Tez özetleri Astronomi ve Uzay Bilimleri Anabilim Dalı

Demand Planning Methods in Enterprise Resources Planning Software and Applications

In  today’s industries which has hard competition and short production lifecycle, companies prefer customer focused production. Now companies can not sell whatever they produce as before and wrong production investments or wrong production desicions causes lot of bad results such as high inventory level, losing customer or losing competition force and at the end getting weaker in the market.

With this improvement of the industry, companies have to forecast what their customers wants and how much they need to produce before customer demands. So companies bended demand forecasting methods and because of this lot’s of forecasting methods and tools improved.

Judgmental methods, causal methods, time series methods, stochastic methods and artificial intelligence solutions are the methods  improved for demand forecasting of companies in different industries. By time data needed for forecast increases and the methods get more complex so for this reason lots of forecasting software or other softwares including forecasting functions are improved.

Lot’s of companies prefer ERP softwares for institutionalization but little of them use forecasting tools of this softwares. ERP sotfwares are powerful and enough in time series forecasting.  But time series forecasting tools forecast next periods sales by only taking past sales values. However ERP softwares has more useful data for forecasting. In practice with notice of this idea, linear relationship between  average currency rates and customer satisfaction with sales,  study with corelation and regression; nonlinear reletionship  study with fuzzy sets. 

In practice this methods gives very good results. The results show that if the methods studied in the thesis integrated with ERP softwares; sotfwares can be used more efficiently, companies get more values from this softwares by giving same amount of data and the most important thing is that companies can get more accuracate forecasts.