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All Journal Journal of Engineering and Management in Industrial System Angkasa: Jurnal Ilmiah Bidang Teknologi
Sinta Rahmawidya Sulistyo
Universitas Gadjah Mada
Computer Science & IT Industrial & Manufacturing Engineering

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LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP Sulistyo, Sinta Rahmawidya; Sutrisno, Alvian Jonathan
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 10, No 2 (2018): November
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2105.33 KB)

Abstract

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.
DEVELOPMENT OF AN ENVIRONMENTALLY-FRIENDLY LOGISTICS MODEL BY INTEGRATING DECISIONS OF LOCATION, MULTI-CAPACITY VEHICLE, AND ROUTING PROBLEM Artya Lathifah; Sinta Rahmawidya Sulistyo; Izzawi Winda Murti; A.A.N Perwira Redi
Journal of Engineering and Management in Industrial System Vol 6, No 2 (2018)
Publisher : Badan Penerbit Jurnal, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1364.518 KB) | DOI: 10.21776/ub.jemis.2018.006.02.1

Abstract

Transportation and distribution are two things that are closely related to logistics problems However, on the other hand this activity can sometimes damage the environment. Emissions from fuels used in transportation and distribution activities accounted for 29.4% of the total costs incurred by the organization in their activities. From this issue many organizations finally make environmentally friendly logistics as priority in their activities, where the goal of minimizing distribution costs and maintaining sustainability of environments. Some factors that can be improved are: determination of the location depot, combination of vehicle and the route. Therefore, this study aims to develop mathematical model that optimize these three factors integration to minimize the emission cost. The results of this research are the mathematical model, optimization of the development of the Simulated Annealing (SA) method that is applied to the problem is able to get a reduction in total emissions costs up to 18.8%.
LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP Sinta Rahmawidya Sulistyo; Alvian Jonathan Sutrisno
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 10, No 2 (2018): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2105.33 KB) | DOI: 10.28989/angkasa.v10i2.362

Abstract

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.
Co-Authors A.A.N Perwira Redi Alvian Jonathan Sutrisno Artya Lathifah Izzawi Winda Murti Sutrisno, Alvian Jonathan