Replenishment Strategy Based on Historical Data and Forecast of Safety Stock

Ongkicyntia, Allysia; Rahardjo, Jani

doi:10.1109/icsiit.2017.65

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Furthermore, the researchers argued that GMDH has good performance in accuracy with simply operation. Ongkicyntia and Rahardjo [22] considered that SS is between forecast data and historical data. In this paper, we tried to fuse forecast value based on neural networks (GMDH) and normal distribution (forecast value of historical data) to approximate the SS truth value.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Given the numerous irrelevant variables found in input, the GMDH-type neural network performs self-learning and forecast through screening criteria [20]. Dempster-Shafer (D-S) data fusion function is used to fuse the forecast data from GMDH-type neural network algorithm and normal distribution, which is an effective method for SS determination [21,22]. Company profitability is directly related to oil price and inventory.…”

Section: Introductionmentioning

confidence: 99%

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A Procurement Method in Oil Marketing Company Based on Forecast Model and Expectation Criterion

Zhi-hong

Wang

2021

Mathematical Problems in Engineering

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Within the oil marketing operation, various entities compete and attempt to maximize their proﬁts by providing sufficient supply to meet needs of market. It is an optimal method for oil marketing company operation with a dynamically reasonable inventory to maximize proﬁt under oil price fluctuation and inventory sales lag. In this paper, we study the optimal procurement method of oil marketing company which confirms the reasonable inventory. We build a data fusion model for the GMDH- (group method of data handling-) type neural network and normal distribution forecast results, what is trying to confirm the safety stock (SS). On the basis of the expectation criterion of risk decision and safety stock limit, oil marketing companies can make scientific purchase decision for inventory income. Numerical results reveal that this method has a good effect for inventory income.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%