Manufacturing Data Management and its Significance
The manufacturing industry creates an enormous amount of data, from raw material sourcing to shipping the product. Properly managing this data properly is integral to driving productivity, product quality, and the cost of operations.
Master Data Management (MDM) has always been a part of the enterprise information strategy, but with the emergence of Artificial Intelligence (AI), MDM has taken on greater significance.
As AI has opened opportunities for organizations to change their approach to data, it is an integral step for organizations working in the manufacturing environment. MDM is essentially the centralized data management in one ‘place’ instead of data being disbursed across the enterprise.
A report from IBM states that “84% of Data Transformers have significantly integrated artificial intelligence (AI) and machine learning in their data platforms.” An integrated data view enables one to make better informed decisions and collaboration amongst functions, from the shop floor all the way to the executive’s office. When data is made available to the organization, real-time and accurate, MDM enables manufacturers to develop new product opportunities, effectively manage improvement initiatives, and maintain a competitive advantage in a rapidly changing marketplace.
Key Components of Effective Manufacturing Data Management
1. Master Data Management
At the centre of production operations lies keeping master data up to date, the fundamental data that describes products, materials, equipment, suppliers, and customers. This article explores strategies organizations can implement to harmonize their master data across systems. Effective master data management ensures:
- Consistent product definitions across all systems.
- Standardized naming conventions and attributes.
- Elimination of duplicate or conflicting information.
- Clear data ownership and governance protocols.
When master data is properly managed, it eliminates confusion and creates a solid foundation for all subsequent data activities.
2. Bill of Materials (BOM) Management
The Bill of Materials functions as the DNA of manufacturing, defining exactly what components, materials, and quantities are required to produce a finished product. Sophisticated BOM management provides:
- Maintains version control across product iterations.
- Keeps a track on engineering changes and approvals.
- Connects design data with manufacturing requirements.
- Supports accurate costing and procurement activities.
Without proper BOM management, manufacturers risk production delays, excess inventory, and quality issues stemming from outdated or incorrect specifications.
3. Work Order Management
Work orders translate plans into executable manufacturing instructions. Effective work order management systems include:
- Schedule production efficiently.
- Allocate resources appropriately.
- Track progress in real-time.
- Collect vital production data.
- Document deviations and quality results.
Digital work order management creates a closed-loop system where production data feeds back into planning processes, enabling continuous improvement.
4. Data Quality Management
Perhaps the most fundamental aspect of MDM is ensuring data quality. This involves:
- Data Cleansing: Identifying and correcting inaccurate records. There are different data cleansing strategies that organizations can adopt.
- Deduplication: Eliminating redundant information.
- Standardization: Ensuring consistency across systems.
- Enrichment: Adding context and metadata to enhance usability.
- Validation: Verifying data meets quality standards.
Poor data quality leads to inefficiencies, inaccurate decisions, and lost opportunities in manufacturing. As per a Gartner report, “poor data quality costs organizations at least $12.9 million a year on average”.
5. Data Integration
Manufacturing environments typically contain numerous systems—ERP, MES, PLM, quality management, maintenance, and more. Data integration creates connections between these systems:
- Eliminating manual data re-entry.
- Reducing transcription errors.
- Enabling end-to-end process visibility.
- Supporting automated workflows.
- Creating comprehensive analytics capabilities.
Modern integration approaches, including API-based architectures and middleware solutions, make it possible to connect even legacy systems without complete replacement.
1. Preventive & Predictive Maintenance
Application:
- Asset master data (e.g., make, model, maintenance history) is used to set condition-based or time-based preventive maintenance schedules.
- Predictive analytics systems use sensor data combined with equipment master data to forecast when a machine is likely to fail.
Example:
A bottling plant uses real-time vibration data from a conveyor system. Cross-referencing with equipment specs in the master data, the system predicts bearing wear 10 days in advance and triggers an automatic maintenance order.
This article discusses how feature engineering plays a pivotal role in building predictive maintenance models. As per a report published by McKinsey, “Successful Predictive Maintenance programs reduce the requirement for both planned and unplanned maintenance interventions.” It also stated that “Lower-maturity PdM systems typically deliver only a fraction of the benefits (10 percent or less) of a fully scaled system. Ultimately the decision on where and at what level to implement predictive maintenance requires an asset-by-asset validation of the potential benefits and data availability.”
2. Production Planning & Scheduling
Application:
- Accurate BOM and routing data ensures the right materials and production steps are used for each product variant.
- Master data supports constraint-based scheduling to balance workloads across machines and shifts.
Example:
A manufacturer of electric motors uses master data to determine lead times, resource availability, and tool changeovers. This enables the MES system to generate optimized weekly schedules with minimal downtime between product types.
3. Inventory Optimization & Procurement
Application:
- Material master data (e.g., unit of measure, reorder point, lead time) drives automated re-ordering and just-in-time procurement.
- Vendor master data ensures parts are sourced from approved suppliers with known quality and delivery metrics.
Example:
An aerospace supplier integrates vendor lead time and part classification data into its ERP system, reducing inventory levels by 18% while improving on-time delivery.
4. Regulatory Compliance & Traceability
Application:
- Manufacturing master data helps trace every component, supplier, and process step used in production.
- Ensures conformance with FDA, ISO, or automotive industry standards (e.g., IATF 16949).
Example:
In pharmaceutical production, the batch master record includes ingredients, equipment used, and operator info—all drawn from validated master data. This supports fully traceability for audits and recalls.
5. Product Costing & Profitability Analysis
Application:
- Material master data and BOMs feed into cost rollups for finished goods.
- Accurate labor, material, and overhead data help identify unprofitable SKUs.
Example:
A consumer electronics company uses updated BOMs and machine hour rates from the master data to recalculate product costs, revealing that two products were consistently sold below margin.
6. Quality Control & Defect Analysis
Application:
- Master data enables automatic assignment of inspection protocols based on part category or supplier.
- Defect codes and equipment IDs allow for structured root cause analysis.
Example:
A Tier 1 auto supplier links master data with in-line quality inspections. When a defect is flagged, they quickly trace it to a specific press machine and material lot, enabling rapid containment.
7. Automation & Robotics Integration
Application:
- Robotics and automated lines require accurate master data for coordinates, part dimensions, tool change instructions, etc.
- Master data helps synchronize PLCs, HMIs, and SCADA systems.
Example:
A packaging line uses product master data to automatically reconfigure robotic arms when switching between bottle sizes and cap types—reducing setup time by 40%.
8. Sustainability & Energy Optimization
Application:
- Master data links materials to environmental impact data (e.g., carbon footprint per kg).
- Enables reporting for ESG and sustainability initiatives.
Example:
A building materials manufacturer uses material and process master data to calculate carbon emissions per batch, helping it meet ISO 14001 targets and provide transparency to customers.
9. Digital Twin & Simulation Modeling
Application:
- Digital twins require up-to-date master data to accurately represent physical assets and operations.
- Used for scenario testing, what-if analysis, and design validation.
Example:
A food processing plant simulates a line changeover using digital twin software. Accurate master data allows for accurate time, resources, and cost modeling before making physical changes.
10. Mergers, Acquisitions & System Consolidation
Application:
- Master data consolidation is a critical step in harmonizing operations post-merger.
- Standardized naming conventions, unit conversions, and supplier records ensure continuity.
Example:
After acquiring a competitor, a chemical company harmonizes material and equipment master data across both ERP systems, enabling unified reporting and procurement.
Role of Digital Twins in Manufacturing Data Management
Digital twins have emerged as a transformative technology in manufacturing, revolutionizing how companies manage and leverage their data. At its core, a digital twin is a virtual representation of a physical asset, process, or system that enables real-time monitoring, simulation, and optimization.
In manufacturing data management, digital twins serve as a central hub that connects disparate data sources and provides actionable insights throughout the product lifecycle. Let me break down their critical roles:
Data Integration and Contextualization
Digital twins integrate data from multiple sources sensors, IoT devices, enterprise systems, and historical records creating a comprehensive view of manufacturing operations. This integration transforms raw data into contextual information by:
- Consolidating real-time operational data with historical performance metrics
- Mapping relationships between equipment, processes, and outputs
- Providing a single source of truth for all stakeholders
Predictive Maintenance and Quality Control
One of the most valuable applications of digital twins is in maintenance and quality:
- Predicting equipment failures before they occur by analysing performance patterns
- Simulating various operating conditions to identify potential failure points
- Optimizing maintenance schedules based on actual usage rather than fixed intervals
- Tracking product quality metrics against digital models to detect deviations early
Process Optimization and Decision Support
Digital twins enable manufacturers to simulate process changes before implementing them:
- Testing new production parameters virtually without disrupting operations
- Identifying bottlenecks and inefficiencies through data visualization
- Providing decision support through what-if scenario analysis
- Optimizing resource allocation and energy consumption
Knowledge Capture and Transfer
Manufacturing organizations face significant challenges with knowledge retention. Digital twins help by:
- Capturing tacit knowledge from experienced operators in digital form
- Creating a persistent record of operational decisions and outcomes
- Enabling new employees to understand complex systems more quickly
- Preserving institutional knowledge during workforce transitions
Supply Chain Visibility and Resilience
Beyond factory operations, digital twins extend to supply chain management:
- Modelling supplier networks and identifying potential disruptions
- Simulating inventory levels against various demand scenarios
- Optimizing logistics and distribution networks
- Enhancing traceability throughout the product lifecycle
A report published by PWc highlighted that “Organisations that have developed digital capabilities to transform their linear supply chains into ecosystems — achieved savings of 6.8% annually in supply chain costs, along with a 7.7% revenue increase.”
Critical Link Between BOM and Work Orders
Bill of Materials (BOM)
BOM management is the capturing, configuring, and managing of all product data that is developed throughout the lifecycle of a product. Effective BOM management is critical to a manufacturer’s success no matter the size or the number of parts in the product. Think of it as a complete “recipe” or “ingredient list” for producing a finished good.
Work Order Management
Work Order Management is the systematic process of creating, tracking, and completing manufacturing tasks. A work order is a document that contains detailed instructions for a specific production run, including which product to make, the quantity, timeframe, resource requirements, and sequential steps in the manufacturing process.
Relationship Between BOMs and Work Orders
BOMs and work orders are intrinsically linked in the manufacturing process:
- Data Flow: When a work order is created, it references the appropriate BOM to determine exactly what materials are needed.
- Resource Planning: The BOM provides the “what” (materials required), while the work order adds the “how,” “when,” and “who” (production methods, scheduling, and assigned resources).
- Execution Support: Together, they guide the entire production process from material requisition through manufacturing to completion and quality control.
Challenges in Manufacturing Data Management
1. Data Silos
Disconnected systems create redundancies, delay decision making and hinder cross-functional collaboration. A few things create silos of data such as legacy technology, lack of integration capabilities, and the way organizations manage and operate in departments rather than as an integrated organization with combined performance and impact.
2. Scalability
As devices and sensors build on scale manufacturers will experience bottlenecks in data collection, data batch and storage, and computation and analysis. Without scalable systems, costs will increase and performance from data will fall short.
3. Integration complexities
Different systems have different protocols and data models. Integrating and transforming legacy systems, proprietary platforms, and real-time operations is often expensive and consumes a great deal of time and resources.
4. Uncertainty of information quality
Uncertainty in both data and information quality exists in a myriad of forms. Sensor drift, manual entry of data, differences in timestamps, and differences in reference datasets can influence your datasets, which does not bode well for planning, analytics, and/or automation.
5. Regulatory Compliance
Highly regulated industries with strict requirements for traceability, data retention, and audit trails often lose track of disconnected or undocumented processes for data; this makes compliance, when necessary, a challenge, which is not only a risk in and of itself but carries a tremendous cost for day-to-day operations.
Advanced Software Solutions in Manufacturing Data Management
Selecting and leveraging specialized software tools can differentiate successful manufacturers from their competitors:
1. IBM Maximo
IBM Maximo is a complete EAM solution with robust asset lifecycle and maintenance management. Its primary features are:
Integration of IoT devices for real-time monitoring and analysis.
Artificial intelligence-driven predictive maintenance features, minimizing unplanned downtime.
Supports flexible configuration models to address several industries’ demands.
Enhanced reporting and analysis dashboards for more informed decision-making.
2. SAP S4/HANA
SAP S/4HANA is a future-proof ERP system that is agile, quick, and data-centric. The key strengths are:
In-memory processing allows for rapid analytics and optimizations in operational effectiveness.
Integrated predictive analytics for proactive maintenance, and demand forecasting.
Smooth integration with IoT platforms, with real-time visibility.
Strong support for compliance, traceability, and regulatory compliance.
While S4/Hana is flexible, the functionalities and capabilities for managing master data is limited and generally requires a custom implementation or third-party solution to ensure data quality remains intact over time.
The team at Verdantis have compiled a detailed article that details the journey of managing Master data in SAP source systems. [Website Page]
3. Verdantis
Verdantis directly integrates with SAP, Oracle & Infor, and the team assists with directly integrating into any of other ERP systems. It provides manufacturing data management solutions, MRO master, Vendor master, Customer master data.
The software is trained on 3 million+ Material records across industries and caters to asset intensive industries spanning Oil, Gas & Energy; Mining, Minerals & Metals; packaged goods, Chemicals; Packaging & building materials.
Among the other companies in this list, Verdantis seems to have an edge as far as embedding Agentic AI solutions are concerned – Almost all use cases around categorizing, cataloguing, data enrichment, duplicate identification has their own AI-agents that manage master data records autonomously. Several Fortune 500 companies have been their clients; and their leadership team has solid pedigree across data management and enterprise resource planning businesses.
Specialty: Manufacturing Data, Supplier Data, Vendor Data, Customer Data, Maintenance Operations, MRO, BOM, Work Order Management.
Target Industries: Oil, Gas & Energy, Manufacturing, Packaging, Food & Beverages.
4. Oracle Cloud Maintenance
Oracle Cloud Maintenance offers intuitive, cloud-based asset management designed for simplicity and strategic impact. Key functionalities encompass:
Simplified maintenance task automation and streamlined workflows.
Embedded machine learning algorithms predicting potential asset failures.
Real-time operational analytics, increasing transparency and efficiency.
Scalable cloud infrastructure to accommodate evolving business needs.
Impact of Vendor Master Data on Manufacturing Data Management
Vendor master data represents a critical function of manufacturing data management to provide a source of truth that links manufacturing systems to the outside supply network. It provides accurate tracking supply of materials, consistency of data across enterprise systems, and gives anchor to end-to-end supply chain visibility.
Effective use of vendor data means effective product quality and regulatory compliance because of the ability to trace components back to original suppliers and document supplier qualification and supplier performance.
Good vendor data also helps with production planning. Reliable vendor data improves calculations for lead time, inventory management, capacity management, and planning schedules. Good financial decisions are possible around product costing, payment terms management, compliance with contracts, and spend analysis.
Good data on vendors reduces risk related to manufacturing. Good data helps manufacturers manage supplier diversity, identify single-source supplier dependency, and better anticipate supply chain disruptions.
When combined with digital twins and advanced technology in planning, good data can enhance simulation models which are instrumental to carrying out different scenarios and predictive analysis.
Assurance in the governance and quality of vendor data is imperative for maximizing performance, improving supply chains, and ultimately supporting organizational success in complex industrial environments.
Manufacturing Master Data in Asset-Intensive Industries
Manufacturing Master Data (MMD) is the crucial data that makes effective operations across asset-intensive industries such as manufacturing, energy, utilities, and mining possible. It is the single source of truth for the crucial business assets, processes, and relationships.
Key Components of Manufacturing Master Data
Asset Data: It includes detailed information regarding physical equipment, such as specifications, location, operating parameters, and maintenance record.
Material Data: Raw material, component, spare part, and finished product data
Vendor/Supplier Information: Information on suppliers, capacity, contracts, and performance metrics
Process Data: It contains data that describes manufacturing processes, workflows, and standard operating procedures.
Resource Data: Information of human resources, skills, qualifications, and duties
Conclusion:
Manufacturing Data Management is the foundation of operational excellence within the highly competitive and data-driven environment of manufacturing today. By effectively centralizing and managing vital data like master data, BOMs, and work orders, manufacturers can achieve greater efficiency, product quality, and supply chain resilience. Further leveraging new and exciting technologies like digital twins and predictive analytics can make even better decisions, as well as be more agile in their operations. With all industries changing quickly, we believe there is value in investing in data management and manufacturing knowledge strategies that help keep market share, optimize resources, and ensure sustainability.
