Before automating a manual process, leaders should deeply engage with the people on the line. These operators possess invaluable, often un-documented, knowledge about process nuances and potential failure modes that are critical for a successful automation project.
When preparing to scale a manufacturing team, the highest priority is the well-being of the core, foundational members. They hold the critical tribal knowledge and culture. Losing them to burnout right before a major expansion can cripple the entire operation.
In R&D-heavy organizations like life sciences, operations leaders face an uphill cultural battle to secure resources for proactive maintenance. The company's focus is typically on scientific discovery and new product development, not on sustaining existing operational equipment.
You can quickly gauge if a manufacturing process was rushed into production by checking for in-process quality control measures. The absence of tools like vision systems or torque testers indicates a lack of thought given to measuring and controlling critical process parameters.
Companies, especially in early stages, should resist outsourcing production too quickly. Keeping a new process in-house is essential for understanding its pain points, which is a prerequisite for being able to specify clear, effective requirements to an external vendor later on.
Collaboration between scientists and engineers requires acknowledging their different mindsets. Scientists operate with a 'freedom of thought' to prove a novel concept works once. Manufacturing engineers must translate that concept into a robust process that works consistently every time.
To ensure a smooth transition from development to production, an operations or manufacturing SME must be part of the design process from the start. Otherwise, products are developed without manufacturability in mind, leading to expensive, reactive fixes and subjective quality control during scale-up.
Instead of just collecting all data and hoping AI finds insights, Industry 4.0 is about intentionally architecting systems to capture specific data needed to make predetermined operational and quality decisions faster and more effectively.
Choosing a modular, reworkable product architecture can save money during early development. However, this approach often creates operational complexity that is difficult to scale. This strategy is only viable if there's a clear plan and trigger point to transition to a more fixed, scalable design.