Strategy Workbench v2 — Saurin Jani

For the early steps of M&A — the analysis that decides what you hunt and why, before any banker is briefed

Snapshot · [date removed] · public-source snapshot · all company identifiers anonymized for confidentiality
Currently viewing
Company A (Pharma-Machinery Group) · steady-pace acquirer
About this build

What this is

A skill-showcase build of an interactive workbench for the early steps of M&A analysis — external scan, theme generation, investment criteria. The intent is to demonstrate what's possible with a small, well-stitched tool stack, not to ship a production system on day one.

The three steps are the early end of a longer corporate-development funnel. Many other elements — internal capability audit, build-vs-buy-vs-partner stress-tests, M&A pace selection, balance-sheet capacity sizing, sourcing, valuation, integration — sit downstream and would each get their own module in a full version. This build focuses on the three steps where structured frameworks plus public data give the strongest return on effort.

What changed in v2

v1 was a deal & signals tracker — monitoring already-public M&A news and filtering it for relevance. Useful for late-stage situational awareness, but commodity intel. v2 is a complete strategic tool — it attacks the earlier analysis where most of the M&A value is actually determined: what to hunt, why, and how to filter targets against a consistent gate.

v2 also adds a backchannel-insights channel inside Step 1: lead-voice mining from LinkedIn, conference roster cross-references, and a working colleague intake form. This addresses the part of M&A signal that never reaches public sources.

This build deepens each step with six consulting-grade analysis modules: a profit-pool map and market map (TAM/SAM/SOM) in Step 1; an assumption audit, build-buy-partner options, and competitive-intel read in Step 2; and an interactive business-case (DCF) in Step 3. Each is grounded in a named framework — see Sources & citations.

How "live" works (and how to make it actually live)

The data on this page is a snapshot pulled fresh on [date removed], embedded into the HTML — not a real-time feed. That keeps the file self-contained, with no API keys needed at your end and no risk of it going stale-but-silent.

Making it actually live is straightforward: the companion n8n workflow already wires the source pulls (DrugPatentWatch, SEC EDGAR, Tracxn, CMS, EC AI Act, FDA, Firecrawl) and runs on schedule. Replacing the embedded snapshot with a fetch('/dataset.json') on page load — and hosting the workflow output on any small VPS or serverless endpoint — flips it from snapshot to live in under a day.

How to use this file

  • Left sidebar is the navigation. About this build is the home page; the three M&A Strategy Process steps and the four Company Profiles are clickable independently.
  • Picking a company opens that company's Overview. Switching to a Step keeps the current company selected — useful for comparing how the same step looks across acquirers.
  • Bain map (Step 2) — click any numbered bubble or any legend row to expand its definition.
  • Theme Thesis builder (Step 2) — every field updates the preview on the right live as you type.
  • Investment criteria (Step 3) — sliders update the weighted score live; the test-target form runs the full gate in real time.
  • Backchannel intake form (Step 1) — type a fake note, click Normalize, see what the AI step would produce in production.
  • Tooltips — hover the small i icons for plain-English definitions.

What's deliberately out of scope (v3 candidates)

  • Internal capability audit — needs real internal capability heat-map data.
  • Portfolio-level deal-capacity calculator — the Step 3 business case sizes a single target; a full balance-sheet capacity model across the whole acquisition programme still needs internal inputs.
  • Pace-pattern configurator — useful only for acquirers choosing a pace, not for Company A (already steady-pace).
  • Quality-check harness for the implicit theme map inference — would need team validation, not a single-analyst golden set.

Built by

Saurin Jani — as a skill showcase for the role of Senior Research & Intelligence Analyst (AI-Enabled) at Company A (Pharma-Machinery Group). Tool stack: n8n for data orchestration, Gemini (web research + structured generation) as the AI helper, this single-file HTML for the interactive surface. The companion n8n workflow (importable JSON) regenerates this page from live data.

Strategy Workbench v2 · [date removed] · single-file HTML · works offline · no API keys required at your end

Company A (Pharma-Machinery Group) i
· steady-pace acquirer

Implicit theme map — reverse-engineered from public deals

A decade of acquisitions tells you what the strategy was, even when it was never written down on a slide. White-space themes are my read on what the implied logic next points to.

Deals on the public record

Source: company press hubs, Tracxn, public filings · refreshed [date removed].
What this step does: Maps what's changing in the outside world that bends our hunting map. Three angles on public data — value chain (where the money is), patent cliff (where the demand is coming from), and PESTEL (what regulation is doing to the map) — plus a fourth channel below for the off-internet signal that public sources can't reach. Together they produce the input for Step 2.

Pharma value chain — where the profit lives

For each step of the pharma value chain: how big the revenue pool is, how high the margin, how fast it's growing, how much M&A activity is happening there, how much disruption risk it carries, and how strong the selected acquirer's fit is.
Source: McKinsey biopharma services report · Roots Analysis 2026
Value chain step Revenue pool ($B) Margin Growth (annual) M&A activity Disruption risk Preset fit
Low Mid High Strong preset fit Mid fit Out of scope
How to read this: dark heat = priority pool. Strong-fit rows are where the selected acquirer already plays. Bright heat + weak fit = a candidate white space to investigate in Step 2.

Patent cliff calendar 2025–2032 — where the demand pull comes from

About $300–400B of branded drug revenue loses patent protection by 2030. That replacement need is the demand pull behind almost every aggressive 2026 acquirer — and the indirect demand pull behind everything downstream (manufacturing, fill-finish, packaging).
Source: DrugPatentWatch · BioPharma Dive · biopharma 10-K filings
Loss of patent in the US Loss of patent in the EU Both same year
For pharma-services acquirers (Company A, Company B, Company C): each $1 of branded revenue that has to be replaced via M&A pulls follow-on capex into the manufacturing supply chain. For biopharma acquirers (Company D): this is the gap that defines their buying agenda.

PESTEL — regulatory & macro pressures shaping the map in 2026

PESTEL = Political, Economic, Social, Technological, Environmental, Legal. Each card is a force already in motion. The impact dots are my qualitative read of how much it bends the theme map.
Sources: CMS · FDA · EMA · EC AI Act service desk

Profit-pool map — where the money actually sitsi

Following the money across the value chain: each step's profit pool (revenue x margin), how attractive it is, and who actually captures that profit. Bars show profit $; stars show the selected acquirer's current position in each step.
Framework: profit-pool analysis (McKinsey)

Value capture — who keeps the margin

PoolCaptureWho captures itStructural reason

Market map — TAM / SAM / SOM & where to play

Framework: market mapping
SegmentSizeGrowthProfitComp.Attract.
= 2/5 rating

White space

    Where to play

      TAM/SAM/SOM sized top-down here; the Step 2 thesis builder lets you cross-check a single theme bottom-up. Attractiveness separates the market's appeal from the acquirer's right to win.
      Beyond public sources

      Backchannel insights — the signal that never reaches the public internet

      Public data covers maybe 80% of what's needed for early-stage M&A analysis. The other 20% — a coffee-talk insight, a conference side-chat, an insider's read on whether a team is actually executing — is what doesn't show up in any feed. Pure AI can't reach it; a workflow around AI can. Below: two scraped channels of quasi-public weak signal + a 30-second intake form that turns a colleague's off-internet conversation into a structured signal record alongside everything else.

      Channel 1 · Lead-voice mining (LinkedIn weak signals)

      A curated whitelist of M&A advisors, sector bankers, and operator-voices in pharma services. Their posts and comments are scraped weekly and parsed for named entities + deal signals. Not perfect, but quasi-public signal that's almost never systematically watched.

      Channel 2 · Conference rosters & side events

      Speaker, exhibitor, and panel rosters for the major pharma events. Cross-referenced against the watchlist and the lead-voice list to surface in-person networking opportunities the team could prioritize.

      Channel 3 · Backchannel insights (colleague intake form)

      Anyone in Strategy/M&A can drop a 30-second note after a coffee-talk, conference side-chat, or call. An AI step normalizes the free text into a structured signal — same schema as everything else above, so it ranks side-by-side in the analysis. Try it: type a note and click Normalize.
      Demo only — nothing leaves this page. In production: posts to Notion, pings Slack, flows into Step 2.
      What feeds Step 2: the scan outputs above (where money lives, where demand comes from, what regulations bend the map) plus the backchannel signal channel define the context in which themes get generated. Move to Step 2: Theme generation to position candidate themes against this context.
      What this step does: Takes candidate themes (existing executed deals + white-space candidates) and stress-tests each across three different lenses simultaneously. A theme that lands well on all three is high-confidence; one that fails on any is a candidate to kill or restructure. The point is not to use one framework — it's to triangulate.

      Ansoff matrix — risk by what's new

      How risky is each theme based on whether the product is new, whether the customer is new, or both? Themes in the top-right (both new) are the riskiest — they're new product AND new customer at the same time.
      ↑ existing market → new market ↓
      EXISTING PRODUCT
      NEW PRODUCT
      Market developmentMed risk
      Same product, new customer or geography
      DiversificationHigh risk
      New product AND new customer — option-value play
      Market penetrationLow risk
      Same product, same customer — defend & deepen
      Product developmentMed risk
      New product, same customer base
      How to read this: risk increases from bottom-left (existing product + existing market) up and to the right (both new). A diversification theme isn't bad — it just needs a different deal structure (smaller, optional, often a minority stake) and a different hurdle rate than a market-penetration bolt-on.

      Three Horizons — what's near, mid, far

      Themes split into three time horizons. Horizon 1 = defend & extend the current core (near-term cash). Horizon 2 = build adjacent businesses that take 3–5 years to mature. Horizon 3 = seed bets on what might matter in 5+ years. A healthy portfolio has all three; one-horizon companies starve themselves later.
      Horizon 1 · Defend & extend the core
      Near-term · bolt-on deals · short payback (3 years) · high return hurdle · integration-heavy
      Horizon 2 · Build emerging adjacencies
      Mid-term · mid-size deals · 3–5 yr payback · expects revenue + cost synergies
      Horizon 3 · Seed long-term options
      Long-term · minority stakes · corporate venture deals · option-value · long payback (5+ yrs)

      Bain Adjacency map — how far from the core

      Each theme placed by how close it sits to what we already do well. The inner ring is our core. Each ring outwards is one step away — different customer, different technology, different geography, different business model. Empirical research (Bain) found that acquisitions inside 1–2 rings work about a third of the time. Beyond 2 rings, success rates collapse. The map is a discipline against over-reach.
      Click any bubble or any row to expand its definition (what's in scope, what's out, why it sits at that distance). White-space themes (dashed) are candidates that aren't acted on yet but the implicit logic suggests.

      Turn a theme into an approval-grade one-pager

      Themes that survive all three lenses get written up as a Theme Thesis — the one-page artifact M&A committees actually approve themes against. Eight fields, live preview on the right.

      Assumption audit — what has to be truei

      Every theme rests on load-bearing beliefs. Each assumption is graded by importance (dots) and evidence strength (pill). The important-but-weakly-evidenced ones are highlighted — those are where the strategy is actually exposed, and each becomes a test below.
      Framework: assumption audit
      Assumption
      Importance
      Evidence

      Test plan — turn weak assumptions into validation work

      Strategic options — build · buy · partner

      Framework: strategic options

      Evaluation (1–5; risk scored so 5 = lowest risk)

      Competitive intel — who else is hunting this space

      Rival acquirers chasing the same themes, modelled from their incentives, capabilities and constraints — not just facts. This is what tells you which themes are contested vs open before you commit.
      Framework: competitive intel
      What feeds Step 3: themes that survive all three lenses, written up as theses, advance to Step 3: Investment criteria — where every target inside any approved theme runs through the same gate.
      What this step does: Once a theme is approved in Step 2, every potential target inside it runs through the same gate. Two layers — hard gates (kill criteria) and a weighted soft score — keep the team disciplined and stop random banker-introductions from consuming bandwidth. The test-target form at the bottom shows the gate in action.
      Purpose of this gate: Without it, corporate development teams end up chasing whatever bankers happen to pitch them. With it, every target gets evaluated against the same criteria — derived from the active theme map and the preset's pace pattern. Defaults below change when you switch preset, because a steady-pace acquirer and a fewer-larger-deals acquirer have very different screening rules.

      Hard gatesKill criteria — if any hard gate fails, the target is rejected before any banker engagement. Faster & cheaper than discovering the deal-breaker in due diligence.

      –

      Soft scoringRanks the targets that survived the hard gates. 1–5 weighted across 6 dimensions — same scorecard for every target inside the theme. Move the sliders to see the weighted total flip the verdict.

      Weighted score (out of 5)
      —
      —

      Financial hurdlesSet per preset's pace pattern. A target that passes the score above still has to clear these.

      Minimum IRRi
      15%
      ROIC > cost of capital byi
      Yr 3–5
      Valuation ceilingi
      12× post-synergy
      Cost synergy payback
      ≤ 3 yrs
      Net debt ceilingi
      3.5×
      EPS accretive byi
      Yr 2

      Test a hypothetical target against the gate

      Business case — does the economics work?i

      A target that clears the hard gates still has to clear the economics. Downside / base / upside on NPV, IRR and payback — base assumptions pre-load per acquirer, and every input is editable so you can pressure-test the deal live.
      Framework: business case builder

      All figures in .
      MetricDownsideBaseUpside
      Screening DCF only: FCF ≈ 72% of EBITDA; synergies ramp linearly over the horizon; terminal value = final-year EBITDA × exit multiple; IRR solved on the resulting cash-flow stream. Downside = 0.4× synergies and −3pts growth; upside = 1.4× synergies and +3pts growth. Not a substitute for a full model.

      Sources & citations

      Every number, theme position, and PESTEL item in this workbench is grounded in a public source — listed below by category. Frameworks reference their academic / consulting origins.
      All links verified [date removed]

      Patent expiries & biosimilars

      Patent cliff calendar 2025–2032; biosimilar entry tracking.

      M&A history & peer benchmarking

      Deal histories, archetype classification, peer activity.

      Industry & market sizing

      Value chain profit pools, CDMO market size, contract packaging trends.

      Regulatory (PESTEL items)

      Live regulatory and macro pressures cited in Step 1.

      Frameworks used

      Source references for the analysis lenses used across the three steps.
      • Profit-pool analysis (Step 1) — Gadiesh & Gilbert (1998), "Profit Pools: A Fresh Look at Strategy," Harvard Business Review (Bain & Co.)source anonymized
      • Market map / TAM-SAM-SOM (Step 1) — standard top-down + bottom-up market-sizing methodology (McKinsey / venture practice)source anonymized
      • Ansoff matrix (Step 2) — H. Igor Ansoff (1957), "Strategies for Diversification," Harvard Business Reviewsource anonymized
      • Three Horizons (Step 2) — Baghai, Coley, White (2000), The Alchemy of Growth (McKinsey)source anonymized
      • Bain Adjacency map (Step 2) — Chris Zook (2004), Beyond the Core (Bain & Co.)source anonymized
      • Assumption audit / "what has to be true" (Step 2) — Lafley & Martin (2013), Playing to Win (HBR Press); McGrath & MacMillan (1995), "Discovery-Driven Planning," HBRsource anonymized
      • Strategic options (build / buy / partner) (Step 2) — McKinsey corporate-strategy "build, buy, or partner" practicesource anonymized
      • Competitive intel / competitor response (Step 2) — Michael E. Porter (1980), Competitive Strategy (Free Press)source anonymized
      • Business case (DCF / NPV / IRR) (Step 3) — Koller, Goedhart & Wessels, Valuation (McKinsey); Brealey, Myers & Allen, Principles of Corporate Financesource anonymized
      • McKinsey — Seven habits of programmatic acquirerssource anonymized
      • Framework inputs (market sizes, profit pools, rival-acquirer & deal intel) draw on the industry, M&A and regulatory sources listed in the other cards — illustrative snapshot, refreshed by the companion workflow.

      Backchannel inputs & sector commentary

      Sources behind the lead-voice list, conferences, and macro commentary.

      Workflow data sources (production pipeline)

      APIs and feeds the companion n8n workflow uses to refresh the dataset.
      • DrugPatentWatch API — patent expiry calendar (paid)source anonymized
      • SEC EDGAR — public company filings (free)source anonymized
      • Tracxn — peer M&A database (paid)source anonymized
      • CMS — IRA negotiated drug list (free)source anonymized
      • EC AI Act service desk — implementation timeline (free)source anonymized
      • FDA — DSCSA enforcement status (free)source anonymized
      • Firecrawl — LinkedIn weak-signal scraping (paid)source anonymized
      • Perplexity Sonar Pro — on-demand deep research with citations (paid)source anonymized