The Model Logic short shelf life model was originally developed for a dairy manufacturer who wanted to investigate the trade-off between lost sales and stock write offs at one of its major yoghurt manufacturing plants.
The dairy management wished to quantify improvements in production planning and sales forecasting and see the benefits in terms of a reduction in finished product wastage and improved service levels to its customers.
The latest short shelf life model is a simplification of its dairy orientated predecessor, as much of the complexity and functionality has been removed to offer a more generic tool with a number of potential applications.
The model requires the usual inputs of weekly sales forecast, daily profile, sales forecast error, lead time, minimum batch quantity and supply interval leading to the calculation of production quantities, daily demand quantities and inventory levels.
However the main difference between the short shelf life model and a standard inventory model is the tracking of date codes and remaining shelf life within the total manufacturing and total distribution shelf life constraints, and also that service level is a model output.
This means that although total stock quantity will cover the forecast period, the various date codes and distribution shelf life of the SKUs will mean that certain elements of stock cannot be used to meet the demand, as the maximum shelf life has been exceeded.
It is particularly useful to understand the sensitivity of the inputs described above, as this dynamic leads to both stock write offs or lost sales or both.
The model works stochastically using multiple replications as prescribed by the end user as the demand is varied around the forecast by means of the forecast error variable.
The results are smoothed keeping track of all key stats such as service level from the multiple iterations.