MENENTUKAN JUMLAH PRODUKSI KAYU BARE CORE MENGGUNAKAN METODE PERENCANAAN ZERO INVENTORY

Encountering the playwood industry competition, companies have to maintain the production flow by producing good quality products, exact production time, and lower production cost. A timber factory that produces bare core in the difficulty to deal with the challenge which is caused by the amount of the work forces that are not proportional with the goods which are being produced, and also bad decision of work time causing the high number of salary to the income of the company become lower. The effort that have to be done are adjust the number of the work force and standard time in order to be proportional to the goods which are going to be produced. Through the zero inventory method, company will only produce goods that are needed by the consumers.The number of the work force will increase when the demand is increasing and will lower the work force when the demand is discreasing, Production plan analysis is conducted to ease the work force number determining process. Hence, the at last the minimum cost production could be determined. The calculation result shows that the costs which are spent by the company on May 2017 is the highest in the amount of Rp 2.306.804 and the lowest cost spent is on December 2017 in the amount of Rp. 1.545.906.000.This strategy is proposed based on the cost calculation which is more precisely spent, resulting to minimized spending and limited without cost calculation, recruiting investment, and workforce training.

Research on Prediction Model of Car Sales Based on Grey Neural Network

Nowadays, zero inventory and just in time production and management technology are got attention by more and more domestic and foreign automobile enterprises. Accurate prediction of car sales is the key technology for ensuring zero inventory and product planning of enterprises. In this paper, a prediction model about car sales based on grey neural network was researched. Combining the advantages of grey model in the way of dealing of small sample data, and BP neural network in finding regularity of weak correlation data, the accuracy of the prediction results was improved. Finally, the model was validated by practical application of automobile enterprise production data.