Imagine customer complaints suddenly increase about texture defects in an established product line. You have limited time and budget. How would you structure your investigation—from hypothesis generation to trials on the line—and decide which corrective actions to implement first?

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VirtualInterview.AI · Accepted Answer

When complaints spike on texture, I first quantify the problem: complaint rate vs. sales, SKUs affected, plants, and time window. In my last role, a yogurt line jumped from 0.5 to 3.2 complaints per 10,000 units in six weeks.

I’d pull a small cross-functional team (QA, production, maintenance, procurement) and build a quick fishbone around texture: raw materials (e.g., protein quality, stabilizer lot), process parameters (shear, hold time, fill temperature), equipment wear, and distribution conditions. Then I’d correlate complaint dates with process and supplier data to narrow to 2–3 hypotheses.

With limited budget, I prioritize low-cost, high-impact checks first: calibrations, recent parameter drifts, and raw-material COAs. We’d run short, targeted trials on the line (e.g., tightening viscosity range, adjusting homogenization pressure) and validate with both instrumental texture and a small sensory panel.

I implement changes that give at least a 50% reduction in defects in factory checks, then lock parameters, update SOPs, and monitor complaints for 4–6 weeks before moving to more expensive reformulation options.

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