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Product Management Root Cause Analysis Question: Investigating spike in support tickets for warehouse management system
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Nextsprints

Updated Jan 22, 2025

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What factors are contributing to the unexpected 30% spike in support tickets for Blue Yonder's Warehouse Management System in the last month?

Problem Solving Data Analysis Customer Support Strategy Supply Chain Logistics Enterprise Software
Root Cause Analysis Supply Chain Support Optimization Warehouse Management Blue Yonder

Introduction

The unexpected 30% spike in support tickets for Blue Yonder's Warehouse Management System (WMS) over the past month is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into product understanding, metric breakdown, and hypothesis generation. We'll then conduct a thorough root cause analysis, propose validation methods, and outline a comprehensive resolution plan.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • Looking at the timing, I'm thinking there might be a recent system update. Has there been any significant change or update to the WMS in the last 1-2 months?

Why it matters: Recent changes often correlate with support spikes. Expected answer: Yes, a major update was rolled out. Impact on approach: If yes, we'd focus on the update's features and potential bugs.

  • Considering user segments, I'm curious about the distribution of these tickets. Are we seeing this increase across all customer types, or is it concentrated in specific segments?

Why it matters: Helps identify if it's a universal issue or segment-specific. Expected answer: The increase is primarily in enterprise customers. Impact on approach: If segment-specific, we'd tailor our investigation to that user group.

  • Given the nature of WMS, I'm wondering about seasonality. Is this 30% increase unusual for this time of year, or do we typically see fluctuations in support tickets seasonally?

Why it matters: Rules out normal seasonal variations. Expected answer: This increase is atypical for the season. Impact on approach: If seasonal, we'd compare to historical data for context.

  • Thinking about the support process, has there been any change in how support tickets are categorized or processed in the last month?

Why it matters: Ensures the increase isn't due to measurement changes. Expected answer: No changes to the support ticket system. Impact on approach: If changed, we'd need to recalibrate our analysis based on the new system.

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