American Software, Inc. (AMSWA)
| Previous close | 10.11 |
| Open | 10.11 |
| Bid | 10.14 x 100 |
| Ask | 10.18 x 100 |
| Day's range | 10.10 - 10.24 |
| 52-week range | 9.38 - 13.72 |
| Volume | |
| Avg. volume | 204,916 |
| Market cap | 337.174M |
| Beta (5Y monthly) | 0.69 |
| PE ratio (TTM) | 32.74 |
| EPS (TTM) | N/A |
| Earnings date | N/A |
| Forward dividend & yield | 0.44 (4.35%) |
| Ex-dividend date | 02 May 2024 |
| 1y target est | N/A |
- Business Wire
Logility named a Leader in 2024 Gartner® Magic Quadrant™ for Supply Chain Planning Solutions
ATLANTA, April 30, 2024--Logility, Inc., a leader in AI-first supply chain planning software, announced they have been positioned by Gartner® as a Leader in the Magic Quadrant™ for Supply Chain Planning Solutions for their offering, the Logility Digital Supply Chain Platform. The evaluation was based the company’s overall Completeness of Vision and Ability to Execute.
- Business Wire
Logility Launches Decision Command Center to Mitigate Supply Chain Risk
ATLANTA, April 24, 2024--Logility, Inc., the leader in AI-first supply chain planning solutions, today announced the new Decision Command Center, offering a holistic approach to supply chain processes, leveraging data and intelligence to enhance decision-making and drive value across the entire supply chain ecosystem. Created for supply chain leaders seeking tighter cross-functional collaboration and higher organizational efficiency, Decision Command Center is designed to provide data-driven ins
- Business Wire
Logility Transforms Network Optimization with AI-Powered Dynamic Inventory Modeling
ATLANTA, April 16, 2024--Logility, Inc., a leader in AI-first supply chain planning solutions, today announced dynamic inventory modeling capabilities as a new function of its cloud-based (SaaS) Logility® Digital Supply Chain Platform. Developed to better manage inventory costs, which are the second highest logistics costs after freight, as well as complex inventory-related issues, dynamic inventory modeling features a wealth of inputs not included in classic network optimization models. With th
