Too often for food shippers, logistics managers, and transportation decision-makers, traditional routing solutions fail to deliver on their promise of cost savings. There are internal and external reasons for this inability to realize a return on investment.
Why traditional routing solutions can fail to deliver ROI
Data processing deficiencies can be a major issue in routing solutions. Legacy routing systems often operate with limited datasets that inadequately represent the complex and dynamic nature of food distribution operations. These systems, which typically process small volumes of historical data in batches, do not account for changing conditions and updated driving or dispatch plans.
Traditional routing systems rely on static parameters and often historical averages, leading to an optimization approach to routing, scheduling, and assignment as separate challenges executed at different points in time, rather than simultaneously. Unable to learn from operational patterns or adapt to changing conditions in real-time, they function as prescriptive tools, providing singular solutions.
Routers, load planners and dispatchers often know exactly what they want, but without meaningful flexibility, current routing solutions don't support their needs and fail to incorporate all relevant data and new constraints.
Integration and workflow challenges exist as well. Often separated from operational workflows, algorithms can create persistent gaps between planning and execution. This is due in part to older operating systems being designed as digital filing cabinets rather than workflow solutions and dispatch execution being handled with disconnected or analog tools, such as text messages, trip sheets or paper forms.
As an example, a food distributor to restaurants, theme parks, and military bases with a $100 million freight spend implemented a routing solution that will potentially generate $15 million in savings. However, since its dispatch occurs by relaying a week’s worth of routes for each day on a paper form, and drivers self-organize routes, the ability to unlock any efficiency gains is undermined. In addition, drivers’ self-dispatching along proposed routes has resulted in 150,000 customer stock-out events per year, curtailing revenue growth and customer satisfaction, and generating excessive costs.
When the single-tenant architecture of traditional TMS prevents effective data exchange with routing systems, it undermines the possibility of routing solutions being natively embedded in real-time operational workflows, whether in the dispatch office or in the cab. As a result, load planning decisions that need to be made in split seconds cannot be supported. This human-machine interface challenge is further exacerbated because legacy routers tend to operate as normative tools rather than decision-support platforms, dictating what a solution rather than engaging in a dialogue with dispatchers to suggest progressive improvements.
New opportunities
While traditional on-premise and private cloud systems face inherent limitations in data processing speed and scalability, an AI-native, multi-tenant cloud infrastructure with better algorithm-derived recommendations enables processing capabilities previously unavailable. This opens up the opportunity for routing, dispatch planning, and delivery management to be delivered natively in one single platform where human planning, management and monitoring skills are augmented, in the moment, by the data processing and unbiased analytical capabilities of the machine.
These modern platforms can ingest and process diverse, real-time data streams at unprecedented volumes. Their inherent benefits include:
● Allowing systems to improve recommendations, including offering adjustable recommendations based on real-time operational data
● Extending native transportation management system functionality to driver interfaces for real time delivery management
● Analyzing dispatcher behavior and incorporating human decision-making patterns
● Continuous learning from both past decisions and new manual overrides to evaluate route optimization capabilities and improve recommendation accuracy
AI-native, multi-tenant cloud infrastructures enable real-time processing capabilities. Large language models, which are taking on new roles in data science and software, can be effectively developed and deployed in cloud environments. This is particularly critical for core operating systems such as ERPs, or in the case of fleets, TMS that inform and help execute routing recommendations.
But the value of these great advances can only be cost-efficiently deployed if the routing algorithms natively connect with the database and workflows in the TMS.
Industry specific
Modern routing platforms can process massive volumes of structured data like GPS coordinates, traffic conditions, and vehicle schedules in real time. These systems can now also process unstructured customer feedback, driver notes, and operational communications, converting them into actionable routing constraints, most of which live in the TMS.
Native workflow integration can dramatically improve delivery plan adherence and operational outcomes by providing drivers with real-time updates and automated rerouting capabilities that adapt to immediate conditions like traffic and weather changes. For example, GPS-enabled monitoring provides immediate visibility into vehicle locations and activities, while automated rerouting capabilities adjust routes dynamically as orders change, with real-time updates on arrivals and departures and instant alerts for potential delays to support proactive management.
Advanced routing algorithms can also produce automated reports and operational summaries driven by a continuous feedback loop between real-world operations and algorithmic optimization. While continuous optimization may not be necessary for all use cases, it can address the interdependencies between routing decisions, driver schedules, and delivery time windows, often improving overall efficiency by 10-15%.
These real-time capabilities translate to significant operational advantages in food distribution through immediate responsiveness to perishable product constraints and time-sensitive delivery windows. For example, some of the most complex route optimization algorithms can now generate plans in under 30 minutes instead of previously taking hours.
Modern algorithms integrate real-time data from electronic logging devices (ELDs) to ensure route plans consider individual drivers' actual available Hours of Service, preventing violations while maintaining cold chain integrity for products shipping at different temperatures.
The importance of incorporating driver behavior into these systems is highlighted by Amazon's Last-Mile Routing Research Challenge, which found that drivers deviated from planned routes in three out of four deliveries, prioritizing their familiarity over algorithmic suggestions. However, when routing systems use machine learning to predict driver preferences and optimization algorithms to stay within those patterns, they can achieve up to a 28% improvement in route quality while maintaining high driver acceptance rates.
Modern routing implementation
Food distribution operations should evaluate their current routing capabilities against benchmarks, including real-time processing, integration depth, and learning capabilities. At the same time, AI-native routing requires compatible transportation management systems so organizations should assess their readiness for cloud-native, integrated platforms that can deliver all-in-one routing and transportation management.
Best practices for implementing modern routing systems:
● Define clear objectives: Set specific business goals (reduce costs, improve customer satisfaction) and identify measurable KPIs.
● Clean and centralize data: Audit all data sources and address quality issues before implementation.
● Ensure your TMS architecture supports natively embedded routing. This includes a multi-tenant cloud architecture, not a private-cloud, single software instance architecture like most legacy systems.
● Start with pilots: Test on a subset of routes and simulate scenarios based on real-world feedback.
● Monitor route adherence: Track every deviation from optimized routes and require drivers and dispatchers to log the reason. Deviations often reveal hidden operational constraints (customer access restrictions, loading dock availability, driver safety concerns) that the optimization algorithm needs to learn.
● Monitor and improve continuously: Use analytics dashboards to track performance and refine the system over time.
Modern routing algorithms embedded in an AI-native TMS optimize routing, scheduling, and asset and driver assignment. The ability to incorporate contextual and dynamic information and enforce execution through dispatch and driver workflows represents a fundamental capability shift.
In food distribution, an industry with razor-thin margins, closing the gap between traditional and modern routing capabilities creates a competitive advantage. Companies using advanced systems can achieve operational efficiencies unavailable to competitors using legacy solutions and realize an ROI significantly faster than with traditional approaches.
