Developing a Multi‑methodology for Identification and Prioritization of Supply Chain Marketing Strategies for Biotechnology Knowledge-based Companies
Year: 2024
Type: article
Abstract: While knowledge-based companies can leverage their specialized human and technological resources to increase market share and compete with established firms, they face the challenging and complex task of evaluating and selecting supply chain marketing strategies. This challenge arises due to differences in company type, objectives, size, location, and the varying experiences of decision-makers. The research aims to identify and prioritize supply chain marketing strategies for biotechnology knowledge-based companies by developing and applying a multi-methodological approach. This approach combines the advantages of Soft Operational Research (Soft OR), SWOT analysis, and multi-criteria decision-making (MCDM) to address the complex nature of real-world problems more effectively. The study consists of two main phases. In Phase I, a mixed-method approach using Soft OR (JOURNEY Making method) and SWOT analysis is employed to assess the current situation and formulate potential strategies. A combined AHP-PROMETHEE approach is proposed in Phase II to prioritize the identified strategies. The model enhances decision-making efficiency by helping decision-makers select the best strategy, benefiting from the strengths of Soft OR and MCDM in tackling real-world complexities. Results show that biotechnology knowledge-based companies should prioritize sustainable international presence through proactive, competitive, innovative, and collaborative behaviors, leveraging distinctive resources and strategic alliances to improve technological innovation and knowledge sharing while employing appropriate models to select effective supply chain marketing strategies based on their conditions. Additionally, a sensitivity analysis is conducted to evaluate the impact of criteria weights on the decision-making process, providing valuable insights and confident recommendations for future research.
Authors: Akram Deirani, Ali Rajabzadeh Ghatari, Mahmoud Dehghan Nayeri, Moustafa Nassereddine
Institution: Tarbiat Modares University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6944996344
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