A Theoretical and Practical Contribution to Supply Chain Robustness: Developing a Schema for Robustness in Dyads

Durach, Christian F.

doi:10.1007/978-3-658-18632-6_1

Cited by 3 publications

(5 citation statements)

References 173 publications

(322 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature review follows a systematic procedure as recommended in [4,7,9]. The step-by-step procedure is shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…An overview of their characteristics can be found in Table A1 in the Appendix A. Seven out of these 28 stood out in terms of their systematic procedure ensuring transparency, replicability, and reduced bias following the conduct of a systematic literature review as described in [5,7,8]. These seven studies will be briefly presented in the following.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

show abstract

“…The literature review follows a systematic procedure as recommended in [4,7,9]. The step-by-step procedure is shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The second part of the data collection is based on a systematic literature analysis by Durach (2016) [17], who describes systematic literature analysis as a process with six phases. Starting from a clear objective formulated in a research question, the need for research should be ensured by involving relevant stakeholders.…”

Section: Methodsmentioning

confidence: 99%

“…However, he describes the importance of an objective and transparent approach and recommends involving several researchers in this step in order to minimize bias. The sixth step includes the dissemination of the results and the use of the research findings [17].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fields of Action for Designing Measures to Avoid Food Losses in Logistics Networks

Kleineidam

2020

Sustainability

View full text Add to dashboard Cite

The literature contains many analyses of measures against food loss. However, there exists no structured analysis of the objective of these measures. This study employs a systematic literature analysis and open expert interviews, combining the perspectives of science and practice. For each analyzed case, we identified the objectives behind the implemented measures. Using qualitative clustering, we categorized the located objectives into fields of action. The identified 13 fields of action provide an overview of the objectives of food loss prevention measures. The results indicate that different levels of importance can be assigned to these fields of action. In particular, the results show the relevance of increased network cooperation and transparency within a company and along the entire value chain. Furthermore, the study indicates that the creation of transparency provides the greatest overall added value in terms of reducing food losses.

show abstract

Supply chain resilience: a multi-level framework

Adobor

2018

International Journal of Logistics Research and Applications

View full text Add to dashboard Cite

A Theoretical and Practical Contribution to Supply Chain Robustness: Developing a Schema for Robustness in Dyads

Cited by 3 publications

References 173 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

Fields of Action for Designing Measures to Avoid Food Losses in Logistics Networks

Supply chain resilience: a multi-level framework

Contact Info

Product

Resources

About