scholarly journals AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND

Transport ◽  
2008 ◽  
Vol 23 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Xin Miao ◽  
Bao Xi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Kalman Filter ◽  
Influencing Factors ◽  
Extended Kalman Filter ◽  
Comparison Analysis ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Logistics Demand

The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results. Comparison analysis shows that the forecasting method has better reliability for agile forecasting of dynamic logistics demand.

Estimation of Critical Platform Integrity Parameters in the Absence of Direct Measurements in the Context of Integrated Marine Monitoring Systems

2013 ◽  
Author(s):  
Soma Maroju ◽  
Kevin Delaney ◽  
Christopher Leon ◽  
Igor Prislin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kalman Filter ◽  
Missing Data ◽  
Extended Kalman Filter ◽  
Working Condition ◽  
Operational Risk ◽  
Monitoring Systems ◽  
Marine Monitoring ◽  
Direct Measurements

Integrated Marine Monitoring Systems (IMMS) are designed to help operators to reduce operational risk by providing information about the environment and the platform responses in real time. In spite of efforts to keep monitoring systems in working condition by following planned maintenance and upgrades, some sensors may fail intermittently or may generate spurious data. Quite often, intervention to repair or to replace a faulty sensor is either difficult, or even not feasible. This paper discusses various methods to estimate critical platform integrity parameters with satisfactory confidence in the cases when direct measurements are temporarily unavailable or questionable. Methods such as Artificial Neural Network and Extended Kalman Filter have been employed and specifically tuned to particular challenges. Estimated results for the missing data, such as platform position or riser loads, are reliable as they have been validated against historically good data. The merit of the paper is to present the methods that can increase reliability of the IMMS, enhance safety, reduce operational risk and decrease cost in maintaining expensive offshore systems.

Survey on Natural Gas Load Forecasting Technology

2012 ◽  
Vol 485 ◽  
pp. 249-252
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Natural Gas ◽  
Statistical Learning ◽  
Learning Theory ◽  
Statistical Learning Theory ◽  
Load Forecasting ◽  
Future Research ◽  
Forecasting Methods ◽  
Forecasting Method

In this paper, we give a short survey on natural gas load forecasting technology. Because the variation of gas load is influenced by weather and people activity, the traditional forecasting results and the precision is low. The forecasting methods based on statistical learning theory and artificial neural network are discussed in detail. We also give some future research on the forecasting method of gas load.

scholarly journals Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain

2019 ◽  
Vol 13 (2) ◽  
pp. 4816-4834
Author(s):  
E. Fradinata ◽  
S. Suthummanon ◽  
W. Suntiamorntut ◽  
Muhamad Mat Noor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Support Vector Regression ◽  
Bullwhip Effect ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Inventory Policy ◽  
Forecasting Method ◽  
Artificial Neural

The objective of this study was to compare the Bullwhip Effect (BWE) in the supply chain through two methods and to determine the inventory policy for the uncertainty demand. It would be useful to determine the best forecasting method to predict the certain condition. The two methods are Artificial Neural Network (ANN) and Support Vector Regression (SVR), which would be applied in this study. The data was obtained from the instant noodle dataset where it was in random normal distribution. The forecasting demands signal have Mean Squared Error (MSE) where it is used to measure the bullwhip effect in the supply chain member. The magnification of order among the member of the supply chain would influence the inventory. It is quite important to understand forecasting techniques and the bullwhip effect for the warehouse manager to manage the inventory in the warehouse, especially in probabilistic demand of the customer. This process determines the appropriate inventory policy for the retailer. The result from this study shows that ANN and SVR have the variance of 0.00491 and 0.07703, the MSE was 1.55e-6 and 1.53e-2, and the total BWE was 95.61 and 1237.19 respectively. It concluded that the ANN has a smaller variance than SVR, therefore, the ANN has a better performance than SVR, and the ANN has smaller BWE than SVR. At last, the inventory policy was determined with the continuous review policy for the uncertainty demand in the supply chain member.

scholarly journals LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP

2018 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Sinta Rahmawidya Sulistyo ◽  
Alvian Jonathan Sutrisno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Spare Part ◽  
Service Level ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Spare Parts Inventory

Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order to gain more understanding on the effect of the forecasting method on spare parts inventory management, inventory simulation using oil and gas company’s data is then conducted. Two inventory parameters; average inventory and service level; are used to measure the performance. The result shows that ANN is found to be the best method for spare part forecasting with MASE of 0,761. From the inventory simulation, the appropriate forecasting method on spare parts inventory management is able to reduce average inventory by 11,9% and increase service level by 10,7%. This result justifies that selecting the appropriate forecasting method is one of the ways to achieve spare part inventory management’s goal.

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