Supply chain · Production

Leading industrials distributor reduces potential stockouts by $27M+ with Ikigai demand and inventory forecasting

The problem

Distribution Co.'s suppliers required lead times far exceeding customers' order horizons, leaving the company with no forecasting solution and relying on manual spreadsheets and terminal queries to manage inventory, resulting in recurring overstocks and stockouts that eroded profit margins.

Workflow diagram · grounded in source
1
ERP data integration
integration
“Integrated Ikigai's solution with Distribution Co.'s ERP to ensure data freshness”
2
Granular demand forecasting
ai_action
“Forecasted demand at a granular level (split by individual product and geographical division) to create 2 years of advance visibility into expected orders”
3
Stockout alert recommendation
ai_action
“Developed stockout alerts to proactively recommend what products to order, when to order those products, and to which facilities to send those products”
4
Stakeholder dashboard
output
“User-friendly dashboard allows key stakeholders to quickly and easily understand state of inventory & anticipated demand”
Reported outcome

Ikigai's demand and inventory forecasting reduced potential stockouts by $27M+ and identified tens of millions of dollars of excess inventory, giving Distribution Co.
2 years of advance demand visibility with monthly updates and dynamic alerts to guide ordering decisions.

Reported metrics
Potential stockouts reduced$27M+
Excess inventory identifiedtens of millions of dollars of excess inventory
Demand forecast visibility horizon2 years
Forecast update frequencymonthly updates
Reported stack
IkigaiaiCastERP
Source
https://www.ikigailabs.io/case-study/distributor-1
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Ikigai's demand and inventory forecasting reduced potential stockouts by $27M+ and identified tens of millions of dollars of excess inventory, giving Distribution Co.

What tools did this team use?

Ikigai, aiCast, ERP.

What results were reported?

Potential stockouts reduced: $27M+; Excess inventory identified: tens of millions of dollars of excess inventory; Demand forecast visibility horizon: 2 years; Forecast update frequency: monthly updates (source-reported, not independently verified).

How is this supply chain AI workflow structured?

ERP data integration → Granular demand forecasting → Stockout alert recommendation → Stakeholder dashboard.