FEIT Research Project Database

Operational Excellence in the fourth industrial revolution era


Project Leader: Guilherme Tortorella
Primary Contact: Guilherme Tortorella (guilherme.tortorella@unimelb.edu.au)
Keywords: 3D printing; artificial intelligence; complex systems; Internet of Things (IoT); machine learning
Disciplines: Mechanical Engineering
Domains:

Industry 4.0 (I4.0) is the new paradigm for organisations, inducing remarkable improvements due to changing operative framework conditions. I4.0 contributes to decentralised and simple structures over large and complex systems, while aiming for small and easily integrated modules with lower levels of complexity. From a business perspective, I4.0 has been claimed as an approach for significantly improving performance through automation and digitalisation. Complementarily, researchers have envisioned I4.0 as a strategic framework that provides competitive advantages through the enhancement of operational performance, such as cost reduction, quality improvement, higher customer satisfaction, and shorter lead times. Such performance improvement corroborates to the achievement of Operational Excellence (OE), which is the execution of the business strategy more consistently and reliably than the competition. OE’s scope goes beyond the traditional event-based model of improvement; it encompasses a long-term change in organisational culture. Significative efforts towards I4.0 have been motivated by the expectation that digitalisation may lead companies to superior levels of OE. Nevertheless, there is a lack of consistent knowledge on how the digital transformation implied by I4.0 is going to affect industries in the future. Furthermore, the integration of I4.0 disruptive technologies (eg: cloud computing, Internet of Things (IoT), machine learning) may imply changes on the concept of OE, as it helps to overcome traditional barriers in operations management.

This research project aims at examining how I4.0 contributes to OE in a variety of industries, considering their contextual characteristics and existing management approaches. For that, complementary research methods (eg: empirical, experimental and analytical) are envisioned as a means to identify such contribution within industries

Schematic representation of the pathway to a high-performing Lean Automation implementation
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