Environmental, Social and Governance (ESG) issues have become even more pressing for executives, boards, and policymakers. These include huge trends worldwide such as climate change, geopolitical shifts, and the continuous emergence of disruptive technologies, as well as the COVID-19 pandemic.
Emerging opportunities such as pioneering technologies and solutions in advanced manufacturing are opening up throughout industries to measure and report accurate and consistent ESG metrics, while enabling both profitability and sustainability in areas such as:
- Sustainability: Providing technologies and solutions to enable sustainability for next generations supporting companies to track and report on the new sustainability metrics.
- Workforce: Empowering workers with new technology, tools and solutions, enhancing the adoption of recent sustainable metrics.
- Innovation: Leveraging innovative technologies and solutions to address business problems while pushing growth ahead vigorously and tracking sustainability impacts.
Solutions range from supply chain sourcing and logistics, product innovation and lifecycle management to net-zero energy and emissions, operations and maintenance, to an empowered workforce (see figure 1 below).
The Davos Agenda is a pioneering mobilization of world leaders to modify the principles, policies and partnerships required in this demanding, innovative context. For leaders from all walks of life, it is crucial to collaborate virtually for a more inclusive, unified and sustainable future as soon as possible in 2022.
In advance of the meeting, we question leaders of the Advanced Manufacturing Industry at the World Economic Forum to give their feedback and concrete actions/examples on how they are transforming operations and business models while assisting other industries in the transition towards stakeholder capitalism metrics:
1. Industry 4.0 and company culture pave the way for accelerating sustainability
Revathi Advaithi, CEO, Flex
Along with Industry 4.0 tools, corporate culture can play a pivotal role in advancing a company’s sustainability agenda. When sustainability is embedded in systems and day-to-day processes, businesses can reach their short and long-term goals faster and more effectively. Our Flex campus in Zhuhai, China, is living proof of this approach. By applying automation, analytics and IoT technologies, the team developed a smart system in 2019 that detects abnormal usage trends for energy, water, and other resources. In these cases, the system either triggers self-correcting optimization processes or automatically sends SMS alerts to engineers to investigate and resolve the issue. This innovative solution reduced energy consumption within various ecosystems between 20-90%. It also contributed significantly to lowering the site’s overall electricity and water consumption by 29% and 31% respectively between 2019 and 2020.
2. Reducing waste through edge AI sensing quality control
Vincent Roche, President and CEO, Analog Devices
To reduce energy consumption and improve operational efficiency, AI-driven sensing solutions at the edge are offering new opportunities over cloud-based alternatives. By deploying edge-based solutions for tasks like condition-based monitoring, an organization can generate insights into asset performance while removing energy intensive processes that transfer data to be analysed in the cloud. This can lead to a 98% reduction in the energy consumed by monitoring the asset.
3. Improving the well-being of people and planet through digital tools
Peggy Johnson, CEO, Magic Leap
Faced with a lack of skilled workers, companies across manufacturing sectors are seeking new opportunities to train their workforce faster and more efficiently. Empowering workers through wearable devices and software, paired with clear definition of success metrics – with continuous measurement cycles and taking an iterative approach to training modules – is enabling industry to achieve exponential cost and environmental savings. This leads to a 50% decrease in carbon footprint due to eliminating travel by 50%, while a 220% higher efficiency in production rates and time savings driven by reduction of ramp up time by 33% for assembly operators.
4. Real-time operational performance to unlock new value
Jim Heppelmann, President and CEO, PTC
With data trapped in siloes across teams, productivity issues arise from unplanned machine downtime and lack of operational visibility to energy consumption. To improve data visibility into machinery performance and energy consumption, an IIoT foundation can be leveraged to enable actionable insights by providing a holistic view into multiple systems. This has allowed companies to reduce impact and at the same time track and report progress towards ESG metrics: reduce work in progress by 16%, unplanned downtime by 30%, and manufacturing cycle time by 16-20%, as well as a 13.2% reduction in energy consumption against the target baseline.
5. Sustainability and energy data driven decision making
Blake Moret, Chairman and CEO, Rockwell Automation
With a focus on achieving net-zero scope 1 and 2 commitments, manufacturers are looking for new ways to leverage technology to reduce their environmental footprints. Intelligent devices, combined with innovative sustainability and energy management software, provide new levels of insight to reduce energy demand and increase efficiency. Real-time energy management is within reach, from simple monitoring to embedded AI tools that enable closed-loop optimization.
By creating a standard energy data model in context to production, energy intensity and other key performance metrics can be measured and improved. In one instance, an automotive company found that 40% of one machine’s energy consumption occurred when it was not producing anything. This simple, yet impactful, insight allowed them to de-energize the equipment when not in use, reducing both costs and greenhouse gas emissions.
6. Energy management to improve sustainability
Barbara Frei, Executive Vice-President, Industrial Automation, Schneider Electric
Today, many manufacturers lack visibility into when and where energy is being used. In order to capture greater energy consumption granularity, when and where it happens in the plant, the Schneider Electric Lexington Smart Factory (part of the Global Lighthouse Network), in Kentucky, US, leveraged IoT connectivity with power meters and predictive analytics to optimize energy costs. This has led to a 26% energy reduction (GWh), 30% net CO2 reduction, 20% water use reduction, and a Superior Energy Performance 50001TM certification by the US Department of Energy.
7. Automate calculation of product carbon footprints
Cedrik Neike, CEO Digital Industries, Siemens
In order to measure and validate a product’s footprint to consumers, customers, or regulators and to derive targeted reduction measures with their suppliers, companies are depending on information at a product level. Today, gathering trustworthy and accurate data across supply chain partners requires significant efforts and the available solutions are not suited to calculate Product Carbon Footprints (PCF) at scale. Siemen’s solution automates the calculation of PCFs from cradle-to-gate using a new method for exchange of certified product-level information that addresses requirements on data quality, trustworthiness and confidentiality.
8. Supply chain resiliency enabled by digital twin
Axel Stepken, Chairman, TÜV SÜD
Global supply chain disruptions are impacting the availability of products and raising costs more than ever. And at the same time, stakeholders are requesting greater ESG data transparency and traceability. To address this, digital twin of advanced manufacturing and value chains can optimize supplier landscape and logistics systems to increase resilience by automating optimal decision making and incorporating environmental and social factors.
For example, choosing a local supplier reduces the miles travelled, while also resulting in less costs. Application of this had led to 21% supply chain resilience, 75% CO2 emission reduction, 13% cost per box reduction, 10% speed up – go to market, 90% risk management accuracy. In a nutshell, based on standardized datapoints of dependencies generated by certified sources and processes, a digital twin supply chain ecosystem is capable to answer and trace questions about planning, design and operation phase. That backbone will ensure an advances manufacturing business continuity and increasing productivity – even while adoptions on occurrences are processing.