A Model for Calculating Cost of Applying Electronic Governance and Robotic Process Automation to a Distributed Management System

Technical Summary: A Model for Calculating Cost of Applying Electronic Governance and Robotic Process Automation to a Distributed Management System

Problem Statement
The paper addresses a gap in the literature regarding the quantitative analysis of organizational efficiency when integrating Electronic Governance (eGov) and Robotic Process Automation (RPA) into Distributed Management (DM) systems. While DM offers flexibility and agility through decentralized decision-making, and while eGov and RPA are recognized for enhancing operational efficiency and reducing costs, there is currently no formal mathematical model to calculate the specific cost of accomplishing a task within this hybrid environment. The authors note that successful implementation involves complex trade-offs, including infrastructure investment, training, and coordination challenges, necessitating a rigorous cost analysis framework that accounts for both human and automated agents.

Methodology
The authors propose a mathematical model that extends an existing framework for relating economic performance to organizational scale and structure (referenced from [23]). The methodology involves the following steps:

1. Organizational Structure Definition: The model defines an organization by its depth ($D$), representing the number of management levels. Level 1 is the highest (e.g., CEO), and Level $D+1$ represents line workers without management duties.
2. Human Cost Formulation:
   • The cost per unit time for a line worker ($C_{D+1}$) is modeled as a function of a minimum base cost, the organization's depth, and the employee's skill level ($sl$).
   • The cost for managers at level $i$ ($C_i$) is derived based on the line worker cost, scaled by a ratio ($1+r$) that increases with organizational depth.
   • The total cost for humans ($C(\text{humans})$) is calculated by summing the product of individual costs and their proportionate time contribution ($p_{ij}$) across all levels.
3. Bot/Automation Cost Formulation:
   • The model introduces "bots" (artificial agents) that operate alongside humans within the same hierarchical structure.
   • Bot costs are calculated using analogous equations to human costs, distinguished by prime notation (e.g., $C'_{D+1}$, $sl'$, $r'$). This allows for distinct parameters regarding bot skill levels, base costs, and salary ratios.
4. Integrated Cost Calculation:
   • The total cost per unit time ($C$) for accomplishing a task is defined as the weighted sum of human and bot costs, normalized by a constant ($\kappa$).
   • The final equation (Equation 8) aggregates the costs of all human and bot employees, weighted by their respective time contributions ($q_{ij}$ and $q'_{ik}$).
   • The model specifically decomposes the cost for line workers (Equation 9) to show how the total cost is a linear combination of base costs and skill-level-weighted time proportions for both humans and bots.

Key Contributions

• Novel Mathematical Framework: The paper presents what the authors claim is "one of the first of its kind" mathematical model specifically designed to calculate task costs in a DM system utilizing both eGov and RPA.
• Hybrid Agent Modeling: Unlike previous models that may focus solely on human labor or purely automated systems, this framework explicitly integrates human employees and software bots into a single hierarchical cost structure, allowing for the comparison of skill levels and time contributions across both agent types.
• Formalization of eGov and RPA Costs: The model provides a structured way to incorporate the specific cost drivers of eGov (e.g., software, digital storage, online forms) and RPA (e.g., implementation, maintenance, bot purchasing) into the broader organizational cost equation.

Results
The paper does not present empirical data, experimental results, or case study simulations. Instead, the "result" is the derivation of the mathematical equations themselves. The model demonstrates that the cost of accomplishing a task in a distributed system is a function of:

• Organizational depth ($D$).
• The skill levels of both human and bot agents.
• The proportionate time contributed by each agent.
• The specific cost parameters (base costs and scaling factors) associated with the organization's structure and the technologies employed.

Significance and Claims
The authors position this work as a foundational step for future research. They claim that the model is significant because:

• It fills a critical void in cost analysis for organizational efficiency in the context of unprecedented advancements in electronic and automation technologies.
• It provides a formal tool to analyze the trade-offs between the potential for increased efficiency/agility and the challenges of implementation (such as coordination and investment costs).
• It is expected to "spark further research" on how eGov and RPA impact organizational performance as these technologies continue to evolve.

The paper maintains a modest tone, acknowledging that while the combination of eGov and RPA has immense potential, the challenges of implementation (planning, design, security) are complex, and the model serves as a theoretical basis for analyzing these dynamics rather than a prescriptive solution for immediate deployment.