Inventory management is essential for satisfying customer demands and reducing
logistics costs. Extensive material portfolios, balancing inventory costs versus customer
service levels as well as fluctuating demand are factors that influence inventory
levels. Inventory management is often considered as inventory reduction; quick
reduction activities are carried out without knowing the exact root causes of nontarget
inventory levels. A sustainable and comprehensive approach could be, to consider
those factors which have a strong impact on inventory and to use this information
for further inventory optimization activities. This paper therefore gives an answer
to the question, how to systematically identify main drivers on inventory, using
multiple linear regression analysis and how to quantify their impact considering
company-specific data and structures. The described approach is applied in a case
study at a company in the commercial vehicle industry. Data sets from different locations
are analyzed and compared. It will be shown in a methodical way that few
factors have a strong linear influence on inventory level and differ depending on the
characteristics of the respective location. Companies can thereby analyze main drivers
on inventories e.g. per location, region, sales channel or company-wide, depending
on the chosen data set. The results can be used to identify root-causes for nontarget
inventory levels and form the basis for company-specific inventory optimization
activities.