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TruKKer for Business

Going Green Smartly: 7 Ways AI Helps Sustainable Logistics

Forbes rightly asserts that sustainable practices have shifted from optional to imperative – given the escalating climate crisis. This transformation has the potential to reshape not only our approach to environmental sustainability but also the management of logistics operations.


In particular, AI holds promise in optimizing various facets of logistics, ranging from efficient route planning and carbon tracking analytics to enhanced asset utilization and intelligent inventory management. It can make supply chains more efficient, cost-effective, and environmentally sustainable.


Let’s see some use cases:


Sustainability Assessment: AI empowers platforms to track carbon emissions in upstream activities, providing the tools to identify opportunities and assess risks in ESG ratings. These end-to-end solutions cover a broad spectrum of sustainability and performance management, enabling comprehensive supply chain risk screening, mapping, and actionable scorecards.


Circularity Design: AI can optimize product designs for circularity by suggesting opportunities for reuse, refurbishment, or recycling within a company’s supply chain. Software solutions aid in route optimization, leveraging insights to enhance last-mile deliveries and return management processes. AI’s role extends to improving reverse logistics infrastructure, enhancing visibility and actionability in sorting, disassembling products, remanufacturing components, and recycling materials.


The TruKKer platform picks up patterns from each trip, benchmarks them, and then clubs loads in a vicinity – mapping the entire service area. Saving almost a million tonnes of CO2 emission every year – we help our clients meet their environmental requirements and planet Earth with lesser wastage.


Delivery Optimization: AI-driven algorithms empower companies to optimize their supply chain operations, reduce emission intensity, and forecast demand-supply patterns while identifying potential disruptions. By analyzing data from multiple sources, including weather patterns, consumer behavior, and transportation trends, AI aids in operational planning and adjustment to enhance availability, minimize waste, and reduce carbon footprint during deliveries.


Energy Optimization: AI aids in optimizing energy consumption by analyzing usage data, identifying efficiency improvement opportunities, and minimizing electricity requirements in offices and facilities. It can also enable distributed or combined energy storage solutions.


Optimizing Transportation Modes: AI-powered software enhances the efficiency and integration of ocean, inland, and air freight. These AI-enabled models work to reduce emissions, forecast needs, and enhance operational efficiency.


Demand Prediction: AI contributes to reducing surplus inventory or production by offering more accurate demand sensing. A deep understanding of demand and buying patterns results in reduced production wastage, leading to less resource consumption and transportation needs.


Partnership Evaluation: AI assesses the sustainability performance of partners and suppliers while identifying areas for improvement. By analyzing data on supplier performance, including carbon emissions, businesses can make informed decisions about their supply chain partners.


The development of sustainable AI promises to minimize waste, reduce carbon emissions, and optimize resource utilization throughout the supply chain, from product design to recycling. However, it’s essential to exercise caution in utilizing AI technology. Achieving fully decarbonized supply chains hinges on data accessibility, necessitating targeted investments. Visibility into supply chains beyond the first tier remains a challenge for most global companies. Moreover, successful AI algorithms rely on training data sourced from the company and public data devoid of alternative motives or inaccuracies.


Additionally, the environmental footprint of AI must not be disregarded. AI systems can be energy-intensive, prompting the need for environmental considerations in their development and operation. While AI holds immense potential, it’s not a universal solution but a tool that demands qualitative data access.

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