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Use of big data analysis to identify possible sources of supply chain disruption through the DOTMLPFI method

Nagy Judit, Foltin Pavel, Ondryhal Vojtech

LogForum

2022

Vol. 18, no. 3

309--319

Backgrounds: The presented research deals with the investigation of how big data analytics can help predict possible disruptive events in supply chains. The supply chain can be considered a complex system with a wide spectrum of possible sources of internal and external disruptions. Since the individual entities of the supply chains operate in a particular environment and interact with this environment, there is a certain level of mutual interdependency. This set of interconnected interactions within the supply chain will be the unit of analysis. Methods: There are many internal and external sources of supply chain disruption, which opens up the potential application of Big Data Analysis (BDA) as an early warning tool. To analyse the possible application of the BDA to identify sources of supply chain disruptions, we conduct a bibliometric analysis to define an appropriate structure for supply chain risk classification as well as appropriate keywords that make data collection quicker and easier. The DOTMLPFI methodology was used to systematically identify the most relevant risks threatening the supply chains. Results: The proposed research approach creates a possible framework to support the operational sustainability and resilience of the supply chain as a system, toward internal and external disruptions. The research results also point out the most explored attributes of supply chain disruption. The conducted bibliometric research and content analysis support the theoretical framework of using BDA as a possible early warning tool, especially for the identification of possible sources of supply chain disruption. The approach of grouping Big Data sources into categories based on DOTMLPFI groups allows to identify the appropriate keywords for their later BDA analysis. The analytical framework provides a starting point for individual supply chain entities to understand risks and systematically collect the appropriate data in the required structure about them. Conclusion: The complexity of supply chains, together with the increasing possibility of digital applications, requires a new analytical framework for evaluating the overall supply chain, with the possible application of new data sources and analytical approaches regarding the risks threatening the chain. DOTMLPFI methodology allows covering all the relevant categories of supply chain risks, and by proposing relevant keywords and data sources it can help companies to find the appropriate open-source, up-to-date information and be prepared for disruptive events.

Applications of performance indicators for optimization of humanitarian chains

Repík Dušan, Foltin Pavel

LogForum

2022

Vol. 18, no. 4

495--504

Background: Humanitarian operations are a key contribution to alleviating human suffering and reducing property damage. The success of these measures is conditioned by the implementation of efficient and sufficiently flexible logistics chains. The specificity of the conditions for the distribution of material aid requires the preparation of a suitable distribution system, the necessary capacities, and the creation of conditions to achieve the flexibility of individual solutions. The key issue for the implementation of the humanitarian aid distribution itself is the identification of suitable performance indicators, considering the specifics in the individual phases of humanitarian aid implementation. Methods: The approach is based on the research and content analysis, followed by creating a conceptual model and workflow diagram using interviews with experts, and identifying key performance indicators for optimization within the case study. Results: The humanitarian supply process was identified through a workflow diagram. A model was built to illustrate a case study of the use of rail transport to the provide humanitarian aid to Ukraine crisis in 2022. Performance indicators are compiled for the model. Recommendations are formulated for the creation of suitable indicators of the performance of humanitarian logistics chains. Conclusion: A gap and research problem in performance improvement and its measurement is identified within humanitarian logistics. This study examines the opportunity to improve the performance of humanitarian distribution to Ukraine in 2021.