This work examines the fundamental concepts for improving plan‑execution flexibility, namely partial‑order planning, the producer‑consumer‑threat formalism, and a suite of deordering and reordering techniques.\ \ A practical way to obtain a partial‑order plan from a sequential one is to drop unnecessary ordering constraints, thereby increasing execution flexibility. Several methods have been proposed for this task; we analyze them along six dimensions—ordering capability, action handling, parameter handling, plan structure, concurrency, and computational complexity—and evaluate them on a common benchmark.\ \ The central finding is that block‑deordering approaches substantially outperform MaxSAT‑based approaches. Block deordering restructures causal dependencies by grouping actions into blocks and substituting subplans, which changes the underlying causal structure and reveals orderings that would otherwise appear mandatory. In contrast, MaxSAT seeks a minimum reordering within the existing causal graph, limiting its effectiveness.\ \ From a practical standpoint, block‑deordering methods are anytime algorithms that always produce a valid result, whereas MaxSAT encodings fail on a significant portion of plans and provide no partial solution when they succeed.\ \ Block substitution further extends parallel execution by formalizing non‑concurrency constraints, but its impact is confined to domains with pronounced resource‑based interactions.\ \ In terms of efficiency, block‑deordering achieves the highest flexibility gain per unit of computation time, while MaxSAT encodings incur substantial computational overhead.\ \ Review: By restructuring the plan’s causal architecture, block‑deordering delivers greater parallelism and robustness, making it the preferred technique for enhancing execution flexibility in automated planning.