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Article
Publication date: 21 May 2021

Rohit Agrawal, Vishal Ashok Wankhede, Anil Kumar and Sunil Luthra

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify…

Abstract

Purpose

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

Design/methodology/approach

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

Findings

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

Originality/value

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

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Article
Publication date: 3 October 2019

Hannu Hannila, Joni Koskinen, Janne Harkonen and Harri Haapasalo

The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on…

Abstract

Purpose

The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance.

Design/methodology/approach

The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size.

Findings

Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation).

Practical implications

The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio.

Originality/value

The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

Details

Journal of Enterprise Information Management, vol. 33 no. 1
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 8 August 2019

Vahid Ghorbanian, Mohammad Hossain Mohammadi and David Lowther

This paper aims to propose a data-driven approach to determine the design guidelines for low-frequency electromagnetic devices.

Abstract

Purpose

This paper aims to propose a data-driven approach to determine the design guidelines for low-frequency electromagnetic devices.

Design/methodology/approach

Two different devices, a core-type single-phase transformer and a motor-drive system, are used to show the usefulness and generalizability of the proposed approach. Using a finite element solver, a large database of design possibilities is created by varying design parameters, i.e. the geometrical and control parameters of the systems. Design rules are then extracted by performing a statistical analysis and exploring optimal and sub-optimal designs considering various targets such as efficiency, torque ripple and power factor.

Findings

It is demonstrated that the correlation of the design parameters influences the way the data-driven approach must be made. Also, guidelines for defining new design constraints, which can lead to a more efficient optimization routine, are introduced for both case studies.

Originality/value

Using the proposed approach, new design guidelines, which are generally not obtainable by the classical design methods, are introduced. Also, the proposed approach can potentially deal with different parameter–objective correlations, as well as different number of connected systems. This approach is applicable regardless of the device type.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 38 no. 5
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 10 June 2020

Maxat Kassen

The peer-to-peer perspective on open data is an interesting topic to research, taking into account that data-driven innovations and related startups are often developed…

Abstract

Purpose

The peer-to-peer perspective on open data is an interesting topic to research, taking into account that data-driven innovations and related startups are often developed independently by civic and private stakeholders in a highly collaborative manner and are tentatively beginning to directly compete with traditional e-government solutions, providing arguably better services to citizens and businesses. In this regard, the paper aims to further debate on the potential of such independent data-driven collaboration not only to transform the traditional mechanisms of public sector innovations but also provide more democratic ways to ensure greater transparency of government and its responsibility before the society.

Design/methodology/approach

The research is based on a cross-country case study, resorting to the content analysis of three demonstrative cases in the development of open data-driven projects, which specifically promote peer-to-peer communication between its stakeholders. In this regard, the case study itself relies heavily on the analysis of rich empirical data that the author collected during his field studies in the Northern European region in 2015–2017, particularly in Estonia, Finland and Sweden. The practical research itself consists of three major parts, which reflect peer-to-peer perspectives of correspondingly civic, public and private stakeholders through manifested examples of related independent projects in the area.

Findings

The paper's results demonstrate that the use of peer-to-peer mechanisms in advancing related public sector reforms allows to transform the traditional understanding of e-government phenomena in a conceptually new way. E-government or its last more political interpretation – from the perspective of its peers could be regarded not necessarily as a platform to provide digital public services but as a source of raw material for various third party projects in, respectively, civic, government and business peer-to-peer dimensions of such reforms. As a result, open data provides an interesting playground to change the very nature of public sector innovations in the area.

Research limitations/implications

The choice of countries for research was motivated by purposive and convenience sampling because all these countries are situated in one region, have both similarities and differences in historical, political and socioeconomic backgrounds and, therefore, provide an ideal playground to investigate open data as a context dependable phenomenon. In this regard, the unique political and socioeconomic contexts of these countries provide an interesting playground to debate on the potential of social democracy, egalitarian society and social equality, i.e. public values that are deeply embedded into the fabric of societies there, to benefit the open data movement in a fundamental manner.

Practical implications

This paper reports on unique practical approaches for peer-to-peer collaboration and cooperation in advancing open data-driven platforms among stakeholders. The results of the case studies in three Nordic countries, which are currently among global leaders in advancing the concept of open government, are presented in an intrinsically illustrative manner, which could help practitioners and policymakers to understand better the potential of such a peer-to-peer perspective on open data. In this regard, the models proposed, of citizen-to-citizen, business-to-business, government-to-government interactions, could be interesting to a wide audience of e-government stakeholders in many nations.

Social implications

The paper also enters into philosophical debates about societal implications of digital peer-to-peer data-driven communication among people. Recent efforts to digitize almost every part of social life, starting from popularization of solutions for distant work and ending to online access to various public services, incentivize individual members of civil society to communicate in an inherently peer-to-peer way. This fact will definitely increase the demand for related digital services. Social distancing in a digital context will allow to paradoxically emancipate technically savvy and entrepreneurial people in creating new services, including using open data, which could meet the demand.

Originality/value

The research is intrinsically of an empirical character because recent e-government reforms in the public sector in many countries, including in the open data area, provide rich practical knowledge to test the limits of new technologies to advance society in socioeconomic and, more importantly, political development. In this regard, this paper provides the first research in analyzing open data from a unique peer-to-peer perspective with an ultimate goal of the whole investigation to draw the attention of other e-government scholars and initiate debates on the collaborative nature of the phenomena to empower civil society and ensure transparency of government.

Details

Aslib Journal of Information Management, vol. 72 no. 5
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 9 October 2017

Guangming Cao and Yanqing Duan

Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational…

Abstract

Purpose

Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.

Design/methodology/approach

Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.

Findings

Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.

Practical implications

Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.

Originality/value

This study provides useful management insights into the effective use of BA for improving organizational performance.

Details

Journal of Enterprise Information Management, vol. 30 no. 6
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 17 February 2021

Ming-Lang Tseng, Tat-Dat Bui, Ming K. Lim, Feng Ming Tsai and Raymond R. Tan

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving…

Abstract

Purpose

Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.

Design/methodology/approach

A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.

Findings

The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.

Originality/value

This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

Details

Industrial Management & Data Systems, vol. 121 no. 3
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 1 February 2007

Silvia Rita Viola, Sabine Graf, Kinshuk and Tommaso Leo

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying…

Abstract

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important issue and has to be investigated to guarantee that the questionnaire really assesses what the learning style theory aims at. In this paper, we focus on the Index of Learning Styles (ILS), a 44‐item questionnaire to identify learning styles based on Felder‐ Silverman learning style model. The aim of this paper is to analyse data gathered from ILS by a data‐driven approach in order to investigate relationships within the learning styles. Results, obtained by Multiple Correspondence Analysis and cross‐validated by correlation analysis, show the consistent dependencies between some learning styles and lead then to conclude for scarce validity of the ILS questionnaire. Some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of the ILS questionnaire. Both the results and the comparisons show the effectiveness of data‐driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for effective, precise and accurate modelling.

Details

Interactive Technology and Smart Education, vol. 4 no. 1
Type: Research Article
ISSN: 1741-5659

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Article
Publication date: 12 June 2019

Pengyu Zhu, Jayantha Liyanage and Simon Jeeves

Emergency shutdown (ESD) systems play a critical role in ensuring safety and availability of oil and gas production. The systems are operated in on-demand mode, and the…

Abstract

Purpose

Emergency shutdown (ESD) systems play a critical role in ensuring safety and availability of oil and gas production. The systems are operated in on-demand mode, and the detection and prediction of their failures is deemed challenging. The purpose of this paper is to develop a logical data-driven approach to enhance the understanding and detectability of ESD system failures.

Design/methodology/approach

The study was conducted in close collaboration with the Norwegian oil and gas industry. The study and analyses were supported by industrial data, failure data generated in a test facility in Norway and domain experts.

Findings

The paper demonstrated that there is a considerable potential to improve the decision process and to reduce the workload related to ESD systems by means of a logical data-driven approach. The results showed that the failure analysis process can be executed with more clarity and efficiency. Common cause failures could also be identified based on the suggested approach. The study further underlined the requirements regarding relevant data, new competence and technical supports in order to improve the current practice.

Originality/value

The paper leveraged the value of real-time data in identifying failures through mapping of the interrelationships between data, failure mechanisms and decisions. The failure analysis process was re-designed, and the understanding and decision making related to the system was improved as a result. The process developed for ESDs can further be adapted as a common practice for other low-demand systems.

Details

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

Keywords

Content available
Article
Publication date: 12 November 2019

Henny Kupferstein

The purpose of this paper is to explore why autistic people and their caregivers choose interventions other than applied behavior analysis (ABA), and how their decision…

Abstract

Purpose

The purpose of this paper is to explore why autistic people and their caregivers choose interventions other than applied behavior analysis (ABA), and how their decision impacts them over their lifespan. The focus group was divided into those who pursued augmentative and alternative communication (AAC)-based supports, received ABA, selected other interventions or received no intervention at all. The reported posttraumatic stress symptoms (PTSS) of ABA recipients were compared to non-ABA recipients in order to evaluate the long-term impacts of all intervention types. Using a mixed-method thematic analysis, optional comments submitted alongside a quantitative online survey were reviewed for emergent themes. These comments augmented the survey Likert scores with a qualitative impression of the diverse intervention-related attitudes among participants. Investigating the lived experiences of autism intervention recipients illuminated the scope of the long-term impacts of each intervention that was chosen. Overall, autistics who received no intervention fared best, based on the lowest reported PTSS. These findings may inform the potential redesign of autism interventions based on the firsthand reported experiences and opinions of autistics.

Design/methodology/approach

The aim of this study was to conduct research that is both question-driven and data-driven to aid in the analysis of existing data (Van Helden, 2013). In the research question-driven approach, the independent variables were the intervention type and duration of exposure relative to lifespan; the dependent variables were the PTSS severity score and binary indicator of meeting PTSS criteria. The analyses that were conducted included linear regression analyses of severity score on intervention type and duration, and χ2 tests for independence of the probabilities of PTSS criterion satisfaction and intervention type. This experiment was designed to test the data-driven hypothesis that the prevalence and severity of PTSS are dependent on the type of autism intervention and duration of exposure. After reviewing the primary data set, the data-driven inquiry determined that the sample for secondary analysis should be categorized by communication-based vs non-communication-based intervention type in order to best complement the limitations and strengths of the published findings from the primary analysis.

Findings

Autistics who received no intervention had a 59 percent lower likelihood of meeting the PTSS criteria when compared to their ABA peers, and they remained 99.6 percent stable in their reported symptoms throughout their lifespan (R2=0.004). ABA recipients were 1.74 times more likely to meet the PTSS criteria when compared to their AAC peers. Within the 23 percent who selected an intervention other than ABA, consisting of psychotherapy, mental health, son-rise and other varying interventions, 63 percent were asymptomatic. This suggests that the combined benefits of communication-based interventions over behaviorism-influenced ABA practices may contribute to enhanced quality of life. Although not generalizable beyond the scope of this study, it is indicated from the data that autistics who received no intervention at all fared best over their lifetimes.

Research limitations/implications

The obvious advantage of a secondary analysis is to uncover key findings that may have been overlooked in the preliminary study. Omitted variables in the preliminary data leave the researcher naive to crucially significant findings, which may be mitigated by subsequent testing in follow-up studies (Cheng and Phillips, 2014, p. 374). Frequency tables and cross-tabulations of all variables included in the primary analysis were reproduced. The secondary analysis of existing data was conducted from the design variables used in the original study and applied in the secondary analyses to generate less biased estimates (Lohr, 2010; Graubard and Korn, 1996). Inclusion criteria for each intervention group, PTSS scores and exposure duration, were inherited from the primary analysis, to allow for strategic judgment about the coding of the core variables pertaining to AAC and PTSS. The data sample from 460 respondents was reduced to a non-ABA group of n=330. An external statistician scored each respondent, and interrater reliability was assessed using Cohen’s κ coefficient (κ=1).

Practical implications

Including the autistic voice in the long-term planning of childhood interventions is essential to those attempting to meet the needs of the individuals, their families and communities. Both parents and autistic participant quotes were obtained directly from the optional comments to reveal why parents quit or persisted with an autism intervention.

Social implications

Practitioners and intervention service providers must consider this feedback from those who are directly impacted by the intervention style, frequency or intensity. The need for such work is confirmed in the recent literature as well, such as community-based participatory research (Raymaker, 2016). Autistics should be recognized as experts in their own experience (Milton, 2014). Community–academic partnerships are necessary to investigate the needs of the autistic population (Meza et al., 2016).

Originality/value

Most autistic people do not consider autism to be a mental illness nor a behavior disorder. It is imperative to recognize that when injurious behavior persists, and disturbance in mood, cognition, sleep pattern and focus are exacerbated, the symptoms are unrelated to autism and closely align to the diagnostic criteria for posttraumatic stress disorder (PTSD). When PTSD is underdiagnosed and untreated, the autistic individual may experience hyperarousal and become triggered by otherwise agreeable stimuli. Since autism interventions are typically structured around high contact, prolonged hours and 1:1 engagement, the nature of the intervention must be re-evaluated as a potentially traumatic event for an autistic person in the hyperarousal state. Any interventions which trigger more than it helps should be avoided and discontinued when PTSS emerge.

Details

Advances in Autism, vol. 6 no. 1
Type: Research Article
ISSN: 2056-3868

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Article
Publication date: 27 June 2019

Yinhua Liu, Shiming Zhang and Guoping Chu

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship…

Abstract

Purpose

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Design/methodology/approach

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

Findings

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

Originality/value

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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