scholarly journals Perancangan Distribusi Produk Tepung Bumbu PT.SI Dengan Metode Distribution Requirement Planning

2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Marcia Devana ◽  
Nunung Nurhasanah

<div class="WordSection1"><p><strong>PT. SI is an FMCG (Fast Moving Consumer Goods) of seasoning and foods corporate in Indonesia. PT. SI’s products such as MNG (Mono Natrium Glutamate), Seasoning Flour (SF), a range of condiments, coconut milk, and instant seasoning. PT SI has SF product’s selling data from 2017 until 2020. However, that data yet to be used optimally. That data could be processed further for the organization’s advantage. For example, to knowledge the trend that’s happening, and, for predicting sales value with doing some forecasting and designing DRP. Based on the calculation of the forecasting, we’re obtained the best results using Double Exponential Smoothing by Brown, whereas Mean Absolute Percentage Error’s value is the smallest between the other methods. DRP’s methods make scheduling for the demand of products for determining times and the amounts that needed, and determining plans for arriving products to anticipates sales. With the scheduling, therefore, the distributions can go smoothly and meet the needs of every distribution center. For acknowledging the amounts of trucks that we use, the sums of PORelease is divided with the trucks’ capacities which are 18 tonnages for one delivery, therefore, the delivery costs that we got are multiplied by the transport’s cost for every destination. For distribution costs, company costs are as much as Rp. 20,831,275,897 whereas with DRP method acquired the value for Rp. 17,611,094,522 margins 15,45% from company cost.</strong></p><p><strong><em>Keywords – </em></strong><em>Distribution Requirement Planning, Forecasting, Distribution cost.</em></p></div>

Omega ◽  
2020 ◽  
pp. 102389
Author(s):  
Xavier Andrade ◽  
Luís Guimarães ◽  
Gonçalo Figueira

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Faisal Iqbal ◽  
Muhammad Zahid ◽  
Durdana Habib ◽  
Lizy Kurian John

Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.


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