Planning for Intermittent Demand: Optimizing Inventory Using Probabilistic Modeling

How do you plan the right inventory levels when you have intermittent demand, the seemingly random, highly sporadic usage pattern that is especially prevalent with spare parts?

Most parts and materials planning organizations rely on traditional forecasting approaches, rule of thumb methods, and “planned” maintenance schedules to determine stocking requirements. This Video will discuss these approaches, why they often fail, and how new probabilistic forecasting methods can make a big difference to your bottom line.

Probabilistic forecasting: A fundamental aspect of supply chain management is accurate demand forecasting.

We address the problem of forecasting intermittent (or irregular) demand, i.e. random demand with a large proportion of zero values. This pattern is characteristic of demand for service parts inventories and capital goods and is difficult to predict. We forecast the cumulative distribution of demand over a fixed lead time using a new type of time series bootstrap. To assess accuracy in forecasting an entire distribution, we adapt the probability integral transformation to intermittent demand. Using nine large industrial datasets, we show that the bootstrapping method produces more accurate forecasts of the distribution of demand over a fixed lead time than do exponential smoothing and Croston’s method.