Advances in Computer and Communication Article Recommendation | Data-driven and enterprise resources

"In an era of data deluge, are enterprises truly learning to 'speak' with data, or are they merely 'drowning' in a sea of numbers?" "As resource allocation shifts from 'relying on experience' to 'following algorithms,' have we really found the 'master key' to efficient business operations?" These questions not only concern the survival of enterprises but also determine the trajectory of commercial competition in the digital economy era.

In the paper “Research on Evaluation and Optimization Models of Enterprise Resource Allocation Efficiency from a Data-driven Perspective”, published in Advances in Computer and Communication by Niming Wang from Boston University, the author systematically reveals how data-driven models can be leveraged to accurately evaluate and optimize enterprise resource allocation efficiency, injecting a new gene of intelligence into modern business management.

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From "Empiricism" to "Dataism": The Paradigm Shift in Enterprise Resource Allocation

Traditional enterprise resource allocation often relies on managers' personal experience, intuitive judgment, or even inter-departmental博弈, akin to a strategic gamble played in a fog—inefficient and fraught with risks. The introduction of a data-driven perspective, however, acts as a "digital eagle eye" for enterprises, illuminating the blind spots in decision-making. By integrating vast amounts of operational data, market dynamics, and employee behavior information to construct scientific evaluation models, enterprises can precisely quantify the marginal benefits of every resource input, achieving a qualitative leap from "roughly right" to "just right." This is undoubtedly a silent yet profound management revolution.

The Efficiency Dilemma and the Data Solution: When Enterprises Hit the "Resource Ceiling"

Amid slowing growth and intensifying competition, many enterprises are stuck in the mire of "high input, low output" resource allocation. Marketing departments squander budgets with meager conversions, R&D teams thirst for funds yet struggle to innovate, and human and material resources are severely depleted behind departmental silos. Wang's research highlights that data-driven models are the key to breaking this impasse. By constructing a multi-dimensional efficiency evaluation index system encompassing finance, operations, and human resources, and employing models such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis, enterprises can clearly identify the "efficiency frontier" and "weak links" in their resource allocation. A case study shows that after a multinational retail corporation applied a similar model to optimize its inventory layout, warehousing and distribution costs were reduced by 18%, and in-stock rates increased by 12%. Data is becoming the "guidance system" that penetrates the fog of management and enables precise resource deployment.

The Gap Between Ideal and Reality: The "Three Gates" Challenge of Data-Driven Implementation

However, the path from evaluation models to optimization practices is far from smooth. Enterprises first face the "Data Gate": isolated data silos and inconsistent data quality render models like a river without a source. Next is the "Decision Gate": how can the optimization solutions output by models integrate with complex organizational structures, human factors, and existing processes? Will managers choose to trust cold algorithms or rely on warm, old-fashioned experience? Finally, there is the "Evolution Gate": with markets changing rapidly, how can static optimization models develop dynamic adaptability and continuous learning capabilities? Crossing these "three gates" requires not only technological upgrades but also a fundamental transformation in organizational culture and mindset.

The Future is Here: The "Digital Twin" and Intelligent Synergy of Resource Allocation

Looking ahead, data-driven enterprise resource allocation is moving toward real-time simulation and prediction in the form of a "digital twin." By creating a virtual mirror of enterprise operations, managers can pre-simulate the outcomes of various allocation strategies in the digital world before committing real resources. Furthermore, artificial intelligence will enable intelligent, autonomous scheduling and synergy of resources across departments and business units, allowing enterprises to achieve efficient, self-adaptive resource flows—much like a living organism. This is not merely the pursuit of ultimate efficiency but a critical step in the evolution of organizational forms toward intelligent ecosystems.

"The art of management is being redefined by the science of data." In the wave of the digital economy, research on resource allocation efficiency from a data-driven perspective is like providing a precise navigation system and power optimization mechanism for a fleet of enterprises sailing in an ocean of uncertainty. It shows us that the future core competitiveness of enterprises may lie not only in how many resources they possess but also in how they use data intelligence to make every resource shine with its greatest possible value.

The study was published in Advances in Computer and Communication

https://www.hillpublisher.com/ArticleDetails/6343

How to cite this paper

Niming Wang. (2026) Research on Evaluation and Optimization Models of Enterprise Resource Allocation Efficiency from a Data-driven Perspective. Advances in Computer and Communication, 7(1), 43-46.

DOI: http://dx.doi.org/10.26855/acc.2026.03.006