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If there's one word we've heard extensively over the past three years, it's "visibility." Recent trends, from rate fluctuations to tight capacity, have underscored the need look deeper at shipping networks, whether plumbing for more efficient routes or revisiting carrier contracts.
The focus on visibility has generated an onslaught of transportation data — often from numerous sources. This rise in information sharing is a positive development but can also be overwhelming. Shippers receive massive amounts of freight data, but they don't always have the bandwidth to clean or standardize it. Until they do so, they risk missing out on many actionable insights.
"Shipping data" refers to any underlying, quantifiable data related to any aspect of a company's transportation structure. For example, it might measure how long deliveries take to arrive, how much a shipper spends on transportation, or drill down into the types of surcharges a company tends to accrue. It can even be used to detect fraud. The data provided through freight audit payment services can create enormous opportunities for shippers.
Shipping data encompasses thousands of inputs across hundreds of metrics. A few examples of shipping data include shipping rates, on-time performance, or transportation costs per package or pound.
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Shedding Light on Transportation Data
Shipping data is crucial to understanding a company's performance and seeing where there's room for improvement. No matter what aspect of transportation performance a business wants to improve, data provides insights that might have been missed by simply "hunching it out."
Unfortunately, just having the data is not enough. Instead, messy transportation data needs to be standardized and normalized. What does this mean? Shipment data can be collected in various, slightly different ways and formats. Normalizing and standardizing order data into a single source of truth makes it easier for shippers to analyze and use it more effectively. In other words, standardization and normalization clean up the data.
Normalized data is vital because raw shipping data will come in with inherent inconsistencies. For example, a dataset might have dollar signs in some entries but not others. Or it might list "Amazon" and "Amazon.com" when referring to the same entity.
There are endless examples of this. When a raw dataset gets fed into a program for analysis, the computer will miss these subtleties and spit out inaccurate information.
The term "big data" has been thrown around a lot in the past five years and has even become prevalent in logistics as of late. Oracle defines big data as "data that contains greater variety, arriving in increasing volumes and with more velocity." It consists of data sets that are too large and complex to be processed using traditional methods. Big data can be difficult to manage, but it can also shed light on business challenges in a new way.
Shipping constantly generates enormous amounts of data that needs to be ingested, normalized, and cleaned to be helpful to an organization. Once standardized, transportation data can be a valuable tool to optimize shipping operations.
So, yes, the phrase "big data" has gotten a little buzzy lately. But in logistics, using big data just means a lot of information can help companies if appropriately handled.
Shippers who fail to take advantage of their shipping data face many challenges and pay the price in both actual and opportunity costs. Here are a few examples.
Shippers who aren't tracking shipment data may have less understanding of costly transportation errors. Businesses can face a host of invalid charges from penalties, late payment fees, or duplicate billing — and that doesn't begin to cover the cost of damage to a brand's reputation.
In 2021, the Ever Given container ship got stuck in the Suez Canal, blocking nearly $10 billion in commerce daily. How would such an isolated event (or even something anticipated, like the ebb and flow of the trucking market) impact a U.S.-based shipper? It's only possible to quantify the effect by turning to transportation data.
Carriers who consistently make late deliveries will cost a company their reputation and customer's trust. It doesn't matter who dropped the ball in a transportation network — all customers see is that the shipper did not fulfill its promise to deliver on time.
Shippers who don't collect or analyze data won't know which carriers provide the best performance. They aren't able to see where their costly errors and bottlenecks are. Then, when RFP season rolls around, they'll have to guess who gets awarded which contracts.
No matter how optimized a network is, errors are inevitable. Shipping labels get lost or printed incorrectly. Drivers get lost, and deliveries arrive late. Packages incur damage. These are called freight exceptions and they are manageable — if a business can draw on historical shipment data to isolate problem areas.
Carriers don't intentionally overcharge for services, but like any other company, they make mistakes. Here are a few examples:
In the age of social media, news of a poor delivery experience can spread far and wide in minutes. A few bad reviews can translate into a damaged reputation, lost customers, and, ultimately, lower sales.
Conversely, shippers who can draw upon the rich, actionable insights of their organized, cleansed shipping data make decisions confidently, yielding many benefits. Here are a few examples.
Most logistics data today is spread out and cobbled together from a patchwork of TMS, OMS, ERP, and other management systems. Having disparate sources turns even basic questions into an exercise in guesswork. Management may not have a straightforward answer on annual transportation spend, cost per shipment, carrier performance, and other essential, crucial business matters. Only by obtaining clean, standardized shipping data will shippers get an accurate picture of their transportation spend.
Management teams are constantly pursuing more efficient, cost-effective transportation. But reaching that goal isn't always a straightforward process. Messy, unstructured freight data can overwhelm a finance team, and cost allocation can be difficult to estimate if many businesses are operating out of one distribution center, for example. To truly know where a company has been and where it's headed, executives and managers need access to one consistent source of information.
Relationships matter for carrier selection. But performance is even more critical, especially given that everyone has a different definition of exceptions. Enriched freight data gives shippers the complete picture of their transportation spend, all in one place. Having one data source helps shippers detect anomalies and risks, ward off delays and provide a better overall customer experience.
Freight audit payment isn't about trying to squash carrier or driver margins. Instead, the goal is to ensure carriers get paid the total amount owed on time. Carriers are often the face of a business. If they aren't happy, the customers they serve will not be, either. Nobody wants to realize that they've made a mistake. But carriers do want to know where they can improve. Access to clean, accurate data helps them identify recurring issues and address their root cause.
Simply put, shippers and carriers must have ready access to data related to contract rates, transit time, and on-time performance. Not every company has the time or resources to use a dynamic routing guide. Instead, the right combination of technology and data standardization, like that provided by Intelligent Audit, helps shippers identify their top performers and helps carriers find their weak points.
If something unusual happens, machine learning identifies the problem, where it happened, and why. For example, a misplaced comma could vastly increase the size of a planned shipment. Intelligent Audit's proprietary algorithms detect and mitigate anomalies before they become huge expensive errors.
Freight audit business intelligence normalizes and provides visibility into shipping data, helps eliminate costly shipping moves, finds inefficiencies, lowers cost-per-package, and ultimately, helps realize tangible savings. Managers can leverage the information gleaned from real-time data to make smarter decisions and guide their business forward.
Shipping data and visibility have been hot topics since COVID first snarled supply chains in 2020. But these aren't just buzzwords or passing fads. Shipment visibility is critical in managing an effective transportation strategy, no matter what is happening worldwide.
Data is the lifeblood of visibility, but it has to be clean and accurate to be impactful. This can be hard to come by when transportation data flows from numerous sources using different standards and metrics. Intelligent Audit transforms your data from a messy quagmire to useful information that can drive your business forward. Schedule a demo to learn more today.
Set up a call with one of our experts to discuss how Intelligent Audit can help your business uncover opportunities for cost reduction and supply chain improvements through automated freight audit and recovery, business intelligence and analytics, contract optimization, and more.
