Supply Chain Standardization Constructs for Relevant Analytics
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Solution Overview
Problem
Existing CRM and SCM software often report irrelevant and overly complicated information, preventing organizations from making effective changes to their supply chain and organizational operations.
Innovation Solution
A system and method for generating standardization constructs based on data analysis, using an analytics search engine to filter and generate relevant information through standardization constructs, including domain weights, time components, and peer comparisons, to provide actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing CRM and SCM software report comprehensive supply chain information, then information completeness is improved, but information relevance deteriorates
Solution Approach 1:
The system extracts only the most relevant supply chain data elements from comprehensive datasets by applying predefined data models and industry standards. This selective extraction removes irrelevant information while preserving critical supply chain metrics, thereby maintaining information completeness while improving relevance.
Solution Approach 2:
The system transforms raw supply chain data into standardized parameters using industry-specific data models. By changing the representation format from generic comprehensive data to standardized relevant parameters, the system maintains the quantity of information while improving its relevance to specific supply chain analytics needs.
2Quantity of substance
If existing CRM and SCM software provide detailed supply chain data, then data comprehensiveness is improved, but ease of operation deteriorates
Solution Approach 1:
The system changes the parameter representation of supply chain data by applying standardized data models that map comprehensive raw data to meaningful standardized parameters. This transformation maintains data comprehensiveness while improving ease of operation by presenting data in a standardized, easily interpretable format that simplifies decision-making and operational changes.
Solution Approach 2:
The system creates a universal standardized data representation that serves multiple supply chain analytics functions. By establishing a common standardized format that can be applied across different supply chain contexts, the system maintains comprehensive data coverage while improving ease of operation through consistent, universally applicable data structures.
3Quantity of substance
If existing CRM and SCM software report granular supply chain information, then information detail is improved, but device complexity deteriorates
Solution Approach 1:
The system applies parameter changes by transforming granular detailed supply chain information into standardized parameters using industry data models. This transformation maintains the detail and granularity of the information while reducing system complexity by organizing detailed data into standardized, manageable parameter structures that are easier to process and analyze.
Data Source
AI summary
Provided are methods, systems, and apparatuses for improving supply chain management interfaces and functionality. The supply chain management interfaces may provide visualizations of one or more standardization constructs associated with an organization and locations thereof. A standardization construct may be generated using supply chain records associated with the organization and locations thereof. The visualizations of the one or more standardization constructs may be adjusted at a user interface. The adjusted one or more standardization constructs may be indicative of ways in which the one or more standardization constructs may be improved.