Industry 4.0 is driving a digital revolution across all aspects of manufacturing processes. As there are a variety of processes involved across the manufacturing sector, the utilisation of transformative technologies has the potential to impact: energy efficiency, consumption and demand, inventory management, and operational controls such as lighting and cooling.

Deloitte Consulting, through its source from, has elaborated further discussion on Digital Transformation on
Industry 4.0 and Transformative Technology. To some extent, the concept refers to Digital Transformation Future Scenarios 2030.

The relevancy of this Digital Transformation refers to the readiness for both Programs of Product Design Engineering (PDE) and Automotive and Robotics Engineering (ARE) in Binus Aso School of Engineering (BASE) to embark BASE’s Students toward Future and Beyond.

Digital Transformation on Industry 4.0

The reduction of carbon emissions in manufacturing is primarily driven by the ‘digitisation’ of the sector through ‘Industry 4.0’. This refers to a new Industrial Revolution, driving a digital transformation of manufacturing practices focusing on interconnectivity, automation, machine learning and real‑time data.

Examples of digital transformations in the manufacturing sector include: automated workflow management, predictive maintenance, inventory optimisation and predictive modelling that estimates market demand. Whilst about two thirds of manufacturing‑related emissions can be eliminated with a switch to 100% renewable electricity, eliminating the remaining third of emissions is complex. Manufacturing creates carbon emissions at all points of the value chain, from raw material mining and material sourcing, to industrial processes and non‑electrical energy consumption, all the way through to up – and downstream transpor tation and distribution.

Digital Transformation on Transformative Technology

Advanced digital technologies, such as Internet of Things (IoT), Artificial Intelligence (AI), Digital Reality and Blockchain, are applied to enable greater interoperability, flexible processes, and intelligent manufacturing.

In the era of Big Data, machine learning demonstrates considerable potential to drive the reduction in carbon‑equi valent impact by streamlining the supply chain, improving production quality, predicting machine breakdowns, optimising heating and cooling systems, and prioritising the use of clean electricity over fossil fuel consumption. The utilisation of machine learning is dependent on the availability of high‑quality data and transparency across the sector.