supply chaincase studycourse module

Unit Plan: Logistics and Supply Chains — Using Autonomous Trucks as Case Studies

UUnknown

2026-03-06

8 min read

A 2–4 week unit using Aurora TMS integration and MySavant.ai nearshore models to teach autonomous trucks, TMS workflows, and real-world supply chain design.

Hook: Teachers and instructional designers struggle to connect classroom supply chain theory with real-world systems — fragmented datasets, inaccessible vendor systems, and fears about automation's workforce impact make authentic projects hard to run. This 2–4 week unit uses Aurora's TMS integration and MySavant.ai's nearshore model as contemporary case studies so students design, test, and evaluate autonomous trucking workflows inside an LMS-ready course.

Why this unit matters in 2026

By 2026 logistics education must move beyond static case studies to reflect integrated automation, API-driven operations, and hybrid human-AI teams. Aurora's industry-first connection between autonomous trucks and a TMS (delivered ahead of schedule due to customer demand) and MySavant.ai's AI-powered nearshore workforce model are examples of the new operating reality: systems that tender, dispatch, track, and manage exceptions programmatically while nearshore analysts augmented by AI handle complexity and change.

Learning snapshot

Audience: High school business/IT classes, undergraduate logistics/IT courses, professional development for instructors
Duration: 2–4 weeks (modular; adaptable to single unit or part of a semester)
Core concepts: autonomous trucks, TMS integrations, API workflows, nearshore AI-assisted operations, KPIs and ethics
Key outcome: Students design and present a working supply chain workflow that tender, monitor, and resolve exceptions for autonomous truck shipments

Learning objectives (measurable)

Explain how a TMS interacts with autonomous truck providers and describe data flows for tendering, dispatch, and tracking.
Map a supply chain process that integrates autonomous capacity and a nearshore AI-assisted operations team.
Use simulated TMS APIs and datasets to tender a load, monitor telemetry, and generate exception reports.
Evaluate cost, service, and labor trade-offs of autonomous trucks plus nearshore support using KPIs and a short business case.
Reflect on ethical and workforce impacts and propose mitigation strategies for displaced roles.

Unit structure: Week-by-week plan

Week 1: Foundations and systems thinking

Lecture: Evolution of freight tech in 2025–2026, highlighting Aurora-McLeod TMS integration and MySavant.ai's nearshore model.
Activity: Diagram a current carrier-TMS-customer ecosystem; identify where autonomous capacity and nearshore teams plug in.
Assessment: Short quiz on TMS functions, API concepts, and autonomy vocabulary.

Week 2: Hands-on simulation and API basics

Lab: Connect to a sandbox TMS simulator (see Data & tools) and walk through tendering a load to a driverless provider endpoint.
Activity: Students extract load status and telematics, then plot ETA vs. actuals.
Deliverable: Log of API calls and a 1-page summary of data flows.

Week 3: Nearshore workflows and exception management

Case study: Review MySavant.ai's nearshore approach to intelligence-driven operations and contrast with traditional BPO scaling.
Simulation: Route an exception (damaged documentation, border delay, unexpected detour) to a nearshore analyst queue; automate standard responses with templates, escalate novel cases.
Deliverable: A decision tree and SLA table for exceptions.

Week 4: Capstone — student project and presentation

Project: Teams propose a deployment that integrates autonomous truck capacity into a regional supply chain, supported by nearshore AI-assisted operations. Include KPIs, cost model, and a change management plan.
Presentation: 10-minute pitch + 10-minute Q&A; peer review using rubric in LMS.
Assessment: Instructor rubric and optional industry mentor feedback.

Classroom activities and practical labs

1. Tendering drill (60–90 minutes)

Provide students with a test order and credentials to a sandbox TMS or mocked API endpoints.
Students construct the tender using JSON payloads, submit to the autonomous provider endpoint, and capture response codes and load IDs.
Reflect: What fields mattered most for acceptance? How would you redesign the tendering form in your TMS?

2. KPIs and business case lab

Students compute cost-per-mile, dwell time changes, on-time delivery (OTD), and projected CO2 impact comparing human-driven vs. autonomous legs with nearshore support.
Deliverable: 2-page business memo recommending adoption timeline and measurement plan.

3. Exception triage roleplay

Create realistic exception packets (e.g., customs hold, reroute, mechanical issue). Assign students to nearshore analyst, TMS operator, and carrier roles.
Run through handling steps and measure resolution time and workflow bottlenecks.

Data, tools, and sandbox setup

Authenticity is key, but vendor access can be limited. Here are practical options:

Vendor sandboxes: Contact McLeod Software or Aurora for education access to sandbox APIs. Aurora's TMS integration demonstrates how tendering and tracking can be automated; many vendors provide limited academic access for pilots.
Mock APIs: Use tools like Postman collections and local JSON servers to simulate TMS endpoints, autonomous provider acceptance, and telematics streams.
Open datasets: Use public freight and telematics datasets to simulate route and ETA analytics.
Nearshore simulation: Model MySavant.ai's approach by giving student analysts AI-assisted toolsets — canned LLM prompts, automated document parsing templates, and KPI dashboards.

Assessment and rubric (LMS-ready)

Use a multi-criteria rubric in your LMS gradebook. Typical criteria:

Technical integration (30%): Correct API flows, payloads, and data handling.
Business analysis (25%): Sound KPI selection, cost modeling, and clear recommendations.
Operational design (20%): Exception workflows, nearshore roles, and SLA design.
Ethics & workforce strategy (15%): Analysis of labor impact and mitigation plans.
Communication & teamwork (10%): Presentation quality and peer evaluations.

LMS best practices for course creators

Modularize content: Build each week as an LMS module with micro-learning assets: video explainer, PDF case brief, API sandbox link, and assignment.
Embed rubrics and checklists: Attach the grading rubric to the assignment so students know expectations before they begin.
Use LTI integrations: If your school uses McLeod, Aurora, or specialized sandboxes, connect them via LTI where possible for seamless single sign-on and grade passback.
Automate formative feedback: Use auto-graded quizzes for foundational knowledge and peer assessment for presentations to scale instructor effort.
Version control: Store API collections and templates in a Git repository or LMS file library so cohorts can reuse and extend previous work.

Differentiation and accessibility

For mixed-skill classrooms:

Provide guided templates and simplified datasets for beginners.
Offer extension tasks for advanced students: build a predictive ETA model or implement an automated escalation rule using simple business rules engines.
Ensure materials meet accessibility standards: caption all videos, provide alt text, and use clear, plain language in templates.

Industry integration & real-world alignment

Showing students real examples strengthens credibility and engagement. In 2025–2026 we saw:

"Eligible customers with an Aurora Driver subscription can book and manage autonomous truck capacity directly within existing TMS workflows," with early adopters reporting operational gains by tendering autonomous loads through existing dashboards (example: Russell Transport).

Use this momentum to invite industry partners for guest lectures, live demos, or project judging. MySavant.ai's nearshore model provides a frank conversation starter about performance, scaling, and the limits of headcount-based nearshoring.

Ethics, labor, and policy — discussion prompts

Which roles are most likely to change or be displaced? How can nearshore AI-assisted teams reskill to higher-value tasks?
How should companies measure social impact when adopting autonomous fleets (e.g., community job impacts, safety improvements, emissions)?
What regulatory and cross-border issues matter when a TMS assigns autonomous legs across states or international borders?

Advanced strategies and future predictions for 2026 and beyond

As we move through 2026, expect these trends to affect classroom units and industry practice:

Integrated automation stacks: Warehouse and transport automation will be architected as interoperable services, not siloed systems. Educators should emphasize API literacy and service orchestration.
Hybrid human-AI ops: Nearshore models will scale by intelligence — using AI to amplify analyst productivity rather than hiring linear headcount.
Data-driven governance: Companies will publish more operational KPIs; students can analyze quasi-real datasets to perform authentic research and recommendations.
Ethics & regulation: Policy labs will be required as part of supply chain education to prepare students for compliance and stakeholder engagement roles.

Practical checklist for instructors

Confirm vendor sandbox access or prepare mocked APIs and datasets.
Create assignment templates, API collections, and grading rubrics in the LMS before week 1.
Line up industry speakers early; propose short asks (15 minutes + 10 minutes Q&A).
Test accessibility and device compatibility for all labs.
Prepare alternative offline assignments if network or vendor access fails.

Sample student project brief (one paragraph)

Your team is the operations improvement group for a regional freight forwarder evaluating autonomous truck capacity on a 400-mile corridor. Design an end-to-end workflow that: 1) tender and accept autonomous loads via a TMS API; 2) monitors telemetry and ETA; 3) routes exceptions to an AI-assisted nearshore team; and 4) presents a 3-year ROI and workforce transition plan. Include mock API payloads, KPI dashboard mockups, and a 7-slide executive brief. Submit code, documentation, and a recorded 10-minute pitch.

Actionable takeaways

Start small: Use mocked APIs to teach the core concepts before attempting vendor sandboxes.
Center human work: Teach how AI and nearshore models augment, not just replace, human decision-making.
Measure what matters: Choose 3–5 KPIs (cost per mile, OTD, dwell time, exception MTTR, emissions) and track them across scenarios.
Embed ethics: Make workforce transition and regulatory thinking part of every deliverable.

Resources and further reading

Industry announcements on Aurora and TMS integrations (refer to vendor releases for technical specs)
MySavant.ai whitepapers on AI-powered nearshore operations
2026 warehouse automation Playbook webinars and articles for broader automation context

Closing and call-to-action

Supply chain education must reflect 2026's integrated, API-first reality. This unit gives students the frameworks and tools to analyze autonomous trucks and nearshore AI-assisted operations critically and practically. Ready to implement this unit in your LMS, or want a customized instructor pack with sandbox links, rubrics, and slide decks? Request the edify.cloud unit kit and a 30-minute consultation to adapt this plan to your course and institution.

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