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Life cycle targets applied in highly automated car body manufacturing – Method and algorithm

Author:
Rödger, Jan-Markus, Bey, Niki, Alting, Leo, Hauschild, Michael Z.
Source:
Journal of cleaner production 2018 v.194 pp. 786-799
ISSN:
0959-6526
Subject:
algorithms, automation, business enterprises, computer software, consumers (people), manufacturing, planning, population growth, stakeholders
Abstract:
Automotive companies are striving for higher productivity, flexibility and more sustainable products to meet demands of central stakeholders (e.g. regulation, customers, investors). New drive systems or lightweight-design of cars often imply an environmental burden shifting from one life cycle stage to another, e.g. from the use-stage to the manufacturing stage. More products will be manufactured for an increasing population and higher efficiency effort may lead to increased consumption (rebound effect). An optimization of the manufacturing stage is thus increasingly important but it has to be done from the perspective of bringing the product's life cycle performance in accordance with sustainability requirements. In order to support the companies in finding effective solutions, the framework “Sustainability Cone” was applied and an algorithm developed guiding the definition of economic and environmental target states (TS) in automotive manufacturing. Especially during the early phase of planning, largest improvements can be achieved, however target states are not yet integrated in production simulation software (e.g. PLM tools). This paper describes the approach and its application in the planning of a body shop, being one of the most relevant and complex steps of car production. The approach addresses all relevant levels, e.g. a robot, a production cell and the entire production line. So-called life cycle targets (LCT) are introduced, which represent a specific share of the target state, reflecting the importance (i.e. activity-based) of each level. Using this approach, a product and production system can be planned holistically and any rebound effect factored in and sub-optimization can be avoided.
Agid:
5974953