Objectives
During a black swan event like the CoViD-19 pandemic, a major healthcare provider faced a sudden drop in patient volumes and the existing forecasting algorithms started failing. The provider wanted us to bring out solutions that could help in cutting costs due to the disruptions in supply chains.
Outcomes
With our highly flexible Decision Engine, our solution – developed within 4 days’ timeframe – could help the provider make multiple snap decisions. The Supply Chain leadership could instantly analyze multiple parameters to zero in on the causes for excess inventory holding and propose next steps for immediate action.
A leading tertiary hospital chain reduces its inventory levels dramatically after using a custom-made Material Planning Logic to identify excessive medicines and consumables.
A key pillar in effective Supply Chain Management is minimizing the cost of inventory holding. Holding Cost is the cost associated with storage of inventory that is yet to be consumed for business operations. It is a tightrope walk for the Supply Chain Manager to balance between under-stocking an item and over-stocking it. While under-stocking can lead to stockouts and business stoppages; over-stocking, on the other hand, ends up increasing Holding Costs.
Identifying the perfect balance between excess stocks and understocking
While theoretical knowledge on the ideal reorder level determination is widely available, managing the stock levels due to erratic demand/ consumption patterns, inconsistent delivery schedules and the general variation of goods’ availability across a large spread of hospitals. Couple these issues with 5,000-6,000 SKUs required at any point-of-time in a multispeciality hospital, it becomes a tedious ask for the Supply Chain Manager to manage the overall Holding costs.
Further, abrupt business disruptions and Black Swan events such as pandemics and lockdowns can only upset the predictions and forecasts made by Material Planning algorithms. To assist Supply Chain teams in arriving at the ideal stock levels for each SKU at each network hospital and adapting to the rapidly changing consumption patterns, a 'Material Spot-Optimizer' program was developed. The objective was to provide insights to Supply Chain Managers on stock levels in the hospital chain and the possible reason for unforeseen accumulation. Utilizing the near-term consumption pattern, the staff were able to make appropriate corrective actions.
Data Analytics helps Supply Chain leadership to extract insights & make meaningful decisions
The core issue that needed to be analyzed was the extent of the consumption drop for each SKU. Ergo, the same quantity of material that seemed non-excessive prior to the quarantine shutdown would suddenly seem excessive during the shutdown. The ideal metric that can be used as a pivot to analyze this form of stock upsurge is the Inventory Days. A simulation was run to analyze the variation in the Days of Inventory during the two time periods. Then, the potential reasons for the surge in inventory holding viz. recent Goods Receipt (GRNs) were pulled out to highlight the movement of stock levels. Also, immediate actions, wherever possible, were suggested by revealing any open and pending Purchase Orders and Requisitions that were placed prior to the quarantine shutdown with the estimate of higher consumption pattern.
Senior members of the Supply Chain teams were furnished with an automated summary visual with the stock patterns that led to accumulation along with a forecast for accumulation. For the executives raising the purchase orders and making GRNs at the store level, the system intelligently identified high-impact interventions and presented a real-time action list.
Timely Information means Money!
Due to the timely adaptation of the framework to counteract the variations in the forecast input conditions, a huge saving of 32% of total inventory value were achieved in the health system. The Corporate headquarters could rapidly identify the key issues for increase in inventory costs and dial-up the specific hospitals located 1,000s of miles away to correct their purchase pattern on certain SKUs.
Deviations in the purchase protocols were pulled up and information was relayed across the network in a flash. A new system of monitoring the progress was implemented and changes could be visualized.
For instance, an order placed from a hospital in the far East of the country for a large shipment of Blood Glucose Strips was immediately put on hold due to the excess stock it already had.
Also, the excess Albumin Injection vials were shifted from one hospital to another network hospital located in the same city.
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