Overview
Completing a cycle count is only half the process. The true value comes from analyzing the results to understand your inventory accuracy, identify the root causes of discrepancies, and monitor the performance of your team. This guide will walk you through the key reports and screens in Logiwa IO that allow managers to turn count data into actionable insights.
Using the Count Plan Details Screen for Analysis
The primary screen for analyzing a specific count is the Count Plan Details screen. It provides a granular, line-by-line breakdown of what was expected versus what was physically counted. This is where managers can pinpoint discrepancies and understand the state of their inventory.
To access it, navigate to the Count Plan screen, select a completed plan, and go to the Location Details or SKU Details tab.
Key Columns for Analysis
Understanding these columns is crucial for accurate inventory analysis.
Initial Stock Qty: This shows the system's inventory quantity at the exact moment the count job was created. It is the baseline for what the system believed was in stock before the physical count began.
Counted Qty: This is the exact physical quantity that the warehouse operator submitted through their mobile device during the count. This value represents the "ground truth" at the time of the count.
Approved Stock Qty: This is an inventory snapshot taken at the moment the inventory is updated. If other warehouse operations (like picking or transfers) occurred in the counted location after the count was submitted but before it was approved, this value will reflect those changes. This is critical for preventing data errors when Lock Locations is disabled.
Approved Count Qty: This is the adjusted value of the Counted Qty. The system intelligently amends the initial counted quantity to account for legitimate inventory movements that happen between the time of the count and the final inventory update. This prevents errors like duplicating stock adjustments.
Difference (%): This column provides the clearest indicator of a discrepancy. It calculates the percentage difference between the Approved Stock Qt
yand the Approved Count Qty. A high percentage here immediately draws attention to significant inventory inaccuracies.
Understanding Discrepancies: End-to-End Examples
Example 1: Basic Under-Count Scenario (Locations Locked)
In this scenario, Lock Locations is enabled, so no inventory movements can occur during the count process.
Initial State: The system shows 50 units of SKU-A at Location-01.
Count Execution: The operator physically counts only 48 units and submits this quantity.
Review Screen Analysis:
Initial Stock Qty: 50
Counted Qty: 48
Approved Stock Qty: 50 (No changes occurred because the location was locked).
Approved Count Qty: 48 (No adjustments were needed).
Difference (%): 4.0%
Result: The manager sees a 4% discrepancy. Upon approving and completing the count, the system will adjust the inventory for SKU-A at Location-01 down to 48 units, reflecting the physical reality.
Example 2: Complex Over-Count with a Post-Count Shipment
This scenario demonstrates how Logiwa IO maintains accuracy even when other operations happen after a count is performed. In this case, Lock Locations is disabled.
Initial State: The system shows 20 units of SKU-B at Location-02.
Count Execution: The operator physically counts 22 units (an over-count of 2) and submits this quantity.
Post-Count Movement: Before the manager approves the count, a high-priority sales order is allocated, and an operator picks 5 units of SKU-B from Location-02.
Review Screen Analysis:
Initial Stock Qty: 20
Counted Qty: 22
Approved Stock Qty: 15 (The system correctly adjusts the initial stock: 20 units - 5 units picked = 15).
Approved Count Qty: 17 (The system intelligently adapts the physical count. It knows there were 22 units, but since 5 were legitimately picked, the expected final quantity should be 17).
Difference (%): 11.76% (Difference between 17 and 15).
Result: The manager sees that a shipment occurred after the count. Upon completing the count, the inventory for SKU-B at Location-02 will be set to 17 units. This is the correct physical quantity (22 units were there, 5 were picked, leaving 17).
Example 3: Under-Count with a Post-Count Receiving Event
This example shows how the system handles inventory being added to a location after the count is submitted.
Initial State: The system shows 30 units of SKU-C at Location-03. Lock Locations is disabled.
Count Execution: An operator physically counts only 28 units (an under-count of 2) and submits the count.
Post-Count Movement: Before approval, a new purchase order is received, and a putaway task adds 10 more units of SKU-C to Location-03.
Review Screen Analysis:
Initial Stock Qty: 30
Counted Qty: 28
Approved Stock Qty: 40 (30 initial + 10 received).
Approved Count Qty: 38 (28 counted + 10 received).
Difference (%): 5.0% (Difference between 40 and 38).
Result: The system correctly identifies the final physical quantity should be 38. The final inventory adjustment will set the stock to 38, accurately reflecting both the original discrepancy and the subsequent receiving event.
Example 4: "Perfect" Count with a Post-Count Movement
This example demonstrates how the system prevents "false" discrepancies caused by legitimate warehouse operations.
Initial State: The system shows 100 units of SKU-D at Location-04. Lock Locations is disabled.
Count Execution: An operator counts exactly 100 units and submits a perfect count.
Post-Count Movement: Before the manager reviews the count, an operator picks 10 units for a shipment.
Review Screen Analysis:
Initial Stock Qty: 100
Counted Qty: 100
Approved Stock Qty: 90 (100 initial - 10 picked).
Approved Count Qty: 90 (100 counted - 10 picked).
Difference (%): 0.0%
Result: The manager correctly sees a 0% difference. The system's logic confirms the count was accurate and the subsequent pick was processed correctly, preventing any confusion.
Connecting Count Plan Details to Task Details
For an even deeper dive, managers can navigate from the Count Plan Location Details screen to the Count Task Details screen. A hyperlink on the Job Code column provides direct navigation. The columns on these two screens are directly related, providing a consistent view of the data.
Here is how the key columns map between the two screens:
Count Plan Location/SKU Details Column | Corresponding Count Task Details Column |
Initial Stock Qty | Initial Task Pack Qty |
Counted Qty | Initial Task Approved Pack Qty |
Approved Stock Qty | Approved Task Pack Qty |
Approved Count Qty | Approved Pack Qty |
Tracking Adjustments in the Transaction History Report
After a count is completed and approved, the system makes an inventory adjustment. The Transaction History Report provides a permanent audit trail of these changes.
How to Use the Report for Count Analysis:
Navigate to the Transaction History report.
Add a filter for Transaction Type and select Count.
This will display all inventory changes that resulted from a cycle count.
The Action Pack Quantity and Action UOM Quantity columns will show how the inventory was adjusted. A positive number indicates that inventory was added, while a negative number indicates that inventory was removed to match the physical count.
The Transaction User column shows which supervisor approved and completed the count, providing accountability for the changes.
This report is essential for understanding the financial impact of inventory adjustments and for maintaining a clear record of all changes made due to cycle counts.
Monitoring User Performance
To track the efficiency and productivity of your staff during counting operations, use the User Performance Report. This report aggregates data to show how much work each user is completing.
Learn more about this report: User Performance Report
Key Columns for Count Performance:
Counted Item Qty: This column displays the total number of individual items (in UOM) that a user has counted within the selected time frame.
Counted Location Qty: This shows the total number of distinct locations a user has successfully completed counts for.
By reviewing these metrics, managers can identify top-performing employees, spot potential needs for additional training, and ensure that counting tasks are being completed efficiently across the team.
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