Managing an inventory of spare parts to support an organization’s industrial operations is critical, yet is one of the most challenging operations for most organizations. Being able to keep up with the complexities that come with spare parts inventory management enables your organization to optimize inventory, minimizes operational and financial risk, and maximizes return on assets.
One way to help with spare parts inventory management is to run a critical analysis. This critical analysis provides guidance on how critical a part is to have in stock and what type of inventory management strategy should be put in place. How critical a spare part is to an organization’s operations varies dramatically across parts. Sometimes the most inexpensive part could have the largest risk associated to it if it is not readily available. The goal is to balance the cost impact of stocking out of a part versus the cost to procure and hold the part in inventory.
In many organizations, multiple parts can be substituted when needed. Examples could be a commodity part or if multiple vendors produce the same custom part. Grouping these substitute parts can reduce risk, which will help with overall demand uncertainty.
It is hard enough to forecast demand for finished goods, and even hard to do for spare parts and materials. Although, forecasting your organization’s spare parts demand is key to spare parts inventory management. Without and accurate estimate of each parts’ demand, it will be impossible to planning for spare parts. Demand for spare parts may seem unforecastable, but leveraging a purpose-built forecasting solution enables forecasting with ease. These solutions analyze historical data, drills down into different demand streams, and applies different forecasting algorithms to predict future part usage, trends, and patterns. There are several demand forecasting methods that can help:
- Croston’s method: This classic method deals specifically with intermittent demand. It is based on the Simple Exponential Smoothing method, but greatly increases its accuracy.
- Syntetos and Boylan Approximate (SBA) method: This method is similar to Croston’s but includes an added correction factor, which decreases errors.
- Artificial intelligence (AI): Advancements in AI have made possible new forecasting methods that leverage neural networks, for example.
- Simulations: These simulations predict when parts will fail over a defined period of time with different variables.
Tracking your spare parts inventory is an obvious best practice, but many organizations fail to do it well. Not knowing what you have on hand or where parts are located is never a good thing. You won’t know if you have too much or too little. You won’t know if you need to replenish today or tomorrow. You won’t know if you need to place an order to a vendor or transfer parts from another location.
Spare parts inventory management often does not get the attention it requires. An accurate inventory system, as well as efficient spare parts management, allows parts usage to be much more proactive and responsive to operational demands. Organizations can save money by knowing which part plays a critical role in your day-to-day operations, and stocking them accordingly.
View our latest infographic to learn about the 8 Best Practices for Managing Spare Parts Inventory.