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The kiss of death for an R&D program is discovering critical issues one by one, late in the process. This serial approach to problem-solving fails to retire risk quickly enough, causing the project to run out of time and budget, ultimately leading to its cancellation.

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Disastrous projects rarely fail overnight. They suffer a 'death by a thousand cuts,' where a series of small compromises and ignored red flags accumulate. Teams become so invested that continuing with a flawed plan seems less 'irksome' than admitting the core concept is broken, leading to an inevitable disaster.

True innovation isn't about brainstorming endless ideas, but about methodically de-risking a concept in the correct order. The crucial first step is achieving problem clarity. Teams often fail by jumping to solutions before they have sufficiently reduced uncertainty about the core problem.

Since most biotech programs fail (as few as 1 in 10 succeed), a company's survival depends on running a portfolio of multiple programs simultaneously. This requires the discipline to quickly terminate unsuccessful projects and the foresight to have subsequent programs already underway.

To avoid accumulating sunk costs on doomed projects, solve the most difficult, uncertain part first (the "monkey"). Avoid easy tasks (building "pedestals") that create a false sense of progress. This framework, from Google's Astro Teller, forces an early confrontation with a project's core viability.

A study of 16,000 projects found successful ones invest heavily in upfront problem definition. This "think slow, act fast" approach prevents the costly, slow-moving execution that plagues projects that rush into production without a clear plan.

Indecision is more damaging than a bad decision because it doesn't just waste time; it dramatically reduces the team's available options. Delaying a hard choice (e.g., on a compliance issue) eats up the time needed to develop creative workarounds, forcing last-minute cuts to essential elements.

A major source of unproductivity in drug development isn't the time spent reaching a clinical milestone. Instead, it's the 'white space' after data is received—the delay in analyzing results and making a firm go/no-go decision, which stalls the entire program.

The primary cause of failure in engineering projects is not technical incompetence but a lack of visibility into budget, schedule, scope, and risk. Successful project execution hinges on addressing these core management areas before they derail the work.

Pharma companies launch countless pilots that fail to scale. This happens because they lack sufficient time to show traction, budgets get cut prematurely, and companies needlessly reinvent the wheel instead of adopting proven solutions from peers.

A key indicator of a well-run project is a reliability model that exists from the beginning. Teams that plan to 'verify reliability in testing' are discovering risk too late. A proactive reliability model should guide design and identify risks early to prevent late-stage failures.

R&D Programs Get Canceled for Serial Problem-Solving, Not for Having Problems | RiffOn