The world of logistics and transportation cannot afford to slow down, and the trend toward sustainable logistics is working hard to catch up. Demand for shipping and delivery services is only increasing in a fast-moving, interconnected global economy. But with this growth comes greater pollution levels from vehicles and logistics networks.

For this reason, it shouldn’t be surprising that 75% of logistics companies have a focus on sustainability, according to a May 2024 survey by the Germany-based Logistics Hall of Fame. A majority of respondents rated the risk of financial loss due to climate change as “medium to high.”

This is why major efforts are underway to reduce trucking-generated pollution by implementing a sustainable logistics program. These include efforts to optimize routing, grow alternative fuel fleets, increase the use of automation, enforce stricter emissions standards, and ramp up intermodal transportation.

Optimized routing, in particular, has emerged as a powerful tool to not only reduce transportation costs but also significantly minimize carbon emissions, making logistics operations more sustainable.

The Environmental Impact of Logistics

Transportation contributes more than a third of all CO2 emissions globally, with nearly two-thirds of that coming from trucking, according to the International Energy Agency (IEA). In addition, transportation relies on petroleum products for nearly 91% of its energy, down just 3.5 percentage points from the early 1970s.

Excessive fuel usage in everything from long-haul trucking to last-mile delivery, traffic congestion, and poorly planned routes contribute to higher CO2 emissions. At the same time, regulatory pressure and consumer demand for more sustainable business practices are forcing companies to adapt.

The Basics of Route Optimization

Route optimization tools use advanced algorithms and predictive analytics, parsing historical and real-time data to identify the most efficient delivery routes. Factors taken into account include traffic conditions, weather, vehicle capacity, delivery windows, and road restrictions.

Many routing tools within a transportation management system (TMS) also include load optimization capabilities, enabling companies to maximize their available freight capacity and improve key metrics like revenue per mile, cost per delivery, and fleet utilization.

State-of-the-art route optimization tools employ dynamic multi-day route planning and scheduling across trucking modes, everything from LTL, TL, middle mile, last mile, private fleet and dedicated transportation services.

Reducing Fuel Consumption and Carbon Emissions

As route optimization enables companies to reduce the number of miles driven, an immediate, tangible benefit is a significant saving on fuel consumption.

These tools also allow drivers to avoid areas of traffic congestion, helping to improve on-time delivery performance. This not only reduces truck dwell time and, thus, tailpipe emissions, but also increases customer satisfaction.

By optimizing load capacity, companies can put fewer trucks on the road, further reducing fuel usage and emissions — further driving sustainable logistics.

The Role of Real-Time Data in Sustainable Routing

Access to real-time data on traffic, weather, and road conditions allows for dynamic rerouting to avoid delays and minimize emissions. Traffic data can come from GPS systems and platforms like Google Maps or Waze for information on congestion, accidents, and road closures.

Weather data from apps like The Weather Channel or OpenWeatherMap provide updates on conditions (thunderstorms, high wind, fog, snow) that might affect a trip, rerouting if necessary. Telematics and sensors take in data on vehicle location, speed, fuel usage, and status from onboard systems or fleet management software.

Updates from order management systems (OMS) on delivery windows and customer availability also become part of the routing equation, as does data on weight or height restrictions and road closures from transportation departments. Routing software also tracks fuel efficiency and vehicle performance, reducing environmental impact.

Other Benefits of Route Optimization

In addition to reduced fuel consumption and lower operating costs, route optimization enables faster, more reliable deliveries, improving customer relationships and fostering loyalty.

Optimized routing also helps companies meet their environmental goals through sustainable logistics. This includes compliance with government regulations and qualification for certifications such as LEED, ISO14001, and SmartWay.

Route Smarter, Stay Greener

Route optimization improves logistics efficiency and plays a crucial role in reducing carbon emissions. An investment in this technology helps drive both sustainability and competitiveness in a business climate that increasingly values environmental responsibility. By embracing sustainable logistics practices through the use of these tools, companies can reach their goals while driving down costs through efficiency gains.

Tramm, an innovator in supply chain technology, has developed an end-to-end TMS platform that combines route planning, load building, transporter management, a supply chain control tower, and a powerful mobile app. With this end-to-end approach, Tramm has solved the problem of fragmented supply chain systems by integrating key functions into a unified, scalable platform.

Tramm’s Planning and Optimization Module handles complex routing environments, accounting for variables such as rules and constraints that accurately reflect on-the-ground transport operations. It allows users to easily adapt their plans based on changing needs, including “what if” visualization and scenarios that enable real-time adjustments. A load builder/stackability app lets them automatically plan loads that are tightly synced with capacity and route optimization.

To find out how Tramm’s unified approach can drive transportation efficiency and cost savings while helping you realize sustainability goals, request a demo today.

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As we’re not yet living in the age of “Star Trek,” where people and things can be transported by breaking down and then reassembling molecules thousands of miles away (Elon Musk: take note), we here on Earth still count on shipping and logistics. By land, sea, and air, massive amounts of cargo are moved every day to keep global commerce humming. And a modern supply chain TMS (transportation management system) is a key technology that helps make it happen more efficiently.

Intelligent shipping combines business intelligence, planning, and execution tools, along with strategy and process, to optimize routing, scheduling, loading, communication, transporter management, etc. There are many primary and secondary benefits, including faster, more accurate deliveries, cost savings, improved customer satisfaction, better driver engagement and retention, and improved inventory management and demand forecasting.

A true supply chain TMS can help you achieve your goal of intelligent shipping by enhancing business intelligence and optimizing all aspects of logistics and transportation.

The Role of Supply Chain Analytics in Global Transport Operations

Supply chain analytics help organizations both keep track of and improve operational performance across various interdependent functions. All of that activity is throwing off tons of data, requiring sophisticated systems to synthesize and analyze, produce insights, and make recommendations. Increasingly, this is done by automating what had been manual processes, such as demand forecasting and route optimization.

The capabilities of supply chain data analytics have advanced significantly in recent years, increasing operational efficiency across all phases of transportation and logistics. For example, modern systems can assess risks associated with everything from geopolitical disruptions and natural disasters to freight capacity and supplier performance. Companies can then develop contingency plans and make better-informed decisions to mitigate these risks.

An advanced supply chain TMS can track KPIs such as the percentage of on-time deliveries, delivery error rate, cost per mile and cost per unit, load utilization percentage, average delivery time, and order cycle time. All of these metrics act as a barometer of business performance and efficiency.

Building Business Intelligence: Supply Chain Analytics Via a Powerful Supply Chain TMS

Tramm, an innovator in supply chain technology, has developed a platform based on the idea that a TMS can only be effective if every aspect of transport management is modeled, integrated, and visible from order inception to final delivery. The end-to-end Tramm TMS platform features route planning and optimization, transporter management, a supply chain control tower, and a powerful mobile app. Comprehensive reporting tools enable strategic planning, and performance analysis and performance management are enhanced via live tempo dashboards, trip scores, and more.

Tramm’s Dashboard Module provides powerful insights built from real-time, integrated external and internal data. Its control tower and visualization module, FleetVision, empowers managers to go from reactive (due to gaps between reported vs. actual performance) to proactive (based on live transportation data). With a better understanding of fleet operations on a granular level, loads are handled more efficiently, freeing up managers to implement performance improvement strategies.

A configurable Order Management module automates order processing, providing detailed visibility into order attributes. Integrated into Tramm TMS for seamless planning and execution, it automatically splits orders based on operational and resource requirements. A client portal allows end customers to capture, manage, and review order status.

Tramm centralizes all supply chain data, improving consistency and accuracy, eliminating discrepancies, and providing a single source of truth. Real-time updates and automated workflows further enhance data reliability, improving decision-making and overall supply chain efficiency.

Three Tactics to Build Business Intelligence through Supply Chain Analytics

To fully leverage the capabilities of a supply chain TMS and enhance business intelligence, companies need to implement specific tactics. Here are three tactics designed to help you achieve these goals, and how Tramm empowers you to do so.

Integrated Supply Chain Analytics

Supply chain analytics need to be woven into every aspect of transportation management. Tramm TMS centralizes data from multiple sources (orders, routes, transporters, etc.), ensuring that critical information is captured and processed in a unified manner.

Tramm eliminates data discrepancies and inconsistencies, creating a reliable, accurate foundation for all decision-making processes. It also provides a comprehensive view of the supply chain, enabling managers to make informed decisions quickly. In addition, internal and external teams have access to the same information, enhancing coordination and cooperation.

Real-Time Data and Insights

Tramm’s TMS and its real-time data processing capabilities allow transport managers to monitor and analyze activities as they happen. Continuous data updates provide a live view from order inception to final delivery, for improved route optimization, tracking, and inventory management.

This capability also allows for proactive identification of bottlenecks and inefficiencies, so managers can quickly implement corrective action and minimize disruptions.

Advanced Reporting and Visualization

Tramm’s supply chain TMS has powerful reporting and visualization tools that turn complex data into actionable insights, helping managers understand and act on key insights effectively.

The platform’s automated reporting on KPIs, such as delivery time, costs, and load utilization, surfaces performance trends and areas for improvement. Tramm’s Dashboard Module translates complex datasets into intuitive graphs, charts, and dashboards to aid decision-making.

Today’s Complex Logistics Call for a Supply Chain TMS That Does More Than Route Shipments

With global freight capacity tightening once again, a volatile market environment, and disruptions the norm, the limited functionality of a legacy TMS isn’t up to the task of managing complex functions across the supply chain.  

Tramm TMS leverages the power of advanced analytics, real-time visibility, comprehensive reporting, business intelligence, and performance management tools. With it, organizations can get a better handle on transporter management, improving order flow, optimizing routing, and improving customer satisfaction. To learn how Tramm can transform your supply chain operations, request a demo today.

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In the world of B2B commerce, having an efficient order management process is a baseline capability. Especially when large order loads are involved, the ability to achieve a high percentage of orders shipped complete drives significant transportation savings, as split shipments eat away at margins.

There are several factors that drive an increased reliance on split shipments for large order loads, sometimes acting in combination to exacerbate the issue. These include how inventory is distributed in a network, the pressure on stock levels from large orders, and the operational strain it can cause. That’s why order management precision and efficiency is at such a high premium, especially when disruptions like a major port strike throw a wrench in the works.

This article examines split shipments and the issues they cause within the supply chain and distribution and explores some best practices to avoid them. The aim is to help reduce operating expenses, improve product availability, and increase customer satisfaction.

Definition and Causes of Split Shipments

A split shipment happens when an order is divided into multiple shipments due to low stock levels, logistical constraints, or both. Often, businesses feel forced to ship what’s available immediately and send the rest later, hoping that customers can get by on the partial order in the meantime.

Some common causes of split shipments on large B2B orders include the following:

• Multiple suppliers: Variations in supplier lead times or stock availability can cause parts of the order to arrive at different times.

• Order complexity: Large orders often involve diverse products with different handling, storage, or shipping requirements. For instance, temperature-sensitive or hazardous goods requiring specialized transportation often lead to separate shipments.

• Order prioritization: High-priority, in-demand products are time-sensitive, which can lead to shipping what’s on hand separately while waiting for replenishment to send the rest.

• Warehouse fulfillment: Large orders can tax the resources of warehouse operations. SKUs stored in different locations or those requiring a different picking process can cause delays, leading to split shipments.

• Backorders and lead time variation: Some items have significantly different supplier lead times, forcing businesses to ship only a portion of their available inventory and put the balance on backorder.

• Inefficient systems: Without an advanced order management system (OMS), businesses lack real-time inventory visibility across multiple locations. This leads to fragmented fulfillment decisions and more split shipments.

The High Cost of Split Shipments

While some split shipments are inevitable or unavoidable due to external factors, costs go up along with higher frequency. Transportation is clearly the highest direct cost, as it’s generally the second highest expense as a percentage after labor, and more trips are required. There’s also the added complexity of scheduling additional pickups with transporters.

Speaking of labor, split shipments also hit this cost center, as they lead to more man-hours spent preparing orders through picking and packing.

Indirect costs include damage to customer relationships and reputation and the potential loss of future business. B2B customers rely on complete orders to meet production schedules or product demand. As a result, many retailers have implemented on-time in-full (OTIF) requirements. The idea is to enforce greater supply chain optimization by tight collaboration among retailers, brands, transporters, and logistics service providers.

Failure to meet OTIF requirements results in fines and penalties. A McKinsey study found that many retailers in North America are tightening OTIF requirements by narrowing delivery windows and increasing fines while revising the definition of in-full delivery.

A higher incidence of split shipments can reflect deeper issues of poor inventory management, such as stock imbalances or inefficient replenishment processes. Finally, they contribute to higher carbon emissions from the additional truck trips required.

Other Split Shipment Headaches in B2B

Split shipments introduce complexities that impact multiple areas of operations, including order tracking, invoicing, and customer communication. Each contributes to inefficiencies, often leading to higher costs, increased labor, and a decline in service quality.

Tracking Complexity

When shipments are split, tracking the order status becomes more complicated because different segments move independently through the supply chain. Logistics teams have to track each portion of the order separately, from inbound inventory to fulfillment and delivery. This involves monitoring multiple tracking numbers, the status of transporters, and estimated delivery times.

Managing Multiple Invoices

Split shipments often lead to multiple invoices, complicating accounts payable for both the shipper and customer, including reconciliation of multiple invoices against the original order. Each invoice might include different shipping costs, taxes, and fees, adding to administrative overhead. In some cases, customers might withhold payment until the entire order is delivered, adding more complexity and delaying closing the transaction.

Customer Communications

In B2B transactions, logistics and delivery impact a customer’s production schedules, inventory levels, and downstream operations. Shippers need to manage expectations around timing, coordination, and delivery schedules. Customers often rely on complete shipments to avoid production downtime, which causes frustration and the pain of adjusting internal processes.

Split shipments also require customers to coordinate with receiving teams across multiple deliveries. Additional labor and resources are needed to accept, inspect, and verify each portion of the order upon arrival.

Leveraging Technology to Optimize B2B Order Management

Advances in technology for supply chain and logistics have made it easier for businesses to optimize their inventory and order management processes in order to reduce the incidence of split shipments. Here are some examples.

WMS

A warehouse management system (WMS) provides real-time updates on inventory levels, enabling better decision-making and reducing stock-outs that lead to split shipments. Most systems can also automate inventory replenishment to ensure that items in high demand remain in stock, reducing the need for split shipments.

OMS

An order management system (OMS) integrates order data from various sources (e-commerce platform, ERP, CRM, WMS, etc.), providing visibility into inventory availability. This helps drive a higher percentage of “order complete” confirmations before shipment. An OMS also has consolidation logic for prioritizing full order fulfillment vs. partial shipments.

Tramm, a developer of a leading unified supply chain management platform, combines a powerful OMS within its transportation management system (TMS) suite. This integrated approach provides end-to-end visibility and control over order fulfillment and shipping processes. Tramm’s Order Management Module ensures that orders are fully consolidated before being released for shipment, while the TMS manages transportation in the most efficient, cost-effective manner.

Using real-time data synchronization, Tramm’s integrated approach ensures that any changes in order status, inventory levels, or shipping schedules are immediately reflected across both systems. This allows the OMS to quickly adjust fulfillment strategies if stock availability or transporter issues arise, reducing the need to rely on split shipments.

Predictive Analytics and Demand Forecasting

Predictive analytics tools tap the power of AI to enable proactive inventory management. By more accurately forecasting demand and optimizing inventory levels, both stockouts and split shipments are reduced. This technology enables users to foresee potential supply chain disruptions and adjust fulfillment strategies accordingly.

Automated Fulfillment and Transporter Routing

Fulfillment automation reduces errors in picking/packing, allowing a higher percentage of full orders to be processed and minimizing the need for split shipments.

Smart routing automatically directs orders to the most appropriate warehouse or fulfillment center, optimizing shipments and reducing transportation costs. Tramm’s Planning & Optimization Module uses advanced algorithms and visualization tools to drive better, faster load and routing decisions. The result is operational cost savings, including reduced split shipments and highly responsive planning capabilities.

Supplier Collaboration and Data Sharing

Collaborative planning includes sharing inventory and demand data with suppliers, improving stock replenishment, and reducing the likelihood of split shipments. By employing Vendor-Managed Inventory (VMI), suppliers manage stock levels, helping ensure that products are consistently available. This minimizes the need for split shipments due to out-of-stocks.

Best Practices to Minimize Split Shipments in B2B Order Management

Companies can take several steps to improve their order management processes, increase efficiency, and increase the percentage of “shipped complete” orders.

Centralized Inventory Management

Centralized inventory involves keeping all stock in one primary location, such as a warehouse, fulfillment center, or other storage facility. This central hub typically manages not only inventory but also key logistics functions like order processing and fulfillment.

In a centralized inventory system, the majority of operations, including order management and distribution, are handled from this single location, even when there are multiple sales channels or retail outlets. This strategy reduces the need for split shipments due to inventory imbalances.

Order Consolidation Policies

Order consolidation involves combining multiple orders into a single shipment. This can be done by centralizing items from multiple orders into a single location or by combining multiple orders from different sales channels into a single system.

Order consolidation can benefit businesses in several ways. This includes lower transportation costs based on economies of scale, better management of stock levels due to fewer shipments, overall simplified logistics, and a better customer experience through more complete orders.  

Smart Reordering Strategies

Smart reordering is a rules-based, automated process utilizing data analytics and AI to manage inventory levels and optimize replenishment. Unlike traditional reordering, which relies on manual tracking or fixed schedules, smart reordering uses real-time data (sales trends, demand forecasting, lead times, supplier performance, etc.) to predict when SKUs will need to be replenished.

This approach helps ensure that critical and/or high-demand products are always in stock, preventing backorders and reducing the need for split shipments.

Transparent Customer Communication

This seems like an obvious point, but too many organizations fail to keep customers updated on order status. This is especially important when unexpected drops in inventory levels or logistics snafus require a split shipment.

Always give customers the option of waiting for replenishment to send a complete order or receiving a partial shipment first. In some cases, the added expense of expedited transportation comes into play to make things right and keep to their schedule.

Continuous Monitoring and Adjustment

Regularly analyzing order data helps identify patterns that lead to increases in split shipments. By tracking trends in demand, stock levels, and fulfillment performance, you can proactively adjust inventory management, including redistributing stock across locations or improving order consolidation processes.

Leaning On Technology to Optimize Large Order Loads

Minimizing split shipments is important to maintain supply chain efficiency and reduce transportation costs. Software such as OMS, WMS, and predictive analytics tools can improve inventory management and streamline fulfillment processes. Best practices such as smart reordering, order consolidation, and continuous monitoring help reduce the incidence of split shipments.

Tramm integrates transporter management, route optimization, order management, customizable dashboards, and a control tower to optimize routing and order allocation. When shipments do need to be split, Tramm automates the process based on operational and resource requirements.

Tramm’s FleetVision Module gives users access to precise real-time data and insights. With a better understanding of fleet operations on a granular level, they can manage each load more effectively and implement performance improvement measures. The result is greater efficiency, reduced costs, and improved customer satisfaction.

To learn more about how Tramm can drive measurable improvement in your supply chain and logistics operations, request a demo today.

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It’s hard to say “supply chain” without throwing in the word “complexity” as a qualifier. Supply chain management has grown more challenging for a number of reasons. Many businesses, both B2B and B2C, have become “omnichannel,” mixing online and offline, while geopolitical strains have disrupted shipping. The pandemic effect led to widespread supply chain redesign and rethinking, including diversifying sourcing, carriers, ports and lanes, etc., to guard against future shocks.

Predictive analytics has become a key capability across industries and business functions. Smart digital tools are used to analyze vast amounts of data, helping companies gain insights and plot strategies with a more forward-looking bent. In logistics and supply chain, predictive analytics has become an invaluable tool, leveraging historical and real-time data to more effectively set transportation, logistics, and warehousing strategies.

We will explore how predictive analytics is transforming supply chain management. It has become essential for everything from demand forecasting to shipping schedules and route optimization.

Supply Chain Analytics: Insights Made Actionable

Predictive analytics applies advanced algorithms and statistical models to identify patterns, trends, and correlations within the data to generate forecasts about future events or behaviors.

In the context of supply chain management, predictive analytics takes historical and transactional data to create models that identify patterns for risks and opportunities. This helps guide decision-making by anticipating specific future events.

For instance, predictive analytics can be used to help a company shift from a just-in-case to a just-in-time model for sourcing products and materials, or use analysis of next quarter’s freight rates to optimize carrier selection and contracting.

Effective Supply Chain Management Begins with Effective Analytics

The proactive approach enabled by predictive analytics helps businesses optimize key supply chain functions such as inventory management, transportation planning, and demand forecasting. It does this by making resource allocation more efficient while minimizing the impact of disruptions.

Common techniques in predictive analytics for supply chain management include regression analysis, time series forecasting, decision trees, and neural networks. Each has its unique strengths and application to specific areas, such as route planning and risk mitigation.

Ensuring compatibility between predictive analytics tools and existing IT infrastructure can be challenging. Balancing innovation with operational continuity is key to leveraging predictive insights effectively. And breaking down silos to enable seamless data flow across systems is critical but often difficult to achieve. This is why a smart option is to go with an end-to-end supply chain management platform approach, using an integrated suite of tools that comes pre-built with powerful analytics capabilities.

Three Ways Predictive Analytics Can Revolutionize Supply Chain Management

Let’s dive a little deeper into specific areas of supply chain management where predictive analytics can drive operational efficiency.

Enhancing Demand Forecasting and Inventory Management

Predictive analytics significantly enhances demand forecasting accuracy, enabling businesses to better align inventory levels with future demand. By analyzing historical sales data, market trends, and external factors, predictive models provide insights into upcoming demand patterns.

This leads to more precise inventory planning, reducing the risk of overstock and stockouts. Benefits include cost savings from reduced holding costs and waste, along with improved customer satisfaction due to consistent product availability.

Optimizing Transportation and Route Planning

Predictive analytics also plays a crucial role in optimizing transportation and route planning. By anticipating potential delays, disruptions, and inefficiencies (traffic congestion, weather conditions, etc.), predictive modeling drives more efficient route planning. This reduces transit times, lowers fuel costs, and minimizes carbon emissions, saving money while taking a more sustainable approach.

Tramm, the creator of a true supply chain TMS, has a Route Optimization Module that utilizes predictive analytics to adjust routes in real time based on a variety of factors, ensuring timely deliveries while maximizing efficiency. It also helps companies optimize cross-dock and multi-modal planning, including LTL, TL, and multi-fleet.

Proactive Risk Management and Issue Resolution

By identifying potential risks (supplier delays, transportation bottlenecks, port disruptions) before they materialize, businesses can get ahead of these risk factors. Early warning systems and scenario planning, powered by predictive analytics, help maintain supply chain resilience and continuity.

Tramm’s Control Tower Module uses the power of predictive analytics to monitor and manage potential disruptions. A dashboard view provides real-time vehicle tracking and status updates via third-party integration, GPS, or Tramm’s Mobile App. Customizable alerts notify managers about unplanned stops, deviations, and performance issues, minimizing the impact of unforeseen events.

Tapping the Power of Predictive Analytics for Supply Chain Optimization

Predictive analytics is transforming supply chain management by allowing companies to optimize demand forecasting and transportation, and proactively manage risk. These capabilities allow them to make data-driven decisions, improving efficiency and supply chain resilience. The efficiencies gained help reduce costs and improve competitiveness.

Tramm’s platform is built on a simple yet rarely implemented idea: a TMS can only be effective if every aspect of transportation management is modeled, integrated, and visible from order inception to final delivery. The power of Tramm comes from its ability to capture and leverage details in a user-friendly way. Users gain planning and route optimization, transporter management (Transact Module), real-time visibility (FleetVision Module), and impactful business insights (Dashboard Module). To learn more about how Tramm can transform your supply chain management, request a demo today.

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