The Principles of Product Development Flow¶
Reinertsen, D. G. (2009). The Principles of Product Development Flow: Second Generation Lean Product Development. Celeritas Publishing.
Cited by¶
7 citations across 7 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Batch Size
- And the feedback-lag dimension means that when the operation itself is being improved, small batches accelerate the learning signal and compound over time, so they can dominate at long horizons even where they lose on any single trade-off snapshot.
This sourceArgues that reducing batch size shrinks feedback lag and risk in product development (the Batch Size Feedback and Risk Principles), with batch size as a central control knob.
- And the feedback-lag dimension means that when the operation itself is being improved, small batches accelerate the learning signal and compound over time, so they can dominate at long horizons even where they lose on any single trade-off snapshot.
- Diminishing Incremental Gains
- Reinertsen (2009) builds his entire Principles of Product Development Flow program on this premise: queue-length, cycle-time, and cost-of-delay metrics surface the marginal economics that intuition silently mis-prices.
This sourceBuilds an economic framework around queue size, cycle time, and cost of delay so the marginal economics of work-in-progress — normally hidden by aggregate metrics — become continuously visible.
- Reinertsen (2009) builds his entire Principles of Product Development Flow program on this premise: queue-length, cycle-time, and cost-of-delay metrics surface the marginal economics that intuition silently mis-prices.
- Optionality
- Distinguishing rational optionality from indecisive option-hoarding requires discipline and clear triggering conditions for exercise, a discipline Reinertsen (2009) operationalizes as work-in-progress limits and cost-of-delay accounting in product-development flow.
This sourceBuilds an explicit economic framework around queue size, cycle time, and cost of delay so that the marginal economics of work-in-progress and batch sizes—normally hidden by aggregate metrics—become continuously visible to product-development decision-makers.
- Distinguishing rational optionality from indecisive option-hoarding requires discipline and clear triggering conditions for exercise, a discipline Reinertsen (2009) operationalizes as work-in-progress limits and cost-of-delay accounting in product-development flow.
- Prioritization
- Many failing initiatives persist because the true opportunity cost remains hidden; prioritization exposes it, as Reinertsen (2009) argues in his economic framing of cost-of-delay-driven sequencing.
This sourceBuilds an explicit economic framework around queue size, cycle time, and cost of delay so that the marginal economics of work-in-progress and batch sizes—normally hidden by aggregate metrics—become continuously visible to product-development decision-makers.
- Many failing initiatives persist because the true opportunity cost remains hidden; prioritization exposes it, as Reinertsen (2009) argues in his economic framing of cost-of-delay-driven sequencing.
- Resource Management
- Static Quotas Waste Capacity; Dynamic Allocation Causes Unpredictability** (a tension Reinertsen (2009) treats as central to product-development flow, where over-reservation of capacity compounds queue costs while excessive variability destabilizes throughput):
This sourceBuilds an explicit economic framework around queue size, cycle time, and cost of delay so that the marginal economics of work-in-progress and batch sizes—normally hidden by aggregate metrics—become continuously visible to product-development decision-makers.
- Static Quotas Waste Capacity; Dynamic Allocation Causes Unpredictability** (a tension Reinertsen (2009) treats as central to product-development flow, where over-reservation of capacity compounds queue costs while excessive variability destabilizes throughput):
Domain-specific¶
Mechanisms¶
- Portfolio Tradeoff Review
- Loading a portfolio past its capacity does not politely queue the extra work — it degrades throughput for everything, because high utilization multiplies delay across the whole system
This sourceShows that rising capacity utilization drives nonlinear queue growth and therefore longer waiting time.
- Loading a portfolio past its capacity does not politely queue the extra work — it degrades throughput for everything, because high utilization multiplies delay across the whole system
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