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An ice cream brand needing to test cold-chain reliability by tracking availability at pincode and time-of-day level across q-commerce platforms.

Industry
FMCG • Frozen / Ice Cream
Region
India — multi-city
Signal
Availability × pincode × time-of-day
Pincode
Level Granularity
Time-of-Day
Availability Windows
Cold-Chain
Real-World Test
Multi-City
Coverage

Key Takeaways

Actowiz tracked ice cream availability at pincode and time-of-day granularity across q-commerce, turning stock-out patterns into a practical test of cold-chain reliability. Availability was captured across defined day-parts — morning, afternoon, evening, night — for each pincode, so evening stock-outs and peak-demand gaps became visible and fixable. Because cold-chain performance is intensely local and time-dependent, a single daily snapshot or national number hides it entirely. The result was a concrete availability metric, by location and hour, that surfaced exactly where and when the client's cold chain was failing across multiple cities.

What did the client need?

The client is an ice cream brand for whom availability is a cold-chain problem as much as a distribution one. A product listed in the morning can vanish by evening as freezers, delivery capacity, and demand fluctuate — and national availability numbers hide this entirely.

Actowiz tracked ice cream availability at pincode and time-of-day granularity across q-commerce platforms, turning stock-out patterns into a practical test of cold-chain reliability by location and hour.

What made this hard?

  • Time-of-day volatility. Availability shifts through the day, so single daily snapshots miss the real picture — multiple day-part captures were needed.
  • Pincode-level truth. Cold-chain performance is intensely local, demanding pincode-level capture.
  • OOS as cold-chain signal. Out-of-stock had to be interpreted as an availability/cold-chain indicator, not just a listing state.
  • Multi-city scale. Patterns had to be captured across cities to compare cold-chain strength.
  • Consistent day-parts. Capture windows had to be consistent to make time-of-day comparisons valid.

How did Actowiz solve it?

Actowiz deployed a pincode-level, multi-time-of-day availability tracker across q-commerce platforms, modelling stock-out patterns as a cold-chain reliability signal.

Approach
  • Day-part capture. Availability captured across defined time-of-day windows (morning, afternoon, evening, night).
  • Pincode targeting. Location context set per pincode for granular local truth.
  • Availability modelling. In-stock/out-of-stock tracked per SKU per pincode per day-part.
  • Cold-chain read. Stock-out frequency and timing mapped to surface reliability gaps.
  • Multi-city rollup. City-level comparison of availability stability.
Data Attributes Extracted
Attribute Description
Brand / SKU Ice cream brand and product
Platform Source q-commerce platform
Pincode / City Location context
Time-of-Day Capture window
Stock Status In-stock / out-of-stock
Availability % Share of windows in-stock
Scrape Timestamp Date and time of capture

What were the results?

  • Cold-chain visibility. Availability by hour and pincode exposed where and when cold-chain broke down.
  • Time-of-day patterns. Evening stock-outs and peak-demand gaps became visible and fixable.
  • Location prioritization. Weak pincodes flagged for distribution and freezer-capacity action.
  • Availability as KPI. A concrete, trackable availability metric replaced anecdote.

Project at a Glance

Metric Value
Industry FMCG • Frozen / Ice Cream
Region India — multi-city
Signal Availability × pincode × time-of-day
Interpretation Cold-chain reliability test
Granularity Pincode × day-part
Cadence Recurring multi-window
Output Availability dataset / dashboard

Client Feedback

“Seeing our stock-outs by pincode and time of day was a wake-up call — it was really a cold-chain scorecard. We knew exactly which zones and which hours were letting us down.”

— Supply Chain Lead, Ice Cream Brand

Frequently Asked Questions

Q: How does availability test the cold chain?

A: Stock-out frequency by pincode and hour reveals where and when cold-chain capacity fails, beyond simple listing data.

Q: Why track time-of-day?

A: Availability shifts through the day; multi day-part capture exposes evening and peak-demand gaps a daily snapshot misses.

Q: What granularity is captured?

A: Pincode-level availability across defined day-parts, across multiple cities.

Q: How often is it refreshed?

A: On a recurring, multi-window cadence to build the availability timeline.

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