Chapter 3: How to Read ABC Analysis Results

Introduction

The importance of "permissible error" and "general overview" in logistics data analysis.

• Data Fluidity: Numerical values in logistics (shipment volumes and order counts) constantly fluctuate due to seasons, days of the week, or campaigns; therefore, one should consider that there is no "fixed correct answer" as an exact numerical value.

• 10-20% Error is an "Acceptable Range": Rather than fixating on overly precise calculations, it is practically important to grasp general trends, treating differences of about 10-20% as margin of error.

• "Direction of Judgment" Over "Precision": The essence of analysis is to capture the general outline to inform decision-making, such as determining if something belongs to Group A or B, rather than worrying about single-digit precision.

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Advice

When designing distribution centers or improvement plans, being overly bound by minute figures risks creating an inflexible system. As noted, the secret to effective planning is to grasp the overall structure with "about 80% certainty".

I. How to Read EQ ABC Analysis Results

The differences are summarized below:

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IQ vs. EQ Analysis: Differences in Interpretation and Logic

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Concrete Examples of "Answers"

• Response to "A" in IQ Analysis: A "slotting" answer emerges, such as: "Since this item moves frequently, let's place it on the most accessible flow racks instead of the automated warehouse."

• Response to "A" in EQ Analysis: An "operational" answer emerges, such as: "Since this customer orders in large quantities daily, let's ship using dedicated pallets" or "let's schedule them at the very beginning (or end) of the delivery route."

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Summary

• Danger of Averages and Grasping Max/Min: Order volumes range widely (e.g., average 140 vs. max 365 and min 8); planning based solely on averages is dangerous. It is crucial to estimate max/min values if unknown.

• Grasping Concentration via Thresholds (50%/80%): Identify workload bias by seeing how many customers account for 50% or 80% of volume. For example, Q80=E50 means 50% of customers account for 80% of volume.

• Classification by ABC Groups (80-15-5% Rule): Divide volume into 80% (A), 15% (B), and 5% (C) and verify the number of customers in each.

• Importance of Period Analysis: EQ analysis on monthly cumulative data (not just daily) clarifies center-specific characteristics.

II. Interpretation of EQ Analysis

• Packaging Inference from Max Values: If max order is 365 cases (~15.2 pallets), it implies a mix of pallet and case orders. Few SKUs are expected for such large orders.

• Identifying Shipping/Storage Forms: For large orders, both "P⇒P" (pallet shipping) and "P⇒C" (case shipping from pallet storage) are expected.

• Selecting Optimal Picking Methods: Single Picking is efficient for customers with such large volume orders.

• General Overview of Center Characteristics: If most orders are >1.5 pallets, the center has low-variety, high-volume characteristics.

III. How to Read IQ ABC Analysis

• Shipping Ranges and Average Limits: Shipment volumes fluctuate greatly (e.g., average 51 vs. max 267); designing based only on averages is risky. Min values are often "1" when variety is high.

• Grasping Concentration via Thresholds: Understand that a small % of SKUs often account for 50% or 80% of volume. For example, Q82=I24. Generally, ~20% of SKUs account for ~80% of volume.

• ABC Group Classification: Divide shipments into 80% (A), 15% (B), and 5% (C) groups.

• Storage/Picking Equipment Inferred from Data:

  • High-Volume (A-Rank): Floor stacking is suitable for items reaching 22-30 pallets at peak.
  • Medium-Volume: Items shipping ≥1 pallet at peak (approx. 20 types expected).
  • Low-Volume: For <1 pallet shipments, consider pallet flow racks or pallet racks for "P⇒C" operations.