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 Forecasting spare parts demand using condition monitoring information

2019 ◽  
Vol 26 (1) ◽  
pp. 53-68
Author(s):  
Nzita Alain Lelo ◽  
P. Stephan Heyns ◽  
Johann Wannenburg
Keyword(s):  
Natural Frequency ◽  
Demand Forecasting ◽  
Fatigue Tests ◽  
Spare Parts ◽  
Just In Time ◽  
Base Excitation ◽  
Content Type ◽  
Forecasting Methods ◽  
Spare Parts Management ◽  
The Cost

Purpose The control of an inventory where spare parts demand is infrequent has always been difficult to manage because of the randomness of the demand, as well as the existence of a large proportion of zero values in the demand pattern. The purpose of this paper is to propose a just-in-time (JIT) spare parts availability approach by integrating condition monitoring (CM) with spare parts management by means of proportional hazards models (PHM) to eliminate some of the shortcomings of the spare parts demand forecasting methods. Design/methodology/approach In order to obtain the event data (lifetime) and CM data (first natural frequency) required to build the PHM for the spares demand forecasting, a series of fatigue tests were conducted on a group of turbomachinery blades that were systematically fatigued on an electrodynamic shaker in the laboratory, through base excitation. The process of data generation in the numerical as well as experimental approaches comprised introducing an initial crack in each of the blades and subjecting the blades to base excitation on the shaker and then propagating the crack. The blade fatigue life was estimated from monitoring the first natural frequency of each blade while the crack was propagating. The numerical investigation was performed using the MSC.MARC/2016 software package. Findings After building the PHM using the data obtained during the fatigue tests, a blending of the PHM with economic considerations allowed determining the optimal risk level, which minimizes the cost. The optimal risk point was then used to estimate the JIT spare parts demand and define a component replacement policy. The outcome from the PHM and economical approach allowed proposing development of an integrated forecasting methodology based not only on failure information, but also on condition information. Research limitations/implications The research is simplified by not considering all the elements usually forming part of the spare parts management study, such as lead time, stock holding, etc. This is done to focus the attention on component replacement, so that a just-in-time spare parts availability approach can be implemented. Another feature of the work relates to the decision making using PHM. The approach adopted here does not consider the use of the transition probability matrix as addressed by Jardine and Makis (2013). Instead, a simulation method is used to determine the optimal risk point which minimizes the cost. Originality/value This paper presents a way to address some existing shortcomings of traditional spare parts demand forecasting methods, by introducing the PHM as a tool to forecast spare parts demand, not considering the previous demand as is the case for most of the traditional spare parts forecasting methods, but the condition of the parts in operation. In this paper, the blade bending first mode natural frequency is used as the covariate in the PHM in a laboratory experiment. The choice of natural frequency as covariate is justified by its relationship with structural stiffness (and hence damage), as well as being a global parameter that could be measured anywhere on the blade without affecting the results.

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 Improving intermittent demand forecasting based on data structure

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Fatemeh Faghidian ◽  
◽  
Mehdi Khashei ◽  
Mohammad Khalilzadeh ◽  
◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Moving Average ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Percentage Error ◽  
Complex Nature ◽  
Intermittent Demand ◽  
Classical Models ◽  
The Cost

Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classical models while simultaneously using their unique advantages in dealing with the complexities in intermittent demand. The strategy of the proposed hybrid model is to use the three individual autoregressive moving average (ARMA), single exponential smoothing (SES), and multilayer perceptron (MLP) models simultaneously. Each of them has the potential of modeling a different structure and patterns of behavior among the data. The accuracy in forecasting ability is also increased by the suitable examination of these in the intermittent data. Croston’s method is the backbone of the suggested model. The proposed hybrid model is based on CV2 and ADI criteria, which improve its efficacy in examining inappropriate structures by reducing the cost of inappropriate modeling while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The accuracy of prediction models was evaluated and compared using mean absolute percentage error (MAPE) by implementing an example, and promising results were achieved.

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 Intermittent Demand Forecasting for Spare Parts: A Critical Review

Omega ◽  
2021 ◽  
pp. 102513
Author(s):  
Çerağ Pinçe ◽  
Laura Turrini ◽  
Joern Meissner
Keyword(s):  
Critical Review ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Intermittent Demand

Big data analytics and demand forecasting in supply chains: a conceptual analysis

2018 ◽  
Vol 29 (2) ◽  
pp. 739-766 ◽  
Author(s):  
Erik Hofmann ◽  
Emanuel Rutschmann
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Conceptual Analysis ◽  
Scientific Literature ◽  
Design Science ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Scientific Work ◽  
Content Type ◽  
Forecasting Methods

Purpose Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy. Design/methodology/approach A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry. Findings The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

scholarly journals A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

2019 ◽  
Vol 5 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Iman Ghalehkhondabi ◽  
Ehsan Ardjmand ◽  
William A. Young ◽  
Gary R. Weckman
Keyword(s):  
Design Methodology ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
Transportation Industry ◽  
Content Type ◽  
Forecasting Models ◽  
Passenger Transportation ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Combined Forecasting

Purpose The purpose of this paper is to review the current literature in the field of tourism demand forecasting. Design/methodology/approach Published papers in the high quality journals are studied and categorized based their used forecasting method. Findings There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods. Originality/value This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

scholarly journals USING A MIXTURE OF EXPERTS’ APPROACH TO SOLVE THE FORECASTING TASK

Aviation ◽  
2014 ◽  
Vol 18 (3) ◽  
pp. 129-133
Author(s):  
Victor Sineglazov ◽  
Elena Chumachenko ◽  
Vladyslav Gorbatyuk
Keyword(s):  
Neural Networks ◽  
Demand Forecasting ◽  
Real Data ◽  
Air Transport ◽  
Aviation Industry ◽  
Mixture Of Experts ◽  
New Approach ◽  
Multiple Neural Networks

The forecasting problem appears frequently in the aviation industry (demand forecasting, air transport movement forecasting, etc.). In this article, a new approach based on multiple neural networks of different topologies is introduced. An algorithm was tested on real data and showed better results compared to several other methods. This shows its suitability for further usage in aviation forecasting tasks.

scholarly journals Demand forecasting methods for spare parts logistics for aviation: a real-world implementation of the Bootstrap method

2021 ◽  
Vol 55 ◽  
pp. 500-506
Author(s):  
M. Baisariyev ◽  
A. Bakytzhanuly ◽  
Y. Serik ◽  
B. Mukhanova ◽  
M.Z. Babai ◽  
...  
Keyword(s):  
Real World ◽  
Bootstrap Method ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Forecasting Methods ◽  
The Bootstrap Method
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