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Data · Analysis

Warehouse picking error heatmap

Cedar Warehouse plots picking errors by day and aisle to identify a location-specific operating problem. Aisle C has the strongest concentration, particularly midweek, while the other locations remain comparatively low. The visual gives a supervisor a focused starting point for checking slotting, labels or replenishment timing. Count errors consistently and consider displaying the number of picks as a companion measure, since a high-volume aisle can have more errors in absolute terms without having the worst error rate. Before sharing it, compare the chart with the source table and make the scope explicit. A clear date range, population and unit help another reader interpret the pattern correctly and decide whether follow-up analysis is needed.

種類 ヒートマップエンジン echarts-heatmap更新日 2026/9/24
Cedar Warehouse Picking Errors
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依頼内容

“Make a heatmap of Cedar Warehouse daily picking errors by aisle, showing Monday to Friday rows and Aisles A through E columns.”

次はこちらAdd a Saturday row.Highlight Aisle C as a review area.
この図に含まれるもの

図の背後にある意思決定を読み解く。

01Set the scope

Use one reporting period and one clear unit.

02Read the pattern

Identify the largest concentration or directional change.

03Check the source

Confirm the displayed values against the underlying data.

Use this heatmap when the stated values and labels are the information the reader needs to compare.

ヒートマップのテンプレートをすべて見る →

よくある質問

よくある質問

What does this heatmap show?

A heatmap uses colour to show the size of a value at the intersection of two categories. It helps readers spot concentrations, gaps and repeated patterns quickly.

How should I read this heatmap?

Choose a colour scale whose minimum and maximum match the data, and include cell labels when precise values matter to the decision.

Can I adapt this heatmap example?

Yes. Replace the title, labels and values with your own data, then check that the prompt still describes every important element in the chart.