Business Analytics — Master: EverydayCart — Growth Under Pressure

Your team runs a household-essentials retailer whose sales are growing while its margins fall. Win customers, strike deals with rival teams and use your analysis to get profit and deliveries back on track.

2 weeks · 2–8 teams (Suggested 3–6 students each) · tools: Excel, SQL, Python, Power BI, GenAI · instructor-led, private cohorts

The challenge

Sales have grown by 25%, but operating margin has fallen from 8% to 3%. Customers face late deliveries and unavailable products while cash is tied up in inventory. You have 14 simulated trading days to improve profitability, protect cash, and deliver reliable service.

Your company sells household and personal-care essentials through an app, a website, and neighborhood pickup locations. Discounts, expensive deliveries, product returns, and poorly allocated stock are eating into profit.

Improve profitability, protect cash, and keep your service promises. You decide what to charge, what to buy, where to spend, and who to work with. Your instructor will ask you to show the numbers behind each choice.

How the simulation unfolds

• Days 1–2 · Excel: Find the margin. Build a margin and cash model. Set prices, buy capacity, and negotiate your first partnership.
• Days 3–6 · SQL: Find the signal. Trace orders, promotions, returns, and customers. Use what you find to fix the business while it keeps trading.
• Days 7–11 · Python: Model the future. Forecast demand and model uncertainty. Rework your inventory and deals when the market changes.
• Days 12–14 · Power BI + GenAI: Defend the decision. Build a board dashboard. Check what the AI suggests and explain your final plan with numbers.

Day by day

1. Day 1 · The growth trap: Why is EverydayCart selling more but earning less?
2. Day 2 · Cash before growth: Can you afford the growth you want?
3. Day 3 · One version of the numbers: Does everyone in your team get the same starting numbers?
4. Day 4 · Growth with a cost: Which categories and segments contribute after costs?
5. Day 5 · The service bottleneck: Where is growth breaking the customer promise?
6. Day 6 · The SQL investment memo: Which intervention deserves the next rupee?
7. Day 7 · Forecast under pressure: How much cash should survive the supplier payment?
8. Day 8 · Quantify uncertainty: What if demand is different from your forecast?
9. Day 9 · Design an allocation: How should a limited budget be divided?
10. Day 10 · Stress the strategy: Can your strategy survive a disruption?
11. Day 11 · A model others can trust: Can another analyst reproduce your recommendation?
12. Day 12 · The board's view: Which few measures explain performance?
13. Day 13 · Challenge the AI: Can a persuasive recommendation be wrong?
14. Day 14 · Defend the turnaround: Did you build a healthier business?

How teams are assessed

• Business decision quality — 20 points
• Technical execution — 35 points
• Collaboration — 15 points
• Negotiation — 15 points
• Business outcomes — 10 points
• Responsible practice — 5 points

Rules

• Decisions, negotiation, and market surprises start on Day 1. Each day has two rounds: a trading round, then a response round to react and renegotiate.
• Standings update every time a round closes. They only count points that have been scored for every team; work still being reviewed never counts as zero.
• Cash, stock, supplier bills, and deals carry over from day to day. Delivery capacity is a daily allowance; unused slots expire when the day’s results are calculated.
• A saved decision sets aside the cash it needs. Your latest decision of the day goes through once, when the day’s results are calculated; anything you leave out counts as zero.
• Inventory costs ₹200 per unit, emergency delivery capacity ₹50 per slot, and the reference selling price is ₹300. A price index of 100 means the reference price.
• All teams share a finite daily market. Demand reflects relative prices and marketing; events affect everyone equally. Fixed daily operating costs are ₹4,667.
• Supplier liabilities of ₹150,000 fall due on Days 7 and 14. Payment uses available cash; unpaid liabilities remain due.
• Operating costs are recognized in full. If cash is exhausted, unpaid operating costs remain liabilities rather than disappearing.
• Capacity deals move spare delivery slots for that day. Research deals share the seller’s research note. In a joint campaign, both teams put in the same amount of cash.
• Offers nobody accepts expire when their round closes. To renegotiate, send a new offer in the next round; accepted deals stay on the record.
• Each day’s outcome points: 40% margin toward 6%, 40% service toward 95%, and 20% cash liquidity toward ₹500,000 with obligations met. The outcome allocation totals 10 points.
• Excel and Power BI use the actual Microsoft tools, with evidence submitted in Bartman. SQL and Python workspaces are built in.
• Your instructor scores the work you hand in. Deals, messages, code runs, and AI answers don’t earn points on their own.

Before you start

• Desktop or laptop
• Access to Microsoft Excel and Power BI
• Basic analytical familiarity
• An instructor-managed team

Questions

What is the Business Analytics — Master?

Your team runs a retailer for two weeks, using Excel, SQL, Python and Power BI to decide prices, stock and deals with rival teams. It is set at EverydayCart, a fictional company with synthetic data.

How long does the Business Analytics — Master take?

It runs for 14 days (2 weeks). The instructor sets the pace and can pause the clock or extend deadlines.

How many students and teams can take part?

Between 2 and 8 competing teams. Suggested 3–6 students each.

Which tools and skills does it use?

Excel, SQL, Python, Power BI, GenAI. The simulation moves through them in stages — Days 1–2: Excel (find the margin); Days 3–6: SQL (find the signal); Days 7–11: Python (model the future); Days 12–14: Power BI + GenAI (defend the decision).

How are students assessed?

Out of 100 points: Business decision quality (20), Technical execution (35), Collaboration (15), Negotiation (15), Business outcomes (10), Responsible practice (5). The instructor reviews the evidence each learner submits, so individual contributions stay visible inside a team.

What do students need before they start?

Desktop or laptop, Access to Microsoft Excel and Power BI, Basic analytical familiarity, An instructor-managed team.

How do students join?

Each run is a private cohort. The instructor invites students by email or with an invitation link, and students sign in to Bartman to enter their simulation.

How can an instructor run it with a class?

Instructors request creator access for the simulation. Once approved, they create a private run, invite students, assign teams and control the schedule from the instructor studio.