Industry 4.0 is the future pathway to meeting legally binding net zero emissions targets in business and industry by 2050.
Industry 4.0 will help achieve these ambitions by enabling the power of digital technology to deliver increased efficiencies, more targeted intervention and future innovations.
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.
Advanced digital technologies such as Internet of Things, Artificial Intelligence, Digital Reality and Blockchain, are applied to enable greater interoperability, flexible processes, and intelligent manufacturing.
Examples of Industry 4.0 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 transportation and distribution.
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.
Most notably, there is great enablement potential across the sector to streamline efficiency and reduce energy demand within manufacturing processes.
In the era of Big Data, machine learning demonstrates considerable potential to drive the reduction in carbon‑equivalent 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.
Supply chains across the manufacturing sector are notoriously complex, with production often utilising cheaper labour in less‑developed markets, where the environmental impact of this additional transportation is a secondary concern.
As more organisations recognise the often unmeasured carbon impact of manufacturing within their supply chain (i.e. Scope 3 emissions), scrutiny over environmentally friendly manufacturing practices has increased, particularly where hardware manufacturers have set science‑based targets.
Manufacturing currently accounts for around 55 Mt CO2e in the UK, approximately 12% of total emissions.11 Based on existing trends, manufacturing emissions are expected to decrease over the next decade to around 24 Mt CO2e. The increased deployment of Industry 4.0 technologies such as smart factories is expected to reduce this further through productivity and efficiency gains. For example, IoT in factories will ensure machines are used more efficiently, optimising their operations and energy consumption.
Increased adoption of Industry 4.0 is expected to potentially decrease manufacturing emissions by a further 7% to 22 Mt CO2e in 2030, compared to a scenario without increased adoption.
