Supply Chain BI Concept for Adidas By Anna Kudyba

Supply Planning Intelligence Cockpit

One version of truth.

Supply planning decisions become harder when demand signals, confirmed supply, inventory exposure, replenishment delays and data quality do not tell the same story. This concept shows how a BI cockpit could prioritize exceptions, explain root causes and support faster planning decisions.

Forecast accuracy 82.4%

Below target in Footwear / Europe

Confirmed supply coverage 91.7%

Healthy globally, uneven by region

Open high-risk exceptions 14

Concentrated in launch products

Inventory exposure CHF 42.8M

Aging risk in Apparel pockets

Avg replenishment delay 6.3 days

Driven by transport and allocation

Data quality score 87%

Late refresh still creates noise

01 / Supply vs demand

Strong supply plan helps demand follow the long-range planning.

Units

02 / Exception radar

Priority queue

Risk weighted
Priority Region Category Product family Exception Impact Risk Owner Recommended action

03 / Root-cause logic

Why does the red KPI exist?

Selected gap

Root-cause analysis of a red KPI proactively helps in a better planning.

04 / One version of truth

Data quality checks

Governance

A metric is trusted when the definition, owner, refresh logic and validation rule are clear.

05 / Stakeholder adoption

Same data, different decisions

Supply Planning

Exception queue, supply gaps, replenishment risk

Finance

Inventory exposure, working capital, risk value

Market Operations

Availability risk, delay impact, action owner

Leadership

Top risks, trend, decision needed

06 / From dashboard to adopted capability

Good BI becomes an operating routine.

KPI definition agreed Source tables confirmed Refresh logic validated UAT completed with pilot users Documentation published Exception ownership agreed Monthly feedback loop active

Good BI is a whole operating routine around the reports and dashboards.