Alabama Layoffs — September 2019
Employers in Alabama recorded 2 WARN Act notices in September 2019, covering approximately 196 workers — marking a sharp increase from August and up 73% versus September 2018. The average filing covered 98 workers, with 1 closure among the notices.
Industry Breakdown
| Industry | Notices | Workers |
|---|---|---|
| Manufacturing | 1 | 146 |
| Finance & Insurance | 1 | 50 |
The Manufacturing sector accounted for the largest share of job cuts with 146 workers across 1 notice. At the same time, Finance & Insurance reported 50 workers.
Geographic Hotspots
| County | Notices | Workers |
|---|---|---|
| Lee | 1 | 146 |
| Houston | 1 | 50 |
Lee felt the sharpest impact, accounting for 74% of all affected workers with 146 workers across 1 notices.
| City | Notices | Workers |
|---|---|---|
| Opelika | 1 | 146 |
| Dothan | 1 | 50 |
Layoff Type Analysis
| Type | Notices | Workers |
|---|---|---|
| Closure | 1 | 146 |
| Layoff | 1 | 50 |
The high proportion of closures (74% of affected workers) suggests structural shifts rather than temporary cutbacks in Alabama's labor market.
Largest Layoffs
| Company | City | Workers | Type | Date |
|---|---|---|---|---|
| Flowers Baking | Opelika | 146 | Closure | |
| Ameris Bank | Dothan | 50 | Layoff |
Leading the list was Flowers Baking at its Opelika facility, reporting 146 affected workers. Ameris Bank followed with 50 workers.
Trend & Outlook
After a dip last month, layoff activity has ticked back up.
The filings reflect mounting pressure on the Alabama labor market, with activity running above both recent and year-ago benchmarks. The Manufacturing sector warrants close attention heading into the next period.
This analysis is based on official WARN Act filings reported by Alabama. The Worker Adjustment and Retraining Notification (WARN) Act requires employers with 100+ employees to provide 60-day advance notice of mass layoffs and plant closings. Data is updated daily by WARN Firehose. View all Alabama WARN notices, browse layoffs by state, or download the full dataset.