Managing inventory effectively is a critical aspect of successful retail, manufacturing, service and distribution companies. Stocking inventory can be viewed as straightforward and static, however understanding customer demand is more challenging and is constantly fluctuating. Inventory that has been sitting on the shelf for a while becomes costly and loses its value quickly. To keep inventory constantly moving, inventory stocking has to be directly in step with customer demand. Analytics can serve as a guide for how to keep a lean yet diverse inventory to minimize stock out and over stocking costs. As a mini-guide for where to focus attention on analytics in inventory, we’ve classified the 4 Components of Inventory Analytics.
1) Tracking Activity
The most important component in inventory analytics is tracking inventory activity effectively. While there is always a little trial and error that goes into efficient inventory, companies become more successful the more relevant
metrics they track and use effectively. Basic tracking such as number of stocked items, number of sold items and age of inventory are all important metrics to keep a pulse on for smooth operations. What about an instance where a customer comes in looking for a product but finds an “out of stock” label on the shelf? They are not able to signal to the store that they wanted this item. To start tracking this unrecorded demand, a company could have its employees keep track of items customers were looking for but found out of stock. This will allow greater accuracy in assessing demand to stock accordingly and will keep customers returning. Finding ways to keep track of variables that your competitors are not is a powerful strategy for pulling ahead of the competition.
2) Item Categorization
The basis of inventory analytics is item categorization. If your company stocks more than 30 items, the analytics can quickly become messy when the items are not categorized. A commonly used categorization method is the ABC method which groups items into three categories based on frequency of sales and percentage of inventory. Category A items account for 70% of sales but only 20% of inventory, category B items account for 20% of sales and 30% of inventory. Category C items account for only 10% of sales but take up 50% of inventory. By analyzing and classifying inventory, store managers can make more accurate assessments on where to place certain items, how often to restock, and use these categories for further retail analytics.
3) Reducing Obsolescence
An issue for any company carrying inventory is assessing product obsolescence. While the primary focus is ensuring that your most popular products are restocked at frequent intervals, there can be a tendency to let less popular products sit in inventory. Analytics can help your company determine the least popular products to reduce their count in inventory proactively, or even predict the risk of certain SKU’s becoming obsolete before ordering.
4) Predicting Demand
Predicting demand is complex as it requires attention to many moving parts. Seasonality, price, location, current trends and even the shade of the sky on a particular day can all affect product demand. As a start, the newsvendor model is a good analytical tool used to determine the critical fractal of an item or group, or in other words, to balance the cost of being understocked with the cost of being overstocked for optimal stock keeping. This newsvendor model is just the bare-bones model, other components will be added as need be to determine the optimal amount of an item to keep on the shelves. For example, to stock snow blowers efficiently, statistical models to predict the likelihood of snowfall will be combined with the newsvendor model. Predicting demand is analytically intensive and can give a company the information it needs to be able to stock items optimally.
AAARL can help you find the right tools and implement optimal ways of managing inventory. Contact us or explore our other materials to learn more about analytics!