Tapping into existing operations data can be the driver for manufacturers who are taking on sustainability goals.
The production of greenhouse gases, inefficient water and energy usage, and significant harmful emissions have earned the manufacturing industry a less-than-sterling reputation for its impact on the environment. Adding to these concerns, manufacturers are now being asked to do their part in the fight against climate change by setting sustainability goals and showing their progress toward them, as companies can no longer operate without addressing their environmental impact.
Improving key performance indicators for environmental, safety, and governance provides multiple benefits to manufacturers. A good score will provide better access to capital for new investments, along with more efficient, climate-friendly operations. The efficient use of raw materials to reduce waste and lower energy consumption provides significant cost savings. Sustainable practices boost a company’s reputation, giving it a competitive advantage in the market.
But despite these benefits, obstacles remain when striving to reach sustainability goals, and they start with money.
Challenges to Investing in Sustainability
According to the Climate Action 100+ initiative,1 159 companies around the world are responsible for 80% of the global industrial greenhouse gas emissions. The initiative’s most recent data, published in January 2021, reports that 46 of the top emitters are in the industrial manufacturing space (29%), 75 in energy (47%), 12 in consumer packaged goods (8%), and 26 in transportation, which includes automotive (16%).
Of this group, 90% of companies have acknowledged concern for climate change and have assigned a C-level executive to their board to lead sustainability efforts and create concrete KPIs for greenhouse gas reduction targets. Despite the public steps taken to address the environmental and societal demands concerning sustainability, there is still a significant disconnect between words and their actions as 90% of these organizations do not have a clear allocation of capital for sustainability initiatives
In its global 2021 Climate Check report2, Deloitte reported that the lack of capital allocation is due to several factors. The main one is difficulty quantifying the return on investment assigned to reducing an organization’s impact on the environment because of the volume of possible outcomes and the undetermined timeline of when these outcomes will be realized.
For example, in the energy industry, green and blue hydrogen are options for transitioning into less carbon intensive processes, but these are new technologies, and only green hydrogen is viable at an industrial scale. In other industries, such as consumer packed goods, reducing energy use is a significant step toward sustainability, and it is relatively simple to do so.
Sustainable practice boost a company’s reputation, giving it a competitive advantage in the market
While a lack of capital allocation and clear ROI calculations remain top challenges for process manufacturing industries looking to improve sustainability KPIs, many overlook a valuable resource, namely data. Process manufacturers have collected time-series process data in data historians for decades, and with the right tools they can empower their employees to make sustainability a constant focus, and thus begin making progress toward goal attainment immediately.
Empowering the Workforce
Sustainability is an area that all process industries should be investing in due to changing political, regulatory, and market trends. For example, sustainability has become a deciding factor for job candidates, particularly with younger generations. Forbes has dubbed Gen Z as “the sustainability generation.”
The perception that these industries are harmful to the environment, are not taking action to reduce their impact, or are not innovative enough not only impacts corporate image, but also the ability to hire new talent. Luckily, digital technologies can provide important benefits in reporting and reducing greenhouse gas emissions by implementing energy models while requiring minimal capital expense.
Digital transformation is not exclusive to the process design teams with a fancy digital twin running simulations, or the maintenance groups doing predictive maintenance, and neither is sustainability. These and other technologies can also be used, for example, by an environmental engineer and a process engineer collaborating with other groups to make the necessary progress toward more sustainable operations.
As with safety, reducing an organization’s environmental impact should be everyone’s responsibility, and it can be achieved without a large investment. It just takes the right technology applied to the right data.
The Answer Is in the Data
With the right advanced analytics solution, process engineers can gain insight into the organization’s historical and near-real time process data, including access to environmental process data. Thanks to automated data cleansing and contextualization, engineers can significantly reduce the time spent wrangling data to generate reports, and instead focus on process improvement projects, like optimizing environmental performance.
Additionally, advanced analytics provide users with the capability to switch from a compliance-focused and reactive approach to a more proactive approach by continuously monitoring parameters to detect and mitigate environmental violations. If a violation is detected, users can assess process performance to identify events that led to the incident.
Subject matter experts using advanced analytics solutions can also better understand how process changes will impact the organization’s environmental performance by building models to predict process or equipment behavior based on operating conditions. For example, energy models based on steam generation and consumption in the plant can be used to reduce steam use, and therefore overall plant energy use.
With the real-time collaboration capabilities provided by advanced analytics software, sustainability becomes a common goal among all employees. Accelerated insights can be communicated with the broader organization, alleviating siloed and error-prone processes that exist when employees are dependent on more traditional technologies for analysis, such as spreadsheet applications.
Achieving Goals with Global Impact
Software combined with self-service advanced analytics and machine learning tools can lower the risk on investing in sustainability initiatives with unknown ROI, but it can also help organizations work toward Agenda 2030. In 2015, the United Nations adopted 17 sustainable development goals3 to address the global concern for sustainability, noting that the goals were “a universal call to action to end poverty, protect the planet and ensure that all people enjoy peace and prosperity by 2030.” When it comes to the process industries, there are four common goals where progress can make the most impact:
- Clean Water and Sanitation: Ensure availability and sustainable management of water and sanitation for all. Process industry organizations can reduce water consumption and optimize treatment processes.
- Affordable and Clean Energy: Ensure access to affordable, reliable, sustainable and modern energy for all. Process industry organizations can predict and report on energy consumption to lower use.
- Responsible Consumption and Production: Ensure sustainable consumption and production patterns. Process industry organizations can improve process efficiencies to minimize waste and rework, while mitigating safety risks.
- Climate Action: Take urgent action to combat climate change and its impact. Process industry organizations can monitor, report on, and predict emissions—and then take steps to reduce them.
Use Cases: Putting It into Action
Greenhouse emissions reporting: Reporting greenhouse gas emissions is a challenge for any company for two reasons: lack of standardization and regulatory requirements. Most often, greenhouse gas reporting is done with spreadsheets that need to be massaged, and in some cases manually updated, to provide a single status report that takes several days to create. Digital tools can provide the ability to locate, aggregate, and analyze the data necessary to provide overall emissions numbers.
Despite the public steps taken to address sustainability, 90% of these organizations do not have a clear allocation of capital for sustainability initiatives.
Once a model for reporting is in place, reports can be created in a matter of hours, as opposed to days using spreadsheets. The ability to create a model to report emissions does more than save hundreds of hours of engineering hours, it also addresses the problem of auditing the process. Every step of the calculations and data sources selection can be recorded, so if there is a question on excursions from the goals or achievement toward the goals, root causes can be determined.
Energy consumption prediction models: A specialty chemical company wanted to create energy models of their critical assets. Creating prediction energy consumption models is difficult, and in most cases the models are rarely updated, which makes them obsolete quickly. Relevant data needs to be cleansed, aligned, and then featured in a sample that can be put into a mode. Advanced analytics applications enabled the chemical company to develop and update models, many with multivariate complexity, to consistently identify energy use reduction opportunities.
It is important to note that any models developed should represent the process, and simple tests can be done to confirm conformity. For example, when opening a valve feeding a steam jet, if something does not look right, the issue may be in the instrumentation, or the control loop associated with it. A model allows an organization to discover relationships defined by energy models, creating an effective energy reduction program that consistently delivers results and attractive ROI.
Digital tools can provide the ability to locate, aggregate, and analyze the data necessary to provide over-all emissions numbers.
Boosting production and safety: A critical aspect of sustainability is the impact on people. Safety first is a motto that all manufacturers live by, which means anything that can help reduce risk and improve work conditions is highly valued.
One example of how digital technology helped to improve safety is in fire prevention for a maker of paper consumer goods. In the tissue making process, high temperatures are critical for drying the tissue paper so that it has precise strength and softness qualities, but there can be a fire risk in the hood where those fibers dry if proper care is not taken.
Deploying condition-based monitoring helped the company manage temperatures during the process to ensure the proper high temperatures and other conditions were maintained for evaporating water off the fibers while also preventing dust collection – the primary ignition source for fires. After experiencing eight fires in the first half of 2021, the manufacturer has had none since deploying the monitoring solution and has since expanded its use to all similar equipment across the company.
Regardless of the industry or use case, the common thread among these examples is that these projects don’t require significant capital investment. Instead, positive impacts can be realized through better use of existing assets, specifically by analyzing existing data to create insights and perform actions.
Sustainability initiatives do not need to be complicated or capital-intensive, and they don’t have to be manual actions relegated to an area detached from operations. Instead, self-service analytics provides a platform for analyzing operational data that can give manufacturers insights to enable cleaner, safer, and more efficient operations.
Digital technologies provide the means for collaboration and access to the many years of operational data that is already available to process manufacturing organizations. Many manufacturers are ready to make sustainability a top priority, and by leveraging existing data, they can start their sustainability journey today. M