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Situation analysis forecasting: the case of European maritime clusters

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  • Peter J. Stavroulakis
  • Stratos Papadimitriou

Abstract

Within the literature concerned with aspects of competitiveness, innovation and strategic management of industrial clusters, the body regarding forecasting of strategic management is still nascent. This work aspires to render a contribution within the domain of strategic management forecasting, through the indicative case of European maritime clusters. For this end, a two-tier model is formulated. A quantitative strengths, weaknesses, opportunities, and threats (SWOT) methodology that derives from the fusion of a quondam situation analysis and crosstabs’ theory is generated. This leads to the utilization of the crosstab’s conditional probabilities as transition probabilities; the latter are called to compile the transition matrix of a Markov chain. Through this methodology, and the successive Markov chain’s transition matrices, strategic forecasts are extracted for a devised European maritime cluster case. This work relinquishes a novel application with respect to strategic management forecasting, that provides a dichotomy of practical interpretations and scenarios for quantitative situation analysis. Thus, it may enable effective real-time decision-making for strategic management and/or policy drafting. The situation analysis forecasting model may find applicability in a plethora of practical and theoretical cases, wherein forecasting may be desirable. It may as well pertain to an intrinsic methodology for situation analysis forecasting of maritime clusters.

Suggested Citation

  • Peter J. Stavroulakis & Stratos Papadimitriou, 2017. "Situation analysis forecasting: the case of European maritime clusters," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(6), pages 779-789, August.
    • Handle: RePEc:taf:marpmg:v:44:y:2017:i:6:p:779-789
    DOI: 10.1080/03088839.2017.1330560
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    4. Pache, Hannah & Grafelmann, Michaela & Schwientek, Anne Kathrina & Jahn, Carlos, 2020. "Tactical planning in tramp shipping - A literature review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 281-308, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    5. Huang, Dong & Grifoll, Manel & Sanchez-Espigares, Jose A. & Zheng, Pengjun & Feng, Hongxiang, 2022. "Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic," Transport Policy, Elsevier, vol. 128(C), pages 1-12.
    6. Koliousis, Ioannis G. & Papadimitriou, Stratos & Riza, Elena & Stavroulakis, Peter J. & Tsioumas, Vangelis, 2019. "Strategic correlations for maritime clusters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 43-57.

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