Goal of the Day

Today, you will build a strong understanding of demand forecasting fundamentals and explore Titan’s Forecast Datasets.
You’ll link theory (forecasting metrics) to real-world application (analyzing forecast errors) — essential for supply chain analytics roles.

Detailed Tasks with U2xAI Prompts and Interview Preparation Focus

1. Study Forecasting Basics

  • What to Do:
    Understand what demand forecasting means and why it’s critical.
    Focus on types of forecasting:
    • Qualitative vs Quantitative
    • Time Series Forecasting (moving averages, exponential smoothing)
    • Causal Forecasting (based on external variables)
  • U2xAI Prompt:
    "Explain demand forecasting basics, types of forecasting methods, and why it is important for supply chain management."
  • How This Helps for Interviews:
    Interviewers often start with: “What is demand forecasting?” or “Why is it important?” You must be able to answer clearly and confidently.
  • Time Recommendation: 1 hour

2. Explore Forecast Datasets (Sales Forecasts, Forecast Accuracy Metrics)

  • What to Do:
    Open the datasets provided. Look for:
    • SKU-level forecast data (SKU ID, region, forecast quantity, forecast date)
    • Actual sales data if available
    • Forecast error columns (MAPE, MAD, Bias)
  • U2xAI Prompt:
    "List common fields found in a sales forecast dataset and explain their business significance in supply chain analytics."
  • How This Helps for Interviews:
    You may be handed a forecast dataset and asked, “What does this dataset tell you?” Understanding the fields is critical to demonstrating immediate practical ability.
  • Time Recommendation: 1.5 hours

3. Summarize Key Forecasting KPIs and What They Mean

  • What to Do:
    Understand and document important forecasting KPIs:
    • MAPE (Mean Absolute Percentage Error)
    • MAD (Mean Absolute Deviation)
    • Bias (Tendency to over-forecast or under-forecast)
    • Forecast Accuracy %
  • U2xAI Prompt:
    "Explain MAPE, MAD, and forecast bias in simple terms with examples relevant to sales forecasting."
  • How This Helps for Interviews:
    If asked “How would you measure forecast performance?”, you’ll be ready to answer with clarity and simple examples.
  • Time Recommendation: 1 hour
  • What to Do:
    Write how each forecasting metric impacts supply chain operations:
    • High MAPE ➔ Inventory shortage risk
    • High Bias ➔ Systematic planning errors
    • Low MAD ➔ Stable demand = easier inventory planning
  • U2xAI Prompt:
    "How do forecast accuracy metrics like MAPE and Bias impact supply chain decision-making, such as inventory planning and production scheduling?"
  • How This Helps for Interviews:
    You'll be able to answer deep questions like, “If you see a high forecast bias, what supply chain actions would you take?”
  • Time Recommendation: 45 minutes

5. Document Metric Calculations (with Example Formulas)

  • What to Do:
    Create a table or a one-pager with:
    • KPI Name
    • Formula
    • Example Calculation (using sample data)
    • Business Interpretation
  • U2xAI Prompt:
    "Help me create a one-page cheat sheet summarizing forecast KPIs (MAPE, MAD, Bias) with formulas, examples, and business impact notes."
  • How This Helps for Interviews:
    A crisp, organized formula sheet will allow fast revision before interviews. Shows you can connect technical calculations to business interpretation.
  • Time Recommendation: 45 minutes

6. Plan Forecast Error Analysis for Next Session

  • What to Do:
    Write a mini-plan listing:
    • Which SKU, region, or time period you will start analyzing tomorrow.
    • What questions you aim to answer (e.g., “Which SKUs have the highest forecast errors?”).
  • U2xAI Prompt:
    "Suggest 5 important analysis questions I should answer while analyzing forecast error metrics in sales forecasting datasets."
  • How This Helps for Interviews:
    Prepares you to discuss your analytical thinking: “After seeing errors, here’s how I investigate patterns and root causes.”
  • Time Recommendation: 15 minutes

Step-by-Step BUILDUP Application for Day 6

  • Breakdown:
    Understand today's mission: learn forecasting basics and connect them to dataset exploration.
  • Understand:
    Study forecast accuracy metrics like MAPE, MAD, Bias.
  • Implement:
    Summarize KPIs with formulas, examples, and business impact notes.
  • Link:
    Map how forecast errors affect demand management, inventory planning, and service levels.
  • Document:
    Create a forecast KPI cheat sheet + notes about dataset fields.
  • Upgrade Progress:
    Plan tomorrow’s forecast error analysis (what to explore, what to find).

Deliverables for Today

  • Forecasting Concepts Summary (with examples)
  • Forecast KPIs Cheat Sheet (formulas, examples, interpretations)
  • Notes summarizing important fields in the forecast datasets
  • Mini-Plan for Forecast Error Analysis (questions to investigate)

Practice Interview Questions for Day 6

  1. What is MAPE, and why is it important for sales forecasting accuracy?
  2. How would you explain forecast bias to a business stakeholder?
  3. If you observe a high MAD value across SKUs, what would that indicate?
  4. How do poor forecast accuracy metrics impact inventory and production planning?
  5. What steps would you take if you consistently see over-forecasting for a product line?

Bonus Practice Tip:
Try explaining in under 90 seconds:
“If MAPE is 30% for a product, what does it tell you, and what action would you suggest?”