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N3· Engineering: product, sales and operations roles

AI for Supply Chain & Operations

You learn to forecast demand, set safety stock, compare fulfilment options by real cost and score couriers on RTO, using AI to build the models and your own checks to trust them.

Choose a planRead the syllabus
Who it is for
Mechanical, industrial engineering and management students, or anyone from any branch, who enjoys making physical things move on time and at the right cost.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 3.4 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Operations Executive
  • Supply-chain Analyst
  • GCC Operations Associate

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • Google SheetsFree tier
  • Power BI DesktopFree tier
  • Claude (free plan)Free tier
  • Google ColabFree tier
  • n8n Community Edition (Saarthi-hosted)Saarthi sandboxVerified
  • Saarthi sandbox (anonymised seller data pack + MCP server)Saarthi sandbox

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · AI for Supply Chain & Operations

01The marketplace operations map2 lessonsHow an order turns into cash for an Indian seller, where the costs hide, and why AI-produced numbers must always be recomputed.
  1. Order to cash: how goods and money move8 min
  2. Lab: map one seller's operations from their data20 min
02Demand forecasting2 lessonsForecast sales for a marketplace seller, including festive peaks, and measure how wrong the forecast is.
  1. Forecasts that respect Diwali8 min
  2. Lab: forecast three SKUs through a festive season25 min
03Inventory and safety stock2 lessonsTurn a forecast into stock decisions: reorder points, safety stock and the cost of holding too much.
  1. Safety stock without the mystery8 min
  2. Lab: a reorder plan for the festive season25 min
04Cost-to-serve and fulfilment choice2 lessonsWork out what each order really costs to deliver through each channel, and decide between marketplace fulfilment and self-ship.
  1. What an order really costs8 min
  2. Lab: per-SKU FBA versus self-ship model30 min
05Logistics and returns analytics2 lessonsScore couriers fairly, find the causes of RTO, and turn return reasons into fixes.
  1. Courier scorecards and RTO root causes8 min
  2. Lab: courier and RTO scorecard25 min
06Operations automation2 lessonsAutomate stock alerts and supplier follow-ups with n8n and AI, with a person approving every action that matters.
  1. Agents for ops, with humans in charge8 min
  2. Lab: stock alert and PO follow-up flow in n8n30 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Demand forecast and safety stock for a festive season

    A fictional home-and-kitchen seller wants to know how much stock to hold for the coming festive sale. Using six months of anonymised daily sales, forecast demand for five SKUs through the sale, measure your forecast error on held-out weeks, and turn the forecasts into safety stock, reorder points and last safe PO dates.

    You hand in

    • Forecast workbook or Colab notebook for five SKUs with a moving-average baseline and one better method, handling stock-out weeks as censored and estimating festive uplift from in-period sale events
    • Error table with WAPE and bias per SKU and method on the last 4 held-out weeks
    • Reorder-plan sheet: safety stock (variable lead time), reorder point, stock cover and last safe PO date per SKU
    • Supplier-delay scenario showing which SKUs risk a stock-out if lead time grows by 7 days
    • One-page recommendation to the owner, leading with the POs to raise this week

    Data: Saarthi seller data pack v1 (frozen): six months (April–September 2026) of daily sales with in-stock flags, the sale-event calendar and ad spend for fictional home-and-kitchen seller S-022, plus a synthetic supplier PO log for this project.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    FBA versus self-ship cost-to-serve and courier scorecard

    A fictional seller uses both marketplace fulfilment and self-ship and wants to know which SKUs belong where, and which couriers deserve more volume. Build a per-SKU cost-to-serve model and a courier and RTO scorecard. Your contribution-per-order and RTO figures are checked automatically against Saarthi's P&L engine outputs for the same frozen dataset version.

    You hand in

    • Cost-to-serve workbook with an assumptions tab (a source for every input) and per-order contribution for FBA and self-ship
    • Engine-check column comparing your figures with the reference outputs, with a note explaining every mismatch
    • Per-SKU fulfilment recommendation with a 5-point RTO sensitivity
    • Courier × zone × payment-mode scorecard with cost per delivered order, RTO root causes and return-reason chart
    • One-page owner summary with the expected monthly contribution change on the sandbox data, labelled as such

    Data: Saarthi anonymised seller data pack (frozen version): SKU master, marketplace rate card version for the period, self-ship courier rate card, three months of orders, self-ship status history, returns and RTO reports, and Saarthi P&L engine reference outputs.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Anjali, a fictional operations manager at a Pune home-and-kitchen D2C brand that sells on its own site, Amazon.in and Flipkart, interviews you for a supply-chain analyst role. She hands you last year's festive-season sales, asks how much stock to send to the warehouse before the next big sale, and keeps asking 'what if the supplier is a week late?' and 'how sure are you?'. She is unimpressed by any number you cannot explain.

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for AI for Supply Chain & Operations.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy