Discussion: Target Canada (Due in 24h)

Case: Target Canada

Empty Shelves, Full Stockrooms:

The Target  Links to an external site.

Canada Lesson Links to an external site.

 

Synopsis

In 2013, a major U.S. retailer launched more than a hundred stores in Canada. Within months,

shoppers found empty shelves, mismatched price labels, and inconsistent assortments—even as

distribution centers and backrooms reportedly held plenty of product. Leadership had deployed new

inventory systems and dashboards but relied on high-level averages and rollout milestones, not the

descriptive basics that reveal floor reality (e.g., shelf availability, item setup completeness, exception

rates). The venture was closed less than two years later, becoming a cautionary tale about how poor

descriptive visibility and data quality can sink strategy.

Real-world Anchor

Public accounts of the failure cite dirty product data (wrong units, incorrect dimensions, missing

codes), system integration problems, and untested process changes that produced empty shelves

despite inventory. Data accuracy in the new system was described as far below U.S. baselines, and

heavy manual fixes further eroded reliability.

Situation

· Company: Large U.S. big-box retailer entering Canada.

· Initiative: Launch >100 stores in under two years; implement new ERP and replenishment systems.

· What leaders saw (dashboards): rollout counts green; aggregate inventory looked adequate across DCs and store backrooms; average in-stock metrics suggested acceptable coverage.

· What customers saw (floor reality): empty shelves for staples; mislabeled prices and scan errors; backrooms and DCs with product not making it to the shelf.

· Reported root causes: data quality breakdowns (units, dimensions, tariff codes), category misclassifications; system integration issues; aggressive timelines that skipped basic descriptive checks like item data completeness and shelf fit.

Decision Moment

You are the VP of Operations four weeks after launch. Corporate dashboards still show "green"

averages, yet store managers report: "We have product; we can’t sell it." Your CEO wants a 90-day

recovery plan anchored in descriptive analysis—no advanced modeling—so leaders can see the real

problem and act.

Your Task (Conceptual, No Numbers)

Outline five descriptive artifacts (variables) and how you would use them to surface what’s broken and where.

Student Deliverable

A one-page memo titled “Make Reality Visible” listing the five artifacts with a clear definition and how each view informs an operational fix. Keep it conceptual—no calculations.

Discussion Prompts

· Why did “green averages” mislead leaders? What did those views fail to separate?

· Which descriptive views would have surfaced the crisis earliest? Explain your choices.

· What basic segmentation matters here (store, category, DC, label language/region)? Why?

· How can descriptive analysis drive a 90 day fix without algorithmic forecasting?