Identifying Data Misalignment Between Suppliers and Trading Partners

A fully stacked pallet leaves a warehouse at the scheduled time. The advance ship notice (ASN) was transmitted days prior to ensure clear communication. All parties involved believe the pallet is moving exactly as planned.

A few days later, the retailer’s dashboard shows a short quantity. The supplier system shows the order as complete. The carrier insists it was delivered in full. No one appears to be wrong but nothing lines up. This is what data misalignment looks like in the real world. The same physical shipment is interpreted in three separate ways by three different systems.

In this article, we’ll look at the key indicators and red flags that can hint at data misalignment as well as the tools and systems needed to future-proof your data.

Key Indicators and Red Flags of Supply Chain Data Misalignment

Data misalignment rarely appears as a single, obvious failure. More often, it surfaces through patterns. These issues tend to fall into three categories: operational symptoms, data quality issues, and performance problems. Understanding how each one shows up helps teams identify issues earlier and address the root cause instead of treating the symptoms.

Operational Symptoms

Operational symptoms are usually the first visible signs of misalignment. These are the day-to-day issues that teams feel immediately like missed time slots, confusing statuses, and extra steps of work required to ensure shipments move accurately.
One common operational symptom is repeated manual intervention. Teams may find themselves manually adjusting quantities/dates, resending ASNs, calling carriers to confirm delivery details, and cross-checking multiple systems to answer a single question. When normal workflows require constant overrides, it often indicates that systems are not properly communicating.

Data Quality Issues

While operational symptoms are what teams experience, data quality issues are often the underlying cause. These issues occur when the data itself is incomplete, inconsistent, or structured differently across platforms.

One of the most common issues is mismatched inventory quantities. Purchase orders, ASNs, and receiving reports may all show different unit counts due to:

• Case level vs. each level reporting
• Partial shipments
• Damaged or missing cartons
• Formatting errors

Ten items each vs. ten cases vs. ten pallets can equate to a difference of tens of thousands of dollars. Particular attention should be given during setup to ensure inventory quantities are reported cohesively across all systems.

Another frequent issue is inconsistent timestamps. Systems may record ship dates, delivery dates, and receipt dates differently depending on when transactions are updated. Without alignment on which timestamp matters, compliance reporting and disputing deductions can become complicated.

Missing or delayed data is also a major contributor to misalignment. Late ASNs, incomplete shipment confirmation, or delayed carrier updates can cause downstream systems to make assumptions that later prove incorrect.

Performance Problems

At this stage, data misalignment shows up in the retailer’s supplier scorecard, where performance metrics are tracked, scored, and used to assess supplier compliance.

Retailer scorecards may reflect:

• Late or incomplete shipments
• Poor fill rates
• Missed compliance thresholds
• Increased deductions

From a supplier perspective, the results may feel unfair or disconnected from reality. For example, a supplier may ship an order on time and in full based on their internal system, using the carrier pickup dates as the shipment timestamp. However, the retailer’s system may measure performance using the receipt date at the distribution center. If that receipt is delayed (even by a day), the shipment can be marked late on the supplier’s scorecard even without a breakdown in execution.

Disputes are another performance level indicator. When teams repeatedly submit supporting documentation only to have those disputes denied, it often signals that underlying data does not match what the retailer sees. Over time, these performance issues impact more than individual transactions. They can affect supplier reputation, limit growth, and strain the supply chain overall.

Related Reading: What is EDI (Electronic Data Interchange)?

How to Detect Supply Chain Data Misalignment Early

Spotting data problems does not require a full system overhaul, nor a forensic-level investigation. In many cases, it simply starts with alignment on definitions, knowing where to look, and what questions to ask. The most effective methods focus on connectivity, intentional (and consistent) reviews, and making the most of existing data.

Assessing System Connectivity

One of the fastest ways to detect data misalignment is to assess how well systems are connected and not just whether they are technically integrated.
On paper, suppliers, retailers, and 3PLs often appear fully connected. EDI transactions are flowing, portals are accessible, and dashboards are populated. But true connectivity goes far beyond data movement. It requires consistent timing and shared interpretations.
Warning signs of weak connectivity include:

• Data arriving late in one system and on time in another
• Status updates that do not trigger actions
• Transaction data that lacks detail
• Microshifting metrics (lead times extending by 1–2 days)

One useful test is to track a single order across systems in real time. Does the purchase order match the ASN? Does delivery data align with receipt records? Do confirmations update consistently? Did any issues arise?

If teams cannot follow the same transaction smoothly from system to system, connectivity may exist, but alignment does not.

Targeted Audits

Targeted audits are another effective method for detecting misalignment. The key here is focus. Teams should audit where issues are most likely to happen.
Strong candidates for targeted audits include:

• Orders with repeated shortages
• Orders with repeated overages
• Shipments tied to deductions and compliance fines
• New distribution center shipments

During these audits, the goal is not to assign blame. It is to compare how the same transaction appears in each system and identify where the story changes.

Often the root cause will become clear quickly. Missing timestamps, unit conversion issues, and delayed automated updates are usually found to be the source of misalignment problems.

Regular targeted audits help teams detect misalignment early, which then prevents the same issues from happening across hundreds of future orders.

Transaction Mapping

Another simple and effective detection method is transaction mapping. This approach is highly practical and immediately actionable. It involves laying out a transaction (order, shipment, and invoice) across every system it touches and comparing how it appears at each step.

When the same transaction tells a different story in different systems, misalignment becomes immediately visible. Transaction mapping helps teams ask critical follow-up questions about the data itself, including:

• What calculations are made with data?
• What processes are dependent on this information?
• Who interacts with the data?

These questions help teams identify not just where misalignment exists, but why it matters and which errors pose the greatest operational or financial risk.

Related Reading: Risk Audits for Your Supplier Business

Tools and Solutions that Prevent Data Misalignment Across the Supply Chain

Data will never tell you if it is wrong. Instead, there will be inconsistencies within it. Slight differences and variables might be noticed across orders, shipments, and invoices. The most effective solutions here focus on automation, visibility, and pattern recognition. These tools help teams reduce friction while making data easier to trust.

Automated Software

Automation software is often the first line of defense against misalignment, especially when paired with built-in reconciliation software. Instead of relying on people to compare systems manually, these tools continuously align data as it flows.

Reconciliation focused automation works by:

• Matching purchase orders, ASNs, invoices, and receipts
• Flagging mismatches in quantities, dates, or statuses
• Highlighting exceptions instead of requiring full data reviews

This approach shifts from reactive troubleshooting to proactive oversight. Rather than discovering issues weeks after the fact through deductions, teams can address them while shipments are still in motion.

Advanced Analytics

As data volume grows, patterns become harder to spot manually. This is where advanced analytics and machine learning add meaningful value. Machine learning models can analyze historical data across thousands of transactions to identify trends humans are more likely to miss, such as:

• Recurring discrepancies tied to a specific stock-keeping unit (SKU)
• Seasonal patterns in shortages or delays
• Correlation between data gaps and performance penalties

Rather than gaining insight into one issue, teams are likely to gain understanding around the systemic issues. This allows them to prioritize fixes that have the greatest impact across the business.

Over time, these analytics help organizations move from asking “what went wrong?” to understanding “why this keeps happening.”

AI-driven forecasting takes analytics one step further by helping teams anticipate misalignment before it happens. By learning from historical patterns, AI models can predict where future issues are most likely to appear.

Examples include:

• Flagging orders likely to generate a discrepancy based on past behavior
• Identifying shipments at a higher risk of being late
• Anticipating capacity and timing mismatches across partners

While no single tool solves all misalignment issues on its own, a combination of tools can help boost strategies with automation, advanced analytics, and a cohesive system.

Systems and Processes for the Future

Tools can help surface the misaligned data, but systems and processes determine whether it stays fixed or quietly returns. As supply chains grow more complex, future-ready organizations focus less on reacting to data issues and more on building repeatable ways to confirm, protect, and manage data across partners.

The work is not flashy or fun, but it is foundational. The strongest supply chains are built on clarity, consistency, and shared accountability.

Confirm the Data

Before data can be trusted, it must be confirmed. Data confirmation requires validating that what appears in one system truly reflects what happened in the physical world and that it matches what other partners see.

Confirm:

• Order details before fulfillment begins
• Shipment data as inventory moves
• Receipt and invoice data before payment

Confirming data early helps prevent assumptions. It also helps prevent the need for rework later, when discrepancies will be harder to resolve.

Teams that make confirmation a standard step (rather than a reactive one) catch misalignment closer to the source.

Protect Data Integrity

Data integrity is all about ensuring data remains accurate, complete, and consistent as it moves through the supply chain.

This requires paying attention to:

• Validation
• Consistent formatting
• Duplicate data

Without integrity safeguards, even well-connected systems can drift out of alignment. Over time, small issues compound and create confusion that dashboards can’t fully explain. Protecting data integrity is less about perfection and more about reliability.
Establish Benchmarks
Benchmarks provide context. They help teams understand what normal looks like, so exceptions stand out quickly.

Examples include:

• Typical timing between order, shipment, and receipt
• Expected variances in quantities or delivery windows
• Historical performance by partner, SKU, or season

With benchmarks in place, teams can detect misalignment sooner and prioritize issues that fall outside expected ranges. Instead of reacting to every discrepancy, they can focus on the ones that truly matter.

Create Clear Policies

Clear policies reduce confusion, especially when issues arise.

Policies should define:

• Which system is the system of record for each data point
• How discrepancies are investigated and resolved
• When data corrections are allowed and who approves them

When policies are unclear, teams will rely on tribal knowledge. This increases risk, especially as teams change and scale. Clear policies create consistency, reduce debate, and speed up resolution.

Define Data Ownership

One of the more overlooked aspects of data alignment is ownership. Every critical data piece should have a defined owner. The owner is responsible for accuracy, updates, and issue resolution. Without ownership, problems bounce between partners and often without resolution.
This doesn’t mean working in isolation. It means knowing who leads resolution when misalignment shows up and ensuring accountability does not get lost between systems.

Identify High-Risk Phases

Certain moments in the supply chain lifecycle are more vulnerable to misalignment than others. Identifying these high-risk phases allows teams to apply extra inspection when it matters most.

New partners, systems, and processes can introduce uncertainty. During onboarding, definitions, mappings, and workflows are actively being tested, often while teams are focused on forecasting, margins, and assortment planning. Extra validation during this phase helps prevent early issues from becoming long-term patterns. If the underlying supply chain data is not aligned during this phase, none of those downstream plans will hold. Extra validation during onboarding helps ensure foundational processes are sound before early issues harden into long-term patterns.

Proactive communication and testing are also essential during policy shifts. When regulations change, data requirements often change with them. New fields, formats, and reporting timelines introduce misalignment if systems are not updated and reviewed consistently.

During peak sales seasons, delays and capacity constraints become more likely. Teams that prepare for peak seasons by reinforcing processes and monitoring high-risk metrics reduce surprises when spikes occur.

Related Reading: How Suppliers Should Handle Seasonal Peaks with WMS

Future-Proofing with SPS Commerce

As supply chains evolve, future proofing is less about predicting every change and more about building a flexible, connected foundation that adapts as complexity grows.

SPS Commerce supports this by helping organizations standardize and align data across suppliers, retailers, and 3PLs.

With capabilities spanning assortment, analytics, and EDI certification, SPS Commerce helps ensure that data is not only flowing but flowing correctly. This alignment allows teams to move faster, resolve issues earlier, and scale operations with confidence.

When systems speak the same language and processes reinforce consistency, misalignment becomes an exception and not the norm.