Spare Parts Forecasting

When equipment inevitably breaks down, the right spare parts must be on-hand. Problem is, forecasting demand for these parts is complex and difficult, far more so than for ordinary production components. One spare part may be critical to zero-downtime, but rarely needed. Another may be expensive, carried for a long period, and prone to obsolescence. These and other factors can stymie spare-parts demand planning.

Regardless of the difficulty, achieving the optimal size and configuration of spare-parts inventory is critical. Too little and you run the risk of costly or even fatal operational delays. Too much and you tie up cash in spare-parts safety stock — carrying costs kill too.

The most accurate forecasting methods for spare parts are those able to handle intermittent demand, which is characterized by randomness and long periods of zero demand. Indeed, spare-part usage often vacillates between erratic and zero. There are many ways to get around this lack of obvious recurring patterns. One popular method is Croston’s intermittent demand model. A more advanced approach is Monte Carlo simulation, which uses probability theory to model the time between orders, as well as order sizes. Other approaches include increasing the forecast time scales from daily to weekly to monthly and so on, to reveal more stable and predictable patterns.

In all cases, optimizing spare parts inventory should take into account a number of key factors — Is the part expensive? Is it critical? What is your storage capacity? What is the lead time for purchase/manufacturing of this part? Which parts should be ordered on demand, versus kept in stock?

The image below is an example of Vanguard Inventory Optimization: