This intensive five-day program offers a deep dive into the strategic and operational aspects of Materials Master Data Governance (MMDG). In today's complex supply chains, accurate, consistent, and reliable materials master data is paramount for efficient operations, accurate reporting, and informed decision-making. This course is designed for data governance professionals, IT specialists, supply chain managers, procurement personnel, and anyone involved in defining, creating, managing, or consuming materials data. Participants will learn the principles of Master Data Governance, explore the critical attributes and lifecycle of materials data, and understand how to design, implement, and maintain a robust MMDG framework. Through a blend of theoretical concepts, industry best practices, real-world case studies, and interactive exercises, attendees will gain the actionable knowledge to improve data quality, reduce operational costs, mitigate risks,
This course is essential for Data Governance Managers, Data Stewards, Master Data Specialists, IT Managers, Supply Chain Managers, Procurement Managers, Logistics Managers, Production Managers, ERP Specialists (e.g., SAP Material Master, MM consultants), Business Analysts, and anyone responsible for the integrity and management of materials data.
Introduction to Master Data Management (MDM):
What is Master Data? Definition and types (Customer, Vendor, Material, etc.).
Why Master Data Management? Importance of "Single Source of Truth."
The business value and impact of poor data quality.
Deep Dive into Materials Master Data (MMD):
What is Materials Master Data? Definition and key characteristics.
Core MMD Attributes:
Basic data (material number, description, unit of measure).
Purchasing data (vendor, lead time, purchasing group).
Sales data (sales organization, distribution channel).
Storage data (storage location, batch management).
MRP/Planning data (MRP type, lot size, safety stock).
Accounting/Valuation data (valuation class, price control).
Quality Management data, Plant data.
MMD Data Domains: Grouping related attributes (e.g., Logistics, Finance, Sales, Procurement).
MMD's Strategic Business Impact:
Supply Chain: Procurement efficiency, inventory accuracy, production planning, logistics optimization.
Finance: Accurate costing, valuation, financial reporting.
Sales & Marketing: Product availability, pricing, customer satisfaction.
Compliance & Risk: Regulatory adherence, traceability, audit readiness.
MMD Lifecycle: Creation, update, extension, deactivation, archival.
Workshop: Brainstorming the critical MMD attributes for a specific product type in a given industry and discussing their impact on various business functions.
Introduction to Master Data Governance (MDG):
Definition and purpose of MDG.
Key components of an MDG framework: People, Process, Policy, Technology.
Benefits of effective MDG: improved data quality, compliance, operational efficiency, reduced risk.
MMD Governance Principles:
Stewardship, Ownership, Accountability.
Single Source of Truth, Data Standards.
Transparency, Auditability.
MMD Lifecycle Management in Detail:
Data Creation: Triggers, data sources, required attributes for creation.
Data Change/Update: Triggers for changes (e.g., supplier change, unit change, product revision), impact assessment.
Data Extension: Extending material to new plants, sales organizations, etc.
Data Deactivation/Archival: Criteria for flagging materials for deletion or obsolescence.
Understanding Data Hierarchies: Product hierarchies, classification systems (e.g., UNSPSC, ECLASS).
Practical Exercise: Mapping the lifecycle stages of a material in a typical organization, identifying key decision points and data requirements at each stage.
MMD Governance Operating Model:
Defining Roles and Responsibilities:
Data Owner: Accountable for data domain strategy and quality.
Data Steward: Responsible for operational data quality and process execution.
Data Custodian (IT), Data Consumers.
Centralized vs. Decentralized governance models for MMD.
Developing MMD Policies and Standards:
Data definition standards (naming conventions, attribute definitions).
Data entry rules and validation criteria.
Data security and access policies.
Data retention policies.
Designing MMD Workflows and Processes:
Workflow for new material creation (e.g., initiation, data entry, validation, approval, publication).
Workflow for material change requests.
Automated vs. Manual workflows.
Communication & Training Strategy:
Engaging stakeholders (business users, IT).
Training programs for data stewards and data entry personnel.
Change Management for MMDG Implementation: Overcoming resistance, fostering adoption.
Workshop: Designing a basic organizational structure for MMD governance roles and outlining a high-level workflow for new material creation.
Understanding Data Quality Dimensions for MMD:
Accuracy, Completeness, Consistency, Timeliness, Uniqueness, Validity.
Impact of poor data quality on business processes (e.g., stockouts, incorrect POs, financial errors).
MMD Quality Management Techniques:
Data Profiling: Assessing current data quality, identifying gaps and inconsistencies.
Data Cleansing: Correcting errors, standardizing formats.
Data Validation: Implementing rules at point of entry.
Data Enrichment: Adding missing information from external sources.
Data Deduplication.
Operational MMD Processes in Detail:
Material Creation Process: Form design, required fields, approval gates.
Material Change Request Process: Triggers, impact analysis, approval matrix.
Deactivation/Obsolescence Process: Criteria, workflow, impact on dependent records.
Data synchronization across systems.
Measuring MMD Quality:
Defining MMD Quality KPIs (e.g., % complete, % accurate, % unique).
Establishing data quality dashboards.
Laboratory Session: Analyzing a sample dataset of materials master data, identifying common data quality issues, and proposing cleansing/validation rules.
Technology Solutions for MMD Governance:
ERP Systems (e.g., SAP S/4HANA, SAP MDG): Capabilities for material master data.
Dedicated MDM/MDG Software Platforms.
Product Information Management (PIM) systems.
Data Quality and Data Profiling Tools.
Workflow Management Tools.
MMD Integration Strategies:
Integrating MMD across heterogeneous systems (e.g., ERP, PLM, MES, CRM).
Integration methods: Batch, Real-time, APIs.
Challenges of data synchronization and consistency.
Monitoring & Reporting on MMD Governance:
Governance dashboards and scorecards.
Audit trails and compliance reporting.
Regular reviews of policies and processes.
Continuous Improvement in MMD Governance:
Lessons Learned from MMDG initiatives.
Iterative approach to refining policies and processes.
Adapting to evolving business needs and technological advancements.
Future Trends in Materials Master Data: AI/ML for data quality, blockchain for traceability, master data in the cloud.
Facilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage. Percentages of the total course hour classification are:
At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.