Beyond Reorder Metrics: Complexities in real world inventory optimisation

In our previous articles, we explained inventory control charts and how they are crucial to identify health of inventory and estimate right reorder point and quantity to reorder based on replenishment constraints of each business. While these charts play a crucial role in the inventory optimisation process, it is essential to recognise that they serve as foundational element within a much more intricate inventory solution tailored for practical supply chains.

Here, we highlight five key complexities that demand careful consideration during inventory optimisation, prompting adjustments to reorder points and quantities:

• Running Purchase Orders (POs): At any given time, businesses often have pending purchase orders awaiting delivery. When planning the reorder point and quantity for the next cycle, it is crucial to factor in the stock quantity from these ongoing POs. Neglecting this aspect can lead to excess stock situations, undermining the effectiveness of inventory optimisation.
• Batched Delivery of Running POs: The procurement process for many businesses involves the delivery of purchase orders in batches, adding a layer of complexity. When estimating replenishment times, it is crucial to account for these batched deliveries to prevent the risk of items running out of stock before the next batch arrives.
• Inter-Facility Stock Transfers: In scenarios where a business operates across multiple facilities or warehouses, stock transfers may occur from an overstocked facility to one facing shortages. Proper consideration of such inter-facility transactions is vital in adjusting Stock on Hand (SOH) values and recalculating reorder points.
• Expiry Dates and Dead Inventory Consideration: In evaluating Stock on Hand (SOH) data, it is important to assess the proximity of inventory items to their expiry dates (applicable to FMCG/personal care products) or their classification as dead inventory in other sectors. Neglecting these factors in reorder point calculations can result in businesses being left with wasteful unusable stock, potentially leading to out-of-stock issues.
• Disaggregation to Bill of Materials (BOM) for Raw Material Prediction: For businesses with end-to-end supply chains, accurately predicting the quantity of raw materials is a critical use case. Implementing a robust disaggregation logic ensures preparedness for both the demand for Bill of Materials (BOM) and finished products.

Addressing these complexities is a must for business to increase precision and effectiveness of their inventory optimisation strategies in the dynamic landscape of real-world supply chains.

If you’re looking to refine your inventory planning and drive business success, feel free to contact us at info@truegradient.ai for tailored solutions for your business.