Unlocking sustainable manufacturing with data-driven insights

What if the key to unlocking sustainable manufacturing lies not just in advanced technology, but in how effectively you harness data to align operational efficiency with environmental impact?

In today’s rapidly evolving industrial landscape, sustainability has transcended from a mere buzzword to a critical business imperative. As global attention intensifies on environmental responsibility, companies are increasingly pressured to align their manufacturing processes with the principles of sustainability. But what if the key to unlocking sustainable manufacturing lies not just in advanced technology, but in how effectively you harness data to align operational efficiency with environmental impact?

This article delves into the challenges and opportunities of sustainable manufacturing, exploring how leveraging operational data and integrating smart technologies can lead to significant improvements in sustainability metrics, operational efficiency, and profitability and how data-driven strategies can help industries achieve the triple bottom line of people, profit, and planet.

Understanding the core challenges

Sustainable manufacturing is defined as the creation of products through economically sound processes that minimize negative environmental impacts while conserving energy and natural resources. However, achieving this balance is challenging. Operational inefficiencies, excessive resource consumption, and complex supply chains are just a few of the hurdles that industries must overcome.

One of the most significant challenges is the disconnect between operational efficiency and sustainability. While many companies focus on streamlining processes to reduce costs, they often overlook the environmental impact of these operations. This disconnect is particularly evident in how companies manage their resource usage - such as energy, water, and raw materials - which directly correlates with sustainability outcomes.

If you have the opportunity to visit a manufacturing facility, observe how easily production and operational metrics can be retrieved in near real time. You may be surprised to discover that sustainability metrics often require significantly more time and effort to calculate, let alone integrate into routine decision-making processes.

Operational bottlenecks and sustainability

Industry findings have shown that removing operational bottlenecks correlates strongly - in some cases by over 90% - with improvements in sustainability. This correlation highlights the importance of viewing operational efficiency and sustainability as interconnected rather than separate objectives. For instance, a bottleneck in the production process that leads to excessive energy use not only increases costs but also contributes to higher carbon emissions.

A crucial question for manufacturers is: How are you leveraging operational data and sustainability data for combined optimization?

The power of operational data in sustainability

In the pursuit of sustainable manufacturing, data is one of the most valuable assets a company can possess. Operational data, when integrated with sustainability metrics, provides actionable insights that can drive significant improvements across the board. This integration allows companies to optimize their manufacturing processes, reduce waste, and minimize their environmental footprint.

One of the key challenges is how to effectively leverage this data. Many companies collect vast amounts of operational and sustainability data but lack the tools or expertise to analyse and act on it. This is where advanced analytics or ML/AI come into play, enabling manufacturers to uncover patterns and correlations that might otherwise go unnoticed.

Data Collection for resource management

Effective resource management begins with accurate data collection. Manufacturers need to gather process-specific, meter-specific, and site-specific data on resource and material inputs and outputs. This data forms the foundation for identifying inefficiencies and opportunities for optimization.

The challenge lies in the diversity and volume of data generated by manufacturing processes. Energy, water, steam, gas, waste, raw materials, and emissions data all need to be collected, analysed, and acted upon. Prioritizing which data to focus on can be difficult, but it is essential for maximizing the impact of sustainability initiatives.

Energy and water are two of the most critical resources in manufacturing, and their efficient use is essential for both operational efficiency and environmental sustainability. Industry studies estimate that the potential for energy and water usage optimization in manufacturing ranges from 20% to 50%. This wide range reflects the diverse nature of manufacturing processes and the varying degrees of inefficiency present in different facilities.

Example of data-driven energy and efficiency optimization

Let’s consider a hypothetical example to illustrate the impact of data-driven optimization. Suppose a manufacturing equipment has baseline of 2 KWh consumption while producing good parts and it is running an 8-hour shift or 480-minutes. In the figure, we can appreciate how depending on the OEE losses, several spikes in energy usage occur. These can be reduced if related to wear and tear (e.g. 9. reduced speed) with proper preventive or predictive maintenance or if tool change (i.e. 2. changeover) has an unnecessary number of dry runs before being fully operational. If the machine is waiting for parts, smart start and stop mechanisms would help diminishing the energy usage as well.

As the target energy usage (green bar) differs depending on operating conditions, it is critical to look at primary consumption data (orange bars) to establish dynamic and accurate baselines for proper contextual analysis.

The clear advantage here is that energy savings have a direct 1:1 correlation with monetary savings and scale rapidly across multiple equipment and production line, if the symptoms of high energy usage are common in one facility.

Overlaying and analysing both production and sustainability metrics allow for synergies and savings that lean production techniques alone would not be able to achieve.

Near real-time monitoring for continuous sustainable improvement

Continuous improvement is at the heart of sustainable manufacturing, and near real-time data monitoring is a critical enabler of this process. By monitoring KPIs such as energy consumption, water usage, and waste production in near real-time, companies can quickly identify deviations from sustainability targets and take To do so, we need to leverage as much as possible the existing OT/IT landscape of manufacturing operations and look for dynamic waste, emissions, energy, water, steam, fuel and gas consumptions data to establish a proper baseline, which depends on the type of products, materials, processes operating conditions.

Often these data are scattered across a multitude of systems such as ERP, MES, SCADA, PLC, etc... so that the challenge is developing a solution architecture capable of harnessing near real time data from multiple processes and legacy systems while having the right mix of analytical expertise and ML/AI algorithms tailored to the specific product, processes and consumptions.

If performed correctly, this approach allows in time to move away from proxy or secondary data and to gather primary data that build the basis for reliable and accurate dynamic environmental product footprint (EPF), lifecycle assessment LCA, product carbon footprint (PCF).

For selected potential use-cases, the implementation can be approached step-by-step, focusing on the most promising hotspots within the manufacturing process. By targeting these key areas, companies can prioritize initiatives that are likely to yield the highest impact in terms of sustainability improvements. This method allows for careful monitoring and refinement of practices before broader application, reducing risks and ensuring best practices are developed.

Once these initial sustainable manufacturing lighthouse projects prove successful, they can be rapidly scaled across the organization, provided that strong leadership commitment is in place. Leadership plays a pivotal role in championing these initiatives and ensuring alignment across departments. Additionally, allocating the necessary resources - both financial and human - is critical for effective scaling. This process mirrors the way global enterprises roll out large-scale initiatives, such as enterprise resource planning (ERP) or manufacturing execution systems (MES) projects, which are similarly phased, well-resourced, and strategically aligned with organizational goals. With this structured approach, sustainable manufacturing practices can become deeply embedded in operations, driving long-term value.

Conclusion

Sustainable manufacturing is not just a moral imperative; it is a business necessity in today’s world. By leveraging data, integrating advanced technologies, and adopting innovative approaches companies can achieve significant improvements in sustainability, operational efficiency, and profitability.

The path to sustainable smart factories requires a commitment to data-driven strategies, continuous improvement, collaboration across the value chain, and a willingness to embrace new ideas and technologies. Those who succeed in this journey will not only contribute to a healthier planet but also secure their place as leaders in the future of manufacturing.

To learn more, visit the Sustainability Services section
of our website or contact us.