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 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 AIRCRAFT SPARE PARTS INVENTORY MANAGEMENT ANALYSIS ON AIRFRAME PRODUCT USING CONTINUOUS REVIEW METHODS

2020 ◽  
Vol 2 (1) ◽  
pp. 81-90
Author(s):  
Faikar Ridwan Harimansyah ◽  
Tukhas Shilul Imaroh
Keyword(s):  
Inventory Management ◽  
Cost Efficiency ◽  
Moving Average ◽  
Demand Forecasting ◽  
Service Level ◽  
Spare Parts ◽  
Continuous Review ◽  
Planning Methods ◽  
Abc Analysis ◽  
Single Exponential

The research  aims to find the factors that cause high inventory value, increase the value of forecasting precision, service level and cost efficiency with fishbone diagrams and proposed methods. The research sample is 9 spare parts included in classification A in the ABC analysis and maintenance list 2018. Forecasting methods use Moving Average, Single Exponential Smoothing and Syntetos-Boylan Approximation as well as Mean Square Error calculation, deterministic inventory calculation and Continuous Review Method. The results of this study are an increase in logistics costs by $ 808.71 in the inventory management proposal. An increase in service level from 95% to 99% and the error value in the calculation of the proposal becomes smaller using the proposed method. This study also found that the factor causing the high inventory value was due to inaccurate planning methods so that other comparative methods were needed that could increase the precision of demand forecasting.

scholarly journals Central Jakarta Rainfall Intensity Forecast using Single Exponential Smoothing

2019 ◽  
Vol 8 (4) ◽  
pp. 2105-2108
Keyword(s):  
Rainfall Intensity ◽  
Forecast Error ◽  
Precipitation Amount ◽  
Exponential Smoothing ◽  
Vital Role ◽  
Capital City ◽  
Good Prediction ◽  
Intensity Prediction ◽  
Forecasting Method ◽  
Single Exponential

Rainfall is the precipitation amount that is falling from clouds. In extreme conditions, rainfall could arise many problems. It is the leading cause of landslides and flood disasters. In D.K.I. Jakarta, the capital city of Indonesia, rainfall intensity plays a very vital role since it could easily be puddled and caused floods in many areas. Therefore, in this study, we try to make a rainfall intensity prediction in Central Jakarta using a very popular forecasting method, i.e., the Single Exponential Smoothing (SES). Based on the experiments conducted using Phatsa, it can be concluded that the SES method has been successfully used to predict rainfall intensity. However, it cannot give a very good prediction result due to its high forecast error values.

scholarly journals The Lean Management of Spare Parts in Automotive Manufacturing

2018 ◽  
Vol 214 ◽  
pp. 04005
Author(s):  
Dongdong Guo ◽  
Xingwu Yu
Keyword(s):  
Inventory Management ◽  
Spare Part ◽  
Spare Parts ◽  
Automotive Manufacturing ◽  
Purchasing Cost ◽  
Manufacturing Enterprises ◽  
Spare Parts Management ◽  
Purchase Process ◽  
Spare Part Management ◽  
Maintenance Process

Spare part management is one of the most important work for enterprises, especially for manufacturing enterprises; however, the spare part management problems trouble enterprise operators a lot. In this article, implementation methods of lean spare parts management are illuminated. Spare parts purchase process is declared to reduce the purchasing cost and inventory value. We had established a suitable lean spare parts inventory management model for consumable parts, wear parts, insurance parts and accident parts. In addition, methods of lean spare parts management had been created base on optimized supply chain, ERP and integrating repeated material inventory. We used SAP-iPro system and self-developed system to manage spare parts, so that warehouse management process, spare parts purchase process and maintenance process are standardized. According to theory analysis and practice, the remarkable economic benefit is created for enterprise by the means of optimizing spare parts distribution, standardizing and scientific spare parts management.

Multistage Inventory Control Research of Engineering Ships' Spare Parts

2014 ◽  
Vol 909 ◽  
pp. 236-240
Author(s):  
Quan Wen ◽  
Shi Dong Fan ◽  
Pan Jiang ◽  
Shou Hui He
Keyword(s):  
Control Theory ◽  
Inventory Control ◽  
Control Method ◽  
Spare Parts ◽  
Control Research ◽  
The Cost ◽  
Task Types ◽  
Construction Task

This paper aims to using multistage inventory control theory to optimize traditional inventory control method, meanwhile, the spare parts supply of engineering ships are guaranteed and keep the cost in the lowest level. Moreover, in accordance with all kinds of construction task types to ensure the spare parts supply to satisfy engineering ships demand, and reduce the situation of shutdown unattended which is resulted by spare parts supply not in time.

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</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 Peramalan Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hisyam Ihsan ◽  
Rahmat Syam ◽  
Fahrul Ahmad
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Trial And Error ◽  
Squared Error ◽  
Forecasting Method ◽  
Operational Activities ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on  these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.

scholarly journals Decreasing Total Inventory Cost by Controlling Inventory in Motorcycle Dealer

2020 ◽  
Vol 22 (2) ◽  
pp. 41-49
Author(s):  
David ◽  
Engmir ◽  
Irwan Budiman ◽  
Jusra Tampubolon
Keyword(s):  
Inventory Management ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Order Quantity ◽  
Inventory Cost ◽  
Safety Stock ◽  
Safety Stocks ◽  
Forecasting Method ◽  
Total Inventory ◽  
Abc Analysis

This research was conducted at one of the motorcycle dealers in Indonesia. Besides selling motorcycles, this dealer also provides services to repair motorcycles and sells genuine motorcycle parts. Inventory management which the company carried out is still not good enough because there are still demand for spare parts from consumers that cannot be fulfilled by the company. The purpose of this study is to draw up a plan to control spare parts by paying attention to the spare parts that need to be considered, estimating the exact number of spare parts demand, knowing the smallest total inventory cost, knowing the amount of safety stock needed, and knowing when to reorder. In preparing the spare parts control, the methods used are ABC analysis, demand forecasting method, and EOQ method. The results of this study are plans to control the inventory of Tire, Rr. such as the forecasting sales of Tire, Rr. as many as 17338, economic order quantity of Tire Rr are 2158 units, the number of safety stocks of Tire, Rr. needed in 2020 are 1738 units, and the reorder point in 2020 is 8 times with the total inventory cost for Tire, Rr. in 2020 is Rp. 30,009,005.

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