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.

scholarly journals Analisis Pengendalian Persediaan Spare Part Mesin Produksi di PT. Prima Sejati Sejahtera dengan Metode Continuous Review

2017 ◽  
Vol 16 (2) ◽  
Author(s):  
Endah Budiningsih ◽  
Wakhid Ahmad Jauhari
Keyword(s):  
Inventory Control ◽  
Inventory Management ◽  
Control Method ◽  
Spare Part ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Optimal Number ◽  
Continuous Review ◽  
Forecasting Method ◽  
Single Exponential

<em>PT. Prima Sejati Sejahtera as one of the subsidiaries of PT. Pan Brothers Tbk. which is engaged in garment production. The company's mechanical department in managing spare part inventory is still using intuitive method, where the number of spare part order for certain periods based on spare part demands data onto the previous period. The company’s mechanical department often stock out of spare parts. Spare part’s inventory management becomes a complex issue because of the need for fast response to handle the downtime of machines, and the risk of obsolescence of spare parts. So in this research will discuss about spare part inventory control which is started with spare parts grouping by using ABC analysis method to determine the appropriate inventory control method for each group. There are 23 spare parts which included in group A. The forecast of spare part’s demands to use Croston, Syntetos-Boylan Approximation (SBA) and Single Exponential Smoothing (SES). Comparison of each forecasting method will be determined by the value of forecasting errors (MAD). It is known that there are 12 spare parts with Croston method in the best forecasting method, 6 spare parts in Syntetos-Boylan Approximation (SBA) method and 5 spare parts with Single Exponential Smoothing (SES) method. Based on the best forecasting result, it will be calculated the value of safety stock (SS), reorder point (ROP) and the optimal number of ordering (Q) using Continuous Review method for each spare part.</em>

scholarly journals Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 55-58
Author(s):  
Nandita Barman ◽  
M Babul Hasan ◽  
Md Nayan Dhali
Keyword(s):  
Time Series ◽  
Inventory Management ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Demand And Supply ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Exponential Smoothing Method ◽  
Manufacture Company

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed. Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

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.

scholarly journals Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shoujing Zhang ◽  
Xiaofan Qin ◽  
Sheng Hu ◽  
Qing Zhang ◽  
Bochao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Inventory Management ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Spare Part ◽  
Back Propagation Neural Network ◽  
Spare Parts ◽  
Training Data ◽  
Importance Value ◽  
The Impact

The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management. Most of the methods used in previous research are subjective. For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper. First, we classified the spare parts by analyzing their historical maintenance and inventory data. Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data. Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts. The previous importance evaluation methods were susceptible to subjective factors during the evaluation process. The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data. The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation. At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.

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 UPAYA IMPROVEMENT PENGENDALIAN PERSEDIAAN SUKU CADANG DENGAN METODE FIXED TIME PERIOD PADA PT XYZ

2019 ◽  
Vol 3 (2) ◽  
pp. 129-140
Author(s):  
Sazli Tutur Risyahadi (Universitas IPB - Indonesia) ◽  
Hanifah Yunan Putri (Universitas IPB - Indonesia)
Keyword(s):  
Standard Deviation ◽  
Inventory Management ◽  
Fixed Time ◽  
Service Level ◽  
Spare Parts ◽  
Order Quantity ◽  
Time Intervals ◽  
Time Period ◽  
Spare Parts Inventory ◽  
Abc Analysis

Abstract  Perusahaan yang memiliki keinginan untuk memenangkan persaingan yang terus meningkat di era globalisasi, perlu terus menerus melakukan perbaikan metode pengendalian persediaan suku cadangnya. Metode existing di perusahaan memiliki beberapa kekurangan seperti belum mempertimbangkan standar deviasi demand dan service level yang dikehendaki oleh perusahaan. Tujuan kajian ini adalah melakukan perbaikan pengelolaan persediaan suku cadang dengan menerapkan model yang memiliki karakteristik pengadaan yang sesuai dengan perusahaan. Model Fixed Time Period (FTP) adalah model yang sesuai karena telah memenuhi karakteristik pengelolaan persediaan di perusahaan seperti pemesanan barang dengan interval waktu yang konstan dan demand yang berfluktuatif. Hasil menunjukkan bahwa jumlah pemesanan pada suku cadang bandsaw dengan menggunakan metode existing perusahaan di tahun 2018 selalu lebih tinggi quantity ordernya dibandingkan dengan menggunakan metode FTP. Berbeda dengan suku cadang bandsaw, suku cadang thermoc, jumlah order dengan metode FTP tidak selalu lebih rendah; bahkan metode FTP seringkali lebih tinggi atau pun sama quantity ordernya pada tahun 2018.  Kata Kunci: Analisis ABC, Metode Fixed Time Period, Manajemen Inventori The company that has the desire to winning the increasing competition in the era of globalization need to improve the method of spare parts inventory control continually. Existing approaches in companies have several disadvantages such as not considering the standard deviation of demand and service level desired by the company. The purpose of this study is to improve the management of spare parts inventories by implementing a model that have right characteristics that are in line with the company. The Fixed Time Period (FTP) model is an appropriate model because it has fulfilled the characteristic of inventory management in companies such as ordering goods with constant time intervals and fluctuating demand. The results show that the number of orders on bandsaw parts using the existing company method in 2018 always has a higher order quantity than using the FTP method. Unlike bandsaw parts, thermoc parts, the number is not still lower; even the FTP method was often higher or equal to the order quantity in 2018. Keywords: ABC Analysis, Fixed Time Period Methods, Inventory Management

An EOQ Based Multi-Storage Location of Spare Part Inventories: A Case Study

2013 ◽  
Vol 315 ◽  
pp. 733-738 ◽  
Author(s):  
Noor Ajian Mohd-Lair ◽  
Chuan Kian Pang ◽  
Willey Y.H. Liew ◽  
Hardy Semui ◽  
Loh Zhia Yew
Keyword(s):  
Inventory Management ◽  
Spare Part ◽  
Spare Parts ◽  
Inventory Level ◽  
Management Technique ◽  
Inventory Management System ◽  
Spare Parts Inventory ◽  
Storage Location ◽  
Manual Recording

Spare parts inventory management is very important to ensure smooth operation of maintenance department. The main objectives of inventory management of spare parts are to ensure the availability of spares and materials for the maintenance tasks and increase the productivity of the maintenance department. This research centred on the development of the Computerised Inventory Management System (CIMS) for the maintenance team at Weida Integrated Industries Sdn. Bhd. The inventory management technique used to control the spare parts inventory in this research was the basic Economic Order Quantity models (EOQ). However, the CIMS developed is unique as it has the ability in handling inventories in multiple-storage locations. The CIMS was written using the Visual Basic 2010 software. This CIMS has the abilities to keep records and process the spare parts information effectively and faster besides helping the user to perform spare parts ordering tasks compared to the current manual recording. In addition, the ordering quantity and frequency for the CIMS is determined through the EOQ technique. However, observation indicates that the overall average inventory level currently at the factory is lower than the expected overall average inventory level produced by the CIMS. This is due to the fact that the CIMS was unable to consider the opening stock in ordering the inventories. Therefore, further improvements are needed to optimize the performance of the system such as using the EOQ with the reorder point technique, the periodic or continuous review system.

scholarly journals Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

2021 ◽  
Vol 11 (15) ◽  
pp. 7088
Author(s):  
Ke Yang ◽  
Yongjian Wang ◽  
Shidong Fan ◽  
Ali Mosleh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Inventory Management ◽  
Hierarchical Classification ◽  
Critical Issue ◽  
Spare Parts ◽  
Deep Convolutional Neural Network ◽  
Learning Ability ◽  
Second Phase ◽  
Good Decision

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

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