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“How am I doing?” This is a typical question among shippers in a volatile market without visibility into how their network of logistics partners and service offerings measures up against the performance of peers and competitors. It’s a question that requires answers, as it lays the foundation for operational decisions and long-range planning.
The pandemic had significant residual effects in the massive, chaotic North American trucking market. “Beyond the regular cyclical volatility of the freight market, COVID changed the way networks are designed, how companies procure and handle capacity, and how they look at their spend,” says Samuel Parker, shipper segment lead with DAT Freight & Analytics. Among the drivers, he says, are widespread omni-channel fulfillment and parcel delivery, added less-than-truckload (LTL) and last-mile complexity, and the grueling request for proposal (RFP) rate-negotiation process. “All of that volatility is basically here to stay, so you have to either outsource it to a 3PL or 4PL, or else bring it in-house and run it yourself.”
It is increasingly clear that real-time external data generated by supply chain networks delivers essential context to help shippers benchmark performance, and make better operational and planning decisions in the face of uncertainty. Here are five ways supply chains benefit from real-time, real-world data and supporting analytics:
1. Visibility Beyond the Horizon
The disruptions of the pandemic underscored the importance of end-to-end supply chain visibility and market insights, driving shippers to rely more on external data from partner networks and trusted third-party providers. A new sense of urgency is driving supply chain evolution as omni-channel fulfillment, logistics complexity and shorter product lead times demand speed, reliability and flexibility.
“If you want to build a resilient supply chain, ramp capacity for seasonality, build a spot market strategy, redeploy a dedicated asset versus telling your broker to add extra lanes for a particular time period, you need data for all of that,” Parker says.
Analytics provides the ability to interpret data patterns and uncover insights that help determine lane allocations, whether to outsource select functions to 3PLs, how to structure mini-bids, strike the right balance of spot and contract capacity, and other key optimization decisions.
2. Less Money Left on the Table
A strong return on investment depends on operating efficiency, cost savings, and revenue. Yet most discussions among shippers and carriers mistakenly focus almost entirely on the zero-sum issue of rates, to the exclusion of other areas where process changes and collaboration could significantly boost ROI for both parties.
Shippers need a neutral snapshot of comprehensive rate trends to confidently prepare requests for proposals, evaluate carrier bid responses, negotiate contracts or seasonal adjustments. Market visibility allows shippers to assess the comparative risk in utilizing spot or contract capacity on challenging lanes or as they prepare for shifts in the freight market.
That same data can highlight mutually beneficial opportunities for shippers and their partners. Carriers want profitable loads based on revenue per day, not rate per mile, along with principal costs needed to manage fuel consumption, driver hours, dwell time, and deadhead inefficiencies. Brokers make their living from the added value they provide, matching freight with the right capacity and managing the move, especially in difficult lanes. Their shared interests with shippers represent potential incentives in rate negotiations.
3. Alignment with Market Benchmarks
When shippers ask if they’re paying too much for their trucking and logistics, they really want to know what they’re paying relative to competitors. In an $875-billion market, with nearly 2 million companies chasing both spot and confidential contract business, the validation of rate, capacity, and service trends from trusted, neutral data sources is critical.
Anonymized data sourced from real freight and load board transactions provides important and reliable insights into rate, demand, and capacity trends. Shippers can use this data to analyze the impact of seasonality, compare average spot and contract rates in similar market segments over time, and more accurately structure RFPs that align with actual market conditions. Insights from advanced analytics tools help shippers allocate spend efficiently while locking in necessary capacity.
4. Less Friction in Rate Negotiations
Access to more granular and comprehensive market data makes rate negotiation less adversarial by aligning all parties on an agreed-upon baseline. From that point, discussions can move beyond rates to consider areas of cooperation that add mutual value.
Pricing transparency across similarly situated geographies, freight, and industries helps establish a negotiating range for rates, in turn freeing parties to consider non-rate solutions and incentives of mutual benefit.
Bench-marking analytics tools allow companies to compare their current rates against average prices in the market. They can use the results to validate current freight charges, assess carrier and broker rate increase requests, search for alternative carriers or lanes, assess spot premium risks, and prepare more accurate RFPs — all in far less time than outdated or manual approaches.
5. Actionable Insights for Resilience and Agility
The pandemic highlighted the need for proactive detection and response to supply chain disruptions, and subsequent application of insights to build resilient strategies. A highly cyclical trucking market with wide fluctuations in capacity and price makes it critical to have a pulse on the market.
“With that visibility, you know when you need to strategically dip into the spot market, or when you might be better served bringing in a 3PL,” Parker says. “You have a holistic view of the market, instead of just feeling locked into a single route whatever the cost might be.”
When all parties are bench-marking against the same trusted data set, they begin to see the same patterns and shared opportunities to improve performance, stabilize revenues, and lower costs.
DAT Freight & Analytics Removes Uncertainty in a Volatile Trucking Market
DAT Freight & Analytics, established in 1978, is a software and analytics provider that manages global company and shipment data from a network of more than 120,000 trucking and parcel delivery carriers, 1,300 shipper customers, and roughly four in five freight brokers. DAT serves key shipper verticals including food and beverage, retail and manufacturing.
The DAT One network is the world’s largest load board, matching freight with available capacity throughout North America. It represents a combined 449 million loads posted, 52 million transactions and $150 billion in annual freight spend across 68,000 lanes. DAT bolstered the DAT iQ analytics platform with acquisition of the Freight Market Intelligence Consortium from Chainalytics.
DAT iQ helps shippers optimize transportation budgeting and procurement strategies through rate and capacity planning, management and optimization. With $150 billion in annual transactional data, DAT iQ has the deepest, broadest transportation rate data to power freight market intelligence and analytics solutions for shippers, including the following:
RateView Analytics Solutions: Gain visibility into spot and contract market rates for strategic sourcing, planning and cost management. Use freight market intelligence to align bids from carriers with market trends, establish resilient pricing, and set realistic transportation budgets.
Network Analytics Solutions: Evaluate historic, current, and forecasted capacity trends to create a procurement strategy that works for the long haul. Extensive lane and carrier insights to help proactively mitigate routing guide failure, secure reliable capacity, and build resilience.
Analytics Services Solutions: Optimize operations with advanced data services, seamless integration support, and access to consultants with deep industry expertise.
Resource Link: https://data.dat.com/Empower_iQ
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