For hard-to-abate chemicals and materials industries that burn fuels to achieve high-temperature operations, energy requirements are intrinsically linked to emissions. Multiple technologies and applications have arisen from the need to find energy efficiencies to achieve net-zero emissions from manufacturing operations and achieve decarbonization targets.

Utilities optimization with opportunity identification is one such technology focused on optimizing plant processes, allowing operators to manage and optimize balancing energy across the production site. This capability is currently being used by a leading multinational chemical manufacturing company and has enabled it to reduce carbon emissions by 60,000 tons per year, as well as to minimize steam losses and reduce GHG emissions.⁵

Another capability is more suited toward management of the energy system rather than optimization of plant processes. Energy management and information systems (EMIS) monitor, schedule, and optimize in real time to provide insight into emissions, cost-effective energy production, distribution, scheduling, and trading. This type of technology has been implemented by the largest Latin American petrochemical company.⁶ By incorporating EMIS into its processing unit operations, it was able to automatically select the most appropriate energy source for its equipment, thus increasing efficiencies and stability.

Depending on regional availability of support, it may be beneficial to implement different fit-for-purpose vendor EMIS at various global facilities. A metals manufacturer was experiencing rapid growth and needed to implement a solution to contend with stricter emissions regulations, increased gas costs, and improved energy intensity. To meet these goals, the manufacturer looked to implement a solution to dynamically reoptimize inputs based on real-time data and optimize energy usage through a control system. Further, the manufacturer wanted to increase the transparency of the operations by increasing visibility into energy usage at each step.

Working with the manufacturer, Deloitte identified opportunities to improve sustainability, efficiency, and energy usage through a combination of process improvements and software solutions. Within these opportunities, Deloitte provided multiple options for EMIS that would allow the company to choose from selected EMIS that fit each site’s needs. The Deloitte team also provided physical process improvements to reduce downtime and energy losses that coincided with EMIS to achieve sustainability and energy savings goals. The implementation was rolled out utilizing multiple EMIS, driving 10% reduction in energy intensity for each plant. This, in turn, reduced emissions by lowering the amount of energy input needed throughout each step.

Energy and utilities optimization

Beyond energy monitoring and reactive management, two factors largely enable proactive energy optimization to transform energy efficiency: the amount of energy and utilities data a plant can collect and the degree to which it can leverage predictive insights from that data for optimal operations recommendations. While data collection can be highly reliant on hardware and networks, optimization depends on software utilizing AI, machine learning, and advanced analytics platforms to achieve improved efficiencies. Typical key steps in this journey are to define the energy management strategy and road map for operations, develop improved processes and operating procedures to minimize energy usage and loss, and build a business case for advanced energy and utilities optimization solutions for dynamic energy supply/demand balancing.

Smart manufacturing capabilities present a significant opportunity to improve industry energy efficiency. By analyzing historical energy and utilities usage data against associated process data and events, AI models can learn operations patterns and provide insights. These models can determine the optimal operating conditions and process parameter settings to balance energy demand and supply, and to minimize energy usage and losses, leading to cost savings and reduced emissions. The optimization and balancing of planned energy consumption with energy supply from diverse sources, including renewables, can be performed while minimizing energy losses. End-to-end energy optimization starts with energy demand and supply planning. It then entails real-time monitoring and control to balance utilities and energy sources and sinks. Organizations can integrate these advanced energy optimization capabilities with existing EMIS.

As the manufacturing sector advances, the focus on sustainability is diversifying. While monitoring emissions and optimizing energy use remain central, there’s a growing emphasis on proactive quality management and waste reduction. This comprehensive approach to sustainability extends beyond just traditional quality management and overall asset efficiency, encompassing every aspect of the manufacturing process, from design and sourcing to delivery and service. The transition toward a more environmentally conscious future is closely linked with these diverse efforts, ensuring that each stage in the production process is eco-friendly.

The data on waste generation in the United States underscores the importance of quality control and waste minimization in manufacturing processes. In 2018, the United States generated approximately 292.4 million short tons of municipal solid waste.⁷ Concurrently, research from MIT indicates that the generation rates of non-hazardous industrial waste (NHIW) were on the same order of magnitude, with estimates ranging from 246 million to 316 million US tons annually.⁸ The Toxics Release Inventory (TRI) Program, which tracks specific chemicals for their potential health and environmental implications, reported that, in 2021, the United States managed 29.3 billion pounds of production-related, TRI-listed chemical waste.⁹ Of this, approximately 3.22 billion pounds were disposed of or released into the environment. These figures highlight the imperative for industries to adopt rigorous quality compliance and waste minimization approaches.

In the dynamic world of plant operations, Deloitte’s Smart Manufacturing capabilities are pioneering the integration of predictive quality and smart statistical process control (SPC) to optimize production. Deloitte’s approach to smart SPC standardizes data, visualizes performance trends, and dynamically adjusts control limits. In an implementation for a manufacturing company, Deloitte’s Predictor & Solver solution used advanced analytics to optimize machine settings to enhance material flow and reduce waste. This intervention led to an impressive annual EBITDA improvement of $1.3 million, with primary value drivers being reduced waste, increased profitability, and heightened operational efficiency.

Predictive quality and process control to prevent quality deviations

A key challenge for the process industry is that performing quality test analyses of collected samples and receiving results takes a significant amount of time, during which deviations from product quality and wastage can occur. Predictive quality capability enables sustainable and safe production practices by proactively controlling production quality within specifications and minimizing waste. By leveraging AI/ML models, predictive quality can recommend adjustments of process variable parameters and equipment settings based on process data inputs. These predictive quality models can be developed using historical process and events data aligned with quality test results data from laboratory information management systems (LIMS). The models can be trained to predict key quality characteristics properties in real time based on process data and to provide the process control setpoint changes needed to keep the product quality within specifications.

For a plastics products manufacturer, Deloitte developed a model predicting process anomalies well in advance, with troubleshooting and interventions leading to a potential value of $10 million across the plant network and an additional 40,000 pounds of production annually per line. The primary benefits were a significant reduction in waste and downtime and a streamlined root-cause analysis and quality review process. The above-described innovative capabilities in Deloitte’s Smart Manufacturing offerings signify a transformative shift in quality and waste management. They emphasize not just the integration of smart technologies but a holistic approach to quality, efficiency, and sustainability, providing manufacturers a competitive edge in today’s market.