Financial Operations Steering System for Deviation Detection
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Solution Overview
Problem
Current controlling processes in enterprises are inefficient due to manual or semi-automated data consolidation and analysis, making it difficult for controllers to identify and address deviations between actual and plan data in a timely manner, and lack the ability to proactively manage operational drivers impacting revenue.
Innovation Solution
A Financial Operations Steering System (FOSS) that automates data monitoring and alerting, utilizing alert models to detect deviations from plan data, allowing for proactive intervention and collaboration to correct operational issues, integrated with an ERP system and data warehouse for centralized data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automated data consolidation processes are used, then controllers can analyze actual data against plan data, but the process becomes very time-consuming and controllers cannot perform their job effectively
Solution Approach 1:
The system performs preliminary actions by automatically consolidating data and calculating projected results before the controller needs to analyze deviations. The alert model proactively monitors operational drivers and computes variances between projected and plan results in advance, eliminating the need for manual data consolidation at the time of analysis.
Solution Approach 2:
The patent introduces an intermediary alert model that acts as a mediator between raw operational data and the controller. This alert model automatically processes operational drivers, calculates projected results, identifies deviations from plan data, and presents consolidated findings to the controller, thereby reducing the controller's manual workload while maintaining analysis precision.
2Measurement precision
If data is consolidated for analysis, then deviations can be identified, but links to transactional details are lost making root cause analysis difficult
Solution Approach 1:
The system implements feedback by automatically tracing deviations back to their source operational drivers and providing links to underlying transactional details. When a deviation is detected, the alert model provides feedback pathways that allow controllers to drill down into specific transactions and operational data that caused the variance, preserving information links throughout the consolidation process.
3Reliability
If controllers manually analyze all data and coordinate with departments, then root causes can be understood, but controllers do not have enough time to perform their job effectively
Solution Approach 1:
The alert model performs self-service by automatically monitoring operational drivers, calculating projected results, identifying deviations, and even suggesting potential root causes without requiring controller intervention for each analysis step. The system serves itself by autonomously performing data gathering, consolidation, and preliminary analysis, freeing the controller to focus on high-level decision-making.
Solution Approach 2:
The system performs preliminary root cause analysis by automatically tracing deviations to their source operational drivers before the controller needs to review them. This preliminary action includes identifying which operational drivers contributed to variances and preparing analytical insights in advance, so the controller receives pre-processed information ready for review.
4Measurement precision
If current controlling processes are used, then actual data can be compared to plan data, but the system cannot understand analytical models of operational drivers and their impact on revenue
Solution Approach 1:
The patent applies parameter changes by transforming the system's analytical capabilities to understand and process operational driver parameters. The alert model is configured to recognize specific operational driver types (such as sales transactions, production volumes, inventory levels) and automatically calculate their impact on projected revenue, enabling the system to comprehend complex analytical relationships between operational parameters and financial outcomes.
Data Source
AI summary
Systems, methods and computer readable media are provided for controlling operations of an enterprise. In one exemplary embodiment, a method is provided that includes creating an alert model that may be parameterized by a user to monitor the behavior of one or more operational drivers. The method may also include monitoring the behavior of the one or more operational drivers based on the alert model and sending an alert message when the behavior of the one or more operational driver causes a deviation from a preset standard. The method may further include analyzing the alert message to determine one or more behaviors contributing to the alert and communicating the alert message to others in order to collaborate on correcting the deviation from the preset standard. Moreover, the method may include generating a report summary regarding the alert.