Innovation in Scientific Knowledge Based on Forecasting Assessment

2019 ◽  
pp. 247-263
Author(s):  
Ignacio Aranís Mahuzier ◽  
Pablo A. Viveros Gunckel ◽  
Rodrigo Mena Bustos ◽  
Christopher Nikulin Chandía ◽  
Vicente González-Prida Díaz
Keyword(s):  
Scientific Knowledge ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Moving Averages ◽  
Intermittent Demand ◽  
Automotive Market ◽  
Knowledge Based ◽  
Automotive Company ◽  
Forecasting Methods ◽  
Historical Series

This chapter presents a study of forecasting methods applicable to the spare parts demand faced by an automotive company that maintains a share of nearly 25% of the automotive market and sells approximately 13,000 parts per year. These parts are characterized by having intermittent demand and, in some cases, low demand, which makes it difficult for such companies to perform well and to obtain accurate forecasts. Therefore, this chapter includes a study of methods such as the Croston, Syntetos and Boylan, and Teunter methods, which are known to resolve these issues. Furthermore, the rolling Grey method is included, which is usually used in environments with short historical series and great uncertainty. In this study, traditional methods of prognosis, such as moving averages, exponential smoothing, and exponential smoothing with tendency and seasonality, are not neglected.

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.

Parameter optimization of intermittent demand forecasting by using spreadsheet

Kybernetes ◽  
2015 ◽  
Vol 44 (4) ◽  
pp. 576-587 ◽  
Author(s):  
Gamze Ogcu Kaya ◽  
Omer Fahrettin Demirel
Keyword(s):  
Parameter Optimization ◽  
Demand Forecasting ◽  
Real Data ◽  
Spare Parts ◽  
Aviation Industry ◽  
Intermittent Demand ◽  
Demand Characteristics ◽  
Content Type ◽  
Forecasting Methods ◽  
Smoothing Parameters

Purpose – Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization. Design/methodology/approach – Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total. Findings – From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods. Research limitations/implications – Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry. Practical implications – This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods. Originality/value – The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

scholarly journals Intermittent demand forecasting for aircraft inventories: a study of Brazilian’s Boeing 737NG aircraft´s spare parts management

TRANSPORTES ◽  
2019 ◽  
Vol 27 (2) ◽  
pp. 102-116
Author(s):  
Jersone Tasso Moreira Silva ◽  
Luiz Henrique Santos ◽  
Alexandre Teixeira Dias ◽  
Hugo Ferreira Braga Tadeu
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Next Generation ◽  
Intermittent Demand ◽  
Spare Parts Management ◽  
Single Exponential ◽  
Airline Company

Este estudo tem como objetivo avaliar cinco métodos de previsão para demanda intermitente usando uma série histórica de consumo de peças sobressalentes da aeronave 737 Next Generation, fabricado pela Boeing, da maior frota aérea brasileira gerenciada pela VRG Airline Company S/A. Os métodos de Winter, Croston, Single Exponential Smoothing, Weight Moving Average e Método de Distribuição de Poisson foram testados em um histórico de 53 peças sobressalentes e cada uma delas possui um histórico de demanda de trinta e seis meses (janeiro de 2013 a dezembro de 2015). Os resultados mostraram que os métodos Weight Moving Average, Distribuição de Poisson e Croston apresentaram os melhores ajustes. Além disso, observou-se que a maior parte das demandas por peças sobressalentes apresentaram um padrão smooth ao contrário do resultado obtido pelo estudo de Ghobbar and Friend (2003) que apresentou um padrão lumpy. Por outro lado, tem-se que o Método de Winter apresentou-se como o de pior ajuste em ambos os estudos. Conclui-se que os métodos de Weight Moving Average e Distribuição de Poisson são os mais adequados para avaliar a demanda intermitente para o caso da VRG Airline Company S/A.

scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

TAPPING REGIONAL AND CORPORATE SCIENTIFIC KNOWLEDGE FOR INNOVATION: THE MODERATING ROLE OF SCIENTIFIC KNOWLEDGE DURATION

2015 ◽  
Vol 19 (01) ◽  
pp. 1550001
Author(s):  
OLGA BRUYAKA ◽  
FIONA XIAOYING JI ◽  
LINDA F. TEGARDEN ◽  
DONALD E. HATFIELD ◽  
WILLIAM B. LAMB
Keyword(s):  
Scientific Knowledge ◽  
Moderating Effect ◽  
Knowledge Based ◽  
Business Entities ◽  
Moderating Role

We develop and test how business entities tap regional and corporate scientific knowledge for their innovations in a revolutionary technology. Building on the knowledge-based view of the firm, we argue that while both regional and corporate scientific knowledge may improve a business entity's innovation, it is the business entity's own accumulative research efforts that improve its ability to absorb regional scientific knowledge. In contrast, we expect and find that the longer the entity has been researching the revolutionary technology, the less likely corporate scientific knowledge will impact the focal entity's innovation. Our results support this theory regarding the moderating effect of a business entity's own accumulative research efforts with corporate scientific knowledge but not with regional knowledge. Further, we find different effects between regional scientific knowledge and innovation among single and multi-location firms.

scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.

scholarly journals Peramalan Kurs IDR Terhadap USD Menggunakan Double Moving Averages Dan Double Exponential Smoothing

2019 ◽  
Vol 2 (2) ◽  
pp. 48-55
Author(s):  
Padrul Jana ◽  
Rokhimi Rokhimi ◽  
Ismi Ratri Prihatiningsih
Keyword(s):  
Exponential Smoothing ◽  
Moving Averages ◽  
Double Exponential ◽  
Double Exponential Smoothing

Kurs IDR terhadap USD yang fluktuatif sangat mempengaruhi ekonomi Indonesia saat ini, dibutuhkan suatu metode untuk meramalkan Kurs IDR terhadap USD agar bisa diprediksi. Diharapkan  para pemangku kepentingan segera mengambil kebijakan strategis demi stabilitas ekonomi nasional. Metode peramalan dalam tulisan ini menggunakan Double Moving Averages dan Double Exponential Smoothing dengan . Hasil peramalan menggunakan metode Double Moving Averages diperoleh IDR/USD, IDR/USD, IDR/USD dan Double Exponential Smoothing diperoleh IDR/USD, IDR/USD, IDR/USD. 14"> Kata Kunci: IDR, USD, Double Moving Averages, Double Exponential Smoothing.

scholarly journals Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kyle C. McDermott ◽  
Ryan D. Winz ◽  
Thom J. Hodgson ◽  
Michael G. Kay ◽  
Russell E. King ◽  
...  
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Production Capacity ◽  
Spare Part ◽  
Spare Parts ◽  
Mixed Integer ◽  
Network Configuration ◽  
Intermittent Demand ◽  
Content Type ◽  
Demand Patterns

PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

scholarly journals Kajian Peramalan dan Pengaruh Profitabilitas terhadap Harga Saham pada Perusahaan Makanan dan Minuman Tahun 2011-2016

2017 ◽  
Vol 8 (1) ◽  
pp. 75-88
Author(s):  
Octaviani Hutahaean ◽  
Abdul Basith
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Profit Margin ◽  
Moving Averages ◽  
Return On Assets ◽  
Return On Equity ◽  
Purposive Sampling ◽  
Net Profit ◽  
Weighted Moving Average

Laju pertumbuhan industri terbesar selama tahun 2011-2015 yaitu 8,48 persen terhadap Produk Domestik Bruto (PDB) mencerminkan perusahaan yang termasuk dalam industri makanan dan minuman memiliki kinerja bisnis yang baik. Penelitian ini bertujuan untuk mengetahui kondisi harga saham dan profitabilitas pada tahun 2011-2015, mengetahui peramalan harga saham dan profitabilitas pada tahun 2016 dan untuk menganalisis pengaruh profitabilitas terhadap harga saham pada tahun 2011-2016. Analisis profitabilitas dipresentasikan oleh beberapa rasio keuangan yaitu Return On Equity (ROE), Return On Assets (ROA), Net Profit Margin (NPM), dan Earning Per Share (EPS). Penelitian ini menggunakan teknik purposive sampling dan data yang digunakan merupakan data sekunder. Peramalan menggunakan metode moving averages, weighted moving average, dan exponential smoothing dengan nilai MAD terkecil menggunakan aplikasi POM-QM for windows-3. Model analisis yang digunakan dalam penelitian ini adalah regresi linier berganda dengan menggunakan SPSS 18. Hasil penelitian menunjukkan bahwa PT Delta Djakarta, Tbk (DLTA) memiliki kondisi harga saham, ROE, ROA, dan EPS dengan rata-rata tertinggi selama 2011-2015. PT Tiga Pilar Sejahtera Food, Tbk (AISA) memiliki rata-rata NPM tertinggi selama 2011-2015. PT Delta Djakarta, Tbk (DLTA) dan PT Indofood Sukses Makmur, Tbk (INDF) menunjukkan peramalan tahun 2016 terhadap harga saham dan profitabilitas mengalami peningkatan dari tahun sebelumnya. Profitabilitas berpengaruh simultan dan signifikan terhadap harga saham dan secara parsial menunjukkan bahwa ROE dan EPS berpengaruh dan signifikan terhadap harga saham.

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