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.
The application of churn prevention represents an important step for mobile communication
companies aiming at increasing customer loyalty. In a machine learning perspective,
Customer Value Management departments require automated methods and processes to
create marketing campaigns able to identify the most appropriate churn prevention approach.
Moving towards a big data-driven environment, a deeper understanding of data
provided by churn processes and client operations is needed. In this context, a procedure
aiming at reducing the number of churners by planning a customized marketing campaign
is deployed through a data-driven approach. Decision Tree methodology is applied to drow
up a list of clients with churn propensity: in this way, customer analysis is detailed, as well
as the development of a marketing campaign, integrating the individual churn model with
viral churn perspective. The first step of the proposed procedure requires the evaluation of
churn probability for each customer, based on the influence of his social links. Then, the
customer profiling is performed considering (a) individual variables, (b) variables describing
customer-company interactions, (c) external variables. The main contribution of this work
is the development of a versatile procedure for viral churn prevention, applying Decision
Tree techniques in the telecommunication sector, and integrating a direct campaign from
the Customer Value Management marketing department to each customer with significant
churn risk. A case study of a mobile communication company is also presented to explain
the proposed procedure, as well as to analyze its real performance and results.