Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: lumpy demand

Sortuj według:

Ogranicz wyniki do:

Comparing Probabilistic Economic Order Quantity and Periodic Order Quantity Model Performance Under Lumpy Demand Environment

Nurprihatin Filscha, Rembulan Glisina Dwinoor, Pratama Yohanes Dwi

Management and Production Engineering Review

2022

Vol. 13, No. 4

16--25

Improper planning of inventory will affect the factory operating costs, building costs, the cost of loss, and the cost of product defects due to being stored for too long which will eventually become a loss. This research discusses the processing industry which is experiencing lumpy demand. In carrying out the production process, the company has never made plans for future demand, resulting in a waste of message costs due to repeated orders of raw materials ordered to suppliers. This paper contributes to overcoming this issue by simulating future demand by using the Material Requirement Planning (MRP) method with a probabilistic Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ) model. The demand in the coming period is determined using the Autoregressive Integrated Moving Average (ARIMA) method, and an aggregate plan is carried out to determine the regular cost of raw material production and optimal subcontracting. The final analysis states that the calculation of MRP on the selected items using POQ produces the lowest cost for planning S45C-F, SGT-R, and SKD11-R, while SLD-R uses the probabilistic EOQ method.

Artificial intelligence methods in spare parts demand forecasting

Rosienkiewicz M.

Logistics and Transport

2013

Vol. 18, No. 2

41--50

The paper discusses the problem of forecasting lumpy demand which is typical for spare parts. Several prediction methods are presented in the article – traditional techniques based on time series and advanced methods that use Artificial Intelligence tools. The research conducted in the paper focuses on comparison of eight forecasting methods, including classical, hybrid and based on artificial neural networks. The aim of the paper is to assess the efficiency of lumpy demand forecasting methods that apply AI tools. The assessment is conducted by a comparison with traditional methods and it is based on Root Mean Square Errors (RMSE) and relative forecast errors (ex post) values. The article presents also a new approach to the lumpy demand forecasting issue – a method which combines regression modelling, information criteria and artificial neural networks.