Inventory management optimization model with database synchronization through internet network (A simulation study)

Sutanto, Yusuf; Sarno, Riyanarto

doi:10.1109/iceei.2015.7352480

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Compared with the above research work that intended to review inventory management, many other work were more inclined to study the technical aspects of inventory management. For example, reference [12] focused more on addressing the unnecessary out-of-stock and the oversupply issues that happened during the process of inventory management. In order to address these issues, this work proposed to optimize both the transport and inventory, such that the strategies of the single inventory and bulk order were jointly taken into consideration.…”

Section: Related Workmentioning

confidence: 99%

“…By simulation and calculation, the results indicated that the proposed distributed inventory model was correct and effective. However, it should be noted that both references [12,13] did not consider the uncertainty of the market, which may cause great loss especially when the oversupply situations happen.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this awkward situation, some other related studies tried to adapt to the actual supply and demand ratio by using the strategy of single-point inventory and bulk order. For instance, the single-point inventory and bulk order strategy was used by the reference [12] to achieve a reliable inventory management. Despite this, this singlepoint inventory means a centralized processing method, which always suffers from the performance bottleneck.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning‐Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection

Deng

Liu

2021

Wireless Communications and Mobile Computing

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The rapid development of emerging technologies such as machine learning and data mining promotes a lot of smart applications, e.g., Internet of things (IoT). The supply chain management and communication are a key research direction in the IoT environment, while the inventory management (IM) has increasingly become a core part of the whole life cycle management process of the supply chain. However, the current situations of a long supply chain life cycle, complex supply chain management, and frequently changing user demands all lead to a sharp rise in logistics and communication cost. Hence, as the core part of the supply chain, effective and predictable IM becomes particularly important. In this way, this work intends to reduce the cost during the life cycle of the supply chain by optimizing the IM process. Specifically, the IM process is firstly formulated as a mathematical model, in which the objective is to jointly minimize the logistic cost and maximize the profit. On this basis, a deep inventory management (DIM) method is proposed to address this model by using the long short-term memory (LSTM) theory of deep learning (DL). In particular, DIM transforms the time series problem into a supervised learning one and it is trained using the back propagation pattern, such that the training process can be finished efficiently. The experimental results show that the average inventory demand prediction accuracy of DIM exceeds about 80%, which can reduce the inventory cost by about 25% compared with the other state-of-the-art methods and detect the anomaly inventory actions quickly.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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