By Patrick Harkanson, Business Development Manager
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Supply chains today are more complex than ever. Global sourcing, omnichannel fulfillment, and rising customer expectations have forced organizations to move faster while managing greater operational risk. At the same time, companies are investing heavily in analytics, automation, and AI to improve efficiency.
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Yet many supply chain leaders continue to face the same persistent challenges: unexplained shrink, inventory discrepancies, delayed shipments, and operational blind spots that make problems difficult to diagnose.

In many cases, the issue isn’t a lack of effort or technology. It’s a lack of visibility into what is actually happening to items as they move through the supply chain.|
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Here are five common mistakes that continue to hold supply chain teams back.
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1. Confusing Reports with Visibility
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Many organizations believe they have supply chain visibility because they receive dashboards or daily operational reports. While those tools provide useful insights, they typically show what has already happened — not what is happening right now.
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If data updates once a day, teams are still reacting to yesterday’s information. In fast-moving supply chains, that delay can hide problems like shrink, misrouted products, process errors, and inventory mismatches.
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True visibility means being able to answer simple operational questions quickly:
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• Where is this item right now?
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• When was it last moved or handled?
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• Which location or process step touched it last?
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Without timely signals from the physical world, supply chain teams are forced to make decisions based on incomplete or outdated information.

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2. Optimizing Around Averages Instead of Reality
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Supply chains are often managed using averages — average transit times, average dwell times, average shrink, or average inventory accuracy. These metrics are useful for planning, but they often hide the issues that create the most disruption.
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Consider a distribution network where most shipments move between facilities in two days, but a small percentage consistently take four or five days due to routing errors or handling delays. The overall average may still look acceptable, even though those delays create service failures and frustrated customers.
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A similar pattern appears in inventory accuracy. A facility may report strong overall accuracy while still experiencing frequent stockouts because certain products repeatedly fall out of sync with system records.
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Without item-level visibility, these outliers are difficult to detect. They disappear inside aggregated metrics, making it harder for teams to identify where operational processes are breaking down.
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3. Treating Shrink as “Just Part of Retail”
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Shrink is often viewed as an unavoidable cost of doing business, particularly in retail. While theft certainly plays a role, a significant portion of shrink originates from operational visibility gaps.
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Products can effectively disappear when containers move through facilities without being scanned, pallets are broken down incorrectly, or items are misrouted between locations. Over time, these small process failures accumulate into meaningful inventory losses.
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The challenge is that once items go missing, many organizations have no reliable way to determine where the breakdown occurred. The last recorded scan may show a product arriving at a distribution center, but offer no insight into what happened afterward.
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Without a clear record of where items were last handled, investigations quickly become guesswork. As a result, many companies simply absorb the loss instead of addressing the operational issue behind it.
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4. Investing in AI Before Fixing Data Capture
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Artificial intelligence is generating enormous excitement across the supply chain industry. Many organizations are exploring AI-powered forecasting, predictive analytics, and automated decision-making tools.

But AI systems are only as reliable as the data they ingest.
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If item-level data is incomplete, delayed, or inaccurate, AI cannot correct those gaps. Instead, it often amplifies them — producing confident predictions based on an incomplete view of what is happening in the physical world.
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Before investing heavily in advanced analytics, organizations must ensure they are accurately capturing what is happening to physical goods throughout their supply chains. Reliable data collection is the foundation that AI systems depend on.
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5. Accepting Manual Workarounds as Normal
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Despite increasing automation, many supply chain operations still rely heavily on manual work behind the scenes.
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Teams often use spreadsheets to reconcile inventory discrepancies, long email threads to manage operational exceptions, or experienced employees who know how to navigate system limitations.
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These workarounds usually develop gradually. When systems fail to capture certain events or provide incomplete data, employees create manual processes to fill the gaps.
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While these solutions keep operations running, they introduce long-term risk. Processes become dependent on individual knowledge rather than scalable systems. As operations grow more complex — or key employees move on — maintaining consistency becomes much more difficult.
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The Solution: Ambient IoT
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Across all of these challenges, the underlying issue is the same: most supply chains still operate with limited visibility into what is actually happening to individual items.
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Systems track shipments, pallets, and inventory records, but they often struggle to capture real-time signals from the physical world. As a result, teams are forced to rely on delayed updates, incomplete scans, and manual workarounds to understand where products are and how they’re moving.
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That visibility gap is exactly what emerging technologies like Ambient IoT are designed to solve.
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Ambient IoT sensors embedded directly into products or packaging — such as Wiliot IoT Pixels — can continuously capture signals from individual items as they move through warehouses, transportation networks, and stores. Those signals are then transmitted to cloud platforms, like the Wiliot Physical AI Platform, where physical-world data is transformed into dependable, real-time insights about how goods are moving and interacting throughout the supply chain.
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With that level of visibility, many of the mistakes outlined above begin to disappear. Teams can move beyond static reports and averages, investigate shrink with confidence, reduce reliance on manual workarounds, and build AI systems on top of accurate, real-world data.
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Supply chain leaders who can truly see how goods move through their networks will be far better positioned to detect problems earlier, reduce losses, and make smarter operational decisions.
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If your organization is experiencing any of the challenges discussed here — from unexplained shrink to unreliable inventory data — it may be time to rethink how visibility is captured across your supply chain. Ambient IoT and Physical AI are making it possible to understand the physical world in ways that were never before achievable.