Adopting circular economy
Circular economy and zero waste manufacturing concepts are becoming integral parts of process manufacturing companies.
This is due to rising climate advocacy, increasing concerns over depletion of natural resources, high volume of wastages in process manufacturing, regulatory mandates, and improvements in technology.
The global economy is, however, just 7.2% circular, as per the Circularity Gap report of 2023. This means more than 90% of materials are either wasted or lost, making us entirely depends on new materials.
Leveraging Industry 4.0 technologies, such as artificial intelligence (AI), machine learning (ML), internet of things, (IoT), cloud computing, and big data, can help minimize waste generation in manufacturing and pave the way towards a circular economy.
Mirror on the wall
Know your enterprise (KYE) is the first step for businesses to plan a circular strategy.
It is a comprehensive value chain assessment that helps enterprises introspect their operations and digital maturity. It is supported by a weighted score and the scoring methodology aims to provide a structured approach to measure and communicate performance. That enables enterprises to make informed judgements and implement actions in accordance with the computed score and recommendations. This framework is designed based on our contextual knowledge, experience, and on-ground observations from various client engagements in discrete manufacturing across the US and EMEA regions.
Charge of the tech brigade
Technology acceleration and climate change are perhaps two of the most important trends that are shaping our future.
Industry 4.0 is intertwined with circular economy. The following technologies can be leveraged by original equipment manufacturers (OEMs) to reap the benefits of circular economy for maximum value creation:
AI-ML models: A well-trained artificial intelligence (AI) and machine learning (ML) model can recognize useful patterns to trace and predict a materials life cycle. Medical device or automakers can leverage this model for preventive maintenance events. They can build purpose-led neural network algorithms which will send alerts and notifications which will predict the amount of recyclable, reusable, and residue material in the value chain.
Big data: Original equipment manufacturers (OEMs) such as building technology and transportation have begun the journey towards smart manufacturing. In this transition, companies tend to generate data from multiple sources like sensors, machines, and robotics. With this data, enterprises can build data ecosystems for operational excellence. It can also be leveraged for deep learning, predictive modelling, and advanced circularity analytics. Historical and real-time data can predict the carbon footprint, demand, and manage inventory thereby helping in minimizing emissions, waste, and enhancing sustainable operations.
Blockchain: Blockchain, combined with industrial internet of things (IIoT), help to monitor products in their entire value chain. For instance, a wind turbine maker deployed blockchain in field services for managing and sharing maintenance data in the turbine operations. It helped to create a secure and tamper-proof ledger for maintenance and performance records, warranty information, and service history of the turbines in an efficient and transparent manner. It also helped to track the turbine components in the entire value chain that can be reused, remanufactured, or recycled.
Cloud computing: Cloud solutions contribute to a more eco-optimized manufacturing and supply chain operating model. A recent study by TCS Global Cloud showed 67% respondents saying that cloud technologies have helped their organizations achieve sustainability goals. A leading smart meter maker deployed a cloud-based end-to-end solution for flexibility management across diversified market segments. The solution not only reduced downtime, capex cost, and latency but also brought down energy consumption and carbon emissions significantly by eliminating computing hardware and maintenance.
Cyber-Physical Systems (CPS): CPS can maximize the opportunity for manufactures by predicting defects in machines, energy consumption, asset health, and building factories of the future. Automakers can also automate the manufacturing process by deploying robots or collaborative robots (cobots) for waste segmentation. All these can maximize the value of recycling and reduce the volume of materials sent to landfill.
5G: Manufacturers can monitor the lifecycle of the products, even after sales, with 5G-enabled sensors. The data can provide consumers with recycling services and repurposing functional components. One such application could be implementing 5G-enabled smart water systems. 5G networks facilitate real-time monitoring of water infrastructure, helping in instant leak detection and inefficiencies thus reducing water waste and consumption. Such water meters also provide precise information on the consumption, thereby empowering consumers to take informed decisions about their water usage, leading to reduced consumption.
Additive manufacturing (AM): AM is poised to manufacture materials with efficient designs that reduce wastage compared to conventional manufacturing. It facilitates manufacturers to produce on-demand goods locally, which can cut down shipping cost and carbon emissions associated with transportation. Safety-critical industries like medical equipment manufactures can use it for prototyping techniques to quickly build and test anatomical models prior to complex surgical procedures.
Unlocking future value
Zero waste manufacturing is not just an aspiration.
It can help companies halve their costs through improved resource efficiency, increase revenue by about 12% through new business models, and reduce emission footprints by at least 6% through improved recycling. Manufacturers can also grab 4-5% market share by offering sustainable products and services appealing to environmentally conscious customers. All these estimations are based on the benefits realized and value delivered by us to global clients.
The adoption rate, however, can vary significantly across industries and organizations. These technologies are more likely to be adopted by large, well-established firms with more resources at scale, while smaller ones may use these more slowly due to resource constraints. In addition, there may be challenges related to workforce skills, cyber-security, infrastructure, data quality and management, regulatory compliance, and the need for cultural changes within organizations.
The path ahead may hold challenges, but the destination promises a world where manufacturing is not only efficient but also sustainable, contributing to a cleaner, greener, and more responsible industrial future.