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.
가트너에 따르면 2028년까지 세계 최고 실적 기업 4곳 중 1곳이 GenAI를 활용하여 순 배출량을 제로로 줄일 것으로 예상됩니다. 폐기물 관리 및 생산은 기업이 순 배출량 제로 달성에 직면하는 가장 중요하고 막대한 비용이 드는 과제 중 하나이며, 특히 세계 최대 오염 배출원 중 하나인 제조업 분야에서 더욱 그렇습니다. 영국 비즈니스 웨이스트(Business Waste)에 따르면, 이 산업은 매년 약 20억 톤의 산업 폐기물을 발생시키며, 이는 전 세계 폐기물의 50%를 차지합니다. 폐기물의 대부분은 과잉 생산, 불량품, 그리고 최종 제품에 필요하지 않은 원자재의 잔여물인 "잔여" 폐기물에서 발생합니다.
현재의 경제 환경에서 CEO는 탄소 중립을 향한 경쟁과 AI와 같은 혁신 기술을 활용한 폐기물 감소를 포함한 모든 분야에서 경쟁에서 앞서 나가야 합니다. 이는 CEO가 이를 달성하는 데 전략적으로 도움이 될 수 있습니다.
그렇다면 GenAI가 순배출량 제로 달성, 폐기물 대폭 감소, 그리고 동시에 운영 효율성 향상을 위한 묘책일까요? 지름길은 없지만, GenAI는 폐기물 감소, 생산성 향상, 그리고 매출 증대를 이룰 수 있는 잠재력을 가지고 있습니다.
제조업에 GenAI 적용: 혁신 = 효율성
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.
AI와 GenAI의 잠재력을 최대한 활용하려면 CEO는 목적 중심 혁신부터 시작해야 합니다. 이를 통해 도입하는 새로운 솔루션이 목적에 부합하고 비즈니스 목표 및 가치와 전략적으로 부합하는지 확인할 수 있습니다. 기업이 AI를 적용하여 낭비를 줄이고 효율성을 높일 수 있는 다섯 가지 방법을 소개합니다.
AI가 폐기물 관리를 최적화할 수 있는 5가지 주요 방법
1. 지능형 프로세스 최적화
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 엠에스엔.
2. 고급 예측 유지 관리
기존의 유지보수 전략은 사후 대응적이며 기계 고장이 발생한 후에야 효과가 나타나지만, GenAI는 고장 발생 전에 이를 차단할 수 있습니다. AI는 고장 발생 전에 예측하여 예측 유지보수를 지원하며, 이를 통해 과잉 부품 및 과도한 재고 요구량을 줄이고, 낭비를 줄이며, 자원을 절약하는 동시에 최고의 운영 효율성을 유지할 수 있습니다.
3. 향상된 공급망 관리
연구에 따르면 AI 기반 공급망 관리로 인해 상당한 운영 개선이 이루어지고 서비스 수준이 최대 향상되는 것으로 나타났습니다. 65퍼센트 재고를 최대 35% 이상 절감할 수 있습니다. AI는 실행 가능한 통찰력과 실시간 데이터 분석을 제공하여 공급망 효율성을 높이고, 수요 예측을 개선하며 과잉 생산 및 재고를 줄이는 데 도움이 됩니다.
4. 종단간 추적 기술
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. 생성적 디자인 및 수명 주기 관리
생성적 설계는 다음과 같은 친환경적 관행을 가능하게 할 수 있습니다. 지속 가능한 재료 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.
AI의 장점: 지속 가능성 노력 가속화
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 소비자 지속가능성 산업 준비 지수(COSIRI) 지속가능성 노력의 핵심입니다. COSIRI는 생산 현장, 공급망, 물류, 전략, 위험, 인력 개발, 리더십 등 다양한 측면에서 지속가능성 성숙도를 평가할 수 있는 널리 인정받는 프레임워크입니다. COSIRI는 CEO가 전략적 의사 결정에 활용할 수 있는 강력한 통찰력을 제공하여 지속가능성 실천을 운영에 통합하는 데 도움을 줄 수 있습니다. COSIRI에 대한 자세한 내용은 당사 웹사이트를 방문하세요. 코시리 평가 페이지.
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.
