MalaysiaFood and restaurants2018 – ongoingNestlé Malaysia
Pulsifi predictive selection for management trainees
Pulsifi reported saving Nestlé Malaysia 300 hours in management-trainee selection
Problem
Manual screening slowed trainee recruitment and produced inconsistent shortlists. Young applicants' CVs gave limited evidence of leadership potential.
Approach
Pulsifi compared earlier trainees' assessments with internal performance data. It built a tailored algorithm that scored applicants and predicted work outcomes. Nestlé used the scores to shortlist candidates, then fed subsequent performance results into model updates.
Results
Takeaway
The performance check covered 29 hires. It did not establish prediction accuracy across the full applicant pool.
Categories
- Size
- Large enterprise
- Industry
- Food and restaurants
- Business function
- Human resources
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Vendor case study
Sources
Source summaries
Pulsifi described the selection model and reported processing-time savings. Its follow-up compared predictions with the performance of hired trainees.
Full details are in the original sources.
