busINess analyticS In manufacturinG: Husky solutioning lead-Time optimization

SCHEME: Industrial Fellowships

CALL: 2017

DOMAIN: MS - Materials, Physics and Engineering




INDUSTRY / PPP PARTNER: Husky Injection Molding Systems Luxembourg IP Development S.a r.l.

HOST INSTITUTION: University of Luxembourg

KEYWORDS: business analytics, industry 4.0, big data, lead time optimization

START: 2017-03-01

END: 2021-08-31


Submitted Abstract

Project INSIGHT aims at assisting the manufacturing industry derive actionable knowledge from their vast databases segregated in legacy information technology systems in order to motivate investments in technological updates that lead to a full realization of Industry 4.0 principles. As a case study, the University of Luxembourg has partnered with Husky Injection Molding Systems Luxembourg IP Development S.à r.l. to apply business analytics techniques to their databases concerning customers, bottle preforms, steel molds and processing parameters with the objective of reducing the lead-time between customers quotation requests and an effective response from the solutioning team. It is envisioned that such lead-time optimization will enhance customer service, reduce costs and improve profitability.Given the wide variety of products manufactured by the collaborating institution, and to circumscribe the scope of the project to fit the doctoral program, project INSIGHT will concentrate on Husky “weight conversion” business unit. The pertaining data will be collected from the abovementioned databases and analyzed in three incremental phases mirroring traditional value stream mapping. Firstly, customer data will be analyzed for customer segmentation and analysis aiming at the identification of the characteristics of customers and/or their markets of operation that most strongly correlate with the solutioning lead-time. Then, business analytics will focus on the engineering team’s work and model preform injectability and product variability considering geometric, mechanical, and material properties as well as processing parameters. The results of such endeavor will help identify the technologically significant variables in the determination of the lead-time. Finally, the data will be used to model order scheduling and guarantee its optimization. As a result, an interactive, self-adaptive questionnaire will be designed to handle customer quotation orders while facilitating communication between the engineering team and the end customer.

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