From my perspective, I’ve seen firsthand how poor shipping data hobbles both supply chain managers and their attempts to leverage the latest automation. Without a doubt, the complexity of shipping data with its fragmented and inaccurate information, makes it difficult for supply chains to gain visibility and actionable analytics. In this article, I’ll identify the four main challenges with shipping data. Also, I’ll look at how emerging technologies like AI and advanced analytics can help overcome these obstacles, driving better insights and business outcomes.

1. Disjointed Functional Silos Results in Isolated Data, Restricting Shipment Visibility.

A major challenge for shippers is that their various internal organizations are functionally disjointed when it comes to shipping analytics. Indeed, supply chains as a whole have a limited view of shipping data as it is localized and isolated within functional silos. To illustrate:

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• 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 the data challenges with enterprise systems, see my article, Agile Supply Chain Decision-Making: First You Need to Know The Truth About Enterprise Software.

“Visibility emerges when data is connected.”

Grant Sernick

2. Massive Amounts Of Fragmented, Ambiguous Shipping Data With Few Insights.

It is a fact that most companies have better visibility over their purchase orders and product-level data than their 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. On the other hand, shipping data is routinely incomplete and out-of-date. See below for more examples.

a. Data is Fragmented Across the Supply Chain Limiting Visibility and Insights.

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Indeed, regardless of how we query shipping data, the results often prove unsatisfactory. For example, below 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, disjointed, and hard to find. For more information analyzing supply chain data across multiple functional areas, see my article, Multi-Hop Reasoning For Supply Chains: This Is The Way To Make Better Decisions And Avoid Unintended Consequences

b. Data Ambiguity Exists Because Supply Chain Stakeholders Have Different Interpretations of Key Business Terms.

Additionally, it is common for supply chain stakeholders to have different interpretations of shipping terms. As a result, data transferred is 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

A Refocus on Supply Chain Glossaries: The Best Way To Unlock Data Interoperability, Strengthen Collaboration And Leverage Tech

3. Supply Chain Management And Automation Limited Due To Complicated Shipping Data Structures.

The rise of e-commerce has made shipping a critical customer-facing operation. As a result this increases the demand for shipping analytics to optimize operations and identify systemic issues. However, shipping data is scattered across various systems, including order fulfillment, TMS, carrier tracking, and financial systems, making it challenging to link together and analyze. The root cause of many of these shortcomings are inferior supply chain data structures that hinder both supply chain manager and automation. The complexity of these data structures is rooted in several key factors, which are discussed below.

Reasons Why Shipping Data Structures Are Complex

Data-Centric Advice to Reduce IT Complexity and Make Tech Remarkably More Useful

• Complex and Conflicting Data Models Prevent an Unified View of Shipping Data.
• Duplicate Data Complicates Data Relationships and Reliability Across the Enterprise.
• Critical Data Is Not Prioritized and is of Low Quality.
• Bloated Software Code Base Makes Data Dependent on Software.
• Application-Centric Software Solutions Makes Data a ByProduct.
• Data Security Policies Implemented at Application Level Isolates Enterprise Data.
• Too Many Custom Data Integrations Leads to Brittle Digital Structures Prone to Breakage.
• Lack of Mutually-Agreed Business Terms Disjoints and Results in Dumbed-Down Data Structures.

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. For more information on reducing data complexity, see my article, Data-Centric Advice to Reduce IT Complexity and Make Tech Remarkably More Useful.

4. Shipping Data Is Not Well Linked to Financials, Hindering Effective FinTech Solutions.

Also, the quality of shipping data significantly impacts financial operations. Without a doubt, fragmented data complicates comprehensive financial analysis.

a. FinTech Solutions Need Good Shipping Data.

For instance with the emergence of FinTech solutions like digital freight matching and carbon credits, accurate shipping data is crucial for accessing, analyzing, and extracting valuable insights. Also, other emerging financial offerings such as real-time dynamic pricing need high-quality data. Moreover, FinTech providers now offer many new and improved financing options to shippers, carriers, 3PLs, and freight brokers. Lastly, finance departments need accurate shipping data for calculating Total Landed Cost. In all these cases, FinTech solutions require good shipping data.

A Less Painful Way To Unlock Total Landed Cost Insights By First Fixing The Massive Disconnects In Supply Chain Data

b. FinTech Solutions Need a Single Source Of Truth.

Also, 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:

The Best Ways To Make Your Freight Bill Audit Process Better Benefit Your Shipping Operations

Types of Financial Questions That Need Shipping Data

• Did the audit department approve the carrier’s invoice?
• 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?
• Is the carrier double billing?
• 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.

Final Thoughts.

This article makes it clear that poor shipping data is detrimental to both shipping analytics and leveraging data-intensive tech solutions. But, what can we do about it? Yes, there are solutions out there that can improve our shipping data. Below are links to Insights’ and solutions that can help.

• The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status
• Reactive Shipment Tracking Vs Proactive Operational Visibility: Here Is How To Best Overcome Supply Chain Exceptions
• Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials.

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.

For more articles from Supply Chain Tech Insights, see the latest topics on data, shipping, and finance.