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Article
Publication date: 19 March 2024

John Maleyeff and Jingran Xu

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of…

Abstract

Purpose

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Design/methodology/approach

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

Findings

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

Social implications

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

Originality/value

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

Details

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

Keywords

Article
Publication date: 4 January 2024

Nishant Kulshrestha, Saurabh Agrawal and Deep Shree

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this…

Abstract

Purpose

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects.

Design/methodology/approach

Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed.

Findings

The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability.

Research limitations/implications

The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society.

Originality/value

The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 10 August 2022

Bingfeng Bai

Despite the importance of demand forecasting in retail industry, its influence on supply chain agility has not been sufficiently examined. From a total information technology (IT…

Abstract

Purpose

Despite the importance of demand forecasting in retail industry, its influence on supply chain agility has not been sufficiently examined. From a total information technology (IT) capability perspective, the purpose of this paper is to examine the antecedent of supply chain agility through retail demand forecasting.

Design/methodology/approach

Combining the literature reviews, the quantitative method of algorithm analysis was targeted at, and the firm data were processed on MATLAB.

Findings

This paper summarizes IT dimensions of demand forecasting in retail industry and distinguishes the relationship of supply chain agility and demand forecasting from an IT capability view.

Practical implications

Managers can derive a better understanding and measurement of operating activities that appropriately balance among supply chain agility, IT capability and demand forecast practice. Demand forecasting should be integrated into the firm operations to determine the agility level of supply chain in marketplace.

Originality/value

This paper constructs new theoretical grounds for research into the relationship of demand forecasting-supply chain agility and provides an empirical assessment of the essential components for the means to prioritize IT-supply chain.

Details

Kybernetes, vol. 52 no. 10
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 20 March 2024

Vinod Bhatia and K. Kalaivani

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable…

Abstract

Purpose

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

Design/methodology/approach

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

Findings

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

Originality/value

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

Details

foresight, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-6689

Keywords

Open Access
Article
Publication date: 12 January 2024

Patrik Jonsson, Johan Öhlin, Hafez Shurrab, Johan Bystedt, Azam Sheikh Muhammad and Vilhelm Verendel

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

Abstract

Purpose

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

Design/methodology/approach

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

Findings

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

Practical implications

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

Originality/value

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Details

International Journal of Operations & Production Management, vol. 44 no. 13
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 6 November 2023

Zhiying Wang and Hongmei Jia

Forecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with…

Abstract

Purpose

Forecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with grey-Markov method and applied it to the prediction of emergency supplies demand. Therefore, this article aims to establish a novel method for emergency supplies demand forecasting under major epidemics.

Design/methodology/approach

Emergency supplies demand is correlated with the number of infected cases in need of relief services. First, a novel method called the Intuitionistic Fuzzy TPGM(1,1)-Markov Method (IFTPGMM) is proposed, and it is utilized for the purpose of forecasting the number of people. Then, the prediction of demand for emergency supplies is calculated using a method based on the safety inventory theory, according to numbers predicted by IFTPGMM. Finally, to demonstrate the effectiveness of the proposed method, a comparative analysis is conducted between IFTPGMM and four other methods.

Findings

The results show that IFTPGMM demonstrates superior predictive performance compared to four other methods. The integration of the grey method and intuitionistic fuzzy set has been shown to effectively handle uncertain information and enhance the accuracy of predictions.

Originality/value

The main contribution of this article is to propose a novel method for emergency supplies demand forecasting under major epidemics. The benefits of utilizing the grey method for handling small sample sizes and intuitionistic fuzzy set for handling uncertain information are considered in this proposed method. This method not only enhances existing grey method but also expands the methodologies used for forecasting demand for emergency supplies.

Highlights (for review)

  1. An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

  2. The safety inventory theory is combined with IFTPGMM to construct a prediction method.

  3. Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.

The safety inventory theory is combined with IFTPGMM to construct a prediction method.

Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 12 October 2023

Xiaoli Su, Lijun Zeng, Bo Shao and Binlong Lin

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production…

Abstract

Purpose

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

Design/methodology/approach

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Findings

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Originality/value

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 14 November 2023

Flavian Emmanuel Sapnken, Mohammed Hamaidi, Mohammad M. Hamed, Abdelhamid Issa Hassane and Jean Gaston Tamba

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic…

36

Abstract

Purpose

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Design/methodology/approach

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Findings

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

Originality/value

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

Details

Grey Systems: Theory and Application, vol. 14 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 12 September 2023

Mingzhen Song, Lingcheng Kong and Jiaping Xie

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of…

Abstract

Purpose

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Design/methodology/approach

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

Findings

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

Originality/value

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

Highlights

  1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

  2. Investment strategy of energy storage power stations on the supply side of wind power generators.

  3. Impact of pricing method on the investment decisions of energy storage power stations.

  4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

  5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

Details

Industrial Management & Data Systems, vol. 123 no. 11
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 17 March 2023

Zhisheng Wang, Xiang Lin and Huiying Li

Using a video revealing unhygienic practices in Chinese five-star hotels as the case study, this study aims to understand the impact of service failure online exposure on hotel…

Abstract

Purpose

Using a video revealing unhygienic practices in Chinese five-star hotels as the case study, this study aims to understand the impact of service failure online exposure on hotel revenue performance in terms of seriousness, magnitude and duration, as well as to identify the hotel-characteristics and hotel-responsiveness factors that influence revenue recovery.

Design/methodology/approach

This study uses the actual Revenue per Available Room data of ten hotels involved in the incident and five different market segments during 2016–2019. Event study method is used to investigate the effect of online exposure on hotel revenue performance.

Findings

This study confirms the significant negative effect of online exposure and that hotels take nearly nine months to fully recover. The results indicate that hotel size, hotel age and response strategy play an important role in reducing negative impacts. Moreover, this study reveals the dynamic spillover effects of online exposure on different hotel market segments. These effects change from a competitive to a contagious effect with a decrease in class ratings.

Practical implications

Low-class hotel managers should take effective actions to avoid possible negative spillovers from others’ service failure incidents. Hotel managers could consider the synergy of different strategies rather than a single response strategy to minimize losses.

Originality/value

This study theoretically broadens knowledge about the negative impact of online exposure on Chinese hotel revenue. Additionally, the findings examine the dynamic spillover effects on hotels in different segments. Furthermore, they extend the existing findings on the negative impact of online public opinion crises.

目的

本研究以一段揭示中国五星级酒店不卫生行为的视频为案例, 旨在了解网上曝光的服务失败事件在严重程度、规模和持续时间方面对酒店收入绩效的影响, 并确定影响收入恢复的酒店特征和酒店回应因素。

设计/方法/途径

本研究使用了2016–2019年期间10家涉及酒店和5个不同的细分市场的实际每间可用房收入(RevPARs)数据。采用事件研究法(ESM)来研究网上曝光对酒店收入绩效的影响。

研究结果

本研究证实了网上曝光的显著负面效应, 酒店需要近9个月的时间才能完全恢复。结果表明, 酒店规模、酒店年龄和回应策略在减少负面影响方面发挥了重要作用。此外, 本研究还揭示了在线曝光对不同酒店细分市场的动态溢出效应。这些效应随着酒店星级的下降而从竞争效应变为传染效应。

实践意义

低星级酒店管理者应采取有效行动, 避免其他酒店的服务失败事件可能带来的负面溢出效应。酒店管理者可以考虑不同策略的协同作用, 而不是单一的回应策略来减少损失。

原创性/价值

本研究从理论上拓宽了关于网上曝光对中国酒店收入绩效的负面影响的知识。与此同时, 本研究的结果考察了不同细分市场的酒店的动态溢出效应。此外, 还扩展了现有的关于网络舆情危机的负面影响的研究结果。

Diseño/metodología/enfoque

Este estudio utiliza los datos reales de ingresos por habitación disponible (RevPAR) de 10 hoteles implicados en el incidente y cinco segmentos de mercado diferentes durante 2016-2019. Se utiliza el método de estudio de sucesos (ESM) para investigar el efecto de la exposición en línea en el rendimiento de los ingresos de los hoteles.

Objetivo

Utilizando como caso de estudio un vídeo que revela prácticas antihigiénicas en hoteles chinos de cinco estrellas, este estudio pretende comprender el impacto de la exposición online de fallos en el servicio sobre el rendimiento de los ingresos hoteleros en términos de gravedad, magnitud y duración, así como identificar las características y los factores de respuesta del hotel que influyen en la recuperación de los ingresos.

Resultados

Este estudio confirma el importante efecto negativo de la exposición online, tardando los hoteles casi nueve meses en recuperarse totalmente. Los resultados indican que el tamaño del hotel, su antigüedad y la estrategia de respuesta desempeñan un papel importante en la reducción del impacto negativo. Además, este estudio revela los efectos indirectos dinámicos de la exposición online en diferentes segmentos del mercado hotelero. Estos efectos cambian de un efecto competitivo a un efecto contagioso con una disminución de las calificaciones de la categoría o clase hotelera.

Implicaciones prácticas

Los revenue managers de los hoteles de categoría baja deberían tomar medidas eficaces para evitar posibles repercusiones negativas de los fallos en el servicio de otros hoteles. Los directores de hotel podrían considerar la sinergia de diferentes estrategias en lugar de una única estrategia de respuesta para minimizar las pérdidas.

Originalidad/valor

Este estudio amplía teóricamente los conocimientos sobre el impacto negativo de la exposición online en los ingresos de los hoteles chinos. Además, los resultados examinan los efectos indirectos dinámicos en hoteles de diferentes segmentos. Además, amplían los resultados existentes sobre el impacto negativo de las crisis de opinión pública online.

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