Neural network system for operations management

Distribution centers and warehouses are becoming more and more dependent on advanced computer technologies to establish and maintain competitiveness in a global economy. Neural network represent a new technology with a wide scope of potential warehouses applications, ranging from planning and forecasting to overall performance. In this dissertation, numerous results are showing increases in warehouse performance, when using neural network technology. The neural network system is used as a forecasting tool. It is then compared to time series forecasting analysis. The comparison process is designed to increase the warehouse performance understudy. At the end of this process, the results are forecasting variables needed to eventually increase warehouse performance and efficiency. Initially, neural networks along with time series are used to make the forecast on inventory control. Then the following step is to let different neural network modules perform the forecasting analysis on other management operations like inventory adjustments, accuracy and turnover, customer complaints and labor productivity for any distribution center or warehouse. The concept of benchmarking is also used, in order to provide tools which will help warehouse management determining performance levels for each subcomponent of the warehouse operations, and consequently the overall performance of the warehouse or distributor center taken into consideration after feeding in the appropriate data to the system.