5.8 Million Outlets. One Inventory View.

Leading Indian FMCG company × Experidium · Hair & personal care · Edible oils · Foods

How Experidium connected a leading Indian FMCG company's three-tier distribution network to identify, quantify and test inventory decisions before stock moves.

The scale behind the problem

The company's India distribution network operates at a scale where a small inventory mismatch can propagate across multiple layers of the network.

Its published disclosures describe a distribution network of more than 7,500 distributors covering over 5.8 million retail outlets, supported by 24 depots and 6 redistribution centres. The network reaches more than 60,000 villages and almost every Indian town with a population above 5,000.

And the network continues to evolve. The company has outlined a three-year roadmap to increase direct reach from approximately 1 million to 1.5 million outlets, while using technology, analytics and supply-chain efficiencies to support the expansion.

At this scale, inventory planning isn't simply about knowing how much stock exists. It's about knowing:

The challenge

A stockout does not always mean "send more stock."

Consider a simple three-tier network:

1. DepotCan be holding excess stock even when downstream demand is healthy.
2. DistributorCan be holding excess inventory of exactly the SKU its retailer is short on.
3. RetailerCan be below target — or seeing demand accelerate faster than the norm planners use.

Three situations. Three different decisions. A traditional stock report can show all three as an exception.

It doesn't necessarily tell the planner what to do next.

One network. Three tiers. Five data sources.

Experidium created a connected view of the distribution network across:

1. 01 — DepotUpstream inventory and replenishment position
2. 02 — DistributorIntermediate inventory, demand aggregation and downstream coverage
3. 03 — RetailerActual market demand and shelf-level inventory position

The intelligence layer combines 5 operational data sources:

Data source	What it contributes
Network master	Who supplies whom
SKU master	Product, cost and price
Inventory	On-hand stock and target norms
Demand	Weekly consumption
Orders	Replenishment moving through the network

Retail demand rolls upward through the network:

1. Retailer demandIts own weekly consumption
2. Distributor requirementSum of its retailers
3. Depot requirementSum of the retailers under it

This allows planners to see the inventory position as a connected system rather than isolated locations.

3 inventory problems. 3 different actions.

Experidium classifies inventory exceptions into three primary patterns.

From red cells to rupees

Not every inventory exception deserves equal attention. Experidium ranks the imbalance by inventory value, rather than simply displaying another red/amber/green dashboard.

The calculation

Inventory imbalance = excess / shortage units × unit cost = ₹ impact

Every exception carries its evidence:

• On-hand quantity
• Target inventory
• Days of cover
• Demand trend
• Unit cost
• Rupee impact
• Recommended action

The exact financial value is calculated from the underlying inventory and SKU data rather than using a generic percentage estimate.

The operating model

The inventory bands

The modeled network uses tier-specific cover targets.

Inventory health is then evaluated against the relevant tier target:

This gives the planner a common language for identifying where inventory is under pressure and where excess is accumulating.

A week inside the North network

The modeled scenario follows:

1. 2 depotsNorth and South
2. 4 distributorsTwo under each depot
3. Multiple retailersUnder each distributor

across a core basket including:

• Coconut oil
• Edible oil
• Oats
• Hair care
• Control SKU

Three exceptions emerge:

1. A retailer is short on coconut oil while its distributor holds excess.Decision: reposition.
2. The North depot is holding the flagship edible oil at approximately 2.2× norm.Decision: reduce upstream exposure and move excess.
3. Oats demand has accelerated across the recent 10-week window, but the target remains based on the older run rate.Decision: raise the norm and catch up.

The planner doesn't need to rebuild the analysis in another spreadsheet.

What if the situation changes?

Inventory decisions rarely happen under static conditions. That's why Experidium puts 6 scenario levers on the same screen.

The numbers change before the stock does

A planner can test:

• Demand +10%
• Demand +20%
• Norm −3 days
• Norm +5 days
• Dispatch +2 days
• Stock −X units

and immediately see how the change propagates through the network. The system identifies:

• Which node breaks first?
• Which SKU is affected?
• How many days until stockout?
• Where does the imbalance move?
• What intervention resolves it?

This is the difference between a dashboard and a decision layer.

From exception to action

The workflow becomes five steps:

1. FindIdentify the node or SKU that is out of range.
2. ExplainDetermine whether the problem is positioning, trapped inventory or norm lag.
3. QuantifyMeasure the imbalance in units and rupees.
4. SimulateTest the impact of a demand change, norm change, stock correction, lead-time issue or dispatch delay.
5. ActReposition inventory, change the norm or initiate replenishment.

What changed?

The bigger picture

The company's distribution network is already one of its core competitive advantages, with 5.8 million outlets reached and an ongoing push to expand direct reach from approximately 1 million to 1.5 million outlets.

At this scale, better inventory decisions are not about optimizing one warehouse or one SKU. They are about understanding the relationships between thousands of inventory positions across the network.

Experidium's role is to turn those relationships into a decision layer:

1. See the network.
2. Find the imbalance.
3. Quantify the exposure.
4. Test the intervention.
5. Move with confidence.

5.8M+ outlets. 3 tiers. 1 connected inventory view.