A data science and open source software approach to analytics for strategic sourcing

Boehmke, Bradley C.; Hazen, Benjamin T.; Boone, Christopher A.; Robinson, James C.

doi:10.1016/j.ijinfomgt.2020.102167

Cited by 15 publications

(3 citation statements)

References 66 publications

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“…Supply chain analytics (SCA), via the use of cognitive technologies, such as artificial intelligence (AI), helps improve complex supply chain process decisions ( Akter, Michael, Uddin, McCarthy, & Rahman, 2020 ; Asmussen & Møller, 2020 ; Boehmke, Hazen, Boone, & Robinson, 2020 ). Cognitive technologies capability enables machines to understand complex situations at high speed, whilst processing large amounts of data, and to learn and interact like humans ( Duan, Edwards, & Dwivedi, 2019 ; Dwivedi et al, 2021 ; Gupta, Kar, Baabdullah, & Al-Khowaiter, 2018 ; Kelly, 2015 ) and Artificial intelligence-powered supply chain analytics (AI-SCA) has gained increased momentum during a pandemic crisis ( Cankurtaran & Beverland, 2020 ; Ivanov, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context

Dubey

Bryde

Blome

et al. 2021

Industrial Marketing Management

127

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Section: Introductionmentioning

confidence: 99%

Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context

Dubey

Bryde

Blome

et al. 2021

Industrial Marketing Management

127

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“…In the context of COVID-19 as a strong event that brought an uncertain environment for supply chains, this statement of Chen and Guo (2014) reinforces the importance of a deeper understanding of the strategic sourcing process. Boehmke et al (2020) have studied the use of data analytics for the strategic sourcing process, which may enhance its performance. Cox (2015) suggested more research regarding the phenomenon of the strategic sourcing process on the search for a more scientific view in this regard.…”

Section: Introductionmentioning

confidence: 99%

Impact of the strategic sourcing process on the supply chain response to the COVID-19 effects

Frederico

Kumar

Garza‐Reyes

2021

BPMJ

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PurposeThis research investigates the impact of the strategic sourcing process on the supply chain response to COVID-19. The paper presents practitioners' perspectives (experts in supply chain management, especially involved in the procurement field) on the strategic sourcing process's impact on the supply chain response.Design/methodology/approachThe study follows a survey-based approach for data collection. It uses a descriptive survey methodology where questions related to the impact of the strategic sourcing process on the supply chain response in the face of the coronavirus pandemic were explored by practitioners.FindingsIn total, 130 valid responses were obtained. The results showed that the majority of respondents agreed or strongly agreed that strategic sourcing positively impacts the supply chain response amid the COVID-19 effects. Also, for the five phases of the strategic sourcing process, the majority of respondents considered them as a high and very high impact on the supply chain response.Research limitations/implicationsThis paper provides timely insights for practitioners and academics, especially those involved in the supply chain management area, showing how the strategic sourcing process plays an important role in making supply chains more responsive amid disruption situations.Practical implicationsFindings of this paper clearly shows the impact of the phases of the strategic sourcing process on the responsiveness of the supply chains amid the COVID-19 pandemic. This can encourage supply chain leadership to devote more time to strategic sourcing initiatives to generate improvements on the supply chain performance.Originality/valueThis paper is unique since it brings an unexplored relation in respect to strategic sourcing amid disruption situations, such as the COVID-19 pandemic, from a practitioner's perspective. It also significantly contributes to developing new directions for the supply chain management domain to deal with large-scale disruptions, such as the coronavirus pandemic.

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“…equivalence testing, principal component analysis, artificial neural networks and knowledge management Boehmke et al (2020). defined DS as combination of important skills, which are programming skills, analytics skills 2.3 technical parts are related to 'data engineering'.…”

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confidence: 99%

Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry.

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Problem Statement: Predictive Analytics (PA) may effectively support semiconductor industry (SI) companies in order to manage the special challenges in SI value chains. To discover the implications of PA, the realistic benefits as well as its limitations of its application to semiconductor manufacturing, it is necessary to assess in which ways the application of PA affects the production system (PS) performances. However, based on the literature survey, the influences of PA on the various performance characteristics of an SI PS are not as clear as expected for the efficiently operative application. Besides, the existing performance models are not effective to predict the impacts of PA on the SI PS performances. Therefore, the overall aim of this thesis is to analyse and evaluate the impacts of PA on the SI PS performances and to identify under which conditions a PA application would generate the most significant performance improvements. The focus of this thesis is predictive maintenance (PdM). Research Methodology: Based on a post-positivist philosophy, the thesis applies a deductive research approach using mixed-methods for data collection. The research design has the following stages: (1) theory, (2) hypothesis, (3) state of research, (4) case study and (5) verification. Main Achievements: (1) The systematic literature review is carried out to identify the gaps of the existing research and based on these findings, a conceptual framework is proposed and developed. (2) The existing performance models are analysed and evaluated against their applicability to this study. (3) A causal loop model for SI PS is generated based on the assessment of experts with industrial engineering and equipment maintenance expertise. (4) An expert system is developed and evaluated in order to investigate transitive and contradictory effects of PdM on SI PS performances. (5) A simulation model is developed and validated for investigating the strengths and limitations of PdM regarding SI PS performances under different circumstances. Results: The results of the logical inference study show that PdM has 34 positive effects as well as 4 contradictory effects on SI PS performance characteristics. Based on the various simulation experiments, it has been found that (1) ’Mean Time to Repair’ decreases only if PdM supports proportionate reduction of failures and repair times. (2) Logistics performance improves only if the underlying workcenter is limited in capacity or the four partners are nonsynchronous. (3) PdM supports optimal cost decreases for workcenters where the degree of exhausting wear limits can be most effectively improved and (4) the degree of yield improvement gained by PdM is dependent on the operation scrap rate. However, (5) if a workcenter has overcapacity, PdM will potentially worsen PS performances, even if the particular workcenter performance can be improved. These new insights advance existing knowledge in production managements when adopting predictive technologies at SI PS in order to improve PS performances. The findings above enable SI practitioners to justify a PdM investment and to select suitable workcenters in order to improve SI PS performances by applying the proposed PdM. Contributions: The main contributions of this PhD project can be divided into practical application and theoretical work. The contributions from the theoretical perspective are: 1) The critical review and evaluation of the state of the research for PA in the context of semiconductor manufacturing and the models for predicting and evaluating SI PS performances. 2) A new framework for investigating the implications of PA on the challenges such as gaining high utilizations and controlling the variability in production processes in SI value chains. 3) The new knowledge about transitive and contradictory effects of PdM on SI PS performances, which indicates that PdM can be used to improve PS performances beyond a single machine. 4) The new knowledge about strengths and limitations of PdM in order to improve SI PS performances under particular circumstances. The contributions from the practical application perspective are: 1) A practical method for identifying workcenters where PdM delivers the most significant benefits for SI PS performances. 2) An expert system that provides a comprehensive knowledge base about causes and effects within SI PS in order to justify a PdM investment. 3) A concise review of important PA applications, their capabilities for the wafer fabrication and the most suited PA methods. These findings can be adopted by SI practitioners.

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A data science and open source software approach to analytics for strategic sourcing

Cited by 15 publications

References 66 publications

Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context

Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context

Impact of the strategic sourcing process on the supply chain response to the COVID-19 effects

Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry.

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