As the race to net zero accelerates, manufacturing CEOs are poised to transform their entire operations, from the shop floor to waste management and even rethinking land use, with sustainability integrated throughout every aspect. During this era of “green transformation,” leaders ultimately fall into two categories: trailblazers leading the way and slow starters, who trail behind. If McKinsey and Co.’s prediction proves accurate, by 2027, 75 per cent of S&P 500 businesses will disappear entirely. This alarming prediction sends a clear message to CEOs: to remain competitive, leaders must proactively transform their businesses to meet the green demands of today, and groundbreaking technology, such as generative artificial intelligence (GenAI), will play a key role in expediting their efforts.
Según Gartner, para 2028, 1 de cada 4 empresas globales de alto rendimiento utilizará GenAI para reducir las emisiones netas a cero. La gestión y producción de residuos se encuentran entre los desafíos más importantes y costosos que enfrentan las empresas para alcanzar las cero emisiones netas, especialmente en el sector manufacturero, uno de los principales contaminantes del mundo. Según Business Waste del Reino Unido, el sector produce aproximadamente 2000 millones de toneladas de residuos industriales al año, lo que representa el 50 % de todos los residuos a nivel mundial. La mayor parte de estos residuos se genera por sobreproducción, mercancía defectuosa y residuos sobrantes, resultantes de los restos de materias primas que no se requieren en el producto final.
En el entorno económico actual, los directores ejecutivos deben adelantarse a la competencia en todas las áreas, incluida la carrera hacia el cero neto y la reducción de desechos con tecnologías innovadoras como la IA, que pueden ayudarlos estratégicamente a lograrlo.
Esto nos lleva a preguntarnos: ¿es GenAI la solución milagrosa para lograr cero emisiones netas, reducir significativamente los residuos y, al mismo tiempo, mejorar la eficiencia operativa? Si bien no existen atajos, GenAI sin duda tiene el potencial de reducir los residuos, impulsar la productividad y aumentar los ingresos.
La aplicación de GenAI en la fabricación: innovación = eficiencia
The hype surrounding GenAI continues to build, and for good reason. According to Ernst & Young (EY), GenAI is estimated to unlock approximately USD $1.7 trillion to $3.4 trillion in gross domestic product (GDP) by 2033. In manufacturing alone, by 2033, MarketResearch.biz predicts that the global GenAI market will soar to approximately USD$6.4 million. In a world where digital transformation is revolutionising the sector, if CEOs leverage GenAI to suit their business needs, they can enable their businesses to thrive in all areas, including waste reduction and, ultimately, resulting in net-zero operations.
There are many ways manufacturers can apply GenAI to their processes. For example, fashion companies can leverage GenAI in 3D weaving technology. Making clothes made to fit minimises waste, enabling the industry to cut its carbon emissions. In the case of Airbus, their generative design enables their jetliners to consume less fuel and reduce waste and their overall environmental footprint.
Para aprovechar al máximo el potencial de la IA y la GenAI, los directores ejecutivos deben empezar por la innovación orientada a un propósito. De esta manera, pueden garantizar que las soluciones emergentes adoptadas sean adecuadas para su propósito y se alineen estratégicamente con los objetivos y valores empresariales. Aquí presentamos cinco maneras en que las empresas pueden aplicar la IA para reducir el desperdicio y, de paso, aumentar la eficiencia.
Las cinco formas principales en que la IA puede optimizar la gestión de residuos
1. Optimización inteligente de procesos
Imagine rotting food that sits in trucks due to poor planning or overproduction of inventory that happened due to human error. Within the realms of planning, production, etc., AI can support the enhancement of processes, ultimately reducing waste. A new AI-driven system developed by University of Virginia researchers could eliminate these errors and establish new benchmarks for manufacturing efficiency, as reported by MSN.
2. Mantenimiento predictivo avanzado
Las estrategias de mantenimiento tradicionales son reactivas y solo surten efecto cuando la maquinaria falla, pero GenAI puede detener las interrupciones antes de que ocurran. La IA facilita el mantenimiento predictivo al pronosticar fallas antes de que ocurran, lo que permite reducir el exceso de piezas y los requisitos de inventario, disminuir el desperdicio y conservar recursos, manteniendo al mismo tiempo la máxima eficiencia operativa.
3. Gestión mejorada de la cadena de suministro
La investigación descubrió que la gestión de la cadena de suministro habilitada con IA conduce a mejoras operativas significativas, mejorando los niveles de servicio hasta en un 100%. 65 por ciento y el inventario hasta en un 35 % o más. La IA puede impulsar la eficiencia de la cadena de suministro al proporcionar información práctica y análisis de datos en tiempo real, lo que permite mejorar la previsión de la demanda y reducir la sobreproducción y el exceso de inventario.
4. Tecnologías de trazabilidad de extremo a extremo
AI-enabled tech that tracks and reduces waste can help expose the reasons for production errors and help establish best practices to sustainably source, produce and dispatch high-quality goods. CEOs who use AI for digital tracing can uncover inefficiencies and execute targeted waste reduction strategies, leading to cost savings, reduction of emissions, and positioning their firm as a sustainability leader.
5. Diseño generativo y gestión del ciclo de vida
El diseño generativo puede permitir prácticas ecológicas como la utilización de materiales sostenibles that are not only good for the environment but also keep customers happy. These products can have an optimised lifecycle through better integrated sustainable processes to reduce waste and emissions to support net-zero advancement activities.
La ventaja de la IA: agilizar los esfuerzos de sostenibilidad
In summary, the pursuit of reaching net-zero carbon emissions by 2050 is an ambitious goal and something that requires company-wide effort and dedication. Manufacturers are among some industries that have the most work to do, given the change needed to move towards net zero. Leaders must change their mindset on sustainability and embrace innovative technologies like AI that can boost efficiency, expedite efforts to reduce waste and optimise land use. Our top five ways to optimise and address waste management are a start, but CEOs must also categorise business activities into two categories: activities that support sustainability goals and activities that instead sabotage eco-friendly goals.
To develop a plan that addresses business activities that are not aligned with sustainability business goals, a robust Environmental, Social, and Governance (ESG) framework, such as the Índice de preparación de la industria para la sostenibilidad del consumidor (COSIRI) Es fundamental para las iniciativas de sostenibilidad. COSIRI es un marco ampliamente reconocido que permite evaluar la madurez de la sostenibilidad en diversas dimensiones, como la planta de producción, la cadena de suministro, la logística, la estrategia, los riesgos, el desarrollo de la fuerza laboral y el liderazgo. COSIRI puede revelar información valiosa que los directores ejecutivos pueden utilizar para tomar decisiones estratégicas, apoyando la integración de prácticas sostenibles en las operaciones. Para obtener más información sobre COSIRI, visite nuestro sitio web. Evaluación COSIRI página.
Frequently Asked Questions About AI in Sustainable Manufacturing
How Does AI Support Sustainable Manufacturing Practices?
AI supports sustainable manufacturing by optimising energy use, reducing waste, predicting equipment failures, and improving process efficiency. It helps manufacturers align operations with sustainability and ESG goals.
What Are Examples of AI Applications in Reducing Industrial Waste?
Examples include AI-powered quality control to reduce defective products, predictive maintenance to avoid equipment breakdowns, and smart production planning to minimise raw material waste.
Can AI Help Manufacturers Lower Their Carbon Footprint?
Yes, AI can help manufacturers lower their carbon footprint by optimising energy consumption, reducing material waste, and enabling data-driven decisions that support low-emission production.
How Does AI Improve Energy Efficiency in Factories?
AI improves energy efficiency by analysing equipment performance, predicting peak energy usage, and automatically adjusting systems to reduce unnecessary power consumption in real time.
What is the Impact of Predictive Analytics on Sustainable Manufacturing?
Predictive analytics helps sustainable manufacturing by forecasting maintenance needs, reducing downtime, and minimising resource waste. It allows manufacturers to run more efficiently and sustainably.
How Does AI Enable Real-time Decision-making in Manufacturing?
AI enables real-time decision-making by processing live data from machines and sensors to detect problems, adjust processes, and optimise performance instantly, supporting agile and efficient operations.
Why is AI Important for Circular Economy Initiatives in Manufacturing?
AI is important for circular economy initiatives because it helps track resource usage, predict material reuse opportunities, and design waste-minimising production cycles, enabling closed-loop manufacturing systems.
What Challenges Do Companies Face When Implementing AI for Sustainability?
Challenges include high implementation costs, data integration issues, lack of skilled talent, and resistance to change. Companies must align AI with clear sustainability goals to maximise impact.
Why is AI Important in Sustainable Manufacturing?
AI is important in sustainable manufacturing because it enables smarter resource management, waste reduction, energy savings, and process optimisation—all critical for achieving long-term environmental and operational goals.
