Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adve...

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Supply chain risk management and artificial intelligence: state of the art and future research directions

The integration of lean manufacturing, Six Sigma and sustainability: A literature review and future research directions for developing a specific model

Sustainable Value Stream Mapping (Sus-VSM): methodology to visualize and assess manufacturing sustainability performance

Evidence suggests that Lean, Six Sigma and Green approaches make a positive contribution to the economic, social and environmental (i.e. sustainability) performance of organisations. However, evide...

A framework for the integration of green and lean six sigma for superior sustainability performance

Purpose



– Globally expanding supply chains (SCs) have grown in complexity increasing the nature and magnitude of risks companies are exposed to. Effective methods to identify, model and analyze these risks are needed. Risk events often influence each other and rarely act independently. The SC risk management practices currently used are mostly qualitative in nature and are unable to fully capture this interdependent influence of risks. The purpose of this paper is to present a methodology and tool developed for multi-tier SC risk modeling and analysis. 




Design/methodology/approach



– SC risk taxonomy is developed to identify and document all potential risks in SCs and a risk network map that captures the interdependencies between risks is presented. A Bayesian Theory-based approach, that is capable of analyzing the conditional relationships between events, is used to develop the methodology to assess the influence of risks on SC performance 




Findings



– Application of the methodology to an industry case study for validation reveals the usefulness of the Bayesian Theory-based approach and the tool developed. Back propagation to identify root causes and sensitivity of risk events in multi-tier SCs is discussed. 




Practical implications



– SC risk management has grown in significance over the past decade. However, the methods used to model and analyze these risks by practitioners is still limited to basic qualitative approaches that cannot account for the interdependent effect of risk events. The method presented in this paper and the tool developed demonstrates the potential of using Bayesian Belief Networks to comprehensively model and study the effects or SC risks. The taxonomy presented will also be very useful for managers as a reference guide to begin risk identification. 




Originality/value



– The taxonomy developed presents a comprehensive compilation of SC risks at organizational, industry, and external levels. A generic, customizable software tool developed to apply the Bayesian approach permits capturing risks and the influence of their interdependence to quantitatively model and analyze SC risks, which is lacking.

Quantitative modeling and analysis of supply chain risks using Bayesian theory

Fuels from biomass resources have emerged as a promising alternative to fossil fuel. Widely distributed source locations, varying feedstock, and specific regional conditions make it challenging to develop an optimization model that can be applied to any region to estimate the overall economics of producing these biofuels. The lack of a region specific, flexible optimization model makes it difficult for stakeholders like local policy makers, growers, and investors to make informed decisions about the economic viability and social and environmental impacts of biomass utilization. This novel contribution will illustrate an approach to develop a region specific optimization model which links various aspects of the biofuel supply chain such as feedstock source location, upstream and downstream logistics, as well as thermochemical and biochemical processing. This research shows how various individual optimization models can be combined, resulting in a complete, multivariable economic optimization model for a regional biomass network, paving a pathway for future work to develop an integrated framework for sustainability. The research demonstrated in this contribution illustrates the development of a model that can form the basis of a generalizable decision support tool that can guide investors and policy makers in making critical assessments on a local level in any particular region of interest. As a proof of concept, a portion of the described model will be validated using the Jackson Purchase region of Western Kentucky, USA, which is adjoining many coal fields and has various bio-based feedstocks.

A multidisciplinary decision support tool for evaluating multiple biorefinery conversion technologies and supply chain performance

This paper presents the initial results of a comprehensive effort to develop a methodology for sustainable value stream mapping (Sus-VSM) and its application to an industry case study. Suitable metrics and visual symbols were first identified to develop the approach to Sus-VSM. A pilot case study conducted with a local manufacturer of satellite television dishes showed the Sus-VSM approach was able to capture the economical, environmental, and societal sustainability of the line studied. Other and/or additional metrics may be more appropriate if a completely different production line were to be assessed and can be determined through further case studies.

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Visualizing Sustainability Performance of Manufacturing Systems using Sustainable Value Stream Mapping (Sus-VSM)

If lignocellulosic biomass is to become a viable competitor with fossil based resources for the production of energy and chemical products, sustainable sources of this material must be established. To address this issue an understanding about the risks affecting biomass sources and the biorefineries which they supply is crucial. In this paper, a quantitative approach using Bayesian Belief Networks (BBN) to model and analyze risks in a biorefinery's biomass supply chain is presented. Centralized aggregated corn stover supply locations and an integrated biorefinery located in Kentucky's Jackson Purchase Region are considered as a case study for demonstrating the approach.

William Faulkner

Papers

Sustainable Value Stream Mapping (Sus-VSM): methodology to visualize and assess manufacturing sustainability performance

Quantitative modeling and analysis of supply chain risks using Bayesian theory

A multidisciplinary decision support tool for evaluating multiple biorefinery conversion technologies and supply chain performance

Visualizing Sustainability Performance of Manufacturing Systems using Sustainable Value Stream Mapping (Sus-VSM)

A Bayesian Network Based Approach for Risk Modeling to Aid in Development of Sustainable Biomass Supply Chains