In intermediate pharmaceutical distribution — the layer between manufacturer and pharmacy or hospital — delivery route planning has a recurring structure: fixed routes, repeated daily, weekly or monthly, with a stop sequence designed months (or years) ago and kept stable for operational reasons. Customer, driver, depot and pharmacies build routines that reduce errors and increase service reliability.
The structural problem is that the market under fixed routes is never still. Every month new pharmacies open, others close, some hospitals revise reception hours, most renegotiate volumes and delivery windows, the company signs new exclusive distribution contracts on specific segments. Each of these micro-changes erodes the optimality of the original route; the cumulative effect, after 8-12 months, is a plan that works “by inertia” but is significantly sub-optimal versus what would be designed from scratch today.
This article tackles the specific challenge of intermediate pharma logistics: how to keep fixed routes optimal over time, with the right balance between operational stability (preferred by drivers and pharmacies) and adaptation to change (necessary for marginality). Let’s see the four causes of erosion, the scenario-simulation-based approach that today represents the state of the art, and how two types of Italian operators — intermediate distributors and regional wholesalers — apply this model.
The four causes of optimality erosion
A fixed route optimised at design time starts losing efficiency from day one. The four factors systematically eroding it are these.
Pharmacy openings/closures and ownership changes. The Italian pharmacy market has an annual turnover of about 4-6%: new openings, closures, management changes that modify needs (hours, frequency, payment methods). A closed pharmacy leaves a hole in the route the dispatcher typically “ignores” — the vehicle still passes through the area but with extra dead time. A new opening is inserted “by force” into the nearest route without recalculation.
Volume variations per customer. The pharmacy that ten months ago ordered 15 packages/week today orders 25 because management changed and they expanded the assortment; the one that ordered 30 now orders 18 because they lost a public health agency as customer. Trucks are sized on original volume and become under-used or overloaded without the system signalling it.
Regulatory and contractual changes. Changes in hospital reception hours (increasingly frequent for security and traceability reasons), new windows required by healthcare retail, stricter temperature constraints for new drug categories (cell therapies, biologics at -20°C beyond the traditional 2-8°C). Each modification constrains the existing plan at a specific point.
Insertion of new distribution contracts. For intermediate distributors working with multiple manufacturers, acquiring a new mandate — even just one product line — may require additional steps (loading at additional manufacturer, dedicated intermediate ATP depot, specific pickup windows) that didn’t exist in the original plan.
The operational problem is not “one pharmacy more or less” — it’s that after 8-12 months these micro-changes have accumulated by the dozens and no one has the overall visibility of how far the current plan is from optimal. Marginality erodes without visible shocks: one percentage point at a time.
The right model: optimisation + scenario simulation
The answer is not “redo routes from scratch every month” — it would be excessive operational disruption, drivers would lose routines, pharmacies would protest schedule changes. The answer is structurally different: keep the base routes stable, but use an optimisation and scenario simulation system to continuously evaluate alternative scenarios and apply changes only when the advantage is significant.
The operational pattern that works on Optivo’s pharma clients articulates in four components.
1. A digital and analysable “as-is” plan
The first step — sounds trivial but almost no one does it — is having the current plan of fixed routes loaded into a digital system, not just in the dispatcher’s spreadsheet or in the driver’s memory. Without this baseline you can’t compare alternative scenarios or measure the drift accumulated in recent months.
2. Continuous update of pharmacy/hospital data
Real volumes per customer (extracted from historical PODs), updated time windows, temperature constraints per product line, any special requirements (floor delivery, LEZ access). The system must have these data as “primary source” and not as printed cards.
3. Monthly “what-if” simulation
Once a month, the planning manager runs a simulation that recalculates the optimal routes with updated data and compares them with the current plan. Output: how many pharmacies are out of optimal position, how many extra km are being driven versus the recalculated scenario, which recomposition would produce the largest gain with the least operational disruption.
4. Selective application of changes
Not everything is applied immediately. Structural changes (opening of an important new customer, loss of a relevant volume, depot change) are applied quickly; micro-optimisations accumulate and are applied in “plan change” windows — typically quarterly — communicated in advance to drivers and customers.
The leap over the traditional “one-and-done” planning model is qualitative: planning becomes a continuous process instead of an episodic event, and the company recovers the accumulated optimality drift in weeks instead of years.
Logital: simulation as a decision tool
Among our clients in intermediate pharma distribution, Logital uses Optivo paradigmatically for this model. The specific challenge is that of a growing intermediate distributor: the portfolio of served pharmacies changes continuously — new mandate acquisitions, expansion to new zones, integration of single pharmacies into existing routes — and every change requires assessing the impact on overall planning without throwing away the existing.
The operational use of the platform is twofold. On one hand, standard optimisation of current routes, producing the updated reference plan. On the other — and here is the differentiator — scenario simulation: before accepting a new group of pharmacies to serve, the planning manager simulates how they would fit into existing routes, how many additional km they would require, whether they would require an extra vehicle, where the time-critical points are. The commercial decision (accepting the mandate, negotiating specific conditions, sizing a new shift) is based on this projection, not on a feeling-based estimate.
The value of simulation is particularly high in growth: without a tool translating “this new pharmacy requires 8 extra km but saves 2 km on another route because it completes a geographic cluster” into verifiable numbers, development decisions are made blindly or on the commercial manager’s “gut feeling”. A simulation system gives the company back control over service margins before they become a problem.
National capillary distribution: the Univex pattern
A second pattern, parallel to Logital’s, is national capillary distribution. Realities like Univex — a national pharma distributor among our clients — manage larger volumes over a wider geographic area, with planned routes combining high frequency in metropolitan zones (daily or twice-daily deliveries to urban pharmacies) and lower frequency in rural zones (weekly or fortnightly routes, often multi-stop with clusters of nearby pharmacies).
For realities of this size, fixed-route optimisation is a “gym exercise”: the theoretical advantage of perfect planning is significant (on industry benchmarks, 8-15% km savings on routes not re-optimised in 12+ months), but the operational disruption cost of complete application is high. The pattern that works is “progressive recomposition”: every quarter, the system identifies the 2-3 geographic clusters where accumulated drift is greatest and applies recomposition there, leaving well-functioning routes stable.
On industry benchmarks — applicable also to pharma — a structured progressive re-optimisation programme typically produces 5-10% fewer annual km at equal service, with an additional advantage on workload balancing across drivers (reducing the work queues accumulated by the “saturated” route drivers). At average consumption of 25-30 L/100 km for vans and light vehicles used in capillary distribution, and with diesel above 2 €/L (see our analysis on levers to reduce consumption under expensive-diesel pressure), the annual fuel saving for a 20-vehicle fleet is easily €60-90 thousand.
Compliance and traceability: the added value of structured data
In pharma logistics, route optimisation cannot be separated from two specific regulatory requirements: EU Good Distribution Practices (GDP) mandating temperature and delivery time traceability, and transport data access required by AIFA in case of product recall.
A planning system integrating optimisation, telematics and digital POD automatically produces the data needed for compliance: for every delivery the actually-driven route is available, the temperature in the refrigerated cell (for cold-chain products), the delivery time signed by the pharmacy. On the integration between temperature monitoring and route optimisation — a pattern shared between pharma and food & beverage — we have a dedicated deep-dive. This data is also raw material for EU Data Act reporting — the pharmaceutical principal company increasingly requests access to this data for its own internal reporting.
Digital POD for pharmacies replaces the paper signature with a validated digital signature + photo of the receiving counter + timestamp. For the pharmacy it’s faster (5-10 seconds instead of paper’s 30-40); for the distributor it’s the legal proof that delivery happened in the times and ways agreed, useful in case of dispute and fundamental in case of product recall.
KPIs to monitor
For a pharma fleet with fixed routes, six KPIs to monitor at minimum monthly frequency (some weekly).
| KPI | Frequency | Typical target |
|---|---|---|
| Km/delivery on fixed routes | Monthly | Stable or decreasing; rise >5%/year = optimality drift |
| Average time per stop | Weekly | Stable; rise = new inefficiency (to investigate) |
| In-window delivery rate | Weekly | >95% for pharmacies, >98% for hospitals |
| Vehicle load composition time | Weekly | Stable; rise = depot issues |
| % PODs closed in real-time | Daily | >95% (below = app or procedure issue) |
| Geographic clusters with drift > 10% | Monthly | Trigger for targeted recomposition |
On the 7 fundamental KPIs for every fleet manager we already dedicated a specific article; for pharma you add compliance KPIs (GDP compliance, completeness of temperature traceability, average POD return time).
When to redo routes from scratch
Even with a good simulation and progressive recomposition system, every 24-36 months it’s appropriate to redo planning completely from scratch. Not because progressive recomposition doesn’t work — it does — but because starting assumptions (service zones, depot, fleet sizing) may have changed to the point that today’s local optimum is no longer the true optimum.
Natural triggers for a complete redesign: opening of a new depot, significant client portfolio change (acquisition/loss of an important mandate), main vehicle change (transition to electric fleet for urban centres), structural regulatory change (new hospital time windows, expanded LEZs). In absence of specific triggers, every 30 months is a reasonable cadence.
The key point
The “perfect” fixed route doesn’t exist as a stable object over time: it exists as a continuous practice of optimisation and simulation. The difference between a pharma distributor working with positive marginality and one slowly eroding margins is the discipline with which they update their routes to the changing market, without excessive operational disruption.
The required technological investment is manageable for pharma SMEs: a modern planning platform integrates optimisation, simulation, telematics and POD in a single experience; payback on a 15-25 vehicle fleet measures in 4-8 months on fuel savings and dispatcher productivity alone.
If you want to understand how your current plan would compare with an optimised recalculation — and which areas of your pharmacy network have the largest drift — talk to our team: an analysis of three months of operational data is enough to identify intervention priorities.