inventory inaccuracy is a structural problem, not accidental
errors accumulate between each physical inventory count
A structural problem, not accidental
ScienceDirect (2016) publishes an in-depth analysis of the structural causes of inventory inaccuracy in retail and supply chain. The authors emphasize that inventory inaccuracy is not an isolated incident but a recurring symptom in organizations maintaining manual processes. They identify several aggravating factors: movement frequency, product diversity, staff turnover, and the absence of automatic confirmation.
How errors accumulate
Eureka Publications (2025) explains the accumulation mechanism: every movement not confirmed by a scan is an opportunity for discrepancy. Over a month of normal activity in an SME warehouse, this represents dozens to hundreds of operations. Even with a 1% error rate, 100 operations per day statistically generate one error per day. Over a quarter, that's 90 errors that have silently accumulated in the system.
The degradation cycle
iDrive Logistics describes how errors become self-sustaining. Step 1: an error generates an unanticipated stockout. Step 2: the stockout forces an unplanned urgent order. Step 3: the urgent order disorganizes the receiving schedule. Step 4: disorganization generates more errors during receiving. The cycle is closed. Without intervention on the process, the problem mechanically worsens.
Impact on productivity and performance
SciELO (2020) quantifies the operational impact: inventory errors reduce team productivity (time spent searching for products indicated as present but absent), increase lost sales (unanticipated stockouts), and degrade overall warehouse efficiency. The authors conclude that resolving this problem requires improving data collection processes, not increasing headcount.
The negative cycle of inventory errors is a problem that doesn't resolve with more work but with better processes. A WMS that requires scan confirmation at each operation breaks this cycle at the source: every movement is validated in real time, errors are detected immediately, and system data stays synchronized with physical reality.
This summary is a free reformulation of works published by ScienceDirect (2016), Eureka Publications (2025), iDrive Logistics, and SciELO (2020), created for informational purposes. Key ideas are attributed to the cited authors. Consult the original sources for complete analyses.
Original sources
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Improving Warehouse Inventory Accuracy: The Bedrock of Warehouse Productivity
Michael Badwi, SC Junction, 2023
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