Exnovation.jl

Exnovation.jl is a Julia framework for modeling exnovation decisions: phasing out legacy practices, products, or routines to make room for new innovation. It is inspired by the conceptual treatment in Holbek & Knudsen (2020) and is structured to capture drivers, barriers, and decision criteria in a transparent way.

This package does not embed proprietary content; it provides a clean model and simple scoring helpers so you can encode your own organizational context.

Core Concepts

• Exnovation item: a practice, product, or routine being considered for phase-out.
• Drivers: forces pushing toward exnovation (e.g., regulatory pressure, obsolete technology, sustainability targets).
• Barriers: cognitive, emotional, behavioral, or structural resistance.
• Intelligent failure: planned experimentation with bounded risk and deliberate learning checkpoints.
• Decision criteria: weighted factors such as sunk cost bias, strategic fit, performance, and risk.
• Debiasing actions: prompts to counter sunk-cost and status-quo effects.
• Stage-gates: thresholds that stop or advance exnovation decisions.
• Impact model: capex/opex savings plus public value.

Quick Start

using Exnovation

item = ExnovationItem(:LegacyCRM, "Legacy CRM system", "Sales operations")

drivers = [
    Driver(:SecurityRisk, 0.7, "Legacy stack has known vulnerabilities"),
    Driver(:Sustainability, 0.4, "Cloud move reduces footprint"),
]

barriers = [
    Barrier(Cognitive, 0.5, "Sunk-cost framing in past investments"),
    Barrier(Behavioral, 0.3, "Habits and routines tied to old workflows"),
]

criteria = DecisionCriteria(0.3, 0.3, 0.2, 0.2)

assessment = ExnovationAssessment(
    item,
    drivers,
    barriers,
    criteria,
    1_200_000.0,  # sunk_cost
    350_000.0,    # forward_value
    900_000.0,    # replacement_value
    0.4,          # strategic_fit (lower is worse)
    0.6,          # performance (lower is worse)
    0.7,          # risk (higher is worse)
)

score = exnovation_score(assessment)
println(score)
println(recommendation(assessment))

# Intelligent failure readiness
criteria = IntelligentFailureCriteria(
    0.9,  # planned_action
    0.7,  # outcome_uncertainty
    0.8,  # modest_scale
    0.9,  # rapid_response
    0.8,  # familiar_context
    0.7,  # explicit_assumptions
    0.8,  # checkpoint_learning
)

failure = FailureAssessment(Intelligent, criteria, 0.6, 0.7)
summary = failure_summary(failure)
println(summary.intelligent_failure_score)

# Decision pipeline and JSON report
case = ExnovationCase(
    assessment,
    failure,
    RiskGovernance(0.5, 0.7, :govern),
)

report = decision_pipeline(case)
write_report_json("exnovation_report.json", report)

# Portfolio scoring and budget allocation
impact = ImpactModel(100.0, 50.0, 0.9)
item = PortfolioItem(case, impact)

scores = portfolio_scores([item])
allocation = allocate_budget([item]; capex_budget=120.0)

API Snapshot

BarrierType, Cognitive, Emotional, Behavioral, Structural, Political
FailureType, Preventable, Unavoidable, Intelligent
ExnovationItem, Driver, Barrier, DecisionCriteria
ExnovationAssessment, ExnovationSummary
IntelligentFailureCriteria, FailureAssessment, FailureSummary
RiskGovernance, ExnovationCase, DecisionReport
ImpactModel, PortfolioItem, StageGate
sunk_cost_bias_index, exnovation_score, recommendation
debiasing_actions, intelligent_failure_score, failure_summary
decision_pipeline, write_report_json
barrier_templates, run_stage_gates
portfolio_scores, allocate_budget

Conceptual Alignment

The model is aligned with ideas from the Holbek & Knudsen manuscript on exnovation: exnovation as making space for innovation, the role of sunk-cost bias, and the impact of cognitive, emotional, and behavioral barriers.

It also integrates the Hartley & Knell article on innovation, intelligent failure, and exnovation by modeling intelligent failure criteria and making them explicit in the decision flow.

Development

julia --project=. -e 'using Pkg; Pkg.instantiate()'
julia --project=. -e 'using Pkg; Pkg.test()'