1 / 11

Automatic Inventory Control: A Neural Network Approach

Automatic Inventory Control: A Neural Network Approach. Nicholas Hall ECE 539 Final Project Fall 2003. Managing Inventory. Managing inventory is a huge problem for many businesses: How many parts do you order? When do you order? How do you estimate demand?

andrew
Télécharger la présentation

Automatic Inventory Control: A Neural Network Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Automatic Inventory Control:A Neural Network Approach Nicholas Hall ECE 539 Final Project Fall 2003

  2. Managing Inventory • Managing inventory is a huge problem for many businesses: • How many parts do you order? • When do you order? • How do you estimate demand? • Parts should arrive just before a customer orders them, but no sooner!

  3. Managing Inventory • It is impossible to predict customer demand 100% in almost every case. • Instead try to: • Minimize backorders - orders for parts that are not in stock • Maximize inventory turnover - number of times a year, on average, every product in the warehouse is sold. • Some tradeoff must be made.

  4. Prediction Problems • Many products are highly seasonal:

  5. Prediction Problems • Some have impossible to predict buying “spikes”:

  6. Prediction • Fortunately, others show similar patterns each year:

  7. Approach • Use inputs that reflect the data: • # sold for past 12 months • # sold for past 12 months / # sold for 12 months prior • # sold for seasons / # sold for other seasons • # sold for 1 month / # sold all year

  8. Approach • Accuracy can never be 100% on the testing set, but that is not what we are trying to accomplish. • Instead, try to minimize backorders while maximizing the turnover rate. • Created simulation program that pretends it is managing the inventory for an entire year and keeps statistics about product movements.

  9. MLP Results • Using the multi-layer perceptron algorithm with 150 hidden nodes, best compromise was a 54.9% backorder rate with 6.7 turns per year. • Common backorder rate when inventory is managed by humans is 2-5%, so the MLP did not perform very well.

  10. Simulated Annealing Results • Originally, a simulated annealing prediction program was developed as a reference. • It was expected to be outperformed by the MLP. • However, it achieved a very good 9.3% backorder rate with 8.5 inventory turns per year.

  11. Conclusion • The more complex method is not always better, as the much simpler simulated annealing program was better at predicting that the MLP. • However, both are slow. The simulated annealing program used 1 week of CPU time on an AMD Athlon 1800+. • If the amount of training time for the MLP is increased, it may do better.

More Related