Module 8 – Technology in Logistics & Supply Chain

Module Overview

Welcome to Module 8 of the EFOS Logistics & Supply Chain Management course.

This module introduces the digital tools and technologies used in modern logistics and supply chain operations. Students will learn how organizations use enterprise systems, warehouse and transportation software, barcode scanning, RFID, GPS, automation, Artificial Intelligence, and Microsoft Excel to improve visibility, coordination, inventory accuracy, and operational decision-making.

The module focuses on practical technology awareness for entry-level logistics and supply chain roles.

Learning Objectives

By the end of this module, students will be able to:

• Identify common software systems used in logistics and supply chain operations.

• Understand the purpose of ERP, WMS, TMS, and supply chain management software.

• Explain how barcodes, QR codes, RFID, and GPS support logistics activities.

• Understand the role of warehouse automation and e-commerce platforms.

• Identify introductory applications of Artificial Intelligence and predictive analytics.

• Recognize useful Microsoft Excel functions for logistics work.

• Understand how operational data supports decision-making.

• Apply basic technology concepts to practical logistics scenarios.

Topics Covered

1. Introduction to Logistics Technology

Logistics technology includes the software, devices, digital records, and automated systems used to plan, manage, monitor, and improve supply chain activities.

Technology can support:

• Inventory visibility.

• Warehouse operations.

• Transportation planning.

• Shipment tracking.

• Order processing.

• Supplier and customer coordination.

• Performance reporting.

• Operational decision-making.

Technology supports employees and business processes; accurate data, suitable procedures, and human oversight remain important.

2. Enterprise Resource Planning (ERP)

ERP (Enterprise Resource Planning) is an integrated software system used to manage and coordinate business functions.

Depending on the system, ERP may support:

• Purchasing and procurement.

• Inventory records.

• Sales orders.

• Finance and accounting.

• Supplier and customer information.

• Production planning.

• Reporting.

Practical example: When a purchase transaction is recorded in an integrated ERP system, relevant purchasing and inventory information may become available to authorized teams according to the system configuration.

3. Warehouse Management System (WMS)

WMS (Warehouse Management System) supports warehouse activities and inventory movement within a facility.

Common functions include:

• Receiving and put-away.

• Bin and location management.

• Inventory tracking.

• Picking and packing.

• Stock transfers.

• Cycle counting.

• Dispatch processing.

• Warehouse performance reporting.

Practical example: A warehouse employee receives a picking instruction for a particular SKU and location. The WMS helps guide the employee to the relevant stock and record the picking activity.

4. Transport Management System (TMS)

TMS (Transport Management System) supports transportation planning, execution, and monitoring.

Common functions include:

• Shipment planning.

• Carrier or transporter coordination.

• Route planning.

• Freight cost recording.

• Delivery scheduling.

• Shipment tracking.

• Transport performance reporting.

Practical example: A transportation coordinator uses a TMS to review planned shipments, coordinate carriers, monitor delivery status, and identify shipments requiring follow-up.

5. Supply Chain Management Software

Supply chain management software helps organizations coordinate activities across multiple stages of the supply chain.

Depending on the platform, it may support:

• Demand planning.

• Supply planning.

• Procurement coordination.

• Inventory planning.

• Order management.

• Supplier collaboration.

• Shipment visibility.

• Supply chain performance analysis.

ERP, WMS, and TMS systems may be integrated or used alongside other applications. Their capabilities depend on the specific software and organizational setup.

6. Barcodes, QR Codes, RFID, and GPS

Barcode: A machine-readable code scanned to identify an item, package, or shipment.

QR Code: A two-dimensional code that can store or link to information, depending on its implementation.

RFID (Radio Frequency Identification): Technology that uses radio waves to identify tagged items through compatible readers.

GPS (Global Positioning System): Satellite-based positioning technology used by compatible devices to determine location.

Typical applications include:

• Recording goods received.

• Identifying SKUs.

• Tracking stock movements.

• Verifying picked products.

• Monitoring vehicle locations.

• Supporting shipment visibility.

These technologies can improve speed and accuracy when appropriately implemented, but they depend on correct setup, reliable data, equipment, and operating procedures.

7. Warehouse Automation and E-Commerce Platforms

Warehouse Automation uses equipment and technology to support or automate selected warehouse activities.

Examples include:

• Conveyor systems.

• Automated sorting equipment.

• Automated storage and retrieval systems.

• Mobile scanning devices.

• Warehouse robotics where suitable.

E-Commerce Platforms support online product listings, order placement, payment processes, and customer order information. Their integration with inventory, warehouse, and transportation systems can help coordinate order fulfilment.

Automation and system integration should be selected according to operational needs, cost, facility design, safety requirements, and business scale.

8. Artificial Intelligence and Predictive Analytics

Artificial Intelligence (AI) refers to technologies that perform tasks such as recognizing patterns, generating predictions, or supporting decisions.

Potential logistics applications include:

• Demand forecasting.

• Route optimization.

• Estimated delivery time prediction.

• Identifying unusual inventory patterns.

• Shipment risk alerts.

• Warehouse planning support.

Predictive Analytics uses historical and other relevant data to estimate possible future outcomes.

AI-generated recommendations should be reviewed against operational conditions, data quality, company policy, and human judgment. A prediction is not a guarantee.

9. Microsoft Excel for Logistics Operations

Microsoft Excel is widely useful for organizing, checking, analyzing, and reporting operational data.

Important features include:

• Sort: Arranges records in a chosen order.

• Filter: Displays records matching selected criteria.

• SUM: Adds numerical values.

• COUNT: Counts cells containing numbers.

• IF: Returns a result based on a specified condition.

• VLOOKUP: Searches for a value in the first column of a range and returns a corresponding value from another column.

• XLOOKUP: Searches a range or array and returns a corresponding result, where supported.

• Pivot Tables: Summarize and group larger datasets.

• Charts: Present data visually to help identify patterns.

Practical examples:

• Filter delayed shipments.

• Calculate total quantities dispatched.

• Identify orders with a status of “Delayed.”

• Look up product details using an SKU.

• Summarize deliveries by transporter.

• Create a chart showing order status or inventory levels.

10. Data Accuracy and Digital Work Practices

Technology is only as reliable as the data and processes supporting it.

Good digital work practices include:

• Entering correct SKU and shipment details.

• Updating records promptly.

• Checking data before sharing reports.

• Following access permissions and company procedures.

• Protecting confidential business and customer information.

• Maintaining appropriate records of corrections.

• Verifying system alerts before taking action.

• Escalating data mismatches or system issues.

Employees should never assume that a system record is always correct without appropriate verification.

Practical Scenario: Shipment and Inventory Data Analysis

A logistics team maintains a spreadsheet containing order number, SKU, quantity, dispatch date, promised delivery date, actual delivery date, and shipment status.

Students should explain how they would:

1. Filter shipments marked as delayed.

2. Compare promised and actual delivery dates.

3. Identify SKUs with low inventory.

4. Use Excel formulas to summarize quantities.

5. Create a Pivot Table to group shipments by status or transporter.

6. Prepare a simple chart for a supervisor.

7. Verify unusual results before recommending corrective action.

The objective is to show how basic digital skills can help employees organize operational information and communicate findings.

Practical Learning Activity

Prepare a sample logistics worksheet with these columns:

• Order Number.

• SKU.

• Product Name.

• Quantity.

• Transporter.

• Promised Delivery Date.

• Actual Delivery Date.

• Shipment Status.

Perform the following tasks:

1. Sort orders by promised delivery date.

2. Filter delayed shipments.

3. Calculate total ordered quantity using SUM.

4. Use IF to label a shipment as “On Time” or “Delayed” based on the dates.

5. Use a lookup function to retrieve product information by SKU.

6. Create a Pivot Table summarizing shipments by status.

7. Create a chart showing the number of shipments in each status category.

Use illustrative data and verify formulas and results before drawing conclusions.

Module Review

Students should be able to explain the purposes of ERP, WMS, and TMS; identify barcode, QR code, RFID, and GPS applications; describe introductory uses of AI and predictive analytics; and identify useful Excel features for everyday logistics tasks.

Key Takeaway: Technology improves logistics visibility, coordination, and decision-making when supported by accurate data, appropriate processes, and capable employees.

Next Module: Supply Chain KPIs & Data Awareness.