At PrimeNumerics we offer an “Analytics Readiness Assessment” program to measure the quality of your Manufacturing related data.

We also evaluate your Manufacturing data availability to better help your Manufacturing-Maintenance-Reliability Leadership & Practitioners for better analytics decision making Our Analyst Services.

Our PrimeNumerics Manufacturing Assessment Framework & Methodology combined with ERP of Maintenance & Reliability Subject Matter Experts, ERP Functional Business Analysts, Manufacturing Data Scientists and BI Technology Specialists will help you assess.

Taxonomy & Standardization:
  • Shop Floor Data Collection Systems
  • PLC/SCADA/DCS Data Collection Standardization
  • Data sources analysis (applications, data entities, data elements)
  • Data Quality Assessment for various sources (Availability, Accuracy, Usability, Sufficiency , Integrity)
  • Assess Master Data Development (Manufacturing Master, Manufacturing Hierarchy
  • Location Hierarchy Master, Failure, Cause, Remedy Master)
  • Assess current ERP data input practices maturity
  • Data accessibility & visibility analysis
  • Current Report portfolio adequacy & usage
  • Existing BI & tools technology architecture and usage feasibility
Value for the Management
  • Gain holistic view across various business functions
  • Improving the decision-making process
  • Obtain Trustworthy Manufacturing data
  • Improve analytics turnaround time
  • Supporting analysis for Strategies and process changes
  • Identifying process bottlenecks and monitoring collective progress
  • Communicating Goals / Objectives with everyone involved in ERP
  • Monitoring Performance for timely actions
  • Better Performance Measurements
  • Identify areas of improvements for Maintenance & Reliability Excellence
  • Develop understanding on how to leverage existing data & technology
  • Improve analytics turnaround time
Good quality Manufacturing data is hard to get
  • Voluminous data from disparate data sources
  • No common elements to link data sources
  • Improper identification of good & bad data
  • Laborious process of cleaning data
  • Lack of knowledge on usage of data
  • Cultural challenges in gathering data
  • Complex data validation
  • Training of end users
  • Lack of standardization in Taxonomy
  • Lack of standardization for Manufacturing hierarchy, component, hierarchy, failure codes, item master, location master etc.
Manufacturing Data Life Cycle
  • Impact of Poor Manufacturing Data Integrity
  • Cannot trust Manufacturing data quality
  • Lack of data uniformity across organization
  • Data not available in a reportable format
  • Cannot perform comprehensive data analysis
  • Inability to distribute information to stakeholders
  • Regulatory Compliance is difficult & cumbersome
  • Unable to get early alerts
  • Decisions are made solely on judgment
Issues with Manufacturing Data.
  • Consistency
  • Accuracy
  • Integrity
  • Uniformity
  • Completeness
  • Accessibility
  • Matching and Merging
How we can help with Manufacturing Data Quality Issues...
Manufacturing Data Audit & Quality Analysis
  • Data Sources Analysis (Application, Data Entities, Data Elements)
  • Data Quality Assessment (Availability, Accuracy, Usability, Sufficiency, Integrity)
  • Data Security, Accessibility & Visibility Analysis
Manufacturing Data Development
  • Classification
  • Standardization
  • Naming convention
  • Normalization
  • Combining disparate data sources
  • Analyzing/building/reviewing integrity across disparate sources
Manufacturing Master Data Development
  • Manufacturing Master
  • Manufacturing Hierarchy Catalog with Specifications
  • Manufacturing Component Catalog
  • ERP Master Data clearing and Consolidation
  • Failure, Cause, Remedy Master
  • Spare Part Item Master Cataloging
Manufacturing Data Cleansing & Validation
  • Review & validate existing data based on the quality checks & business rules
  • Translate “Text/Notes” into relevant failure codes
  • Make changes necessary to the data
  • Build scripts to automate data scrubbing wherever possible
  • Check and fix abnormalities
  • Perform validation
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