AI-Powered Transport Sourcing: From Volatility to Profitability

The logistics landscape is rapidly evolving, presenting unprecedented challenges for Logistics Service Providers (LSPs). Market volatility, rising costs and supply chain fragmentation are just a few of the hurdles LSPs must overcome to remain competitive. Traditional transport sourcing methods, while once reliable, are now struggling to keep pace with the dynamic nature of the industry.

To navigate this new era of logistics, LSPs must harness technology and data analytics to their advantage. Here, we outline how AI-powered platforms like Ontruck are revolutionising transport sourcing, enabling LSPs to optimise both tender-based and spot sourcing strategies. By combining real-time data analysis, predictive modelling and intelligent automation, LSPs can gain the agility and precision needed to maximise efficiency and profitability in an increasingly complex landscape.

Volatility demands innovation

Transport sourcing – the process of procuring and managing the resources necessary to move goods – is the raison d’être of the logistics industry. Yet, the way companies approach this vital function is undergoing a profound transformation.

In a volatile global marketplace, LSPs face challenges of unprecedented scale and scope, which are making traditional transport sourcing strategies increasingly untenable. Below is a brief outline of the key areas of concern:

• Market volatility and continuous disruptions: A recent survey from McKinsey&Co found that almost every supply chain manager reported experiencing significant challenges in the past year. Some 44% faced major issues that required changes to their supply chain footprint, and almost half (49%) reported major planning challenges due to disruption. These disruptions can cripple operations and erode profit margins.  
• Rising operational costs: The financial landscape has become increasingly challenging for LSPs. Fuel prices, labour costs and regulatory compliance expenses continue to climb, particularly since the COVID-19 pandemic. This creates a precarious situation, especially for companies considering fleet expansion.
• Supply fragmentation (carriers): The logistics ecosystem is becoming more fragmented, with a proliferation of carriers and transportation providers. This can lead to a complex web of outsourcing relationships that demand significant internal resources to manage effectively.

The convergence of these factors has created an urgent need for innovative solutions in transport sourcing. LSPs that adopt a data-driven approach can not only navigate these challenges but also gain a competitive advantage in the marketplace. Nonetheless, traditional approaches remain prevalent. 

Why the tried-and-tested approaches are falling short

Historically, LSPs have relied on two primary approaches to transport sourcing: tender-based sourcing and spot sourcing. Each strategy offers its own set of advantages and drawbacks, and the choice often depends on the specific needs and risk tolerance of the LSP.

Tender-based sourcing prioritises long-term stability and cost control. LSPs negotiate large, often annual contracts with carriers for recurring routes. The appeal of tender-based sourcing lies in predictability, while limited profit margins are the most significant drawback.

‍

In contrast, spot sourcing is the wild card of transport sourcing. It involves securing transport on an ad-hoc basis through open marketplaces or direct negotiations with carriers.

‍

Traditionally, LSPs have struck a balance between these two strategies. Tender contracts provide a stable foundation, while spot sourcing offers the flexibility to adapt to market fluctuations. However, this approach often creates operational complexities and can limit the ability to maximise profitability.

How to leverage AI to optimise transport sourcing

Current market conditions require LSPs to iron out the kinks in traditional approaches to transport sourcing. However, this doesn’t mean re-inventing the wheel but augmenting it with technology. As with traditional approaches, two distinct paths emerge: tender-driven and spot-driven.

AI-powered tender-driven processes emphasise precise price calculations and fleet sizing, where the goal is to align resources with predictable volumes and optimise for cost efficiency. On the other hand, spot-driven strategies prioritise agility across the entire fleet management spectrum, favouring rapid responses to fluctuating demand and maximising responsiveness over static planning.

1. Spot market optimisation

AI-powered tools are transforming spot market transport sourcing. By analysing vast amounts of carrier data from a freight tracking system, sophisticated algorithms can optimise matches based on availability, preferences and historical performance. This streamlines the entire process, ensuring reliable, efficient and cost-effective transportation solutions.

Automated freight bidding systems further enhance efficiency. These systems empower LSPs to secure carriers swiftly by setting competitive starting prices, negotiating strategically when needed, and automating carrier selection based on predefined criteria. The result is a faster, more streamlined bidding process that conserves valuable time and resources.

Integrating AI-powered tools into spot market operations equips LSPs with a significant advantage. Short-term forecasting employs machine learning and artificial intelligence to predict transportation demands, allowing for proactive adaptation to market fluctuations and improved fleet efficiency. Intelligent freight matching considers various factors to connect LSPs with the most suitable carriers for each shipment, optimising reliability and cost-effectiveness. Automated freight bidding streamlines negotiations, ensuring competitive pricing and maximising profit margins.

By embracing these technologies, LSPs can navigate the volatile spot market with unprecedented agility and precision, positioning themselves at the forefront of the market.

2. Tender-based optimisation

In tender-based transport sourcing, the challenge lies in striking the perfect balance between fleet capacity and operational efficiency. Overestimating fleet size leads to underutilised resources and inflated costs while underestimating it can result in missed opportunities and dissatisfied customers. The key is to calculate the optimal fleet capacity that aligns with the projected demand from tenders, ensuring that resources are neither stretched too thin nor left idle.

For instance, Ontruck's approach to tender optimisation involves a systematic process that uses AI and data analytics to streamline decision-making and enhance operational efficiency. The process can be broken down into the following key stages:

1. Base scenario forecast: AI-powered models predict the carrier's daily operations for the year, creating a strategic plan that optimises existing capacity and resources.
2. Tender analysis: The system analyses the tender, forecasting potential orders and requirements.
3. Scenario matching: The solution identifies overlaps between the tender's routes and the carrier's existing operations, creating a new scenario that integrates the tender.
4. Scenario comparison: Routing algorithms compare the base scenario with the integrated tender scenario to assess the operational impact and potential benefits of accepting the tender. This provides insights for informed decision-making.

By deploying this comprehensive approach, Ontruck enables LSPs to evaluate tenders strategically, considering their impact on existing operations and overall profitability. This allows LSPs to make informed decisions about which tenders to accept, ensuring optimal fleet utilisation and maximising returns.

Harness AI to shore up the supply chain

The present logistics landscape demands a paradigm shift in transport sourcing strategies. Traditional approaches are no longer sufficient to navigate the challenges of market volatility, rising costs, and supply chain fragmentation. To thrive, LSPs must embrace AI.

These solutions use real-time data analysis, predictive modelling and intelligent automation to optimise both tender-based and spot sourcing. By analysing vast amounts of data, they accurately forecast demand, streamline carrier selection and automate bidding processes, ensuring reliable and cost-effective transportation solutions. Additionally, these platforms optimise fleet sizing decisions, minimising underutilised resources and maximising operational efficiency.

Leveraging AI-powered solutions is crucial for LSPs to achieve sustainable growth. The future of logistics is data-driven, and those who adapt will thrive. Prepare your operations for the current LSP reality, where data-driven decision-making is the key to success. Get in touch with Ontruck, and we can walk you through the possibilities.  

‍

‍

References:

• Alicke, K., Foster, T., Hauck, K., & Trautwein, V. (2023, November 3). Tech and regionalization bolster supply chains, but complacency looms. McKinsey & Company. https://www.mckinsey.com/capabilities/operations/our-insights/tech-and-regionalization-bolster-supply-chains-but-complacency-looms

‍