Organizations typically make inventory decisions based on their historical records. Data is collected over time and then analyzed to determine inventory parameters such as safety stocks.
however, that the collected data has significant inaccuracies. In a report on the retail industry, Gruen and Corsten (2007)
note that poor data quality can be perpetuated by the disconnects created in demand and sales data from not recording out-of-stocks
in the demand history file.
Cagan (2015) maintains that small businesses often record transactions only when money changes hands. Cagan also points out that such a practice may even be encouraged as it provides a bit of leeway at year-end to minimize taxable income.
Issues of poor
quality daily demand data have also been observed by one of the
authors in the standard operating practices of a local distribution
center, stemming from resource constraints and resulting in peri-
odic demand reconciliation prior to placing replenishment orders.
Practices along these lines call into question the traditional statis-
tical approach used for managing safety stocks. Leveraging the fact
that the above recordation practices might lead to better data
quality when viewed at a higher level of aggregation than on a
daily basis, a computationally straightforward alternative for calcu-
lating safety stocks is presented herein.