The Tramm Blog

Years back, UPS had a catchy marketing tagline that said, “We Love Logistics.” Well, given the complexity and increasing demands on logistics and supply chain teams, you had better love it, as it’s a highly demanding discipline. This is why decision-making in logistics needs advanced analytics backup: There’s little margin for error when contracts and valuable customer relationships are riding along with each delivery.

Advanced analytics are, in fact, transforming logistics processes in everything from route optimization to driver hours of service, dispatch and delivery, transporter selection, and even yard management. Among the many benefits of this kind of digital approach are greater operational efficiency, lower costs, and improved customer satisfaction.

The Role of Advanced Analytics in Strategic Logistics Decisions

Advanced analytics uses sophisticated techniques and tools to analyze data, uncover deep insights, predict trends, and improve decision-making. It goes beyond simple descriptive analytics or a summary of historical data, using methods that provide actionable outputs and strategic guidance.

Predictive analytics, as the name implies, involves using statistical models and machine learning algorithms to forecast future outcomes based on patterns in historical data. The next step beyond that is prescriptive analytics, which recommends specific actions to achieve desired outcomes through analysis of potential scenarios and their implications.

Helping power decision-making in logistics, control towers have risen in importance as a way to manage the complex flow of inbound/outbound assets, loads, and team members. Primarily, it’s a centralized hub that provides end-to-end visibility, real-time monitoring, and coordination of supply chain activities. Logistics managers access a control tower to track shipments, manage exceptions, and make data-driven decisions geared toward optimized operations.

How Analytics Lead To Better Logistics Decision-Making

Advanced analytics plays a transformative role in optimizing transportation and delivery. Here are specific examples that highlight its impact:

Route Optimization

Real-time analytics assess traffic patterns, weather conditions, and fuel costs, as well as factors like delivery windows and vehicle/site/production restrictions, to determine the most efficient routes. For example, a transporter uses GPS-enabled analytics to avoid congested roads, helping speed up delivery and reduce fuel costs.

Route optimization tools leverage advanced algorithms to improve load planning and cross-docking. They can also optimize planning for multi-modal transportation (LTL and FTL, box trucks and sprinter vans for last mile, etc.), multi-day, and multi-fleet scenarios.

ETA Predictions and Updates

Analytics integrates real-time tracking and other telematics data and compares it with planned data and historical patterns to provide accurate delivery ETAs. Transporters and couriers use advanced analytical models to provide updates on delays due to traffic or bad weather, improving transparency and satisfaction.

Evaluating Transporter, Fleet Performance

Advanced analytics help assess and improve both transporter and fleet performance. By tracking key metrics such as on-time delivery rates, claims for damaged goods, and cost efficiency, shippers gain valuable insights into transporter reliability and efficiency. These metrics help identify high-performing carriers and drivers, enabling shippers to negotiate favorable contracts and foster long-term partnerships.

For fleet drivers, analytics can evaluate metrics on adherence to schedules, fuel efficiency, safety records, and driving behaviors like braking or speeding. This helps maintain high standards while identifying areas for training or improvement.

Integrated analytics can also consolidate data across all third-party and fleet operations. This helps shippers optimize fleet utilization, balance workloads, and replace underperforming drivers or carriers, enhancing supply chain's performance and reliability.

Shipment Consolidation and Load Optimization

Data models analyze shipment volumes and destinations to maximize cube utilization and reduce the number of trips. Logistics providers can use this advanced modeling to consolidate LCL freight from multiple shippers heading to the place, lowering operating costs and simplifying scheduling.

Best Practices for Implementing Analytics in Logistics

Successfully integrating analytics into logistics operations requires strategic alignment and thoughtful execution. Start by fostering a data-driven culture. All teams, from operations to management, must understand the value of analytical decision-making. This kind of mindset shift is essential for embracing data analytics as a core part of the logistics workflow.

Invest in integrated technology to streamline data flow and visibility. Combining analytical tools with assets like a Transportation Management System (TMS), a Warehouse Management System (WMS), and a logistics control tower contributes to greater efficiency by consolidating multi-source data. Integrating these complementary systems in a logistics tech stack facilitates seamless communication and better analytical outcomes.

Prioritize quality, real-time data to enable responsive decision-making. Determine what kind of data you need and how often you need it. Do you need weekly or daily logistics reports, or do you need up-to-date information every time you log into the dashboard? Access to real-time data analytics lets you monitor and analyze trends over time, allowing you to better respond to opportunities and eliminate inefficiencies.

Better Decision-Making in Logistics, Fueled By Advanced Analytics

Analytics are transforming logistics by enabling data-driven decisions that improve efficiency and customer satisfaction. Predictive and prescriptive analytics help forecast trends and recommend strategies to address challenges such as route optimization, transporter selection, and improving on-time performance.

A logistics control tower offers comprehensive, real-time visibility into logistics and supply chain activities. Integrating it with TMS and WMS, overlaid with analytics, helps logistics teams reduce costs and respond faster to disruptions and market shifts.

Tramm, an innovator in supply chain technology, has developed an end-to-end TMS platform that combines order management, route planning and load building, transporter management, a logistics control tower, and a powerful mobile app. With this comprehensive approach, Tramm has solved the problem of fragmented supply chain systems by integrating key functions into a unified, scalable platform. Tramm’s business intelligence tools increase performance at the fleet and driver level by empowering data-driven decision-making.

To find out how Tramm’s unified platform and advanced analytics can help you drive transportation efficiency and improve decision-making in logistics, request a demo today.

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It’s hard to imagine the world before everyone had a smartphone in their pocket, yet the mobile era is actually just 30 years old (give or take). In particular, mobile technology continues to transform logistics operations by enhancing flexibility, accuracy, and visibility.

Mobile logistics involves using mobile technology and applications to manage, monitor, and optimize logistics through real-time communication, tracking, and data exchange. Elements include mobile task management, GPS tracking, route optimization, and electronic proof of delivery (ePOD), all of which help streamline operations and enhance efficiency.

As supply chains become more complex and customer expectations rise, mobile logistics will only grow in importance. Therefore, organizations need to adopt best practices in mobile task management to maintain efficiency, reduce costs, and keep customers happy.  

Benefits of Mobile Task Management in Logistics

Mobile technology is used to assign, monitor, and track tasks in real time. This includes delivery assignments, pickups, inventory checks, and maintenance scheduling. Devices include smartphones, tablets, handheld scanners, and mobile apps. The benefits include:

Real-Time Tracking and Monitoring: greater visibility into vehicle location, shipment status, and route optimization.

Enhanced Last-Mile Delivery: improvements in routing, customer notification, and ePOD.

Improved Communication: smoother coordination between drivers and managers for quick decision-making and problem-solving.

Best Practices for Mobile Task Management in Logistics

As in any other technology application, mobile task management won’t get off the dime if it doesn’t have user-friendly interfaces. The digital aptitude of your workforce and partners will vary widely, and a nice, clean UI helps reduce training time and increase adoption.

Assuming (correctly) that hackers get smarter by the minute, data security needs to be a top priority in mobile logistics. Secure access, regular updates, and mobile device management policies are needed to effectively safeguard user and enterprise data.

Data analytics is another important aspect of mobile task management. In just one of many examples, real-time data on traffic, vehicle availability, weather conditions, and demand can be fed directly into mobile task management tools built into a transportation management system (TMS). Dispatchers can reassign or adjust tasks instantly based on current conditions. In another, route optimization software sends real-time updates to drivers’ mobile devices, ensuring they always follow the most efficient path.

To optimize mobile logistics, a system should seamlessly integrate with ERP, TMS, and WMS systems to ensure consistency across logistics functions. Devices should also have long battery life and be rugged to ensure uptime under difficult conditions such as delivery operations and outdoor use.

Two Use Case Examples

E-Commerce Company

An e-commerce company uses mobile task management to ensure drivers make timely deliveries, notify customers, and confirm deliveries in real time. Via mobile tracking, customers receive live updates on order status, estimated arrival times, and delay notifications, enhancing transparency and customer satisfaction.

Customizable workflows allow for simple to complex setups. Examples of workflows for logistics operations include start/end of day, specific job types, and emergency situations. Some workflows, such as those for compliance-heavy sectors like hazardous materials, can have up to several hundred tasks.

Digitized workflows and documentation eliminate paper-based processes. Mobile apps use GPS and traffic data to give drivers the most efficient routes, reducing travel time, fuel consumption, and vehicle maintenance.

With mobile logistics, the company enjoys streamlined POD via e-signatures or photo confirmation, minimizing disputes. Mobile devices consolidate delivery details, reducing paperwork so drivers can access all necessary information from a single interface. Planners can also view real-time delivery data from the mobile app and make changes on the fly.

As a result, optimized routes and real-time updates help drivers consistently meet delivery windows, while tracking and live updates foster loyalty. Customizable workflows ensure business processes are followed and all records are digitally logged for compliance and legal audit purposes.

There are also fewer delivery disputes and less time spent verifying deliveries. Lastly, ERP integration with automated invoicing enables faster payment.

Regional Distributor

A regional distributor uses mobile logistics to streamline its fleet operations. Equipped with GPS-enabled tablets, drivers get real-time route updates, delivery instructions, and optimized schedules directly on their devices.

Fleet efficiency is improved as managers can monitor and adjust routes on the go, avoiding delays due to traffic or road closures. Drivers can report status updates, delays, or vehicle issues, allowing managers to adjust plans in real time.

Drivers log key information (fuel consumption, mileage, etc.) onto mobile devices, feeding it into the TMS to help power smarter decisions. The results include reduced fuel costs, increased delivery accuracy, and consistent on-time performance, delighting customers.

Embrace the Future With Optimized Mobile Logistics

With its versatility and flexibility, mobile logistics has become part of the way the world works. The always-on connectivity and ability to share data that powers optimization help improve the efficiency of delivery and transportation operations.

For mobile data management to succeed in logistics, companies need to invest in a system with a user-friendly UI, strong data security and analytical capabilities, and the ability to connect seamlessly with adjacent software (ERP, WMS, TMS, etc.).

Tramm, creators of a world-class supply chain TMS suite, has a powerful driver app that features comprehensive workflow management and ePOD capabilities. Its digital documentation processes allow both shippers and transporters to get rid of clunky paper-based processes.

Customized workflows allow companies to utilize whatever functionality is available on their mobile devices: photos, scanning, Bluetooth, NFC, sign-on-glass, etc. The workflow editor, powered by sophisticated code, can create detailed tasks or automatically present workflows based on job types, customer settings, or if-then functionality.

Companies using external transporters lacking visibility into GPS data can send a Tramm mobile app link to the driver for a single trip, enabling reporting and customer updates. They can also manage the entire trip lifecycle using Tramm’s Transact module. For transporters, ERP integration with ePOD through the Tramm app means invoicing is triggered automatically for faster digital payments.

To learn more about how Tramm can revolutionize your mobile task management in logistics, request a demo today.

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Fleet operations managers have a lot on their plate, charged with squeezing as much performance and efficiency as they can out of fleet assets. On any given day, they keep track of available loads and load requirements, driver locations, equipment specifications, load rate, expenses, hours of service (HOS), and driver preferences. Toss in an unforeseen disruption, such as traffic, weather, or a port labor stoppage, and execution gets even more complicated.

Suffice it to say that fleet operations are about as critical a piece of supply chain management as can be imagined. When trucks aren’t rolling on schedule consistently, the ripple effect can be pronounced: late orders, lost sales, breached contracts, upset customers, and even line-down events in manufacturing in an extreme instance. And when logistics providers are already challenged in a freight recession that may be finally thawing — as NASA’s mission control flight director Gene Cranz said in “Apollo 13” — “Failure is not an option.”

This is why performance analysis is such an important capability for fleet management. It is a tool used to assess and optimize fleet operations, and that, of course, means data analytics. Technology as varied as transportation management systems (TMS), route optimization, BI software, telematics, fleet management software, fuel management, and data visualization tools is used to keep fleet operations on track.

As in most other supply chain spheres, artificial intelligence (AI) and machine learning (ML) are being leveraged to power real-time fleet analytics and predictive capabilities. The importance of data-driven decision-making in fleet management cannot be overstated.

So, what is involved in performance analysis and performance management for fleet operations? Let’s break it down.

Understanding Fleet Performance Metrics

First, we need to define the metrics fleet managers use to gauge performance, assess the impact, and make adjustments accordingly. Here are some of the primary ones:

• Fuel efficiency: Daily fuel consumption tracking helps fleet managers spot patterns or inefficiencies, such as excessive idling or poor route planning, which lead to higher costs. Improving fuel efficiency not only reduces costs but helps companies meet sustainability goals.
• Vehicle utilization: This tracks how well vehicles are used, including capacity and route efficiency. Ensuring vehicles are neither underused nor overused leads to balanced workloads and better resource management.
• Maintenance costs: Monitoring scheduled and unscheduled maintenance helps managers keep vehicles in service. Proactive maintenance planning reduces breakdowns, lowers repair costs, and extends both vehicle life and the replacement cycle.
• Driver performance: Tracking driver behaviors like speed, braking, and idling affects safety, fuel efficiency, and vehicle wear. Improving driver performance through monitoring enhances safety and reduces operating costs.
• Downtime: Minimizing downtime maximizes fleet productivity. Through proactive maintenance planning, managers can reduce idle periods and improve service reliability.

The Role of Technology in Fleet Performance Analysis

We’ve come a long way from the days of two-way radios and manual fleet/trip/driver logging. Advanced technology has taken much of the guesswork out of performance analysis, not only presenting dashboard views of KPIs but also suggesting ways to optimize fleets based on real-time data.

TMS: A TMS integrates data from various aspects of fleet operations, providing real-time tracking, load management, and cost analysis. It automates route planning and scheduling, reduces manual errors, and provides insights to improve efficiency. As a central data hub, a TMS helps fleet managers streamline operations.

Route optimization: This software tool uses GPS and traffic data to map out the most efficient routes, cutting down on transit time and fuel use. It can also adapt to changes on the fly, such as road closures or heavy traffic, to keep deliveries on track while reducing operating expenses.

Fleet management software: These tools consolidate data on vehicle health, driver performance, and maintenance schedules. Fleet management software reduces downtime and extends vehicle life by providing automated maintenance alerts and tracking vehicle usage. It also supports proactive management, improving efficiency and fleet reliability.

Telematics: Telematics combines GPS tracking, vehicle diagnostics, and driver behavior data, providing a comprehensive view of fleet operations. It helps monitor fuel efficiency, identify unsafe driving habits, and enhance route planning. Telematics helps fleet managers improve safety, cut costs, and meet performance improvement goals.

Analyzing Driver Behavior for Performance Optimization

Driver behavior has a huge impact on fleet operations. Things like speeding, unnecessarily hard braking, and idling all have a negative impact on fleet performance. As mentioned above, Telematics and fleet management software use IoT-based sensors and data feeds to provide a real-time snapshot of driver behavior and how it’s impacting overall fleet performance. Some organizations utilize onboard cameras to track driver behavior.

Using performance metrics from these various tracking tools, fleet managers create driver scorecards to gauge behavior and make recommendations. This is not only a performance improvement exercise but is also used by HR managers to assess advancement, compensation, and discipline or termination where necessary. Companies also implement driver improvement programs based on performance data.

Reducing Costs Through Data-Driven Insights

Fuel monitoring systems track fuel consumption, idle time, and driving habits to identify inefficiencies. Telematics provides data on aspects of driver behavior and vehicle performance that affect fuel usage. Fleet management software keeps track of fuel expenses and generates reports, providing insights for reducing costs.

Fleet maintenance software schedules service appointments based on vehicle mileage and performance data to reduce downtime. Telematics monitors engine diagnostics, alerting managers to issues like overheating or low fluid levels before they become repair issues. And asset management tools log and track vehicle history, so managers can set up cost-effective maintenance schedules.

Route optimization tools generate the fastest possible routes for drivers based on traffic, road conditions, and fuel efficiency. They also analyze past data to predict the most efficient delivery times and routes. GPS systems provide real-time data to reroute vehicles, improving delivery times and lowering fuel usage. And geospatial data tools can analyze geographic data to plan more direct, fuel-efficient routes.

Enhancing Compliance and Safety Standards

Compliance and safety are crucial in fleet operations to protect drivers, reduce liability, and maintain market reputation. Fleet managers must be vigilant in monitoring driver behavior and maintaining a high standard. Performance metrics and data analysis can significantly improve safety, compliance, and efficiency.

Analyzing safety metrics (speed, braking patterns, adherence to traffic rules) with dashboard cameras and telematics provides insights into aspects of driver behavior that might require attention. This allows fleet managers to identify risky driving patterns early. By analyzing these safety metrics, companies can implement targeted training to improve driver habits, increase safety, and reduce accident risk. Continuous safety assessments foster a culture of accountability and safety. Aligning these safety practices with European Union regulations on truck driver safety, such as the European Road Safety Charter, helps companies ensure compliance while promoting safer roads.

Driver fatigue is one of the top causes of accidents, along with distracted driving and substance abuse. While South Africa doesn’t have a law akin to the Electronic Logging Device (ELDs) mandate in the U.S. for tracking driver hours, the National Road Traffic Act (NRTA) requires them to follow driving and rest period guidelines to prevent fatigue and to keep a log of driving hours. In addition, the Road Transport Management System (RTMS) is a voluntary certification program with requirements for driver fatigue management, vehicle maintenance, and overall safety and compliance. Companies certified in RTMS often use telematics to monitor driver behavior, hours on the road, and rest periods.

Fleet management systems integrated with telematics generate compliance reports for agencies and internal audits, helping track adherence to safety and regulatory standards. This includes real-time reporting on driver hours, rest breaks, and miles driven.  Automated compliance tools not only simplify regulatory reporting but also lower the risk of non-compliance and fees from violations.

Performance analysis examines patterns of risky driving behavior, so fleet managers can take preemptive action such as scheduling additional training. Advanced telematics and predictive analytics can even identify high-risk situations or routes with high incidences of accidents, so preventive measures can be taken. Reducing accident risk through proactive analysis can lower insurance costs, protect drivers, and uphold a company's reputation for safety.

Implementing Continuous Improvement in Fleet Operations

Implementing continuous improvement in fleet operations helps maximize efficiency and effectiveness. Using technology and data tools, fleet managers can analyze performance consistently, identifying areas for long-term improvements. By adjusting KPIs based on evolving business needs, they can remain agile and responsive to market changes.

Feedback loops help ensure that improvements are actively monitored and become ingrained in daily operations. This type of iterative approach fosters a culture of accountability and encourages ongoing adjustments, leading to better resource management, lower costs, and enhanced service delivery.

Driving Fleet Optimization Through Performance Analysis

Performance analysis is pivotal in optimizing fleet operations by improving efficiency and accountability. Advanced technology and data analytics allow fleet managers to monitor KPIs effectively, making for smarter, data-driven decisions and continuous improvement.

Tramm, the creator of an innovative supply chain TMS, has a suite featuring planning and route optimization, order management, transporter management, real-time visibility, and actionable business insights. Tramm’s Planning & Optimization module uses advanced algorithms to optimize load and route planning, including cross-docking and multi-modal planning in LTL, FTL, multi-day, and multi-fleet. Its advanced analytics and reporting capabilities enable managers to not only track and manage KPIs but also conduct performance analysis at the driver and fleet levels.  

Tramm’s real-time tracking capabilities can streamline operations and lower operating costs. Integrating Tramm into fleet operations helps improve safety standards, deliver superior service levels, and drive sustainability initiatives. To find out more, request a demo today.

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An integrated supply chain TMS helps make tight partner collaboration and data collection from multiple sources possible so goods flow smoothly, SLAs are hit, and production stays on track.

It’s also a key element in synchronized logistics, a process that businesses lean on to manage multiple complex systems. This involves pulling together and analyzing data from disparate sources, all of which touch the supply chain in some way: transportation management systems (TMS), warehouse management systems (WMS), enterprise resource planning (ERP), and even yard management systems (YMS). Using data analytics, synchronized logistics pulls together insights that can then be applied to various supply chain processes.    

In this context, an integrated supply chain TMS can achieve synchronized logistics by enhancing business intelligence and optimizing all aspects of transportation. This, of course, has multiple benefits up and down the supply chain, such as faster inventory turns, better delivery metrics, and improved asset utilization, among others.

An integrated TMS uses advanced business intelligence to optimize logistics and transportation by analyzing large sets of real-time and historical data. It helps identify patterns and inefficiencies, predict delays, and recommend optimal routes. Using predictive analytics to forecast demand can optimize capacity utilization and automate decision-making, such as selecting the most cost-effective carriers or adjusting delivery schedules.

An integrated TMS enables more precise planning, cost control, and supply chain efficiency by incorporating data from ERP, WMSs, and other systems. This helps companies improve planning and execution, reduce delays, and respond quickly to disruptions. Using AI-powered data analytics, businesses can fine-tune their operations and keep costs in check.

As the need for real-time supply chain visibility grows, the role of an integrated supply chain TMS becomes even more critical. Its ability to synchronize logistics and provide actionable insights helps companies stay competitive in an increasingly dynamic market.

What is an Integrated TMS in the Supply Chain?

An integrated TMS is a comprehensive platform designed to streamline the core functions of supply chain management: planning, execution, and optimization of transportation activities. It helps businesses plan efficient routes, execute shipments, and optimize delivery schedules to reduce costs and improve performance.

It also helps meet heightened performance expectations by providing visibility and agility across the supply chain. Unlike a traditional TMS with limited connectivity, an integrated TMS hooks into multiple complementary software systems. Creating a cohesive information flow across the supply chain enables more informed decision-making and better cross-functional collaboration. Businesses benefit from real-time tracking, updates, and greater visibility into logistics operations.

The Importance of Real-Time Supply Chain Management

Customers in both B2B and B2C are demanding faster delivery times, more accurate ETAs, tight delivery windows, and real-time updates on order status. Call it the “Amazon effect,” driven by the higher service level expectations created by the mega e-commerce company turned logistics powerhouse. Especially in a tighter economy, there is little margin for error in terms of costs, the balance sheet, and customer churn.

Real-time supply chain management involves continuously monitoring and managing supply chain activities as they happen. This is where synchronized logistics comes into the picture. Real-time data from integrated systems provides immediate visibility into inventory, shipment status, and fulfillment operations. This allows businesses to make quick, informed decisions, optimize routes, and adjust to changing conditions. The result is increased efficiency, cost savings, and improved customer service.

Key benefits of real-time supply chain management include:

Quick response to disruptions: Real-time supply chain management enables businesses to respond swiftly to issues like traffic, port congestion, or weather delays. With immediate access to updated data, they can quickly adjust routes, reschedule shipments, and notify customers, minimizing delays and maintaining operational efficiency.

Optimized route planning: Real-time data from TMSs allows businesses to dynamically optimize routes based on current conditions such as traffic or road closures. This reduces transit times, improves fuel efficiency, and enhances on-time delivery rates, leading to cost savings and improved KPIs.

Improved customer satisfaction: Accurate real-time tracking ensures customers receive up-to-date information on shipment status, including greater accuracy in delivery estimates. This transparency and proactive communication build trust and loyalty by enhancing customer experience (CX), helping companies exceed delivery expectations, and improving customer satisfaction.

Core Features of an Integrated TMS That Supports Synchronized Logistics

An integrated supply chain TMS offers a wide array of features designed to improve supply chain management and logistics flow:

Real-Time Tracking and Visibility

A supply chain TMS allows you to monitor each shipment from order to delivery by providing continuous updates on shipment location, inventory status, and delivery progress. This level of transparency allows you to quickly address potential issues (delays, misrouting, breakdowns) before they escalate. The system’s ability to provide real-time views into supply chain flow improves accountability and efficiency.

Advanced Data Analytics

Integrated TMS platforms use predictive analytics to anticipate delays, optimize routes, and improve decision-making. Predictive and even prescriptive insights allow businesses to proactively avoid disruptions and refine logistics strategy.

Automation and Collaboration

Automation tools facilitate freight scheduling, real-time communication with carriers, and stakeholder collaboration. This reduces manual intervention and enables teams to focus on higher-value tasks.

Seamless Integration

An integrated TMS connects with other systems to synchronize supply chain operations. This ensures data flows smoothly between departments, teams, and partners, helping streamline processes and reduce delays.

Overcoming Common Supply Chain Challenges with Integrated TMS

An integrated TMS helps businesses address several common supply chain challenges:

Reducing delays and inefficiencies: Real-time updates allow companies to anticipate and avoid delays. With real-time visibility, businesses can reroute shipments or adjust schedules to minimize disruptions.

Improving demand forecasting and inventory management: Data generated by a TMS helps logistics planners maintain lean inventories while still meeting customer demand. With accurate, up-to-date information on shipment status and inventory levels, they can more closely align supply with demand, reducing the risk of stockouts or overstocking.

Adapting to market changes: Market volatility and changes in consumer behavior can disrupt supply chains. An integrated TMS helps businesses become agile through real-time adjustments to routes, carriers, or schedules in response to changing conditions.

The Role of Data in Synchronized Logistics

Data is the backbone of synchronized logistics. An integrated TMS collects, processes, and analyzes vast amounts of data to drive more informed decision-making.

A TMS provides real-time data, enabling better decision-making in procurement, route optimization, and warehouse management. Accurate, timely information helps businesses be more proactive and improve overall efficiency.

Many advanced TMS platforms incorporate AI and machine learning algorithms to predict risks, optimize freight rates, and automate repetitive tasks. These technologies enhance the system’s ability to make real-time adjustments, delivering improved operational efficiency and reduced costs.

By leveraging real-time data, businesses can provide more accurate information to customers about shipment status, improving transparency and loyalty. Data-driven synchronized logistics enhances CX by ensuring timely deliveries and proactive communication.

Choosing the Right Integrated TMS for Your Business

Some attributes of an integrated TMS include its intuitive UI, which makes it easy for nontechnical users to manage both owned and contracted transportation assets. This includes streamlining aspects of transportation management, such as contracts and payments.

Scalability is another important feature, as well as a cloud architecture and ease of integration with other systems through API connections. A supply chain TMS should also be customizable and rules-based, adaptable to prioritize the execution of last-mile delivery, international shipping, or retail replenishment. Mobile workflows, master data attributes, exception management, self-reporting, and business intelligence should all be easily configurable based on your business needs.

The importance of provider reliability cannot be overstated. Look for a company that offers robust customer support, ongoing training, and regular system updates to address evolving industry and business needs. This is a recipe for long-term success, giving you the ability to maximize system capabilities.

An Integrated TMS: Creating the Future of Supply Chain Efficiency

An integrated supply chain TMS plays a pivotal role in powering synchronized logistics so businesses can streamline operations and respond in real time to supply chain challenges. Consolidating data from multiple systems enhances visibility, optimizes routes, and minimizes disruptions. Companies can make informed decisions, improve delivery performance, and reduce costs through advanced analytics and automation. These next-generation systems ensure a more agile, efficient, and customer-centric supply chain.

Tramm TMS is the first SaaS platform using advanced technology to enable companies to easily manage all transportation assets, both owned and third-party, from the first mile to the last mile.

It is purpose-built to streamline supply chain operations, synching data to create a single source of truth. By eliminating data silos and tapping the power of AI-driven insights, Tramm streamlines processes and ensures a consistency of action across the supply chain.

With its modular, scalable architecture, Tramm’s solution set includes an order management system (OMS), a visibility and control tower module, a global transporter module, a dashboards module, and a driver app. Its platform marries flexibility with optimization, powering a customer-centric approach that delivers measurable results. To learn more about Tramm’s ability to power synchronized logistics and increase supply chain efficiency in your organization, request a demo today.

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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 supply chains become increasingly complex, the demand for efficiency and flexibility has never been greater as companies struggle to meet rising customer expectations and deal with global disruptions. Having detailed order data is one key ingredient in supply chain optimization, enabling better decision-making, improved efficiency, and reduced costs.

The ability to access and analyze clean, accurate data improves demand forecasting, inventory management, and fulfillment operations by providing greater visibility into SKU levels and order status. It also helps streamline transportation, reduce lead times, and optimize carrier selection.

Conversely, a lack of detailed order data leads to a variety of negative consequences. These include mismanaged inventory (more stockouts, overstocks), fulfillment delays, disrupted workflows, inaccurate demand forecasts (over or underproduction), strained customer relationships, and financial impacts.

This is why order data accuracy is critical in supply chain optimization. It improves decision-making, enhances efficiency, and reduces costs.

Understanding Order Data’s Role in Supply Chain Optimization

Order data includes information on products, quantities, prices, customer preferences, delivery schedules, and fulfillment status. Sources of order data include order management systems (OMS), enterprise resource planning (ERP, often the central hub), customer relationship management (CRM), procurement systems, and, if there’s a retail component, point-of-sale (POS) systems.

Detailed order data is the foundation for optimizing inventory management, transportation and delivery, warehousing, and fulfillment — in short, every operational aspect of the business.  

An inability to control how orders are handled from inception contributes to poor decisions, and inaccurate data is often the culprit. The lack of a unified, efficient system for moving orders from planning to execution and doesn’t enable real-time visibility is a recipe for disaster. The result is a limited ability to represent actual order status in a meaningful way.

Enhancing Demand Forecasting and Inventory Management

A business’s success or failure is inextricably linked to its ability to accurately forecast demand and, from there, properly manage inventory levels. Detailed order data (current and historical), as well as factors like market trends and customer feedback, allow businesses to predict demand more accurately and optimize inventory levels.

Using manual processes to handle order data, even in small-to-medium businesses, is a system rife with errors and inaccuracies. The ability to access real-time order data informs smarter reordering and replenishment strategies. This helps reduce overstocks or out-of-stocks by leveraging actual demand patterns, maintaining cash flow, reducing carrying costs, and keeping customers happy.

Improving Order Fulfillment and Reducing Lead Times

Having detailed order data helps minimize fulfillment errors by optimizing picking, packing, and shipping processes. By accurately tracking customer demand, businesses can expedite order processing and optimize transportation routes to reduce lead times.

Order allocation can be fine-tuned based on stock availability and proximity to customers, enabling faster, more efficient fulfillment. This not only speeds up deliveries but also improves customer satisfaction.

Optimizing Transportation and Logistics

Another benefit of having detailed, accurate order data is on the logistics side. With it, businesses can optimize order routing, consolidate shipments, and reduce delivery costs. They can also choose the most cost-effective carriers and shipping methods based on order data.

Order consolidation involves centralizing items from multiple orders into a single location or by combining multiple orders from different sales channels into a single system. This helps lower transportation costs, improve inventory management, simplify logistics, and provide a better customer experience by reducing split shipments.

With the ability to access and analyze detailed order data, businesses can save by taking advantage of bulk shipping discounts, optimizing carrier selection, and avoiding last-minute changes in shipping schedules.

Supporting Customer Satisfaction and Personalization

Detailed order data allows businesses to track customer preferences, providing personalized offerings and recommendations. It also improves proactive communication, informing customers about order status, estimated delivery times, and potential delays.

This type of transparency enhances trust and helps companies to manage customer expectations. It contributes to a consistently excellent customer experience, boosting satisfaction scores, repeat business, and brand loyalty.

Without Detailed Order Data, Things Break Down

Detailed order data is critical for supply chain optimization, enabling better demand forecasting, more efficient inventory management, and faster fulfillment. It allows businesses to streamline transportation, optimize routing, and reduce delivery costs. Accurate order data also supports customer satisfaction by providing real-time order status and personalized offerings, reducing errors and improving efficiency. A lack of such data, for instance, due to manual processes or outdated software, leads to fulfillment delays, inventory mismanagement, and strained operations.

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.

The power of Tramm lies in its ability to optimize the entire process from order management through planning and route optimization, contract management, fleet management, and performance analytics. A versatile driver app provides workflow management, electronic proof of delivery, compliance tracking, and auditing.

Request a demo today to learn more about how Tramm can drive supply chain optimization through its cutting-edge tools and holistic approach.

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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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Supply chain analytics is the process of studying historical, transactional, and real-time data in order to uncover ways to improve supply chain efficiency and performance. As such, it is a vital function of any organization involved in the transportation of goods, addressing not only cost management but performance management.

And supply chain management has grown more complex, with frequent disruptive factors the norm, on top of major pandemic shocks that exposed flaws needing strategic resets. In this context, supply chain analytics are called upon more than ever to surface patterns and insights that drive intelligent decisions.

This article will present three key tactics for effectively implementing supply chain analytics, helping companies drive down costs and improve performance.

Modern Supply Chain Management Demands Modern Supply Chain Analytics

To define it further, supply chain analytics pulls in data from throughout the supply chain (procurement, manufacturing, inventory, orders, and distribution) for deep analysis in order to predict and improve performance. This data comes from external (carriers, partners, suppliers) as well as internal (ERP, SCM, TMS, WMS, inventory management, OMS, etc.) sources.

The ability to gain supply chain efficiency by improving data analytics can achieve a number of important business goals. This includes (but is not limited to):

• Minimizing inventory carrying costs
• Optimizing order fulfillment
• Proactively managing material costs
• Avoiding stockouts and overstocks
• Managing suppliers and purchases

Supply chain analytics also empowers businesses to engage in data-driven decision-making in the supply chain that can lower costs and improve resiliency and responsiveness. They can anticipate challenges and adapt to market fluctuations, helping them maintain a competitive edge even as challenges proliferate.

Three Tactics for Effective Implementation of Supply Chain Analytics

In order to receive the full benefit of supply chain analytics, companies need to look at it strategically and implement tactics designed to drive results in supply chain management. Here are three ways to achieve this.

Tactic 1: Integrate Data Across the Entire Supply Chain

The ability to gain a unified view of data across the supply chain is foundational in making supply chain analytics work effectively and glean actionable insights. This requires data feeds, ideally through API integrations, from all internal and external sources.

It’s often easier said than done. Data silos and fragmented systems, unfortunately, are still a thing. Enterprise systems that can’t communicate without IT intervention pose a challenge to integration.  

One surefire way to achieve effective data integration is by using a true supply chain TMS that centralizes data from across multiple sources, such as those listed above  (orders, shipments, inventory, etc.). A key prerequisite is compatibility with your current tech stack (ERP, WMS, TMS, etc.).

Tramm is a comprehensive TMS that is purpose-built to streamline supply chain operations within a unified platform approach. Its integration capabilities unify data from across the supply chain, creating a single source of truth. Tramm’s consolidated platform helps teams pull together actionable insights based on real-time data. This unified approach eliminates data silos, streamlines processes, and ensures consistency of actions across the supply chain.

Tactic 2: Implement Real-Time Analytics for Proactive Decision-Making

Real-time analytics is the process of analyzing and responding to data as it's generated, with the goal of providing quick insights to help users make better-informed decisions. Conversely, relying on delayed or outdated data acts as a drag on supply chain performance.

It's often used in applications where the timeliness of data is critical, such as in supply chain and logistics. Artificial intelligence and its cousin, machine learning, have added the ability to quickly read, comprehend, and correlate data from across sources, advancing real-time analytical capabilities.

Adopting a TMS with real-time data processing and monitoring capabilities ensures a seamless data flow and communication between systems and stakeholders. For example, Tramm’s FleetVision control tower module provides real-time vehicle tracking through integration with telematics, and status updates. It enables communication with all stakeholders via WhatsApp, SMS, or email for visibility into delivery status, deviations, and reporting.

Tactic 3: Utilize Advanced Analytics for Predictive and Prescriptive Insights

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

Prescriptive analytics takes this process a step further. It pulls together insights gleaned from predictive data analysis to suggest optimal actions, anticipating and solving potential issues before they arise.

This type of advanced capability for supply chain management involves integrating AI and machine learning to drive both predictive and prescriptive analytics. Staff must be adequately trained and processes aligned to take full advantage of these tools for real-time decision-making that drives greater supply chain efficiency.

Tramm’s advanced analytics include both predictive and prescriptive tools that optimize routing, and risk management. Tramm enables businesses to anticipate future challenges and take proactive steps in response. This type of real-time decision-making improves supply chain efficiency and resiliency.

Analytics Power Better Supply Chain Management

In reality, supply chain analytics is what drives supply chain management. The ability to pull in and quickly analyze internal and external data to formulate strategies and inform decisions is a foundational discipline. Mix in a complex global economy and disruptive events, and this capability becomes even more critical.

Tramm gets this, which is why it has developed a comprehensive suite that helps companies address multiple aspects of supply chain management. Its innovative supply chain TMS provides in-depth performance analytics backed by real-time data. Users gain planning and route optimization (Planning and Optimization Module), transporter management (Transact Module), real-time visibility (FleetVision Module) and impactful business insights (Dashboard Module), and much more. To learn more about how Tramm can transform your supply chain management, request a demo today.

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