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.
Menurut Gartner, pada tahun 2028, 1 dari 4 perusahaan global dengan kinerja terbaik akan memanfaatkan GenAI untuk mengurangi emisi bersih menjadi nol. Pengelolaan dan produksi limbah merupakan salah satu tantangan paling signifikan dan mahal yang dihadapi bisnis dalam mencapai nol emisi bersih, khususnya di bidang manufaktur, salah satu pencemar terbesar di dunia. Menurut Business Waste dari Inggris, industri ini menghasilkan sekitar 2 miliar ton limbah industri setiap tahunnya, yang mencakup 50 persen dari seluruh limbah di seluruh dunia. Sebagian besar limbah dihasilkan dari produksi berlebih, barang dagangan yang cacat, dan limbah "sisa", yang dihasilkan dari sisa bahan baku yang tidak diperlukan dalam produk akhir.
Dalam lingkungan ekonomi saat ini, para CEO harus menjadi yang terdepan dalam persaingan di segala bidang, termasuk dalam perlombaan menuju nol emisi dan mengurangi limbah dengan teknologi inovatif seperti AI, yang secara strategis dapat membantu mereka melakukan hal ini.
Hal ini menimbulkan pertanyaan: apakah GenAI adalah peluru ajaib untuk mencapai emisi nol bersih, mengurangi limbah secara signifikan, dan sekaligus meningkatkan efisiensi operasional? Meskipun tidak ada jalan pintas, GenAI tentu memiliki potensi untuk mengurangi limbah, meningkatkan produktivitas, dan meningkatkan pendapatan.
Penerapan GenAI dalam manufaktur: inovasi = efisiensi
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.
Untuk sepenuhnya membuka potensi AI dan GenAI, para CEO harus memulai dengan inovasi yang berorientasi pada tujuan. Dengan cara ini, mereka dapat memastikan solusi baru yang diadopsi sesuai dengan tujuan dan secara strategis selaras dengan tujuan dan nilai bisnis. Berikut adalah lima cara perusahaan dapat menerapkan AI untuk mengurangi pemborosan dan, dalam prosesnya, meningkatkan efisiensi.
5 cara utama AI dapat mengoptimalkan pengelolaan limbah
1. Optimasi proses cerdas
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 Bahasa Indonesia: MSN.
2. Pemeliharaan prediktif tingkat lanjut
Strategi perawatan tradisional bersifat reaktif dan hanya berlaku saat mesin rusak, tetapi GenAI dapat menghentikan gangguan sebelum terjadi. AI mendukung perawatan prediktif dengan memperkirakan kegagalan sebelum terjadi, yang dapat memangkas komponen berlebih dan kebutuhan inventaris yang berlebihan, mengurangi pemborosan, dan menghemat sumber daya sekaligus mempertahankan efisiensi operasional puncak.
3. Peningkatan manajemen rantai pasokan
Penelitian mengungkap bahwa manajemen rantai pasokan yang didukung AI menghasilkan peningkatan operasional yang signifikan, meningkatkan tingkat layanan hingga 65 persen dan inventaris hingga 35 persen atau lebih. AI dapat meningkatkan efisiensi rantai pasokan dengan memberikan wawasan yang dapat ditindaklanjuti dan analisis data waktu nyata, yang mengarah pada peningkatan perkiraan permintaan dan pemotongan produksi berlebih serta inventaris berlebih.
4. Teknologi ketertelusuran ujung ke ujung
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. Desain generatif dan manajemen siklus hidup
Desain generatif dapat memungkinkan praktik ramah lingkungan seperti pemanfaatan bahan berkelanjutan 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.
Keunggulan AI: mempercepat upaya keberlanjutan
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 Indeks Kesiapan Industri Keberlanjutan Konsumen (COSIRI) merupakan inti dari upaya berkelanjutan. COSIRI merupakan kerangka kerja yang diakui secara luas yang dapat mengevaluasi kematangan keberlanjutan di berbagai dimensi, termasuk lantai produksi, rantai pasokan, logistik, strategi, risiko, pengembangan tenaga kerja, dan kepemimpinan. COSIRI dapat mengungkapkan wawasan yang kuat yang dapat digunakan oleh CEO untuk membuat keputusan strategis, yang mendukung integrasi praktik berkelanjutan ke dalam operasi. Untuk mempelajari lebih lanjut tentang COSIRI, kunjungi Penilaian COSIRI halaman.
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.
