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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?

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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
  • 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!
managing inventory3
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.
prediction problems
Prediction Problems
  • Many products are highly seasonal:
prediction problems5
Prediction Problems
  • Some have impossible to predict buying “spikes”:
prediction
Prediction
  • Fortunately, others show similar patterns each year:
approach
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
approach8
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.
mlp results
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.
simulated annealing results
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.
conclusion
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.