e of the retailers and their trading partners’ biggest challenge. Depending on how easy consumer demand is to predict for a product category (some are more predictable than others), some retailers and their vendors have missed sales opportunities of up to 40 per cent of the category. In addition to the missed sales opportunities damaging both the retailers and their vendor’s sales and margin, the out of stock product also leads to creating a level of dissatisfaction with the customer. If items are unavailable often enough it may actually lead to a consumer deciding to switch their retailer of preference.  The vendor is also impacted by their products not being in stock. Depending on the strength of their brands, the consumer may choose to switch products (as opposed to retailer switch) when their product is unavailable, and if satisfied with the alternative, it may lead to the consumer brand switching permanently. So in this world of electronic data interchange using a store sales and inventory data to manage store inventory and forecast future demand, why is it that so many retailers and their suppliers are still challenged with out-of-stocks? Data Daily – Many inventory management solutions use weekly sales and inventory snapshots to create the model stock (ideal inventory level for a SKU at store level based on estimate future demand). The use of weekly data to determine the inventory requirements for a SKU at store level is flawed for many reasons. The most complex and accurate forecasting solutions in the world still struggle to provide accurate consumer demand at the most atomic level (SKU/store). The beauty of refreshing sales and inventory data on a daily basis is that the model stock for the store/SKU can be updated daily thereby providing a more accurate snapshot of any daily inventory requirements. It doesn’t allow for a business to run a replenishment order more than once a week. It doesn’t allow for an inventory management system to review important SKUs (typically new product lines or promotional campaign items) on a daily basis using the most recent daily sales and inventory levels at store. Running analysis on “instock position” on a weekly basis provides false information to management on the true inventory position. The weekly analysis of instock position does not take into account the daily fluctuations. Rigid ERP Systems – Although there are many benefits of running an enterprise and rigid ERP solution, managing inventory across diverse product categories with differing life cycles and decay curves is not one of them. ERP solutions typically are designed to manage inventory for product categories that are easy to predict. When faced with product categories with short life cycles or seasonal activities, they typically are ineffective. Time-poor buying and planning teams – today’s modern retailer runs a very lean buying team. The buyers and planners have responsibility of sales and GP, trading terms, marketing funds, promotional management, range management and overall inventory levels. When challenged with an ineffective ERP and/or legacy system they will attempt to managing inventory using a spreadsheet solution. A time poor buying and planning team will manage the high velocity SKUs and campaign SKUs without having the time to manage other SKUs. The outcome being out of stocks for second tier SKUs that, when consolidated, equate to a significant missed sales opportunity. Unsatisfactory Fulfilment rates – the best consumer demand solutions cannot account for vendors having inadequate demand planning solutions. There are many distributors and/or manufacturers today who have underperforming demand planning processes and systems that contribute to fulfilment rates as low as 50%. These unsatisfactory fulfilment rates are typically the result of poor data and/or science being employed to create forward forecasts. Seasonality – Using most recent historic data to calculate future consumer demand presents a real issue for product categories with seasonal demand influences. Not having a mechanism to account for seasonal fluctuations in consumer demand can lead to major out of stocks on shelf. Product Life Cycle – Many of today’s consumer products have a limited shelf life. This equates to a product life cycle that is very perishable and steep in comparison to items on our shelf 15 years ago. The inability to manage a products life cycle has led to many retailers and their trading partners experiencing out of stocks early in a product’s life, and a large amount of redundant stock late in a products life cycle. Several large Australian Retailers have quietly developed a local solution to the conundrum, through  a system that acts as a managed service and complements existing systems. The cloud-based model creates an inventory management process that solves all the current “out-of-stock” challenges faced by retailers and their trading partners. Daily Data – forecasts utilise daily data ensuring the most up to date “model stock” information (at store/SKU level) when running replenishment order processes. Customised Algorithms – unlike “one size fits all” ERP solutions, the system creates consumer demand forecasts utilising algorithms that are fit for purpose. The consumer demand curves created by the V Inventory module suits the nuances of the respective product category. Fulfilment Rates – The system ensures that a vendor’s demand planning process including a consumer demand forecast by SKU that is built from atomic level (store / SKU). The CPFR forecast process links the weekly demand forecast to the actual replenishment orders created on a weekly basis. The outcome being 95+ fulfilment rates. Seasonality – The system takes into account any seasonal fluctuations specific to a product category. This results in the appropriate amount of stock being at store during seasonal time in the product’s life cycle. Product Life Cycle – The system understands that a product within a specific category will have a growth and decay profile that varies from other products within other segments. This intelligence is used to ensure the correct amount of stock is available for the consumer at store level as the product matures. By solving these challenges the retailers get  a near perfect in stock position, thereby maximising the sales opportunity for both retailer and their trading partners.