Beat Planning and Expiry: Route Order Costs ₹3L/Year

The order your salesman visits retailers is quietly costing you ₹3 lakhs a year

Your salesman visits 30 retailers a day. He starts at 9 AM, finishes by 6 PM. He covers the territory, fills orders, collects payments, drives back. By the standard metrics of FMCG distribution — outlets covered, orders taken, kilometres driven — he's doing fine.

But here's what nobody in the distribution chain is measuring: the order in which he visits those retailers is determining which stock expires and which stock sells, and the difference between an expiry-optimised route and a geography-optimised route is, conservatively, ₹2-4 lakhs a year for a mid-size distributor. That number doesn't show up on any single invoice or return claim. It's distributed across hundreds of small return events, each one individually unremarkable, collectively representing a meaningful percentage of your annual margin.

The reason this stays invisible is that beat planning is treated as a logistics problem — minimise distance, maximise coverage, reduce fuel costs. And it is a logistics problem. But when you're distributing products with shelf life (which, in Indian FMCG, is most of what you distribute), beat planning is simultaneously an expiry management problem. The route sequence determines where fresh stock goes first, and where fresh stock goes first determines whether it sells or comes back.

the scenario that plays out every week in every territory

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 stores that turn inventory quickly), 15 medium-velocity retailers (neighbourhood stores with steady but slower movement), and 5 low-velocity retailers (remote locations, specialised shops, the outlet where the owner is a friend and always takes a few cartons as a favour).

If the route is optimised for geography — which is how almost every distributor in India plans beats — the salesman visits low-velocity retailers first because they're closer to the depot. He drops 20 cartons across five low-velocity stores. By the time he reaches the high-velocity stores in the afternoon, he has 30 cartons left for 10 stores that could have moved the entire 50 between them in two weeks. The high-velocity retailers get partial deliveries, or worse, get told "next week." Meanwhile, those 20 cartons at the low-velocity stores are sitting on shelves, moving slowly, aging toward expiry.

Thirty days later, the low-velocity retailers call for returns. The product now has 15 days left. You can accept the returns (preserving the relationship but eating the cost) or refuse them (saving the immediate cost but damaging a relationship you need for other products). The returns come back to the depot, get loaded onto next week's van, go out to another slow retailer, come back again. The product circulates through your system like an unwanted guest at a party, getting older and less sellable with each trip, until it finally expires in your warehouse.

This cycle is so common that most distributors have normalised it. Returns are "part of the business." They are part of the business — but a much larger part than they need to be.

velocity-first routing and why it works

The principle is simple: deliver to the retailers who will sell the product fastest, first. High-velocity stores get the freshest stock and the largest quantities. Medium-velocity stores get second-tier freshness with quantities matched to their actual capacity. Low-velocity stores get the smallest quantities, and only for products with longer shelf life — or, for short-shelf-life products, they get skipped entirely.

This sounds obvious in theory. In practice, it requires knowing something that most distributors don't systematically track: the actual sales velocity at each retailer for each SKU. Not intuition about who's a "good" store. Not the order size, which reflects what the retailer agreed to buy, not what they actually sold. The actual days-to-sell, which tells you how long the product sits on the retailer's shelf before a consumer picks it up.

Without that data, velocity-based routing is guesswork. With it, you can build a return prediction model that's surprisingly accurate. The formula is straightforward: take the number of days the product has been at the retailer, divide by the historical average days-to-sell for that SKU at that store, then multiply by the ratio of the delivered quantity to the historical average order size. A score above 3 means that retailer is holding more stock relative to their selling speed than is safe. Above 5, the return is nearly certain.

This kind of calculation is impossible to do manually for every SKU at every retailer. It's trivial for a system that already has the sales data. The gap between manual beat planning and data-driven beat planning is the gap between routing that feels efficient and routing that actually prevents expiry.

seasonal velocity shifts that make static routes dangerous

Retailer velocity isn't constant. The store that moves biscuits fast during monsoon (when people stock up on packaged foods) might be slow during summer (when everyone buys beverages instead). Festival periods create surges in specific categories that crash back to below-normal levels post-festival, because every retailer over-ordered in anticipation of demand that partly materialised and partly didn't.

A beat plan that was optimised in January becomes increasingly wrong through March and is actively harmful by June. Static route planning — set the beat once, run it all year — ignores seasonal velocity shifts entirely, which means it's guaranteed to be wrong for at least several months of the year. The distributors who manage this well reassess velocity data monthly, adjust the beat sequence for seasonal patterns, and have different routing modes for peak seasons versus normal periods.

Local events matter too, and they're harder to predict: marriage season in a particular neighbourhood, a factory adding a shift that changes evening foot traffic, a new competitor opening near one of your retailers. These shift velocity at individual outlets in ways that even monthly reviews might miss. The salesman in the field usually knows about these shifts before the data reflects them — which is one argument for giving salesmen the tools and incentive to report velocity changes rather than just optimising for volume.

the salesman incentive problem that makes everything harder

Your beat plan is only as good as the salesman following it. And salesmen, being rational economic actors, optimise for their own incentives: commission (volume sold), convenience (easy routes, friendly retailers who don't negotiate), and speed (finish early, go home). What they should also be optimising for — stock rotation at each retailer, appropriate quantities per store velocity, returns prevention — doesn't appear in their compensation structure.

The misalignment is predictable and expensive. A salesman with a 500-carton daily target can either sell 50 cartons each to 10 appropriate retailers (which requires visiting 10 stores, assessing their stock position, having conversations about quantities) or sell 250 cartons to 2 large retailers who'll take whatever he pushes (which takes an hour and hits target). Option B is faster, earns the same commission, and creates a return liability that won't surface for 45 days — by which time it's someone else's problem. The problem isn't the salesman's character. It's the incentive design.

The fix is straightforward conceptually and difficult organisationally: add FEFO compliance, return reduction, and batch rotation efficiency to the salesman's incentive structure. When the salesman earns more from selling older batches first and loses income from high return rates, the behaviour changes. The challenge is measuring these metrics accurately, which circles back to the systems problem — you can't incentivise what you can't measure.

what the weekly rhythm looks like when it works

Monday: analyse the previous week's delivery data. Which retailers sold through? Which are holding excess? Have velocity patterns shifted? This takes 30 minutes with the right data and is impossible without it.

Tuesday through Thursday: execute beats with adjusted quantities, high-velocity routes prioritised, quantities matched to actual velocity rather than retailer enthusiasm or salesman convenience. Flag any unusually large orders for verification — a retailer suddenly ordering three times their normal quantity is either experiencing a genuine demand spike or being pushed stock by a salesman chasing a target.

Friday: pre-weekend deliveries to high-velocity stores, because the weekend is peak consumer buying and you want fresh stock positioned where it'll turn fastest.

Saturday: review returns and near-expiry alerts. Proactive calls to retailers who are sitting on slow-moving stock. Scheme offers to move at-risk product. Adjustments to next week's beat based on what you learned this week.

This rhythm requires weekly attention from someone who has access to the data and authority to adjust routes. In most distribution operations, that role doesn't formally exist — beat planning is set quarterly by the area sales manager and executed without modification until the next quarterly review. The gap between quarterly reviews and weekly reality is where expiry losses accumulate.

the product category nuance that matters

Not every product in your van needs the same routing logic. Short-shelf-life products (under 60 days — biscuits, bread, dairy) absolutely need velocity-first routing. Skip low-velocity stores entirely for these products. Deliver smaller quantities more frequently. The cost of an extra delivery trip is trivial compared to a carton of expired biscuits.

Medium-shelf-life products (60-180 days — most packaged foods) benefit from velocity-first routing but with more flexibility. Low-velocity stores can receive modest quantities. Monthly velocity review is sufficient rather than weekly.

Long-shelf-life products (over 180 days — personal care, home care) can be routed geography-first. Efficiency matters more than velocity for these categories, because the shelf life is long enough that even slow retailers will move the stock before it becomes a problem. Quarterly velocity review is adequate.

The key insight is that mixed loads — which is what most vans carry — need to be managed by category rather than as a single delivery. Deliver the short-shelf-life products to high-velocity stores first. Deliver the long-shelf-life products based on geographic efficiency. Don't let the routing convenience of long-shelf-life products subsidise the expiry risk of short-shelf-life ones.

ShelfLifePro tracks your products from depot to retailer shelf, with velocity scoring, return prediction, and beat optimisation based on actual sell-through data. Because the route order determines whether your stock sells or comes back.