Modern supply chain operations are dynamic and require professionals to be agile and adaptable. Traditional approaches to transport management, once sufficient for meeting basic needs, are being challenged by new expectations centered around resilience, business continuity, transparency, and consumer expectations.
As the backbone of logistics operations, TMS is a pivotal tool for shippers, carriers, freight forwarders, and brokers, facilitating the efficient movement of goods across the supply chain. The TMS market is set to grow at a CAGR of 17.4% until the end of the decade. This widespread adoption underscores a remarkable shift from 2005, when only 15% utilized such systems, to the present day, where 61% of logistics professionals leverage TMS technology.
The core value proposition of a TMS lies in its ability to optimize transportation operations through centralized planning, enhanced execution, and meticulous tracking, ensuring operational excellence and increased profitability.
This shift towards innovative solutions has fueled the exponential growth of Software-as-a-Service (SaaS) TMS offerings, and may even surpass a few features of the conventional Enterprise Resource Planning (ERP) alternatives that once dominated the market.
Modern technologies and changes in consumer expectations are compelling businesses to adopt innovation to maintain a competitive edge. This article will look at 5 key trends influencing that trajectory.
Trend 1: The growing use of AI and Machine Learning for Better Insights
Artificial intelligence (AI) and machine learning (ML) are two powerful forces behind TMS's changing landscape. They are redefining operational efficiency and responsiveness.
Here are the 3 main ways how AI and ML are changing the future of TMS.
Route Optimization Through AI Algorithms
One of the most prominent use cases of AI and ML in TMS involves optimizing routes to minimize travel time, distance, and fuel consumption. Unlike traditional methods, which might rely on the limited experience of individual forwarders, advanced algorithms can analyze vast amounts of historical data, traffic patterns, weather conditions, and other relevant factors to generate optimal routing plans. These algorithms can account for various variables, significantly outperforming conventional planning methods in speed and efficiency. Another standout feature of AI in route optimization is its capacity for real-time adaptability. AI systems dynamically adjust routes in response to live updates on traffic conditions, accidents, or road closures, ensuring that the most efficient path is always taken.
Predictive Analytics for Demand Forecasting
Machine learning enables TMS to make accurate predictions regarding demand fluctuations. eCommerce has made it important for businesses to manage capacity and resources while setting the right pricing levels. Demand forecasting is among the initial use cases of AI for efficient supply chains, but its current form is completely different from what it was even 2 years ago. Businesses rely entirely on AI to learn from past behavioral patterns to anticipate future demands and respond promptly to changing market dynamics.
Automated & Intelligent Operations
AI and ML automate routine tasks and processes within TMS, from data entry and analysis to complex decision-making processes like carrier selection and freight booking. Automation reduces the risk of human error, increases efficiency, and allows human resources to focus on more strategic tasks that require human intervention.
Beyond automation, AI and ML enable intelligent operations by providing recommendations and insights that go beyond what is humanly possible to analyze manually. For example, AI can suggest the most cost-effective transportation modes and routes based on a comprehensive analysis of past performance data, current rates, and other relevant factors.
Trend 2: Real-Time Monitoring Using IoT
By analyzing live data streams generated by IoT devices, businesses can identify anomalies, detect deviations from standard operating procedures, and initiate corrective actions before it’s too late.
One example of real-time monitoring using IoT is the deployment of IoT-enabled trucks. These trucks are equipped with many sensors that track various parameters in real-time, including location via GPS, temperature control for perishable goods, tire pressure, fuel efficiency, and even the driving patterns of the truck drivers.
Here are a few examples:
● DHL enhances workplace safety in its Singapore regional office through IoT-based tools. It monitors staff rest levels, waste movements, and fatigue while facilitating open communication on potential mistakes or accidents.
● Amazon is building smart warehouses where robots handle errand-running and heavy lifting, improving inventory storage, speeding retrieval, and cutting fulfillment costs.
● Maersk employs Remote Container Management (RCM) technology to monitor container conditions, preserve perishable cargo, and optimize resource use.
The data collected from these sensors is transmitted in real-time to the company’s TMS, where AI algorithms analyze the information to identify any anomalies or deviations from expected norms. For instance, if a truck carrying temperature-sensitive pharmaceuticals deviates from its optimal temperature range, the TMS can immediately alert the logistics manager or the driver to take corrective action, such as adjusting the refrigeration settings or even rerouting the truck to the nearest maintenance facility if a mechanical issue is detected.
IoT systems ensure that the goods are maintained optimally throughout their journey, improving customer satisfaction and trust. By combining them with advanced data mining and reporting solutions, they can be the differentiating factor a business needs for its competitive edge.
Trend 3: Data-Driven Decisions
Advanced TMS solutions are now equipped with sophisticated analytics tools that not only gather and store data, but also provide actionable insights.
Consider the example of carrier and mode selection for efficient supply chain operations. Data from various touchpoints can help businesses select between their in-house capabilities or any 3PL partners they might be working with.
Similarly, businesses can process many more variables while deciding the right transportation mode for a specific shipment. Modern TMS data handling capabilities allow for considerations like location, delivery time, and goods category to be factored into decisions quickly, unlike the slower processes of the past.
Trend 4: Better Fleet Management
Today, innovative TMS solutions are driving enhancements in vehicle tracking, maintenance scheduling, and driver management. Businesses can now achieve real-time visibility into their fleet operations, track vehicle location, check fuel consumption, and monitor driver behavior with unprecedented precision.
UPS - a leading multinational shipping, receiving and supply chain management company, for instance, has been able to significantly enhance its operational efficiency and reduced its carbon footprint by leveraging advanced telematics and AI to optimize routes, prioritize assignments, and manage resources effectively, showcasing a substantial reduction in fuel consumption and improved delivery times.
This connectivity improves route planning and execution and ensures that maintenance needs are addressed proactively, reducing downtime and extending the lifecycle of fleet assets.
Trend 5: Collaborative Operations with All Stakeholders
Another growing trend shaping the future of TMS is collaborative supply chains. Simply put, it’s the sharing of resources for mutual gain. However, it required greater visibility, trust, and data management capabilities for flawless execution. Modern TMS backed with advanced data analytics and blockchain technology can solve this issue and facilitate more effective business collaboration.
Currently, the main focus on collaborative logistics revolves around sharing transportation resources, but many other models and use cases are slowly gaining traction in the industry.
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Scalability, efficiency, and customizability are the 3 key areas where TMS of the future will compete. Businesses today need comprehensive solutions that cover their every need, can grow with the business, and deliver consistent results.
Tata BB Matrix can help with that and do much more!
The resilient supply chain solution promises to deliver results with an accuracy of up to 99% and around 50% of the overall/unit cost. Moreover, Tata BB Matrix ensures up to 100% end-to-end shipment visibility, enabling businesses to use real-time insights and make informed decisions at the right time. All these capabilities and more are what the future of TMS is supposed to look like. With TATA BB Matrix, businesses can experience that future, today.
Connect with a Tata BB Matrix expert today to learn how to build a resilient supply chain that is ready for the future.
FAQs
What is the future of transport management?
In the future, transportation will be dominated by sophisticated AI and ML models that gather, process, and deliver data-driven insights for better decision-making. Apart from that, blockchain and autonomous vehicles are some things that are inevitable in the supply chain sector and will naturally be an integral part of the future.
In essence, the future of transport management will mainly focus on adaptability and performance and ensure that businesses continue to provide the highest service for their customers.
What are the benefits of transport management systems?
An effective transport management system can enhance any business's supply chain operational efficiency and performance. TMS can optimize operations, reduce delays, identify bottlenecks, and offer efficient resource tracking, which ultimately means better brand performance and more profits for the business.
How does SaaS TMS differ from traditional ERP solutions?
ERP solutions are generally (not always) in-house and restricted to a device. On the other hand, SaaS TMS operates on a subscription model, which offers quicker implementation and updates without large upfront costs.
