The application of the 5S methodology to warehouse management represents an important

step for all manufacturing companies, especially for managing products that consist of

a large number of components. Moreover, from a lean production point of view, inventory

management requires a reduction in inventory wastes in terms of costs, quantities and time

of non-added value tasks. Moving towards an Industry 4.0 environment, a deeper understanding

of data provided by production processes and supply chain operations is needed:

the application of Data Mining techniques can provide valuable support in such an objective.

In this context, a procedure aiming at reducing the number and the duration of picking

processes in an Automated Storage and Retrieval System. Association Rule Mining is applied

for reducing time wasted during the storage and retrieval activities of components

and finished products, pursuing the space and material management philosophy expressed

by the 5S methodology. The first step of the proposed procedure requires the evaluation

of the picking frequency for each component. Historical data are analyzed to extract the

association rules describing the sets of components frequently belonging to the same order.

Then, the allocation of items in the Automated Storage and Retrieval System is performed

considering (a) the association degree, i.e., the confidence of the rule, between the components

under analysis and (b) the spatial availability. The main contribution of this work is

the development of a versatile procedure for eliminating time waste in the picking processes

from an AS/RS. A real-life example of a manufacturing company is also presented to explain

the proposed procedure, as well as further research development worthy of investigation.