Beat Planning and Expiry: Why Your Route Order Costs You ₹3 Lakhs a Year

The Route That Costs You ₹3 Lakhs a Year

Your salesman visits 30 retailers a day. He starts at 9 AM, finishes at 6 PM. Seems efficient. But here's what nobody tells you: the order in which he visits those retailers is quietly bleeding your margins.

Beat planning in FMCG distribution is treated as a logistics problem. Minimize distance, maximize coverage, reduce fuel costs. But when you're distributing products with shelf life—and in India, that's most FMCG—beat planning is also an expiry management problem.

The Hidden Cost of Route Inefficiency

Consider this scenario:

Your salesman has 50 cartons of a snack product that expires in 45 days. His beat covers:

10 high-velocity retailers (supermarkets, high-traffic kirana)
15 medium-velocity retailers (neighborhood stores)
5 low-velocity retailers (remote locations, specialized stores)

Inefficient route: Visits low-velocity retailers first (because they're closer to the depot), high-velocity retailers last.

Result: High-velocity retailers, who could have moved the stock quickly, get visited when the salesman is running low on inventory. They get partial deliveries or "next week" promises. Meanwhile, 20 cartons are sitting at low-velocity stores, moving slowly, aging toward expiry.

30 days later: Low-velocity retailers call for returns. Product is now 15 days from expiry. You accept returns (relationship preservation) or refuse (relationship damage). Either way, you lose.

The Velocity-First Principle

Smart FMCG distributors sequence beats by retailer velocity, not geography.

High-velocity retailers first:

They move stock faster
Fresh stock goes where it'll turn quickly
Higher chance of reorder within the month

Medium-velocity second:

Get second-tier freshness (still acceptable)
Sufficient time for normal sell-through
Order quantities adjusted based on their actual capacity

Low-velocity last:

Smallest quantities
Only products with longer shelf life
Or: skip entirely for short-shelf-life products

This sounds simple. In practice, it requires knowing actual velocity at each retailer—which most distributors don't track systematically.

The Data Problem

Ask a distributor: "What's the average days-to-sell at Retailer X for SKU Y?"

Most can't answer. They know who orders frequently. They have intuitions about "good" vs "slow" stores. But they don't have the data to make velocity-based routing decisions.

What you need:

Primary sales data (what you sold to retailer)
Secondary sales data (what retailer sold to consumer)
Time-to-reorder patterns
Category-wise velocity differences (same store might be fast for biscuits, slow for soaps)

What most distributors have:

Invoices
Gut feel
Complaints when things go wrong

The gap between those two lists is where expiry losses hide.

Seasonal Velocity Shifts

Retailer velocity isn't static. It changes with:

Seasons:

Summer: beverages, ice creams move faster
Monsoon: packaged foods spike, some categories slow
Festival periods: specific category surges
Post-festival: velocity crashes (everyone overstocked)

Local events:

Marriage season in the locality
School reopening
Factory shift changes affecting local demand

A beat plan that worked in January might be exactly wrong in June. Static route planning ignores this entirely.

Adaptive routing:

Reassess velocity data monthly
Adjust beat sequence for seasonal patterns
Have "surge" and "normal" routing modes

The Salesman Incentive Problem

Your beat plan is only as good as the salesman following it. And salesmen have their own incentives:

What salesmen optimize for:

Commission (volume sold)
Convenience (easy routes, friendly retailers)
Speed (finish early, go home)

What they should optimize for:

Stock rotation at each retailer
Appropriate quantities per store velocity
Returns prevention

Misalignment example:

Salesman has a target of 500 cartons/day. He can either:

A) Sell 50 cartons each to 10 appropriate retailers
B) Sell 100 cartons to 2 large retailers who'll take whatever he pushes

Option B is faster, hits target, earns commission. Option B also creates return liability in 45 days when those 2 retailers realize they over-ordered.

The Return Prediction Model

Here's a formula that actually works:

Return Risk Score = (Days in Store) / (Historical Days-to-Sell) × (Quantity Delivered / Historical Order Size)

Example:

Retailer typically sells 20 units of Product X in 15 days
You delivered 50 units 30 days ago
Return Risk = (30/15) × (50/20) = 2 × 2.5 = 5

A score above 3 = high return probability. Above 5 = near-certain return.

Use this to:

Flag at-risk stock before it's reported
Proactively offer schemes to move it
Adjust future delivery quantities

Beat Planning for Different Product Categories

Not all products should follow the same routing logic:

Short shelf life (< 60 days):

Biscuits, bread, dairy products
Velocity-first routing essential
Skip low-velocity stores entirely
Smaller, more frequent deliveries

Medium shelf life (60-180 days):

Most packaged foods
Velocity-first with flexibility
Low-velocity stores get modest quantities
Monthly velocity review

Long shelf life (> 180 days):

Personal care, home care
Geography-first routing acceptable
Efficiency over velocity
Quarterly velocity review

Mixed loads:

Separate by shelf-life category
Deliver short-shelf-life first
Don't subsidize convenience of long-shelf-life with returns on short

The Weekly Rhythm

Monday: Analyze previous week's delivery data

Which retailers sold through?
Which are holding excess?
Velocity changes from the week?

Tuesday-Thursday: Execute beats with adjusted quantities

High-velocity routes prioritized
Quantities matched to actual velocity
Flag any unusual orders for verification

Friday: Pre-weekend deliveries to high-velocity stores

Weekend is peak consumer buying
Fresh stock positioned for maximum turn

Saturday: Review returns and near-expiry alerts

Proactive calls to slow-moving retailers
Scheme offers to move at-risk stock
Next week's beat adjustments

Technology-Enabled Beat Optimization

Manual beat planning can't keep up with the data requirements. You need systems that:

Track velocity automatically:

Sales data aggregation
Reorder pattern analysis
Velocity scoring per retailer/SKU

Suggest routes:

Velocity-weighted routing
Seasonal adjustments
Real-time optimization based on stock position

Alert on risks:

"Retailer X has 45 units of SKU Y, hasn't reordered in 30 days"
"Delivery to Retailer Z would exceed 60-day sell-through capacity"
"Route A has 3 retailers with high return risk scores"

Enforce discipline:

Suggested order quantities visible to salesman
Approval required for quantities exceeding recommendations
Return prediction scores on handheld devices

The Distribution Partner Network

Your retailers are your partners in expiry management. They don't want returns either—returns mean cash flow problems, storage hassles, and relationship friction.

Help them help you:

Share velocity data (not just what you delivered, but what you know about their category)
Offer training on FEFO
Provide tools for tracking their own expiry

Build feedback loops:

Regular check-ins on what's moving, what's not
Early warning when they sense a slowdown
Joint planning for seasonal shifts

Align incentives:

Discount structures that reward sell-through, not just purchase
Return policies that don't penalize honest communication
Recognition for retailers with low return rates

Measuring Beat Effectiveness

Primary metrics:

Return rate by beat route
Days-to-reorder by retailer
Expiry-related returns as % of sales

Secondary metrics:

Route efficiency (time per outlet)
Order fulfillment rate (delivered vs requested)
Salesman compliance with recommended quantities

Leading indicators:

Stock aging at retailer level
Velocity trend changes
Return risk scores trending up

Review cadence:

Weekly: Quick check on return risk scores
Monthly: Beat sequence optimization
Quarterly: Full beat restructuring based on velocity data

The Bottom Line

Beat planning isn't just about getting products to retailers efficiently. It's about getting the right products to the right retailers at the right time—so they sell through before they expire.

Every day a product sits unsold at a low-velocity retailer is a day closer to becoming your problem again. The route order matters because it determines where fresh stock goes first, and that determines whether it comes back.

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*Want visibility into retailer-level stock aging and velocity-based route recommendations? ShelfLifePro Connect tracks your products from dispatch to secondary sale. [See how it works →](/fmcg)*