Why Route Planning in Logistics Requires a Revolution

In logistics and courier services, companies face the challenge of efficiently executing numerous transport orders. Route planning in logistics is addressed using technological tools, such as specific routing software or those integrated into Transportation Management Systems (TMS).

Given the current market conditions, it's not surprising that logistics operators are actively seeking solutions to optimise costs. More efficient routing planning is an area with great potential for savings; according to the latest Gartner CEO and Senior Business Executive Survey, CEOs cite cost management and supply chain resilience as key actions for route planning optimisation. 

Although routing planning technology has existed for some time, current systems have only yielded partial results. They have partially improved the efficiency and speed of routing planning in logistics but have yet to provide the intelligence necessary to optimise the process entirely. Here, we’ll clarify this concept, why current solutions are falling short, and how next-generation AI solutions offer answers.

The two dimensions of the same challenge

Route planning in logistics refers to the process of efficiently organising numerous transport orders into a sequence of journeys with stops, utilising different types of vehicles. This process encompasses two closely connected dimensions: financial and operational: 

• On the financial side, escalating cost pressures are significant: pressure caused by inflation persists across most road freight operating costs, further elevating the cost base and maintaining high rates. Labour costs across the 27 European Union countries surged 8.3% in Q2 2023 compared to Q2 2022. Additionally, maintenance costs saw a 4.5% increase, tolls and axle taxes rose by 4.0%, finance and insurance costs increased by 4.3%, and overhead costs surged by 6.9% in the nine months of 2023 compared to the same period last year, as reported by Ti Insights.
• The current operational landscape is highly complex, particularly regarding road traffic regulations, navigating urban restricted zones, and the varying requirements of B2B and B2C clients. This complexity underscores the challenge of last-mile delivery faced by couriers and LSPs, who play a critical role in supply chains.

These things considered, there are three key reasons current route planning technologies are failing the logistics industry, particularly couriers and those operating transport light vehicles. Let’s take a closer look.

3 problems presented by current approaches to route planning

1. Fleet managers need dynamic routing rather than rigid planning

Many of the solutions available in the market offer a planner rather than a router. They provide an optimal solution based on specific operational conditions (such as the number and type of vehicles, autonomy, etc.) and a defined cost function (typically, minimising kilometres or times). This is good on paper, but in practice, there are limitations.

Once the journey plan is formulated, real-world transportation dynamics come into play: last-minute transport orders, unforeseen operational changes, traffic issues, and so on. Ostensibly flawless plans become obsolete, and the planner lacks the ability to adjust or resolve them, necessitating manual intervention by operations personnel to manage this operational volatility.

Route planning in logistics should be flexible and able to adapt to challenges dynamically, optimising differently depending on the circumstances. For instance, if an operation involves a fixed number of vehicles and suddenly one is out of action, how can this be addressed without the ability to adjust routing parameters in real time? 

Similarly, should routing be optimised using the same criteria during peak and off-peak seasons? How could greater forecast accuracy optimise the process? Adjustments are necessary to achieve maximum efficiency and effectiveness in such scenarios.

2. Routes aren’t impacted by only one factor

Route planning in logistics doesn’t entail solving a single problem but rather several that can fluctuate over time, occur concurrently in different areas or intertwine. While some solutions can adjust to changes in transport orders or operational conditions, the challenge lies in the variability of what needs to be optimised. The type of transport, operational model and conditions impact the optimisation process, making it challenging to find adaptable solutions capable of tackling diverse problems and conditions.

For instance, consider operations in low-emission zones where optimisation is based on emissions, others in low-density regional areas with a scarcity of carriers requiring minimising the number of vehicles, and others in high-density areas with ample carriers where optimisation focuses on reducing kilometres travelled. It's not feasible to use the same routing criteria for every scenario. 

In essence, different factors come into play: CO2 emissions for the first case, the number of vehicles for the second, and kilometres travelled for the third. Carriers require a solution that accommodates all these factors, applying the appropriate one in each location.

3. Reality rules in transport operations

Even if the aforementioned challenges are overcome, operational reality often contains information that routers lack. This knowledge, acquired through experience and trial and error, encompasses details such as access restrictions, optimal delivery times and other specific factors of the operational environment. 

For instance, the expertise of an experienced fleet team is crucial for logistics service providers, particularly in courier or distribution operations. They are responsible for managing essential operational details such as distribution centre schedules, road characteristics and access instructions to certain areas. While an experienced team can mitigate incidents, it only offers a partial solution as they may become a closed system of knowledge.

This critical information is not shared with the routing system, resulting in journeys that are prone to failure and require manual adjustments to succeed. Moreover, this information is not effectively transferred to new team members, leading to a steep learning curve for new operators and potential chaos in the absence of key personnel. For a routing system to be effective, it must be capable of integrating and utilising this information to optimise routes, considering the realities of route planning in logistics.

Major trends indicate that there is light at the end of the tunnel

To understand the origin of these aforementioned challenges, we need to return to the foundational issue surrounding route planning in logistics. Although technological solutions have been applied for some time, they have followed a traditional record-keeping model rather than proper automation. Innovative solutions are intelligent and dynamic, based on direct experience operating freight transport and a try-and-test mentality. 

In this context, two major emerging trends offer new solutions for couriers and logistics service providers.

1. The acceleration of technological innovation

Recent innovations, from cloud native systems to artificial intelligence (AI), are broadening the horizons of route planning in logistics, enhancing the adaptability, flexibility and learning capabilities of technological solutions. Algorithms powered by machine learning utilise scenario modelling and decision automation to recalculate journey options with greater precision, aiming to minimise incidents and delays.

With the exponential increase in computational capacity, routing engines have become significantly more adept at recalculating alternative journeys in response to shipment changes. They can now consider a multitude of variables to achieve more accurate results.

2. The start-up ecosystem is changing the game

The start-up ecosystem is utilising these technologies to offer solutions that surpass traditional methods. For instance, Ontruck AI Tech evolved from its operational roots to a software-as-a-service model. This suite of AI-powered software solutions optimises road freight management comprehensively, seamlessly integrating with operators' existing systems and adapting to real-world operations.

Drawing on a firsthand understanding of transport operators' challenges, Ontruck AI Tech not only introduces innovation but also equips logistics companies with tailored tools to enhance their efficiency and productivity. With providers like Ontruck shaking up the industry, route planning in logistics is set to be transformed.

Route planning in logistics is set to transform

Digital tools are optimising route planning in logistics across conception, execution, and settlement. According to research conducted by McKinsey, leading logistics players are already witnessing tangible performance improvements of 10 to 20% in the short term, with even more significant gains of 20 to 40% projected within two to four years.

Given these promising outcomes, current technological innovations hold the potential to revolutionise myriad aspects of the logistics and couriering sector, from dynamic route planning to AI price optimisation. By leveraging advanced digital solutions, businesses can overcome longstanding challenges and achieve greater efficiency and effectiveness in their operations. With the ability to adapt to real-world complexities and dynamically optimise routes, these innovations offer a pathway towards improved performance and profitability.

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References:

• Ti Insight. (2023,December). European Road Freight Market Size & Forecasts 2023, 2024 & 2027. https://www.ti-insight.com/whitepapers/european-road-freight-market-size-forecasts-2023-2024-2027/ 
• Gosling, S., Li, J. D., Martinez, A., Miguel, M., & Perez, F. (2023,November). Digital logistics: Technology race gathers momentum. McKinsey & Company. https://www.mckinsey.com/capabilities/operations/our-insights/digital-logistics-technology-race-gathers-momentum 
• Gartner. (n.d). CEOs Expect Supply Chain Leaders to Balance Optimization and Business Growth https://webinar.gartner.com/429170/agenda/session/1015505?login=ML