Big Data and Artificial Intelligence (AI) are transforming how the logistics industry operates. Here’s how they help optimize energy use and cut costs:
- Reduced operating costs: Applying AI and Big Data cuts average logistics costs by 23% and shortens order processing time by up to 35%.
- Energy savings: Solutions such as route optimization and predictive maintenance reduce fuel consumption and CO₂ emissions by up to 12% compared to 2020.
- Demand forecasting: AI analyzes historical and real-time data to predict trends, enabling more effective inventory management.
- Increased productivity: AI-powered smart warehouses can boost worker productivity by up to 3×.
- AI market growth in logistics: Expected to reach USD 707.75 billion by 2034, at a CAGR of 44.40%.
Challenges in Vietnam:
- High investment costs and a shortage of skilled personnel.
- Data security concerns.
Opportunities:
- 52% of Vietnamese logistics firms have already applied AI and Big Data.
- The government aims for the digital economy to contribute 20% of GDP by 2025.
In summary: AI and Big Data not only save energy but also improve operational efficiency, helping Vietnam’s logistics sector become more competitive and sustainable.
Basic Concepts of Big Data and AI in Logistics Operations
How Big Data Works in Logistics
Big Data in logistics involves collecting information from IoT devices, ERP systems, and customer feedback [5]. By 2025, IoT devices are projected to generate up to 181 trillion gigabytes of data [9].
Analyzing this data gives businesses full visibility into the end-to-end process, improving demand forecasting, inventory control, and supply-chain transparency [5]. For example, real-time GPS, traffic, and weather data help companies adjust delivery routes dynamically [5].
“Big Data provides valuable insights across the supply chain, supporting goals such as operational efficiency, resilience, and sustainability.”
- Klaus Dohrmann, VP of Innovation & Trend Research, DHL Customer Solutions & Innovation [9]
With advanced analytics, companies can cut inventory by 20–50%, boost supply-chain efficiency by 10–15%, and increase revenue by 2–7% [8]. Those using real-time supply-chain visibility solutions have reduced operating costs by an average of 15% and improved on-time delivery rates by up to 25% [8].
A notable case is Procter & Gamble in India, which cut 60% of supply-chain touchpoints, speeding operations, saving costs, and boosting efficiency [5]. Meanwhile, Amazon deployed over 104,000 sensors via Amazon Monitron, monitoring 34,810 assets across 192 facilities, reducing unplanned downtime by 69% and saving about USD 37.83 million [5].
However, the greatest challenge is integrating data from multiple sources, which requires complex systems to merge data points [6]. To address this, businesses use data integration platforms to clean and normalize data, combined with real-time IoT monitoring to detect issues early [5].
When handled properly, Big Data becomes the foundation for AI integration, driving efficiency and energy savings.
AI Integration with Big Data Systems
Once Big Data is collected and analyzed, AI leverages it to optimize energy use, forecast demand, manage smart warehouses, and plan efficient routes [10]. AI algorithms use historical data to predict future trends, enabling inventory optimization and risk reduction [10].
AI in logistics is expected to generate USD 1.3–2 trillion in annual economic value [10]. Early adopters can achieve 5%+ higher profit margins, cut operating costs by 50%, and improve safety by 90% [10].
A standout example is UPS using its AI/ML-based ORION system, saving about 100 million miles and 10 million gallons of fuel annually ORION [10]. This demonstrates AI’s ability to combine multi-source data for route optimization, cost reduction, and service reliability improvement [10].
Additionally, Shell and Equinor use the “Shell Inventory Optimizer” to cut inventory inflow by 13%, saving millions [10]. Meanwhile, Cainiao (Alibaba Logistics) deployed AI-driven AGVs in China, boosting worker productivity 3× [10].
| Benefit | Description |
|---|---|
| Accurate demand forecasting | Uses real-time data and external factors for more precise predictions, enabling better handling of fluctuations [7] |
| Lower operating costs | Reduces warehousing costs, minimizes waste, and saves on fuel, storage space, and labor [7] |
Key Applications for Energy Optimization
Route Planning and Optimization
AI analyzes factors like traffic, weather, delivery schedules, and vehicle performance to propose the most efficient routes [12]. These systems can adjust in real time, cutting fuel consumption and delivery times [12].
Real-world results show AI route optimization can reduce transportation costs by 20%, improve delivery times by 30%, and save 10% on fuel [13] [15]. One global logistics company applied AI to over 60,000 vehicles, cutting total fuel use by 7%, avoiding 100,000 tons of CO₂ emissions, and saving USD 50 million in fuel costs annually [16].
“AI routing ensures that these objectives are met without sacrificing efficiency or profitability.”
– Komal Puri, AVP Marketing, FarEye [11]
AI also identifies inefficient driving behaviors, such as aggressive acceleration, which increase fuel consumption [13], enabling targeted driver training. The global route-optimization software market is projected to grow from USD 8.02 billion in 2025 to USD 15.92 billion by 2030 [14].
Predictive Maintenance
AI plays a critical role in predictive maintenance, detecting equipment issues early and optimizing energy use [20]. Algorithms continuously monitor machinery energy consumption to identify waste sources [21].
DHL has integrated predictive maintenance into its supply chain, using AI to monitor vehicles and sorting equipment, scheduling timely upkeep, reducing breakdowns, and ensuring timely deliveries [19]. Ford Motor uses the same technology in its plants, combining AI and digital twins to detect and reduce energy waste [21].
Thanks to predictive maintenance, maintenance costs can drop by 30–40% while equipment availability rises by 20% [17] [20]. Downtime also falls by up to 15%, ensuring continuous production [21]. Extending equipment lifespan further reduces demand for new machines, saving energy and cutting emissions [18] [21].
Smart Warehouse Energy Management
AI enhances warehouse energy efficiency by optimizing lighting, HVAC, and automation systems using Big Data and AI. This not only cuts operating costs but also minimizes energy waste.
A prime example is Viettel Logistics Park, built to US LEED standards, featuring over 3,300 trees, renewable energy systems, and a circular-economy model. It operates efficiently while protecting the environment [22].
“In the coming period, Viettel will complete its network of logistics centers nationwide to serve key economic zones with five orientations: Smart customs gateways; Agricultural logistics centers; Logistics centers in industrial zones; Supply chain infrastructure; and Multimodal transportation networks. This will create a smart, automated, and multimodally connected logistics ecosystem—from roads, rail, and waterways to aviation—contributing to making Vietnam an important logistics hub in the region, in line with Resolution No. 13-NQ/TW, which underscores logistics infrastructure’s role in enhancing economic efficiency.”
– Major General Tao Duc Thang, Chairman & General Director, Viettel Group [22]
Modern smart warehouses also use energy-saving lighting, temperature control, and eco-friendly materials—reducing energy consumption and environmental impact [23].
Real-World Examples and Measured Results
International Implementation Examples
The following cases illustrate significant gains from AI and Big Data in global logistics.
UPS and the ORION System are a prime example. Their On-Road Integrated Optimization and Navigation (ORION) system uses advanced algorithms, predictive analytics, and machine learning on real-time telematics, GPS, traffic, and weather data [24][25].
The results? UPS saves USD 300–400 million per year. The static version cuts 6–8 miles per route, while the dynamic version saves an additional 2–4 miles—equivalent to reducing CO₂ emissions by 100,000 tons annually [24] [25].
DHL has achieved impressive results with Digital Twin technology combined with AI, using route-planning software, autonomous sorting robots (DHLBots), automated picking robots (AP), warehouse-optimization algorithms, and Digital Twins to simulate and improve layouts and processes [26].
| Company | AI Solution | Results | Source |
|---|---|---|---|
| UPS | ORION route optimization | Saved USD 300–400 M/year; reduced 6–8 mi static, 2–4 mi dynamic; cut 100,000 t CO₂/year | [24], [25] |
| DHL | Digital Twin deployment | Reduced energy costs by 25%, maintenance costs by 20%, warehouse costs by 10% | [26] |
These figures clearly demonstrate AI and Big Data’s potential and their applicability in Vietnam.
Localization for Vietnam’s Logistics Market
In Vietnam, 52% of logistics firms use Big Data and AI for demand forecasting and route optimization [4]. Industry 4.0 technologies reduce logistics costs by 23% and shorten order processing times by 35% through automation [4].
One concrete example is ABeam Consulting Vietnam, partnering with a beverage manufacturer to implement the cloud-based Coupa platform. They analyzed current operations, simulated future scenarios, and optimized supply plans, pinpointing which factories should produce which products and allocating them by regional demand [27].
The results included a 9% reduction in manufacturing and logistics costs and a 25% cut in transport energy use [27]. Ryohei Oda, CEO of ABeam Consulting Vietnam, remarked:
“Digital transformation doesn’t have to start with big changes. After seeing digital-twin success in Japan, we realized it’s vital to begin with small steps, address specific issues first, and then scale up.”
[27]
Green logistics is also gaining traction: 53% of medium and large firms have deployed energy-saving solutions for transport and warehousing, while 38% invest in clean-energy vehicles (electric, CNG) [4]. As a result, CO₂ emissions have fallen by 12% since 2020 [4].
Yoshihiro Wake, Consultant at ABeam, added:
“We believe this technology will perform well in Vietnam. Given the North-South divide, logistics planning requires a multidimensional approach—considering cost, delivery time, and customer requirements. Simulations of different scenarios are essential for designing effective solutions.”
[27]
Moreover, 68% of medium and large logistics firms in Vietnam have integrated IoT into warehouse and transport management, while 35% are piloting blockchain for provenance and supply-chain traceability [4]. These figures highlight Vietnam’s strong potential for energy-optimization tech in logistics.
Implementation Steps for Vietnamese Enterprises
Building Data Infrastructure
A robust data infrastructure is essential for leveraging AI and Big Data in logistics energy optimization. As an expert notes:
“A data infrastructure is the ecosystem of technology, processes, and people responsible for an organization’s data—covering collection, storage, maintenance, and distribution.”
[28]
Technology investment
Enterprises need data-processing servers, cloud storage, and analytics software. These tools automate tasks like resource management, data ingestion, error handling, and route optimization, reducing manual work.
Data governance
Data governance ensures accuracy, consistency, and security, unlocking data’s full potential. Key steps include classifying data logically, storing metadata for provenance, setting security and quality policies, and conducting regular audits.
“Alongside hardware and software investments, data governance is an essential ingredient for unlocking the power of data.”
[28]
Staff training
Ongoing training equips personnel to operate and protect the data ecosystem effectively, keeping teams current with technological changes.
Scalability planning
Infrastructure must be designed to grow with increasing data volumes. Firms should assess current systems’ scalability and plan accordingly.
After establishing infrastructure, businesses should partner with digital-transformation specialists to fully realize system potential.
Partnering with Digital-Transformation Firms
Beyond infrastructure, collaboration with specialized firms accelerates technology deployment. With Vietnam’s logistics market valued at over USD 60 billion and growing 12–14% annually [2], investing in digital transformation is indispensable. As one expert warns:
“However, for sustainable development, businesses need to proactively adapt, invest in technology and human resources, and collaborate closely with partners to overcome challenges and capture opportunities.”
[2]
Developing a transformation strategy
Logistics firms must set clear objectives and roadmap phases aligned with current capabilities.
The role of Xenia Tech Solutions
Xenia Tech Solutions is a trusted partner offering comprehensive digital-transformation services in Vietnam:
- Strategy consulting to define goals and roadmap.
- Custom software development for warehouse management, route optimization, and energy monitoring.
- AI and data analytics integration for demand forecasting and operational improvement.
- Cloud infrastructure deployment with scalability and data security.
AI Applications in Global and Vietnamese Logistics & Supply-Chain Management
The Future of Energy-Optimized Logistics in Vietnam
Vietnam’s logistics sector is rapidly evolving thanks to Big Data and AI. With goals to contribute 6–8% of GDP from logistics services and sustain 15–20% annual growth through 2035 [3], energy-optimization tech is becoming essential.
Role of AI in Logistics
AI is crucial for achieving large-scale goals, extending beyond route optimization to demand forecasting, distribution, and data-driven decision-making. Advances like autonomous trucks promise new industry horizons [29].
Digital Transformation: An Irreversible Trend
Currently, 61% of Vietnamese logistics firms have adopted digital tech in operations [4]. Key stats:
- 68% of medium/large firms integrate IoT for warehousing & transport.
- Big Data and AI adoption rates continue rising.
- 53% deploy energy-saving solutions in operations.
These numbers reflect a strong shift toward digital logistics.
Technology: A Major Competitive Advantage
Investing in technology not only cuts costs but also boosts operational efficiency. Digital transformation can lower logistics costs by 23% and reduce order processing time by up to 35% [4].
“Besides green logistics, digital logistics will serve as key levers to help businesses optimise costs, improve competitiveness and expand markets.”
– Bùi Bá Nghiêm, Senior Expert, Import-Export Department, Ministry of Industry & Trade [3]
A典example is Viettel Post, whose revenue rose from VND 3,643 billion in 2016 to VND 21,743 billion in 2022 after advanced delivery tech adoption [1].
Green and Smart Logistics: Long-Term Goals
Vietnam has set ambitious targets for 2035:
- 80% of logistics firms adopt digital tech.
- 30% of vehicles switch to green energy.
- 70% of workforce receives specialized training.
By 2045, all logistics vehicles in Vietnam are expected to run on green energy [3].
“The combination of green and smart logistics not only contributes to building an effective supply chain, but also creates long-term sustainable value for the Vietnamese logistics industry, expanding opportunities to participate deeper in the global supply chain.”
– Ben Anh, Group CEO, ITL Corporation [30]
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Smart Infrastructure: Key to the Future
Intelligent infrastructure—such as smart roads and IoT-integrated ports—will drive digital transformation, enhancing efficiency and safety. The convergence of AI, blockchain, and 5G promises new possibilities for smart logistics [29].
With 65% of corporate customers prioritizing eco-certified logistics providers [4], investing in energy optimization and partnering with reputable tech firms like Xenia Tech Solutions (xeniatech.vn) offers a significant competitive edge.
FAQs
How do AI and Big Data help Vietnamese logistics firms tackle cost and workforce challenges?
AI and Big Data offer practical solutions for Vietnam’s logistics companies facing high operating costs and skilled labor shortages. By automating processes and optimizing operations, these technologies reduce reliance on specialized personnel while cutting operating expenses by 15–25%. They also enhance forecasting and resource management, minimize human error, and open up training and upskilling opportunities, aligning with Vietnam’s Industry 4.0 vision for a sustainable and efficient logistics sector.
What do Vietnamese enterprises need to build data infrastructure and apply AI for energy optimization in logistics?
To optimize energy in logistics, Vietnamese businesses should focus on three key steps:
- Data system integration: Collect and manage data from diverse sources via IoT and Big Data platforms, enabling real-time insights and faster, more accurate decisions.
- AI-driven analysis and optimization: Use AI algorithms to analyze data, forecast demand, and optimize delivery routes, reducing energy use and operating costs for overall efficiency gains.
- Partnership with technology providers: Develop a comprehensive digital transformation strategy and collaborate with technology solution vendors to implement smart management models, ensuring global standards compliance and local relevance.
Implementing these steps not only boosts operational efficiency but also contributes to environmental protection—a growing priority in today’s logistics landscape.
How will Vietnam’s logistics sector evolve thanks to AI and Big Data, and how can it achieve green logistics targets?
Vietnam’s logistics industry is undergoing a major transformation through the adoption of AI and Big Data. These technologies not only optimize supply chains but also reduce waste and boost operational efficiency. As a result, companies can make faster, more accurate decisions, gaining a clear competitive edge.
To reach the goal of green logistics, Vietnam is focusing on sustainable solutions. Technologies such as AI, IoT, and Blockchain are being deployed to cut greenhouse gas emissions, optimize resource use, and drive comprehensive digital transformation. These efforts aim to lower logistics costs to 12–15% of GDP while building an environmentally friendly logistics sector poised for long-term, sustainable growth.
