Jun 15, 2015

How to optimize inventory of highly strategic, but low demand parts?

Expenditure on aftermarket parts and services accounts for more than US$1.5 trillion annually and constitutes 20–30% of the top line and about 40% of the bottom line for most consumer durable manufacturers. However, not all parts have the same criticality and consumption characteristics. A study by Aberdeen Group indicates that only 30% of the total parts can be categorized as regular moving, while nearly 70% of stock-keeping units (SKUs) have intermittent consumption patterns.

Although statistical modeling approaches are inadequate in modeling inventory patterns of intermittent, low-volume parts, if some mission-critical parts are not available when needed, operations can be affected.

To enable maximum business impact, it is important for companies to differentiate both types of low volume spares, by categorizing the strategic slow-moving items inventory (sometimes referred as insurance spares) from the vast majority that are slow moving, and are consumed less, also they do not pose any kind of serious risk if not stocked. By adopting this approach one can maximize service levels for the overall parts portfolio with limited inventory investment.

Multiple factors must be evaluated before a decision is made.

Demand: Consider the historical consumption rate and the variability. Identify demand patterns and intervals to plan ahead of time. This facilitates accurate forecasting for the parts.

Cost: Given the nature of parts that are consumed infrequently and in low quantity, the cost of holding inventory is crucial. Parts that can potentially cause holdups for customers and take a long time to acquire should be considered. A balanced trade-off angle has to be evaluated.

Supplier profile: Certain equipment parts are unique and need suppliers that can support them. If the supplier has a significant risk profile, the supplier's parts should be stockpiled.

Parts profile: Mandatory safety-related parts must be stocked regardless, especially in installations with long service lead times and strong regulations. These parts can be strategic. Similarly, if the parts are complex, that means longer lead times to build; therefore, stocking them is the best option.

Combinations of these factors can be developed in scenarios to formulate algorithms to drive stocking decisions for low-demand strategic parts. With the initial factors understood, choosing the right methodology and approach to solving the problem is paramount. The optimal way is to model the problem as a hierarchy that contains the decision goal (criticality), the alternatives for reaching it (the spare parts), and the criteria for evaluating the alternatives (for example, purchase cost).

Advanced techniques such as Analytic Hierarchy Process (AHP) are used to model the criteria for stocking and forecasting, such as the parts' criticality (customer need) and availability (supplier fulfillment capability).

The outcome of this stage is to identify the parts that are highly critical but less available (which should always be stocked), the parts that are less critical but highly available (which can be selectively stocked), and the parts that are less critical and less available (which can be held by vendors).

Potential impact
Implementing a solution such as Genpact's Inventory Optimizer (IO) backed by our Inventory Optimizer algorithm set has delivered tangible business benefit for clients such as a global transportation company that improved service parts availability by 15% within 3 months of implementing the solution.

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  • Prasad Havre - Assistant Vice President, Supply Chain Analytics Practice
  • Gaurav Shrikhande - Assistant Manager, Operations - Transportation