Natural-Language Business Analytics for Flexible Supply Chain Reporting
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Solution Overview
Problem
Business data related to supply chain management is complex and difficult to understand, with existing methods like paper reports, phone/email communication, and interactive dashboards being inflexible, resource-intensive, or requiring specialized training, leading to inefficient decision-making.
Innovation Solution
A conversational business tool utilizing a Natural Language Processing Model, intelligent analytics, and data-driven speech to provide real-time, relevant business insights and facilitate collaboration, integrated with a supply chain planning platform for rapid processing and scenario simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If paper reports are used to communicate aggregate business data, then data can be presented in a structured format, but the reports are inflexible and require creating new reports for additional details, consuming valuable time and financial resources
Solution Approach 1:
The system transitions from static paper reports to a dynamic conversational interface that adapts to user needs in real-time. The natural language processing model enables the system to dynamically generate responses based on user queries, eliminating the need to create new reports for different data requests.
Solution Approach 2:
The conversational business tool enables users to independently query and access business data without requiring data scientists or report creators. Users can directly ask questions in natural language and receive immediate responses, making the system self-serving and eliminating dependency on human resources for data retrieval.
2Loss of information
If paper reports provide comprehensive data, then all relevant information is available, but too much data is provided such that irrelevant details obscure important aspects and insights
Solution Approach 1:
The system extracts only the relevant information needed to answer the user's specific question from the comprehensive business data. The natural language processing model identifies key entities and relationships, pulling out only the necessary data points while filtering out irrelevant details, thus providing clarity without losing important information.
Solution Approach 2:
The system provides different levels of data detail based on the specific query context. Rather than uniformly presenting all data, it adapts the level of aggregation and detail to match the user's information needs, ensuring that each response contains the appropriate amount of relevant information for that specific inquiry.
3Adaptability or versatility
If interactive dashboards are used to display business metrics, then data can be filtered and modified, but they require even more training to comprehend the complicated visualizations
Solution Approach 1:
The system replaces complex visual interaction mechanisms (dashboard filtering, graph manipulation) with natural language communication. Users can filter and modify data views by simply asking questions in natural language, eliminating the need to learn complex dashboard interfaces while maintaining full adaptability and filtering capability.
Solution Approach 2:
The system changes the interface parameter from visual-graphic interaction to natural language text interaction. This fundamental parameter change makes the system accessible to users without specialized training in data visualization, while still providing comprehensive filtering and modification capabilities through language-based queries.
4Reliability
If data scientists are used for phone or email communication to report business information, then up-to-date metrics can be provided, but human resources are consumed and results may be delayed
Solution Approach 1:
The conversational business tool is a self-service system that automatically queries the supply chain planning platform and generates responses without human intervention. It provides up-to-date metrics by directly accessing real-time data while eliminating the need for data scientists to manually retrieve and communicate information, thus maintaining reliability while dramatically improving productivity.
Solution Approach 2:
The system enables continuous, uninterrupted access to business metrics through automated natural language processing. Unlike human data scientists who may be unavailable, the conversational tool operates continuously, providing immediate responses to any query at any time, ensuring both up-to-date information and maximum efficiency.
Data Source
AI summary
A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.