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Article
Publication date: 30 December 2020

Majid Eskafi, Milad Kowsari, Ali Dastgheib, Gudmundur F. Ulfarsson, Poonam Taneja and Ragnheidur I. Thorarinsdottir

Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to…

Abstract

Purpose

Port throughput analysis is a challenging task, as it consists of intertwined interactions between a variety of cargos and numerous influencing factors. This study aims to propose a quantitative method to facilitate port throughput analysis by identification of important cargos and key macroeconomic variables.

Design/methodology/approach

Mutual information is applied to measure the linear and nonlinear correlation among variables. The method gives a unique measure of dependence between two variables by quantifying the amount of information held in one variable through another variable.

Findings

This study uses the mutual information to the Port of Isafjordur in Iceland to underpin the port throughput analysis. The results show that marine products are the main export cargo, whereas most imports are fuel oil, industrial materials and marine product. The aggregation of these cargos, handled in the port, meaningfully determines the non-containerized port throughput. The relation between non-containerized export and the national gross domestic product (GDP) is relatively high. However, non-containerized import is mostly related to the world GDP. The non-containerized throughput shows a strong relation to the national GDP. Furthermore, the results reveal that the volume of national export trade is the key influencing macroeconomic variable to the containerized throughput.

Originality/value

Application of the mutual information in port throughput analysis effectively reduces epistemic uncertainty in the identification of important cargos and key influencing macroeconomic variables. Thus, it increases the reliability of the port throughput forecast.

Details

Maritime Business Review, vol. 6 no. 2
Type: Research Article
ISSN: 2397-3757

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Article
Publication date: 22 June 2021

Sonali Shankar, Sushil Punia and P. Vigneswara Ilavarasan

Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing…

Abstract

Purpose

Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.

Design/methodology/approach

A novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.

Findings

The result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”

Originality/value

A novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 10 July 2018

Lang Wu, Felix T.S. Chan, Ben Niu and Li Li

Seru (cell) manufacturing system has achieved huge success in production. However, related research is limited, especially, the problem of cross-trained worker assignment…

Abstract

Purpose

Seru (cell) manufacturing system has achieved huge success in production. However, related research is limited, especially, the problem of cross-trained worker assignment. The purpose of this paper is to solve this problem for two representative seru types, divisional and rotating seru, and subsequently, compare throughput performance between the two seru types under reasonable worker-task assignment.

Design/methodology/approach

For the cross-trained worker assignment problem, this research presents new models aiming at maximum throughput of seru and workload balance of workers under considering skill levels (SLs) and several practical constraints. Furthermore, factorial experiments that involve four factors, the number of tasks (NT), gap of task time, SL and gap of SL, are performed to compare throughput performance between divisional and rotating seru.

Findings

First, the maximum throughput of the divisional seru is better than that of the rotating seru under suitable worker assignment. Second, in the seru which has less difference of task time, throughput performance of the rotating seru is better than the divisional seru when the NT is close to the number of assigned workers. Moreover, the influence tendency of the four factors on throughput gap between the two seru types is significant.

Originality/value

This research addresses the worker-task assignment for divisional and rotating seru based on their characteristics. Several findings can help decision maker select more applicable seru type according to various production environments from the perspective of optimum throughput.

Details

Industrial Management & Data Systems, vol. 118 no. 5
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 14 August 2017

Torsten Franzke, Eric H. Grosse, Christoph H. Glock and Ralf Elbert

Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in…

Abstract

Purpose

Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

Design/methodology/approach

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

Findings

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

Research limitations/implications

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Practical implications

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

Originality/value

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

Details

The International Journal of Logistics Management, vol. 28 no. 3
Type: Research Article
ISSN: 0957-4093

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Article
Publication date: 13 April 2015

Andreas Myrelid and Jan Olhager

The purpose of this paper is to analyze the applicability of lean accounting and throughput accounting in a company with considerable investments in advanced manufacturing…

Abstract

Purpose

The purpose of this paper is to analyze the applicability of lean accounting and throughput accounting in a company with considerable investments in advanced manufacturing technology (AMT).

Design/methodology/approach

The paper compares lean accounting and throughput accounting with the traditional accounting system the company is using today. The authors investigate the differences between the three alternative approaches and use a case study approach to illustrate the effects of applying different modern accounting approaches in a complex manufacturing setting.

Findings

Pair-wise comparisons of the three approaches provide some interesting cost information as to the role of bottlenecks and value streams.

Research limitations/implications

The specific results of this study are limited to the case company, but can hopefully contribute to further research on how to combine lean and throughput accounting for mixed manufacturing environments, involving both value streams and bottlenecks.

Practical implications

Lean and throughput accounting provide other perspectives on cost information to traditional accounting, and can therefore be used in combination. The authors identify some issues and challenges involved in using lean accounting and throughput accounting in an AMT company.

Originality/value

This paper contributes with a comparison of traditional, lean, and throughput accounting in a specific industrial setting characterized by AMT and complex manufacturing.

Details

Industrial Management & Data Systems, vol. 115 no. 3
Type: Research Article
ISSN: 0263-5577

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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

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Article
Publication date: 1 July 1989

R.P. Mohanty

A simulation procedure is presented to estimate the throughputavailability of a serially interconnected manufacturing system. Themodel presented in the article is able to…

Abstract

A simulation procedure is presented to estimate the throughput availability of a serially interconnected manufacturing system. The model presented in the article is able to analyse, predict and suggest ways for improving productivity. The model is applicable to systems with branching and converging process streams, recycling, and intermediate buffer storages. The simulation has been carried out for a wire‐rope manufacturing plant and has been implemented by using a personal computer.

Details

International Journal of Operations & Production Management, vol. 9 no. 7
Type: Research Article
ISSN: 0144-3577

Keywords

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Article
Publication date: 1 April 2004

Lynn Boyd and Mahesh Gupta

A number of attempts have been made to develop theories in operations management (OM) (e.g. trade‐off theory by Skinner, customer‐contact model by Chase and Tansik…

Abstract

A number of attempts have been made to develop theories in operations management (OM) (e.g. trade‐off theory by Skinner, customer‐contact model by Chase and Tansik, product‐process matrix by Hayes and Wheelwright). Researchers in OM acknowledge that there is no widely‐accepted theory on which OM rests or which serves as a unified OM theory to integrate existing theory‐like principles or informal theories. Constraints management (CM) has been developed over the past 20 years by consultants and practitioners but has received little attention from OM researchers. The authors believe that constraints management may serve as a broad theory within operations that will allow integration of a great deal of existing OM research. The main objectives of this paper are to propose a construct, throughput orientation, discuss its core dimensions, and develop a theoretical model of CM. The paper also suggests several hypotheses that might be empirically tested to establish CM as a recognized theory in the field of operations management. The paper concludes with suggestions for future research.

Details

International Journal of Operations & Production Management, vol. 24 no. 4
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 24 April 2007

Dadi Gudmundsson and Ken Goldberg

This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to…

Abstract

Purpose

This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to singulate and circulate parts, bringing them under a camera where their pose is recognized and subsequently manipulated by the robot arm. The problem is addressed of optimizing belt speeds and hence throughput of this feeder that avoid: starvation, where no parts are visible to the camera and saturation, where too many parts prevent part pose detection or grasping.

Design/methodology/approach

Models are developed for intermittent and continuous motion feeding based on a 2D Poisson process. Renewal theory is applied to model intermittent motion and an M/G/1 queue with customer impatience to model continuous motion feeding. These models are verified using discrete event simulation.

Findings

The models predict and optimize feeder behaviour very accurately and it is possible to compute optimal settings for different part sizes and throughput sensitivity.

Practical implications

Feeder belt velocities are currently estimated based on intuition and ad hoc trial and error. The results provide a scientific alternative. The models are straightforward to implement and can provide velocity settings for feeders in industrial use.

Originality/value

This paper advances the scientific understanding of automation and part feeding.

Details

Assembly Automation, vol. 27 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 1 May 1997

A. Andijani

Investigates the trade‐off between the average throughput rate and the average systems time using kanban discipline. Considers a multistage serial production line system…

Abstract

Investigates the trade‐off between the average throughput rate and the average systems time using kanban discipline. Considers a multistage serial production line system with materials in the system controlled by kanban discipline. Presents simulation results to evaluate the production system performance in terms of the average throughput rate and the average system time for a fixed total number of kanbans over a given number of serial workstations. Constructs and compares efficient allocation sets for three and four workstations that are generated by kanban discipline for two processing time distributions, namely, uniform and exponential distributions. Based on the simulation results from three and four work‐stations, develops a general design rule to maximize the average throughput rate and to minimize the average system time. Analyses five and six workstations using the general design rule. Tests the validity of the general design rule by considering five and six workstations with a different number of kanbans. The results show that most of the efficient sets generated by the design rule are identical to those generated by enumerating all combinations of kanban allocations. However, using the general design rule reduces the simulation work tremendously.

Details

International Journal of Operations & Production Management, vol. 17 no. 5
Type: Research Article
ISSN: 0144-3577

Keywords

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