Five Types of Inventory Optimization You Should Know
Here are five types of inventory optimization that you should be aware of if you are considering an optimization solution. Knowing which types are important to your business is a vital step to determining which solution best fits your needs.
- Inventory Configuration addresses the issue of in what form should I hold my inventory; as raw material, finished goods, or something in between?Optimized inventory configuration is achieved via postponement strategy which optimizes the trade-off between upstream and downstream inventory to identify the most globally efficient balance. Positioning inventory downstream nearer demand is traded off against positioning inventory upstream, where it has the broadest range of possible uses. In manufacturing operations, adequate levels of assemblies and finished goods are positioned to deliver the final product as late as possible within an acceptable service-time. Partially processed Bill-of-Material (BOM) inventory is positioned to gain higher commonality of raw material and components. This risk pooling approach frequently requires less inventory. For example, postponing final labeling, packaging or kitting reduces inventory requirements because the same inventory can be satisfy multiple types of demand. This trade-off establishes an extra inventory decoupling point that can be used to optimize the inventory staging problem. Downstream production is “pulled” by actual demand, whereas upstream production is “pushed” by forecasts.
- A second form of postponement strategy is sometimes called Stage Optimization. Stage optimization applies the same postponement principles, but to the distribution network, where the form of the inventory usually does not change, only its place in the network. It addresses decisions about whether to stock inventory at the factory, in a centralized distribution center, at regional network, or some combination of the above. Downstream inventory downstream is closer to customer demand. But upstream inventory allows for risk pooling across as many regions, customers or channels (e.g., retail stores, eCommerce, etc.). Optimal staging considers many variables in this risk pooling trade-off such as service lead time, supply uncertainty, and even issues like inventory value at each location (i.e., holding cost based on where import duties or taxes are levied).
- Inventory Policy addresses the issue of the right inventory targets to address the service level needs of group of products. This form of inventory optimization should be quite dynamic, considering issues such as seasonality, changing demand patterns or fluctuating order lead times. Mix Optimization (also called Service Optimization or Service Level Optimization) allows the business to position the inventory across the distribution network to meet high customer service level objectives in a much more efficient manner than simply creating a homogeneous mix of “one size fits all” inventory. Instead, mix optimization creates a blend of different service level targets for each individual SKU in each location to achieve an aggregated service level target that meets the marketing/business objectives. For instance, instead of assigning all SKUs in a class a 98% service level, a global 98% target is achieved by optimally setting individual SKUs service levels at 95%, 97%, 99.5%, etc., achieving the same overall objective with far less inventory expense.
- Lot Size Optimization is the simultaneous optimization of safety stock versus cycle stock values. Optimal lot sizes are a function of the targeted service levels, safety stocks, set-up cost (for manufacturing) or the handling cost (for replenishment) versus the inventory holding cost and other factors. As the lot size increases, set-up or handling costs decrease while the stock holding cost increases in line with average stock level (but not linearly, as is used in most classic models).
- In two specific cases, it’s also important to consider Prebuild Optimization (also called Build Ahead Optimization).The first case is a finite supply capacity environment with strong seasonal demand or reduced supply due to shut-down or maintenance. The prebuilt inventory should be carefully planned to cover the excess demand in the period of inadequate supply. Inventory optimization dynamically defines the prebuilt inventory requirements on the basis of the updated time-phased forecasts and supply capacity until the end of the season. The inventory mix covers the excess demand during the season without losing sales or accumulating overstocks of wrong inventories. Prebuild analysis may be an important component of Sales and Operations Planning (S&OP).The second case occurs towards end of product life when companies define an end of production date. Service parts for such products should be prebuilt to guarantee service for a period defined by an SLA, company policy or by law. Similar to above, based on the residual supply capacity and the evolving forecast for such parts in the above period, prebuilt inventory requirements should be dynamically calculated to minimize obsolescence or resupply (after end of production).
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Some foundational inventory management and inventory optimization terms:
ABC analysis – also called Pareto analysis or the rule of 80/20, is a way of categorizing inventory items into different types for inventory planning, depending on value and use
Cycle stock – normal stock used during operations
Inventory turnover – a measure of the number of times inventory is sold or used in a time period such as a year.
Lot sizing – combining several small orders into larger ones for material requirements planning (MRP)
Postponement – moves almost-finished products into stock, and delays final modifications or customization until the last possible moment
Pull – system used by JIT, where operations are triggered by customer demand and materials are pulled through the supply chain
Safety stock – a reserve of materials that is not normally needed, but is held to cover unexpected circumstances
Service level – a measure of the proportion of customer demand met from stock (or some equivalent measure)
Stochastic – Probabilistic or statistical