In today’s fast-paced logistics industry, shipping data analytics and the adoption of new data-centric technologies face massive hurdles. At its core, shipping data encompasses a diverse range of systems, various data formats, and numerous communication protocols within supply chains. Complicating matters further, this data is often fragmented, duplicated, ambiguous, and riddled with inaccuracies. Consequently, shipping analytics is sub par and compartmentalized across multiple supply chain organizations. This leads to tedious and error-prone analytics processes yielding pitiful insights. In this article, I’ll share with you the four main challenges with shipping data when it comes to data analytics and leveraging emerging tech such as AI. Also, I’ll recommend resources that may get us out of this mess.
1. Data Is Localized Within Disjointed Functional Silos, Restricting Shipment Visibility.
A major challenge for shippers is that their various internal organizations are functionally disjointed when it comes to shipping analytics. Indeed, their view of shipping data is locallized and fragmented into functional silos. To illustrate:
- Financials. The finance department only sees the shipment’s accounts payable information
- Shipment Operations. At best, the shipping department only has visibility of shipment orders and deliveries.
- Transportation Procurement. Moreover, the transportation procurement office is focused on the request for proposal (RFP) cycles and the lowest costs. At best, they see snapshots of shipment data in different formats from internal systems, third parties, and carriers’ systems.
- Sales Organization. For sales, they are focused on service no matter the cost.
In too many cases, these various departments and organizations are blind to how their actions impact other parts of the supply chain and shipping in general. For more on Enterprise systems, see my article, Agile Supply Chain Decision-Making: First You Need to Know The Truth About Enterprise Software.
2. Massive Amounts Of Fragmented, Ambiguous Shipping Data With Few Insights.
Most companies have much more visibility over their purchase orders and product-level data when compared to shipment-level data. For instance, with a purchase order and its associated product data, most supply chain planners have the ability to “run the numbers” from different perspectives. As a result, analysts can look at demand, capacity, different product characteristics, and so on. With shipping data, analytics gets a bit fuzzy.
Indeed, regardless of the data query performed on shipping data, the results often prove unsatisfactory. For example, here is a sampling of shipping data questions that frequently remain unanswered due to the absence of unified shipping data.
- For product X, how many of these did we deliver on-time last month?
- What was the cost of express shipping last week for aircraft parts under 5 lbs?
- What were the shipping costs last year for product z returns?
Indeed, planners and decision-makers often lack easy access to detailed shipment data because it’s incomplete, inaccurate, and hard to find. Additionally, it is common for supply chain stakeholders to have different interpretations of shipping terms that are many times unclear and easily misunderstood. For example, a buyer might think “shipped” means the shipment is on the truck and on its way, whereas the seller might use “shipped” to mean it’s on the loading dock ready to go. For a more detailed discussion on challenges with supply chain terms and definitions, see my article, A Refocus on Supply Chain Glossaries: The Best Way To Unlock Data Interoperability, Strengthen Collaboration And Leverage Tech
3. Supply Chain Orchestration And Automation Limited Due To Complicated Shipping Data Structures.
In the age of ecommerce, shipping is now a critical, customer-facing operation. As a result, supply chain managers have a greater need for shipping analytics than ever before. In particular, this data is key for root cause analytics and identifying systemic issues to optimize shipping operations.
However, shipping data is routinely in different systems to include order fulfillment, TMS, carrier tracking, 3rd party systems, and financial systems. Even if an analyst has access to the data, it is challenging to link shipping data together. Specifically, depending on the data needed, an analyst will need to use various reference numbers such as tracking numbers, invoice number, or purchase order to access the shipping data. Worse, just to piece the data together, the analyst will need significant transportation expertise and countless hours to make sense of the shipping data.
As a result of bad shipping data, supply chains do not have visibility over their operations. This includes both operationally and strategically. Worse with their disjointed, complicated shipping data, they spend millions on information technology, but receive few insights. Indeed, the technology tools are available such as Business Intelligence (BI), advanced automation, AI, and decision platforms, but they cannot fully leverage these tech capabilities because of their poor shipping data.
4. Shipping Data Is Not Well Linked to Its Financials, Hindering Effective FinTech Solutions.
Surprisingly, the quality of shipping data can significantly impact financial operations. This is because fragmented shipping data complicates comprehensive financial analysis. As more financial technology (FinTech) solutions emerge in the transportation sector, the need for accurate shipping data becomes even more critical. These data-centric technologies rely on high-quality information for accessing, analyzing, managing, and extracting valuable insights. Additionally, businesses require precise data to automate financial processes in real-time. Therefore, it is essential for logistic organizations to maintain high-quality shipping data so that they can optimize their financial operations.
To leverage the latest FinTech solutions for the transportation industry, businesses find it critical to integrate their shipping data with their financials and vice versa. For example, FinTech providers now offer many new and improved financing options to shippers, carriers, 3PLs, and freight brokers. However, these FinTech solutions require good shipping data. For instance, the logistics industry increasingly uses digital freight matching (DFM) with real-time, dynamic pricing. Additionally, new transportation trends like carbon credits and reducing CO2 emissions significantly impact financial operations. In all these cases, FinTech solutions need good shipping data.
One of the biggest challenges with the financial supply chain is having a Single Source Of Truth (SSOT) that businesses can trust. This is especially true with shipping data. Indeed, countless financial questions arise about shipping operations. For example:
Types of Financial Questions That Need Shipping Data
- Is the carrier’s invoice approved?
- Do I have enough info about the shipment to authorize payment?
- What is the best way to allocate shipping costs for accounts payable?
- Did the carrier that is requesting payment actually deliver the shipment?
- Am I being double billed?
- Is there fraud involved where I’m paying for a shipment that was never shipped?
Indeed, supply chains cannot optimize their financials without good shipping data. For more detailed discussion on the challenges with transportation financial analytics, see my article, A Less Painful Way To Unlock Total Landed Cost Insights By First Fixing The Massive Disconnects In Supply Chain Data.
So, What Can We Do About The Poor State Of Our Shipping Data?
This article makes it clear that poor shipping data is detrimental to both shipping analytics and leveraging data-centric tech solutions. But, what can we do about it? Are there any solutions out there to improve our shipping data. Well, one solution that I recommend is to use a Universal Load ID to unify shipping data. For details on this solution, see my article, Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials.
Need help with an innovative solution to make your supply chain systems work together? I’m Randy McClure, and I’ve spent many years solving data interoperability and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in proof-of-concept and operational pilot projects for emerging technologies. If you’re ready to modernize your data infrastructure or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.
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Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.