Mahesh Kumar, Omkarprasad S Vaidya and Rajiv Kumar Srivastava
The purpose of this paper is to improve the understanding of the role of the bottlenecks in the dynamic software development supply chains. The paper examines the effects of the…
Abstract
Purpose
The purpose of this paper is to improve the understanding of the role of the bottlenecks in the dynamic software development supply chains. The paper examines the effects of the task priorities in the software development and investigates the possible strategies to manage them effectively.
Design/methodology/approach
In this paper, a software development supply chain has been simulated. This includes modeling of the various sizes of software requirement, different priorities, variations in development times, quality defects, etc. The model assumes a fixed set of resources of various skills. The model is studied for the bottlenecks, throughput, work in progress (WIP), etc. under various work preemption scenarios.
Findings
The results indicate that job priorities impact the bottleneck formulation, throughput and WIP of the software development. The work interruption policies to accommodate priority jobs adversely impact the throughput. Selective introduction of interruptions by leaving the bottlenecks from interruptions helps balancing the throughput and priorities.
Research limitations/implications
The impact of the learning curve and knowledge acquisition time needed by the resources to restart the interrupted work has not been considered in this paper, which can be a future area of research.
Practical implications
The paper helps the practicing managers evaluate the dynamics of the bottlenecks with various task management approaches and comprehend the possible tradeoffs between priority and throughout.
Originality/value
The paper looks at software development from a perspective of workflow dynamics. This is a pioneer effort, as it utilizes simulation and modeling approach in understanding the software supply chains better.
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Vijaya Dixit, Rajiv Kumar Srivastava and Atanu Chaudhuri
This work aims at integrating materials management with project management in the context of manufacturing of complex products which require a variety of items. To achieve this…
Abstract
Purpose
This work aims at integrating materials management with project management in the context of manufacturing of complex products which require a variety of items. To achieve this, we propose two prioritization measures of items: material criticality (MC) at activity level and overall criticality (OC) at project level by incorporating project network characteristic through activity criticality (AC) values.
Design/methodology/approach
The costs or penalties which determine criticality of items are hidden in nature and are difficult to measure and model mathematically. Hence, Fuzzy Inference System (FIS), which captures experts’ tacit knowledge in the form of linguistic If‐Then rules has been used.
Findings
OC obtained can be used as a measure to prioritize items for procurement aligned with on‐site build strategy and as a surrogate measure of shortage cost coefficient for inventory models. The analyses of output to observe the effect of AC on OC values of items, clearly demonstrate the novelty and importance of incorporating project network characteristics in materials management decision making.
Originality/value
In this work, we are able to leverage managerial tacit knowledge derived through years of experience and convert it into a readily usable quantitative parameter OC for prioritization of items to be procured. For identifying the input parameters for OC, we brought in the new perspective of including project network characteristics to align materials and project management.
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Shekhar Srivastava, Rajiv Kumar Garg, Anish Sachdeva, Vishal S. Sharma, Sehijpal Singh and Munish Kumar Gupta
Gas metal arc-based directed energy deposition (GMA-DED) process experiences residual stress (RS) developed due to heat accumulation during successive layer deposition as a…
Abstract
Purpose
Gas metal arc-based directed energy deposition (GMA-DED) process experiences residual stress (RS) developed due to heat accumulation during successive layer deposition as a significant challenge. To address that, monitoring of transient temperature distribution concerning time is a critical input. Finite element analysis (FEA) is considered a decisive engineering tool in quantifying temperature and RS in all manufacturing processes. However, computational time and prediction accuracy has always been a matter of concern for FEA-based prediction of responses in the GMA-DED process. Therefore, this study aims to investigate the effect of finite element mesh variations on the developed RS in the GMA-DED process.
Design/methodology/approach
The variation in the element shape functions, i.e. linear- and quadratic-interpolation elements, has been used to model a single-track 10-layered thin-walled component in Ansys parametric design language. Two cases have been proposed in this study: Case 1 has been meshed with the linear-interpolation elements and Case 2 has been meshed with the combination of linear- and quadratic-interpolation elements. Furthermore, the modelled responses are authenticated with the experimental results measured through the data acquisition system for temperature and RS.
Findings
A good agreement of temperature and RS profile has been observed between predicted and experimental values. Considering similar parameters, Case 1 produced an average error of 4.13%, whereas Case 2 produced an average error of 23.45% in temperature prediction. Besides, comparing the longitudinal stress in the transverse direction for Cases 1 and 2 produced an error of 8.282% and 12.796%, respectively.
Originality/value
To avoid the costly and time-taking experimental approach, the experts have suggested the utilization of numerical methods in the design optimization of engineering problems. The FEA approach, however, is a subtle tool, still, it faces high computational cost and low accuracy based on the choice of selected element technology. This research can serve as a basis for the choice of element technology which can predict better responses in the thermo-mechanical modelling of the GMA-DED process.
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Atanu Chaudhuri, Samir K. Srivastava, Rajiv K. Srivastava and Zeenat Parveen
The purpose of this paper is to identify various risk drivers which affect a food processing supply chain and to create a map of how those risk drivers propagate risks through the…
Abstract
Purpose
The purpose of this paper is to identify various risk drivers which affect a food processing supply chain and to create a map of how those risk drivers propagate risks through the supply chain and impact important performance measures.
Design/methodology/approach
This study involves experts from food processing companies to elucidate the contextual relationships among the risk drivers and between risk drivers and performance measures. This is used to quantify the relationships and to determine the indirect and overall relationships applying Fuzzy Interpretive Structural Modeling.
Findings
Three categories of risk drivers which Indian food processing companies need to pay maximum attention to minimize risks are identified. These are supplier dependency and contracting, supplier variability, visibility and traceability and manufacturing disruptions. Analysis shows that collaborating with suppliers and logistics service providers, developing mutually beneficial contracts with them while ensuring that adequate technology investments are made can significantly mitigate risks and consequently improve margins and lead to revenue growth.
Research limitations/implications
This study has been carried out with experts from large food processing companies in India, and hence, the results cannot be generalized across other types of food processing companies.
Practical implications
The proposed methodology can help understand the interrelationships between supply chain risks and between those risks and performance measures. Thus, it can help a food processing company to create business cases for specific supply chain risk mitigation projects.
Originality/value
This study is one of the earliest to create a comprehensive risk propagation map for food processing companies which helps in quantifying the impact the risk drivers have on each other and on performance measures.
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Shrawan Kumar Trivedi, Jaya Srivastava, Pradipta Patra, Shefali Singh and Debashish Jena
In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must…
Abstract
Purpose
In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.
Design/methodology/approach
The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.
Findings
It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.
Originality/value
To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.
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Shefali Singh, Kanchan Awasthi, Pradipta Patra, Jaya Srivastava and Shrawan Kumar Trivedi
Sustainable human resource management (SuHRM), which aims to achieve positive environmental, social and economic outcomes at the same time, has gained prominence across…
Abstract
Purpose
Sustainable human resource management (SuHRM), which aims to achieve positive environmental, social and economic outcomes at the same time, has gained prominence across industries. However, the challenges of implementing SuHRM across industries are largely under-studied. The purpose of this study is to identify the grey areas in the field of SuHRM by using an unsupervised learning algorithm on the abstracts of 607 papers published in prominent journals from 1995 to 2023. Most of the articles have been published post-2018.
Design/methodology/approach
The analysis of the data (abstracts of the selected articles) has been done using topic modelling via latent Dirichlet algorithm (LDA).
Findings
The output from topic modelling-LDA reveals nine primary focus areas of SuHRM research – the link between SuHRM and employee well-being; job satisfaction; challenges of implementing SuHRM; exploring new horizons in SuHRM; reaping the benefits of using SuHRM as a strategic tool; green HRM practices; link between SuHRM and organisational performance; link between corporate social responsible and HRM.
Research limitations/implications
The insights gained from this study along with the discussions on each topic will be extremely beneficial for researchers, academicians, journal editors and practitioners to channelise their research focus. No other study has used a smart algorithm to identify the research clusters of SuHRM.
Originality/value
By utilizing topic modeling techniques, the study offers a novel approach to analyzing and understanding trends and patterns in HRM research related to sustainability. The significance of the paper would be in its potential to shed light on emerging areas of interest and provide valuable implications for future research and practice in Sustainable HRM.
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Shekhar Srivastava, Rajiv Kumar Garg, Vishal S. Sharma, Noe Gaudencio Alba-Baena, Anish Sachdeva, Ramesh Chand and Sehijpal Singh
This paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach…
Abstract
Purpose
This paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.
Design/methodology/approach
A systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.
Findings
The findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.
Research limitations/implications
This paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.
Originality/value
This paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.
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Shrawan Kumar Trivedi, Pradipta Patra, Amrinder Singh, Pijush Deka and Praveen Ranjan Srivastava
The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most…
Abstract
Purpose
The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.
Design/methodology/approach
The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.
Findings
Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.
Originality/value
While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.
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Anurag Singh, Ashok Kumar Patel, Shefali Jaiswal, Punita Duhan and Vinod Kumar Singh
This study focuses on Aaker's Brand Equity Model, to check the effect of brand equity determinants on booking intention (BI) for ridesharing in India. The study also explores the…
Abstract
Purpose
This study focuses on Aaker's Brand Equity Model, to check the effect of brand equity determinants on booking intention (BI) for ridesharing in India. The study also explores the moderation of ecologically conscious consumer behavior (ECCB) on the multiplicative effect of brand awareness (BAw), brand association (BA) and perceived quality (PQ) in influencing the BI.
Design/methodology/approach
Responses from 393 Indian ridesharing users were collected using judgmental sampling and were analyzed using Hayes Process macro.
Findings
The study found a direct relationship between BAw and BI, BAw and BA, BAw and PQ, BA and PQ, PQ and BI, and BA and BI. Findings revealed mediation of BA in BAw and BI relationship and PQ in BAw and BI relationship. Results revealed that BA and PQ serially mediate BAw and BI relationship. ECCB moderates PQ and BI relationship but not BAw and BI relationship.
Research limitations/implications
Serial mediation and moderated-mediation results draw various theoretical implications for determinants of Aaker's Brand Equity model and ECCB.
Practical implications
The research has several implications for managers in view of brand equity determinants and ECCB. The study also contributes to policy implications.
Originality/value
Study's novel contributions are mediation, serial mediation between brand equity determinants, and moderation of ECCB between BAw and BI for ridesharing.