Case Study · Supply Chain & Manufacturing

Digital Twin-Powered Production Optimization

Manufacturing · Digital Twin · Scenario Simulation · Inventory Optimization · Dispatch

The challenge

Slow, reactive planning.

Multi-day manual planning cycle

Production scheduling required multiple teams and 3–4 days per supply cycle — blocking rapid response.

High sales losses from planning gaps

Poor stock availability and planning delays caused significant lost revenue across 850+ towns and cities.

No scenario simulation capability

Demand surges and operational disruptions had no structured simulation path — decisions were reactive.

Fragmented inventory & procurement

Inconsistent schedules led to last-minute procurement, idle capacity, and rising operational costs.

What we built

A real-time digital twin.

Digital twin simulation engine

Built a digital twin that simulates production capacity, live inventory levels, and regulatory permit constraints in real time.

End-to-end schedule optimization

Automated scheduling across production, packaging, dispatch, and permit workflows — 5+ plants, 850+ towns.

Forecast-integrated procurement planning

Demand forecasts directly linked to raw material procurement schedules — eliminating ad-hoc buying.

Data-backed planning decisions

Real-time simulation and planning dashboards enabling decision-makers to evaluate scenarios before committing.

Results

Quantified outcomes.

3–4d → 3h

Planning cycle compressed via the digital twin.

~25% → 10%

Sales loss rate reduced.

5+

Plants fully optimized & covered.

850+

Towns & cities in the distribution network.

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