Example 12: Future Reality Tree (FRT)

A solution validation graph showing how proposed solutions lead to desired outcomes:

vgraph salesSolution: FRT "Sales Improvement Plan" {
    // Changeable nodes (injections/solutions we will implement)
    node c1: Changeable "Implement agile product development";
    node c2: Changeable "Hire additional support staff";
    node c3: Changeable "Create customer feedback system";

    // Given (unchangeable facts that still apply)
    node g1: Given "Market is highly competitive";
    node g2: Given "Customer expectations keep rising";

    // Intermediate Effects (expected outcomes from our solutions)
    node ie1: IntermediateEffect "Faster product iterations";
    node ie2: IntermediateEffect "Products match customer needs";
    node ie3: IntermediateEffect "Support response time improves";
    node ie4: IntermediateEffect "Customer feedback drives development";

    // Junctor for combining conditions
    node and1: AndJunctor "";

    // Desirable Effects (the goals we want to achieve)
    node de1: DesirableEffect "Sales revenue increasing";
    node de2: DesirableEffect "Customer satisfaction high";
    node de3: DesirableEffect "Market share growing";

    // Potential negative side effects (to monitor)
    node ude1: UndesirableEffect "Initial implementation costs";

    // Solutions leading to intermediate effects
    edge c1 -> ie1: changeable_causes_intermediate;
    edge c3 -> ie4: changeable_causes_intermediate;
    edge c2 -> ie3: changeable_causes_intermediate;

    // Given facts combining with solutions
    edge g2 -> and1: given_to_and_junctor;
    edge ie4 -> and1: intermediate_to_and_junctor;

    // And junctor combining conditions
    edge and1 -> ie2: and_junctor_causes_intermediate;

    // Multiple alternative paths to customer satisfaction (implicit OR)
    edge ie2 -> de2: intermediate_causes_desirable;
    edge ie3 -> de2: intermediate_causes_desirable;

    // Intermediate effects leading to desirable effects
    edge ie1 -> de3: intermediate_causes_desirable;
    edge ie2 -> de1: intermediate_causes_desirable;
    edge ie2 -> de3: intermediate_causes_desirable;

    // Acknowledging potential downsides
    edge c1 -> ude1: changeable_causes_undesirable;
    edge c2 -> ude1: changeable_causes_undesirable;

    // Given competitive market affects outcomes
    edge g1 -> ie2: given_causes_intermediate;
}

Note: FRT graphs also flow bottom-to-top like CRT, but with a different focus. While CRT starts with problems (Undesirable Effects) and traces back to root causes, FRT starts with proposed solutions (Changeable/injections) and traces forward to show how they achieve desired outcomes. This makes FRT ideal for validating that proposed changes will actually deliver the expected benefits.