The paper presents some aspects of a development project related to Industry 4.0 that was executed at Nemak, a leading manufacturer of the aluminium castings for the automotive industry, in its high pressure die casting foundry in Poland. The developed data analytics system aims at predicting the casting quality basing on the production data. The objective is to use these data for optimizing process parameters to raise the products’ quality as well as to improve the productivity. Characterization of the production data including the recorded process parameters and the role of mechanical properties of the castings as the process outputs is presented. The system incorporates advanced data analytics and computation tools based on the analysis of variance (ANOVA) and applying an MS Excel platform. It enables the foundry engineers and operators finding the most efficient process variables to ensure high mechanical properties of the aluminium engine block castings. The main features of the system are explained and illustrated by appropriate graphs. Chances and threats connected with applications of the data-driven modelling in die casting are discussed.
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.
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