IEEE Access (Jan 2024)
Enhancing Operational Efficiency in Industrial Systems: A DEA-Grey Integration
Abstract
Due to the growth of Blockchain as a Service (BaaS) marketplaces, it is crucial to have evaluation methods that can support decision-making processes. This study presents an approach that combines the Grey Model (GM) and Data Envelopment Analysis (DEA) to maximize efficiency assessment in BaaS marketplaces. By leveraging the strengths of both models, this strategy offers a decision-making framework with insights. Integrating GM’s forecasting capabilities and DEA’s efficiency measurements allows for evaluating BaaS marketplace performance. This research empowers stakeholders, including BaaS providers and users, to make decisions based on data analysis, leading to improved efficiency and maximized outcomes. For each BaaS company, we apply GM (1, 1) to forecast values from 2023 to 2026. We use the Malmquist model to calculate three productivity scores: catch-up frontier shift and Malmquist Productivity Index (MPI). These scores are based on three input variables (assets, total liabilities, and total equity) and one output variable (revenue), considering the top 12 BaaS companies in the global market from 2018 to 2026. On average, the BaaS Company that showed productivity was BC05 Oracle, followed by BC12 Alibaba. Conversely, BC02 IBM was found to be the BaaS business. Moreover, the study reveals that most BaaS enterprises tend to follow patterns of development. These findings highlight the importance of considering technical efficiency change indexes as predictors of productivity growth. The research also emphasizes the need for BaaS companies to broaden their focus beyond technology and prioritize resource utilization. This strategic shift can pave the way for opportunities. Ensure long-term success and resilience. This insightful perspective, backed by evidence, is a rationale for companies to adopt a holistic approach that combines technical advancements with optimal resource management.
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