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Article
Publication date: 23 August 2013

Bright Chisadza, Mike J. Tumbare, Innocent Nhapi and Washington R. Nyabeze

The purpose of this paper is to identify, analyse and document local traditional indicators used in drought forecasting in the Mzingwane Catchment and to assess the…

Abstract

Purpose

The purpose of this paper is to identify, analyse and document local traditional indicators used in drought forecasting in the Mzingwane Catchment and to assess the possibility of integrating traditional rainfall forecasting, using the local traditional indicators, with meteorological forecasting methods.

Design/methodology/approach

Self-administered structured questionnaires were conducted on 101 respondents in four districts of the Mzingwane Catchment area, namely, Beitbridge, Mangwe, Esighodini and Mwenezi from February to August 2012. In addition, key informant interviews and focus group discussions were also used in data collection and the collected data were analysed for drought history and demographics; drought adaptation and the use of drought forecasting methods in the catchment using Statistical Package for Social Science.

Findings

The paper reveals the growing importance of precipitation forecasts among Mzingwane communities, particularly the amount, timing, duration and distribution of rainfall. Rainfall was cited as the major cause of drought by 98 per cent of the respondents in the catchment. Whilst meteorological rainfall forecasts are available through various channels, they are not readily accessible to rural communities. Furthermore, they are not very reliable at local level. The paper shows that communities in the Mzingwane Catchment still regard local traditional knowledge forecasting as their primary source of weather forecasts. The paper finds that plant phenology is widely used by the local communities in the four districts for drought forecasting. Early and significant flowering of Mopane trees (Colophospermum mopane) from September to December has been identified to be one of the signals of poor rainfall season in respect to quantity and distribution and subsequent drought. Late and less significant flowering of Umtopi trees (Boscia albitrunca) from September to December also signals a poor rainfall season.

Originality/value

The paper fulfils an identified need to study and document useful traditional drought indicators. Furthermore, the paper provides a platform for possible integration of traditional drought forecasting and meteorological forecasting and ensure sustainable rural livelihood development. The paper is useful to both meteorological researchers and resource-constrained communities in Mzingwane Catchment.

Details

Disaster Prevention and Management, vol. 22 no. 4
Type: Research Article
ISSN: 0965-3562

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Article
Publication date: 8 January 2020

Sonali Shankar, P. Vigneswara Ilavarasan, Sushil Punia and Surya Prakash Singh

Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This…

Abstract

Purpose

Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.

Design/methodology/approach

In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.

Findings

The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.

Originality/value

The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

Details

Industrial Management & Data Systems, vol. 120 no. 3
Type: Research Article
ISSN: 0263-5577

Keywords

Content available
Article
Publication date: 12 April 2019

Iman Ghalehkhondabi, Ehsan Ardjmand, William A. Young and Gary R. Weckman

The purpose of this paper is to review the current literature in the field of tourism demand forecasting.

Abstract

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.

Details

Journal of Tourism Futures, vol. 5 no. 1
Type: Research Article
ISSN: 2055-5911

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Article
Publication date: 27 March 2020

Luyao Wang, Jianying Feng, Xiaojie Sui, Xiaoquan Chu and Weisong Mu

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Abstract

Purpose

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Design/methodology/approach

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

Findings

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

Originality/value

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

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Article
Publication date: 7 April 2015

Gamze Ogcu Kaya and Omer Fahrettin Demirel

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…

Abstract

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.

Details

Kybernetes, vol. 44 no. 4
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 1 May 2004

Nada R. Sanders and Larry P. Ritzman

Accurate forecasting has become a challenge for companies operating in today's business environment, characterized by high uncertainty and short response times. Rapid…

Abstract

Accurate forecasting has become a challenge for companies operating in today's business environment, characterized by high uncertainty and short response times. Rapid technological innovations and e‐commerce have created an environment where historical data are often of limited value in predicting the future. In business organizations, the marketing function typically generates sales forecasts based on judgmental methods that rely heavily on subjective assessments and “soft” information, while operations rely more on quantitative data. Forecast generation rarely involves the pooling of information from these two functions. Increasingly, successful forecasting warrants the use of composite methodologies that incorporate a range of information from traditional quantitative computations usually used by operations, to marketing's judgmental assessments of markets. The purpose of this paper is to develop a framework for the integration of marketing's judgmental forecasts with traditional quantitative forecasting methods. Four integration methodologies are presented and evaluated relative to their appropriateness in combining forecasts within an organizational context. Our assessment considers human factors such as ownership, and the location of final forecast generation within the organization. Although each methodology has its strengths and weaknesses, not every methodology is appropriate for every organizational context.

Details

International Journal of Operations & Production Management, vol. 24 no. 5
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 30 August 2013

Michael Krapp, Johannes Nebel and Ramin Sahamie

The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed‐loop supply chains.

Abstract

Purpose

The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed‐loop supply chains.

Design/methodology/approach

The approach is based on Bayesian estimation techniques. It permits to forecast product returns on the basis of fewer restrictions than existing approaches in CLSC literature. A numerical example demonstrates the application of the proposed approach using return times drawn from a Poisson distribution.

Findings

The Bayesian estimation approach provides at least 50 percent higher accuracy in terms of error measures compared to traditional methods in all scenarios examined in the empirical part. Hence, more precise results can be obtained when predicting product returns.

Research limitations/implications

The flexibility of the proposed approach allows for numerous applications in the field of CLSC research. Areas that depend on the results from a forecasting system, such as inventory management, can embed our estimation procedure in order to reduce safety stocks. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns.

Originality/value

The generic character of the proposed forecasting approach leaves degrees of freedom to the user when adapting it to a specific problem. This adaptability is enabled by the following features: first, an arbitrary function is allowed for capturing the customers' demand. Second, the stochastic timeframe between sale and product return may follow an arbitrary distribution. Third, by adjusting two parameters finite as well as infinite planning horizons can be incorporated. Fourth, no assumptions regarding the joint distribution of product returns are necessary.

Details

International Journal of Physical Distribution & Logistics Management, vol. 43 no. 8
Type: Research Article
ISSN: 0960-0035

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Article
Publication date: 23 September 2019

Nzita Alain Lelo, P. Stephan Heyns and Johann Wannenburg

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…

Abstract

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.

Details

Journal of Quality in Maintenance Engineering, vol. 26 no. 1
Type: Research Article
ISSN: 1355-2511

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Book part
Publication date: 13 March 2013

Youqin Pan, Terrance Pohlen and Saverio Manago

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to…

Abstract

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

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Article
Publication date: 14 May 2018

Erik Hofmann and Emanuel Rutschmann

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…

Abstract

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.

Details

The International Journal of Logistics Management, vol. 29 no. 2
Type: Research Article
ISSN: 0957-4093

Keywords

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