The document provides a comprehensive overview of Manufacturing Data Management (MDM) focusing on its definition, challenges, benefits, and implementation strategies within the manufacturing industry. It details how MDM improves operational efficiency and data quality, enabling better analytics and customer insights while addressing the complexities of data silos and governance. Additionally, it highlights the significance of real-time data collection and analysis for enhancing productivity and quality in food and beverage manufacturing processes.

1. Manufacturing Data Management (MDM)
Abdiwk Tamene
Desk Head
Beverage Processing RD
November 27, 2024
Addis Ababa
Manufacturing Industry Development Institute
Food and Beverage Industry Research and Development Center

2. 1. Overview of food safety and food safety hazards
2. Importance of food safety: implications of failing to control hazards
3. How do we make safe food?
4. Equipment suitability, cleaning & maintenance
5. Handling, preparation, Processing, Packaging, Transportation and Storage of food products
and materials
6. Personal Hygiene
7. Cleaning & Disinfection
8. Steps to implementing Prerequisite Programs (PRP’s) 2
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Outline

3. 1.1. What is Manufacturing Data Management?
• Definition: MDM refers to the processes, tools, and techniques used by manufacturing
organizations to effectively collect, organize, and analyse data from various sources.
• Industry 4.0 and smart manufacturing, require mastering MDM for manufacturers to remain
competitive.
• Manufacturers gain visibility across the entire production process, from suppliers and inventory
to shop floor operations and product quality.
• MDM also enhances data quality through validation, standardization, and deduplication. 3
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1. Introduction MDM

4. 4
Manufacturing Industry Development Institute
Food and Beverage Industry Research and Development Center
1778 1882 1969 NOW
INDUSTRY 1.0
Mechanization, Steam
Power
INDUSTRY 2.0
Electricity,
Mass
Production
INDUSTRY 3.0
Automation, Computers,
Electronics
INDUSTRY 4.0
IoT, Cloud and
Cognitive
Computing
Industrial Revolution

5. • MDM facilitates integration between different manufacturing systems like ERP, MES, and
automation. This breaks down data silos to provide a unified source of truth.
1.2. Challenges of Data Management
Manufacturing companies often struggle with data management due to disconnected systems and
data silos. Here are some key challenges
1. Data silos: multiple enterprise systems like ERP, MES, PLM, SCM, CRM
2. Multiple versions of data
3. Legacy systems
4. Lack of data governance: Issues like data redundancy, quality and security are pervasive
5. Manual processes 5
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6. 1.3. Benefits of MDM
1. Single Source of Truth: consolidates data from multiple sources into a single master repository
2. Improved Efficiency: saves time and effort while reducing errors and facilitates reuse of master data across
multiple systems
3. Enhanced Analytics: Clean, consistent data leads to better analysis and reporting. Data reliability is improved.
4. Agility: organizations respond more quickly to changing market conditions and customer needs
5. Data Governance: centralizes data management tasks like policies, controls, issue resolution and
ensure data quality and compliance with regulations.
6. Customer Centricity: provides a 360 degree customer view that helps better serve and retain customers
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7. 1.4. Implementation of MDM
1. Assess your needs: clearly identify your use cases, data types, integration requirements, and goals. Involve
key stakeholders.
2. Get stakeholder buy-in: multiple departments sponsorship. Clear to each group like sales, marketing,
finance, IT, etc. Address concerns early and get input on needs.
3. Find the right solution: With requirements defined, research MDM solutions. Consider cloud vs on-premises.
4. Start small: Phase the implementation by data domain or use case.
5. Integrate: Plan how the MDM will integrate with other systems like ERP, CRM, and databases
6. Cleanse data: Before loading into the MDM, data must be cleansed. Standardize formats, remove
duplicates, and fix inaccuracies. 7
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8. 1.4. Implementation of MDM
7. Manage change: manage changes via training, communications, and process updates.
8. Measure benefits: Track metrics before and after like customer satisfaction, operational efficiency, time-to-
market, reporting, and analytics to quantify MDM benefits.
1.5. MDM Architecture
1. A central master data hub: the single source of truth for master data.
2. Data stores: databases and data warehouses
3. Data integration mechanisms: like ETL tools that extract data from sources, transform it to match
business rules, and load it into the central MDM hub.
4. MDM hub integrations: allows seamless exchange of master data 8
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9. 1.5. MDM Architecture
5. Data quality tools: validate, cleanse, match, and de-duplicate master data
6. Metadata management capabilities: to define data models, attributes, hierarchies, mappings, etc.
7. Data governance tools: for stewardship, issue tracking, audit trails, workflows, etc. around master
data.
8. Security controls: for authorizing and protecting critical master data.
9. Reporting, analytics, and business intelligence tools: that consume master data from the hub for
analytics use cases.
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1.7 Data Security
• Access Controls
• Encryption
• Auditing
1.8. Data Quality
• Validation
• Standardization
• Enrichment
1.9. Analytics and Reporting:
Enhanced business intelligence, Improved
reporting, Dashboards, Predictive analytics &
Data mining
1.6. Data Governance
• Rules: how master data is entered, validated, updated, deleted, etc.
• Policies: organization-wide policies for master data use and maintenance
• Roles: responsibilities for different users
• Stewardship: verify data accuracy, work to improve quality, and ensure adherence to rules and
policies

11. 2. Manufacturing Data Collection And Analysis
involves gathering, organizing, and analysing data from various stages of production
2.1. Data Collection in Manufacturing
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1. Manual Data Collection: on
paper or spreadsheets & prone
to errors
2. Automated Data Collection: Uses
sensors, machines, or software to
collect data automatically in real-time.

12. Examples of Auto:
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b. MES (Manufacturing Execution Systems): Track production, material flow, and work-in-progress
c. PLC (Programmable Logic Controller): Captures data from machinery.
d. IoT (Internet of Things): Sensors on equipment transmit data on performance,
temperature, vibration, etc.
a. SCADA (Supervisory Control and Data Acquisition): Systems that monitor and
control processes

13. 2.2. Data Types:
1) Production Data: Throughput, cycle time, scrap rate, downtime.
2) Quality Data: Defects, rework, non-conformance rates.
3) Machine Data: Temperatures, pressures, vibrations, maintenance intervals.
4) Energy Data: Energy usage, waste, sustainability metrics.
5) Resource Data: Human, inventory, financial, knowledge, assets, etc.
6) Customer Data: distributers, agents, end users, suppliers, stakeholders etc.
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14. 2.4. Tools for Data Collection and Analysis
1) ERP (Enterprise Resource Planning) Systems: Integrates various business functions, including
manufacturing data. Reduces waste and downtime, optimizes machine performance.
2) BI (Business Intelligence) Tools: Used for visualizing data, spotting trends, and generating
reports (e.g., Power BI, Tableau).
3) SPC (Statistical Process Control): A method of monitoring and controlling a process to ensure it
operates at its full potential.
4) AI/ML Algorithms: For predictive maintenance, quality control, and process optimization. 14
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15. 2.5. Benefits of Data Collection and analysis:
1) Improving Overall Equipment Efficiency (OEE): By monitoring availability, performance, and quality
of machines.
2) Reducing Downtime: Identifying the root cause of frequent downtime and using predictive
maintenance.
3) Quality Control: Ensuring that products meet specified tolerances through real-time monitoring.
Allows real-time monitoring of defects and immediate corrective action.
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16. 2.5. Benefits of Data Collection and analysis:
4) Supply Chain Optimization: Tracking material flow and ensuring optimal inventory levels.
5) Energy Management: Reducing energy consumption by analysing usage patterns.
6) Cost Reduction: Prevents costly failures and reduces manual labour in data entry.
7) Better Decision Making: Provides real-time insights for management and operational staff.
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17. Benefits of Data Utilization in the Manufacturing Industry
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1) Increased productivity
2) Verbalization of know-how
3) Loss reduction
4) Quality improvement of production
line
5) Increase added value
3. Data Utilization in the Manufacturing Industry
data utilization means leveraging the data generated and accumulated within
the company to improve productivity and quality.

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4.1. How is Data Collected in Manufacturing?
real-time digital data collection is the best method (Lean six sigma and Kaizen
experts agree). Surveys and questionnaires
Interviews
Observations
4. How is Data Collected in Manufacturing?
Data analysis is the heart of any well-functioning manufacturing company. Plants
are left in the dark about costs, areas that need improvements, quality assurance,
employee production,

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20. Data collection methods
• Surveys and questionnaires
• Interviews
• Observations
• Documents and records
• Focus Groups
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21. • When creating the checklist, consider the following points:
1) Define information needs
2) Select a suitable presentation method
3) Make checklists self-explanatory and intuitively understandable for employees
4) Use simple and clear language
5) Use pictures to explain activities and bridge language barriers
6) Identify necessary and optional process steps in an understandable way 21
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