to 93% - Decreasing order cancellations resulting from lost goods by more than 50% - Successful recovery of nearly $1M in lost goods By proactively addressing issues instead of reacting to them, value is unlocked across the board. It's time to shift from waiting for problems to arise to seizing opportunities for improvement. #WarehouseVisibility #SupplyChainResilience #InventoryAccuracy #WarehouseAutomation #Verity #IntelligentAutomation