Grounded in our own engagements — no outside claims, no invented figures.
A feasibility study for an API manufacturing plant sounds like one question — "should we build this?" — but it's really four questions stacked on top of each other, and getting the order right matters.
The first is a market question: how big is the global API market, in the specific segment you're targeting (synthetic, biologic, HPAPI, sterile), and what's actually driving its growth — volume, price, or new product introductions? Regulator databases and past project data can establish the baseline; the goal is a defensible size and forecast, not a headline number.
The second is a molecule question. Not every API in a growing market is worth entering — shortlisting the right ones means scoring them against demand, price trend, competitive intensity, and how well they fit the plant's actual capabilities (sterile handling, HPAPI, or standard). This is also where competitor pricing strategy — spot pricing vs. long-term supply agreements — starts to shape the plan.
The third is a customer question. Generic majors, CDMOs/CMOs and formulation companies don't buy the same way: their procurement cycles, price sensitivity, and vendor-selection criteria (price and credit terms, supply performance, quality, regulatory documentation like DMF/CEP/GMP) differ enough that a one-size answer misses real risk.
Only once those three are answered does the business plan make sense — revenue by product, gross margin after raw material and packaging cost benchmarks, operating costs across manufacturing/commercial/corporate headcount, and finally the capex and working capital estimate that feeds an NPV/IRR view with scenario analysis on volume share and pricing.
The part that's easy to skip — and shouldn't be — is direct primary research: interviews with both competitors and customers, not just published data. That's where pricing strategy, vendor-selection reality, and procurement behavior actually surface.
"How is our competitor's sales force actually deployed?" is one of the most common competitive-intelligence questions in pharma commercial teams — and the honest answer is that how completely it can be answered depends heavily on geography.
For the US and EU, sales force deployment intelligence is available, but it's necessarily narrower — built from expert interviews with people working in the relevant departments, cross-checked against each other, giving a directional but not exhaustive view of headcount and structure by department and region.
India reporting can go considerably deeper. A comprehensive India sales force study covers field-force size broken down by hierarchy and by state and city, salary and incentive structures, coverage, reach and call frequency, margin and trade-scheme structures, and how the brand is positioned between trade and institutional channels — alongside critical success factors and key challenges specific to that brand's execution.
The reason the two differ isn't methodology — it's market structure and the density of the expert and stakeholder network available to draw on in each geography. In India, that network extends beyond internal contacts to distributors, chemists and HCPs, all of whom see a different slice of how a brand is actually being sold.
The most useful version of this intelligence rarely stands alone. Deployment data means more read alongside marketing spend and brand communication activity — because "how many reps and where" only explains part of a competitor's commercial performance; what they're spending on, and how they're positioning the brand to prescribers, explains the rest.
By the time a generic competitor's ANDA filing becomes public, most of the important decisions behind it were made a year or more earlier. That's the core problem with treating generic-launch readiness as a point-in-time check rather than something tracked continuously.
There are three tracks worth following in parallel. The first is R&D and manufacturing: what stage of development a generic is at, what formulation challenges have come up, whether pilot batches and process validation have started, and which production units and SKUs are involved. The second is regulatory: progress through ANDA or 505(b)(2) pathways, GMP compliance reviews, bioequivalence and stability testing, facility inspections, and dossier submissions — each a signal of how close a competitor actually is, independent of what they say publicly. The third is supply chain: manufacturing partnerships, announced launch timelines, API procurement activity, shipment logistics and port activity, which together indicate whether a launch is operationally ready, not just regulatory-ready.
Tracked over an 18-month window rather than checked once, these three tracks stop being separate data points and start forming a timeline — one that can flag whether a generic threat is 6 months out or 18, and which specific competitor is furthest along.
For an innovator planning around an upcoming patent expiry, that timeline is the actual planning input: not "generics are coming," but which ones, how ready they are, and roughly when — turned into something a revenue-erosion model can actually use.