[Paper Review] Intellectual Management of Enterprise

Yuriy Ostapov|arXiv (Cornell University)|Jul 2, 2012

Economic Development and Digital Transformation4 references3 citations

TL;DR

This paper proposes Intellectual Management of Enterprise (IME), a novel technology extending ERP systems by enabling natural language interaction, joint production-sales planning for profit maximization, and adaptive response to internal and external disruptions. By integrating natural language understanding, predictive analytics, and optimization algorithms, IME enhances managerial decision-making, increases profitability, and ensures financial stability through real-time adaptation to market and operational changes.

ABSTRACT

A new technology (in addition to ERP) is proposed to provide an increase of profit and normal cash flow. This technology involves the next functions: forming of intellectual interface on a natural language to communicate with a control system; joint planning of production and sales to get the maximal profit; an adaptation of control system to internal and external events. The use of the natural language permits to overcome a barrier between the control system and upper managers. To solve posed actual problems of management the selection of information from a database and call to mathematical methods are executed automatically. Optimal planning provides the maximal use of available resources and opportunities of market. Adaptive control implements the efficient reaction to critical events that lead up to a decrease of profit and increase of accounts receivable.

Motivation & Objective

• To address critical limitations of ERP systems in enabling direct, effective communication between top managers and control systems.
• To develop an integrated approach for joint production and sales planning that maximizes profit under resource and market constraints.
• To enable real-time adaptation of enterprise control systems to internal and external critical events affecting profitability and cash flow.
• To bridge the gap between textual business information and actionable management decisions through semantic understanding of unstructured data.
• To improve decision-making effectiveness by automating mathematical analysis and presenting results in natural language.

Proposed method

• Implementing a natural language interface to allow top managers to query and control the enterprise system using everyday economic language.
• Using least-squares regression and nonlinear regression models to predict sales based on product characteristics, pricing, and market trends.
• Forming optimal production plans by calculating profit for each production variant, considering direct and indirect costs, and selecting the one with maximum profit.
• Applying constraint-based optimization to align production planning with resource availability and predicted sales volumes.
• Integrating natural language understanding (NLU) algorithms to extract and interpret semantic meaning from unstructured documents, media, and market reports.
• Building a dynamic model of the market that distinguishes between consumer and industrial markets, with separate analytical approaches for each.

Experimental results

Research questions

• RQ1 How can natural language interaction between top managers and enterprise control systems improve decision-making efficiency and reduce communication barriers?
• RQ2 What mathematical modeling techniques can effectively predict sales volume based on product characteristics and market variables?
• RQ3 How can joint production and sales planning be optimized to maximize profit while respecting resource constraints and market predictions?
• RQ4 What mechanisms enable a control system to adapt dynamically to critical internal and external events that threaten profitability and cash flow?
• RQ5 How can unstructured textual data from documents, media, and the internet be semantically processed and integrated into enterprise management decisions?

Key findings

• The system successfully identified that the prime cost of notebook TN20A exceeded its selling price due to overpriced components (motherboard, hard disk, screen matrix) and low assembly productivity.
• For increasing third-quarter 2010 income, the system recommended improving the quality of TN20A, expanding sales into new regions, and increasing market share for both TN20A and TN301.
• Optimal production planning was achieved by calculating profit for each variant based on sales-price relationships and cost structures, selecting the variant with maximum profit.
• The analysis revealed that increasing sales volume for TN20A and TN301 could be achieved by targeting underperforming regions, as both models held less than 50% market share.
• The system determined that the ideal budget-class notebook in 2010 had 2 GB RAM, a price of 4048 hryvnias, and used the DOS operating system.
• The experimental system demonstrated that automated mathematical analysis and natural language output significantly improved managerial insight into financial deterioration causes and improvement strategies.