Supply chain · Production

$100B+ technology company forecasts hardware sales with Ikigai

The problem

Tech Co. had an internal data science team but needed a third-party solution to improve demand forecast accuracy, forecast new SKUs with no historical sales data, and incorporate external event signals like holidays and seasonality.

Workflow diagram · grounded in source
1
Demand planning need identified
trigger
“Tech Co. had a top-notch team of internal data scientists, but they were still looking for a third party demand planning solution that would help them make better business decisions”
2
Granular demand forecasting
ai_action
“Created highly accurate demand forecasts for Tech Co.'s products at a granular level, including region, product category, and SKU”
3
External signals incorporated
ai_action
“Incorporated seasonality, holidays, and promotions into forecasts to uncover key drivers of demand”
4
New product similarity modeling
ai_action
“Implementing solution to enable demand forecasting for new products by modeling similarity between new products and existing products in Tech Co.'s portfolio”
5
12-month forecast output
output
“Enabled sales projections up to 12 months into the future, with transparent error rates available to increase trust from stakeholders”
6
Post-launch forecast refinement
feedback_loop
“Designing demand forecasts to be updatable upon launch of new products to allow for adjustments to forecasts pending receipt of real-world sales data”
Reported outcome

Ikigai created highly accurate demand forecasts at a granular level and enabled sales projections up to 12 months into the future with transparent error rates, while its New Product Introduction module extended forecasting to new SKUs without historical data.

Reported metrics
Demand forecast accuracyhighly accurate, decision-useful
Forecast horizonup to 12 months
Reported stack
Ikigai
Source
https://www.ikigailabs.io/case-study/technology-co-1
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Ikigai created highly accurate demand forecasts at a granular level and enabled sales projections up to 12 months into the future with transparent error rates, while its New Product Introduction module extended foreca…

What tools did this team use?

Ikigai.

What results were reported?

Demand forecast accuracy: highly accurate, decision-useful; Forecast horizon: up to 12 months (source-reported, not independently verified).

How is this supply chain AI workflow structured?

Demand planning need identified → Granular demand forecasting → External signals incorporated → New product similarity modeling → 12-month forecast output → Post-launch forecast refinement.

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