## PART VII - INDUSTRY AND COMPETITIVE ANALYSIS | MODULE 035 # Disruption and Technology S-Curves > Mission. Analyze adoption curves, cost declines, standards, infrastructure constraints, and incumbent response. ## Decision output Objective: Analyze adoption curves, cost declines, standards, infrastructure constraints, and incumbent response. The completed work product must be reproducible from evidence, show the downstream financial or decision effect when material, state the strongest contrary case, and define a dated update rule. ## Explicit operating procedure 1. Break adoption into technical readiness, cost/performance competitiveness, supply capacity, infrastructure, standards, regulation, customer workflow change, and replacement cycle. 1. Estimate the addressable installed base and realistic annual conversion capacity; an S-curve cannot exceed physical supply, skilled labor, power, permits, customer budgets, or implementation capacity. 1. Track learning curves and cost decline using cumulative volume where evidence supports it, but identify floors set by materials, energy, labor, or mature-process economics. 1. Model incumbent response: price cuts, bundling, self-cannibalization, acquisition, standards influence, distribution leverage, or capital redeployment. 1. Distinguish revenue adoption from profit-pool migration. A rapidly growing technology can destroy industry profit if supply enters faster than differentiation. 1. Create early, middle, and late adoption scenarios with explicit triggers that move the probability or economic outcome. ## Required evidence and model bridge - Primary-source set: peer filings, industry data, regulator data, channel evidence, technology roadmaps. Preserve exact document/version, date, period, and source location for every material factual input used in disruption and technology s-curves. - For each key concept - performance, TCO, cost parity, adoption, standards, infrastructure - state whether it is a reported fact, analyst calculation, management claim, external estimate, or judgment. Quantitative concepts must retain raw components and units; qualitative concepts must retain the specific evidence and counterevidence. - Map only economically relevant findings into the model or decision record. Process-control modules such as disruption and technology s-curves may have no direct valuation line; in that case document the downstream error or governance risk the control prevents. ## Metrics and calculation controls | Metric / concept | Construction | Required validation | | --- | --- | --- | | adoption penetration | Installed/active units or users divided by the realistically addressable population or installed base for the relevant use case. | adoption penetration: Recalculate from same-scope numerator and denominator; confirm period, units, cohort/geography, and issuer definition; reconcile material differences to filings or operating data. | | cost-parity gap | Incumbent alternative total cost of ownership minus new-technology TCO, expressed in absolute terms and as a % of incumbent TCO. | cost-parity gap: Recalculate price/cost from underlying dollars and physical units; test mix, rebates, FX, timing, and unit-definition effects; reconcile to reported revenue or expense. | | learning rate | Percentage decline in unit cost for each doubling of cumulative production; estimate from a log-log regression of cost on cumulative output. | learning rate: Recalculate from same-scope numerator and denominator; confirm period, units, cohort/geography, and issuer definition; reconcile material differences to filings or operating data. | ## Worked application > Case: superior battery energy density fails to produce adoption because yield and infrastructure lag. - Reconstruct the relevant reported fact from primary evidence before interpreting the case. For disruption and technology s-curves, show the raw components rather than only the resulting ratio or narrative. - Build the causal chain through performance, TCO, cost parity, adoption, then identify which link is directly observed and which link remains an assumption. - Calculate adoption penetration, cost-parity gap, learning rate from sourced components under the reported/base interpretation and at least one skeptical alternative interpretation. - Translate the difference between cases into the variable that matters for disruption and technology s-curves: evidence quality, revenue, operating profit/NOPAT, free cash flow, invested capital, financing/dilution, risk, or valuation. Mark non-applicable links instead of inventing them. - Expert consistency test: separate technical readiness, manufacturability, bankability, and customer adoption. - Precommit the specific future filing, KPI, customer/supplier observation, regulator action, or market input that would materially invalidate the disruption and technology s-curves conclusion. ## Failure tests - FAIL if performance cannot be defined and reproduced from the source pack. - FAIL if adoption is forecast from technical improvement alone without customer economics, infrastructure, standards, supply, incumbent response, and substitution risk. - FAIL if the disruption and technology s-curves conclusion depends on an unstated assumption, unreconciled definition, or evidence that cannot be traced to its source/version. - FAIL if evidence materially inconsistent with the disruption and technology s-curves conclusion is omitted, reclassified, or dismissed without a documented definition, materiality, causal, timing, and source-quality analysis. ## Completion test A senior reviewer must be able to reproduce the disruption and technology s-curves conclusion, vary the most sensitive assumption independently, trace the change through the model, understand the strongest opposing case, and identify the next evidence that would force an update. If any link is missing, the module remains open.