ASCM - Technology in Supply Chain

Curriculum

Module 1: Blockchain

• Define blockchain technology and discuss how it works.
• Compare and contrast public and private blockchains
• Discuss the benefits and drawbacks of blockchain technology.
• Identify factors to consider when implementing blockchain technology.
• Explain how blockchain technology is used in supply chains.

Module 2: What Is Blockchain?

• Defining Blockchain
• Why Is Blockchain Important?
• Key Elements of a Blockchain
• How Blockchain Works
• Blockchain Consensus

Module 3: Types of Blockchains

• Different Types of Blockchain Networks
• Public Blockchain
• Private Blockchain
• Public versus Private Blockchains

Module 4: Advantages and Disadvantages of Using Blockchain

• Benefits of Blockchain
• Smart Contracts
• Non-fungible Tokens (NFTs)
• Threats to Blockchain Security

Module 5: Implementation Considerations for Blockchain

• Factors to Consider When Implementing Blockchain Implementation Steps

Module 6: Blockchain in Supply Chain

• How Is Blockchain Used in Supply Chain?
• Case Study: An American Multinational Retail Corporation
• Case Study: PharmaLedger Case Study: A Global Leader in Logistics
• Case Study: An American Multinational Automobile Manufacturer
• Case Study: A Swiss Multinational Food and Drink Processing Company

Module 7: Advanced Analytics and Automation

• Define advanced analytics and automation.
• Compare and contrast descriptive, diagnostic, predictive, prescriptive, and cognitive analytics.
• Explain the process of data mining and challenges the process can present to a company.
• Discuss data storage options and cloud computing security concerns.
• Describe how artificial intelligence and machine learning are used in supply chain.
• List some of the skills employees need to work with advanced analytics and automation.

Module 8: What Are Advanced Analytics and Automation?

• Advanced Analytics
• End-to-End Analytics Process
• Automation
• Automation—Robotics

 Module 9: Advanced Analytics Methods

• Operations Research and Management Sciences
• Descriptive, Diagnostic, Prescriptive, Predictive and Cognitive Analytics Dashboards

Module 10: Data Mining

• The Importance of Data and Data Mining
• The Data Mining Process
• Data Mining Tools— Extract, Transform, Load (ETL)
• Data Mining Tools—Relational Databases and SQL
• Data Mining Tools—CRISP-DM

Module 11: Data Storage and Security

• Data Warehouses
• Cloud Computing
• Data Security

Module 12: Artificial Intelligence

• Types of Artificial Intelligence
• Machine Learning and Deep Learning
• Supervised and Unsupervised Learning
• Natural Language Processing

Module 13: Selecting and Implementing New Technology

• Steps to Selecting New Technology
• Total Cost of Ownership
• Implementation Best Practices
• Employee Skills

Module 14: Internet of Things

• Explain what the internet of things (IoT0 is, how it evolved, and how it can be used.
• Describe the need for and benefits of track and trace technology.
• Identify several types of technology needed for data capture, transmission, and access in IoT.
• Discuss what is needed for IoT infrastructure, including platforms and business application integration.
• Explain the concept of digital twins, why they are used, and their benefits and risks.
• Identify IoT applications in manufacturing and retail.
• Identify IoT challenges and possible actions to avoid these risks.

Module 15: What Is the Internet of Things?

• Defining IoT
• Evolution of IoT

Module 16: Visibility and the Need for Track and Trace

• Supply Chain Visibility
• What Is Track and Trace
• Barcoding and QR Codes
• Radio Frequency Identification (RFID)
• Types of RFID
• Not Just “Where Is My Package”—The Importance of IoT IoT and Data Regulation

Module 17: Technology Needed for IoT

• The Cloud
• Data Capture and Translations
• Data Transmission
• Data Access and Actionability

Module 18: IoT Infrastructure

• What Is an IoT Platform?
• Enablement
• Device and Software Management
• Data Synchronization
• Application Programming Interfaces (APIs)
• Data Streaming
• Event Management
• Accessibility to IoT Data Feeds
• Data Intelligence and Analytics
• Big Data Sets
• Edge Computing

Module 19: Digital Twins

• What Is a Digital Twin?
• More than Just Sensor Data
• Containerization
• IT/OT Convergence
• Module 20: IOT and Business Segment Applications
  Integration of IoT into Business Sections
• Shop Floor and Field Operations Visibility
• Predictive Maintenance
• Remote and Outsourced Operations
• Retail

Module 21: Challenges to Implementation

• Financial Impact
• Cybersecurity
• Data Sovereignty
• Monitoring Methodologies

Module 22: Redundancy, Firewalls, and Anti-Hacking Technology

• Redundancy and Methods to Achieve It
• Firewalls
• Anti-Hacking Technology

Module 23: Demand Planning Technologies

• Explain demand planning and the importance of demand planning technologies in the supply chain
• Describe the essential steps in forecasting demand
• Explain how to select the correct forecasting method to predict demand
• Describe the future of demand planning using technology as an enabler
• Identify several types of forecasting errors and their sources
• Recognize the role of demand planning in inventory management

Module 24: The Importance of Demand Planning Technologies in Supply Chain

• What is Demand Planning?
• Demand Planning Technologies

Module 25: Essential Steps in Forecasting Demand

• Steps in the Forecasting Process
• Determining What to Forecast
• Sources of Data for Forecasting
• A Smartphone Accessory Retail Case Study

Module 26: Forecasting Methods Used in Supply Chain

• Types of Forecasting Methods
• Judgmental Forecasting Method
• Statistical Forecasting Method
• Combination Forecasting Method
• Choosing the Right Forecasting Method

Module 27: The Future of Demand Planning

• Elements of Demand Planning
• Why is Demand Planning Important?
• Methods of Demand Planning Using Technology
• Machine Learning (ML) For Better Demand Predictions
• Streamline Forecasting Processes with AI
• Artificial Intelligence versus Machine learning
• Role of IoT in Demand Planning
• Social Media and User-Generated Online Traffic Data
• Time Series Forecasting: Use Cases
• Technology Company Forecasting Problem
• Traditional Planning versus Technology Planning

Module 28: Reliability of Forecasting

• Sources of Forecasting Errors and How to Reduce Them
• Types of Errors

Module 29: The Role of Demand Planning in Inventory Control

• How Demand Planning Fits within Inventory Control
• Methods of Inventory Control
• Strategies Used in Production Planning and Scheduling
• Production Environments

Module 30: Additive Manufacturing (3D Printing)

• Define Additive Manufacturing / 3D Printing.
• Provide examples of how Additive Manufacturing can benefit the supply chain.
• Identify the limitations of Additive Manufacturing.
• Describe when a company should use Additive Manufacturing.
• Summarize the advantages and disadvantages of bringing Additive Manufacturing in-house vs. outsourcing.

Module 32: What Is Additive Manufacturing?

• Defining Additive Manufacturing
• Traditional manufacturing – Subtractive
• Benefits of Additive Manufacturing—Still an Emerging Technology
• Case Study: TARDIS Light Switch Covers
• Types of Additive Manufacturing

Module 33: What Does Additive Manufacturing Mean for Supply Chain?

• Prototypes
• Inventory Implications
• Distribution Implications (Portability)
• Customization

Module 34: State of Additive Manufacturing in Supply Chain

• Today
• Tomorrow

Module 35: Limitations of 3D printing today

• Configuration with Other Technology
• Post-processing Requirements
• Volume Requirements
• Complex Material Sourcing
• Newer Technologies

Module 36: In-house versus Outsourcing Considerations

• Costs
• Value
• Intellectual Property Issues
• Procurement and Partner Integration

Module 37: Additive Manufacturing Implementation

• Parts Specifically Designed for 3D Printing
• Using 3-D Printing as a Backup
• Supply Chain Uncertainty