Closing the Shift Handover Gap in 24/7 Automotive Parts Production
How We Helped an Automotive Manufacturer Eliminate Information Loss Between Shifts While Maintaining Complete Data Privacy
Client Snapshot
| Industry | Region | Scale | Operations | Key Stakeholders |
|---|---|---|---|---|
|
Automotive Parts Manufacturing |
Middle East (Saudi Arabia) |
350 employees operating 3 production shifts around the clock |
Produces brake assemblies and suspension components for regional OEMs |
Plant Manager, Production Supervisors, Operations Director |
The Challenge
This automotive parts manufacturer ran three shifts covering 24/7 production. Every shift change involved a 15 to 20 minute handover meeting between the outgoing and incoming supervisors. These handovers were the critical bridge that kept production running smoothly across shifts, but they were happening entirely verbally with no documentation.
An outgoing supervisor might mention that Machine 7 was running slightly hot, that a quality flag had been raised on a particular batch, that a material delivery was delayed and production priorities needed to shift, or that a safety incident had occurred that needed follow up. The incoming supervisor would try to absorb all of this in a 15 minute conversation while also preparing to manage their own shift.
By the time the information reached the third shift, critical details were being lost, distorted, or forgotten entirely. The plant manager estimated that 30% of production issues during night and early morning shifts were directly caused by incomplete handover information. This translated to rework on batches that should have been flagged, continued use of machines that had been reported as problematic, scrap from missed quality notes, and at least two customer complaints per quarter about batch inconsistencies that traced back to handover failures.
The company had considered cloud based transcription tools, but their contracts with regional OEM clients included strict data confidentiality clauses. Production data, machine performance details, and quality metrics were considered proprietary information that could not be transmitted to or stored on external cloud servers. Any solution had to keep all data entirely within the facility.
Our Approach
CubePeaks deployed an on device audio transcription and AI analysis solution on tablets placed at each of the three handover stations. The solution was designed from the ground up for complete privacy, and every function ran locally on the device with zero cloud dependency.
Audio Recording and On Device Transcription
At the end of each shift, the outgoing supervisor tapped record and conducted the handover as they normally would, with no changes to the existing conversational format. The app captured the audio and transcribed it entirely on device using speech recognition models running locally. The system supported multiple audio formats including MP3, WAV, and M4A, and handled audio processing through a chunked pipeline that split longer recordings into five minute segments, transcribing and analyzing them in parallel for faster results. Even a twenty minute handover was fully transcribed within minutes of the conversation ending.
AI Powered Analysis and Insight Extraction
Raw transcription alone would have simply replaced verbal handovers with walls of text. The real value came from the on device AI analysis. After transcription, a local LLM analyzed the content and extracted structured intelligence from the conversation.
The AI produced a concise summary of the handover, capturing the essential points in a fraction of the original length. It identified and listed every action item mentioned during the conversation, including follow ups, tasks, and checks that needed to happen, so nothing could fall through the cracks. It extracted and categorized the specific topics discussed, making it easy to quickly scan what was covered. It also performed sentiment analysis on the conversation, flagging whether the overall tone was positive, neutral, or negative. A negative sentiment flag on a handover could signal frustration, urgency, or unresolved tension that the incoming supervisor should be aware of.
Custom Scoring Rubrics
The plant manager wanted to ensure that handovers were consistently thorough, not just transcribed. The app supported customizable scoring rubrics that could be defined in the settings. The plant manager created rubrics for key criteria: whether safety items were discussed, whether machine status updates were included, whether quality flags were communicated, and whether pending deliveries were mentioned. The AI scored each handover against these rubrics automatically, giving the plant manager a quantitative view of handover quality across all three shifts.
Dashboard and Historical Records
The app provided a dashboard showing performance trends, recent activity, score distributions across shifts, and topic frequency charts. Supervisors and the plant manager could see at a glance which shifts were producing thorough handovers and which were cutting corners. All transcription records, up to 50 sessions with full analysis data, were stored in a local SQLite database on each device. This created a searchable institutional memory of every shift transition.
Projected Results
| Metric | Projected Impact |
|---|---|
|
70% ↓ |
Reduction in information loss between shift handovers, measured by follow up incident tracking. |
|
25% ↓ |
Decrease in shift related production rework and scrap tied to missed handover information. |
|
Zero |
Data privacy or compliance concerns: all processing and storage happens entirely on device. |
|
15 min |
Saved per shift for incoming supervisors who can now read structured notes instead of relying on recall |
|
0 → 100% |
Handover documentation coverage, from zero written records to every single shift documented and scored |
|
Immediate |
Identification of handover quality trends, enabling coaching for supervisors who consistently miss key topics |
Automotive Parts Manufacturing Company
“We used to start every shift guessing what happened in the last one. Critical machine issues were getting forgotten, quality flags were dropping between shifts, and we had no record of what was actually communicated. Now there’s a clear summary with highlighted action items waiting for every incoming supervisor, scored against our own quality criteria. And our OEM partners are completely satisfied because not a single byte of data ever leaves the building.”