When a Waste Record Looks Complete but the Investigation Isn't
A batch misses spec. The deviation is logged, the batch is flagged, and every field in the record is filled in. Then someone asks why it happened, and the record can't answer.
In this episode from the Canadian Physical AI Institute (CPAI), Isaac Caruso, Principal Solutions Engineer at Uncountable, talks with CPAI's Saeed Sarfarazi about waste and quality investigations in food and beverage manufacturing. Using a beverage and starch example, he walks through why records that look complete still leave teams pulling data from different systems and asking people what they remember.
In this conversation
- Why a record with every field filled in can still leave an investigation stuck
- What a beverage and starch example shows about your own records
- How batch records, material lots and supplier certificates of analysis (CoAs) need to connect
- Which details have to be captured in the moment, and which can wait
- What AI needs from your records before it can help with root-cause analysis
- Where a first pilot should start: one recurring loss on one production line
Who it's for
Quality, food safety, continuous improvement and production teams who run waste and off-spec investigations, and anyone thinking about how ready their manufacturing data is for AI.
Episode details
- Featured speaker: Isaac Caruso, Principal Solutions Engineer @ Uncountable
- Host: Saeed Sarfarazi, Business Advisor @ Canadian Physical AI Institute (CPAI)
- Length: 24 minutes
