SCM310

SC Analytics & Technology

Check a forecast against a baseline, turn forecast errors into inventory buffers, and test an optimization model. Four chapters also examine the data and systems needed to use these methods across a product catalogue.

  • 4 chapters
  • ~36 min of reading
  • 12 learning objectives
  • 12 practice questions

Course overview

The course runs to 4 chapters and about 36 minutes of reading. Every chapter is self-contained: read it in one sitting, work the example on paper, then check yourself against the practice solutions before moving on.

The learning path below shows how the chapters build on one another, and each chapter card lists what you should be able to do once you have finished it.

How each chapter works

  1. Objectives — what you should be able to do afterwards.
  2. Reading — short sections that build the idea in plain language.
  3. Worked example — a full problem solved step by step.
  4. Practice — questions with solutions you can expand once you have tried them.
  5. Takeaway — the one sentence worth remembering.

Learning path

Chapters build on each other. This is the arc from first principles to the end of the course.

  1. Choosing and Validating a Forecast Model

    A forecast is only as credible as the design that tested it. Score on a holdout the model never saw, always report a naive benchmark alongside the candidate, and watch the tracking signal in production, because bias is the error type that turns directly into inventory.

  2. Sizing Buffers From Forecast Error at Portfolio Scale

    Turn measured forecast error into sigma before it touches an inventory formula, then set policy from a segmentation rather than item by item. Where a budget is fixed, buy service where sigma over lead time and unit cost make it cheap, and judge the result on demand-weighted service, because that is the number customers experience.

  3. Optimization Models for Network and Transportation Decisions

    Formulate network flow problems as linear programs, then use reduced costs to prove the answer and duals to price capacity and incremental demand. Match the method to the question: optimize to decide, use heuristics where re-running fast matters more than the last percent, and simulate to find out what the deterministic model quietly assumed away.

  4. Digital Supply Chain Stack and Data Quality

    Technology enables strategy; it does not substitute for it. Invest first in data quality, because a better model on worse data just reaches the wrong answer faster, and be precise about which component of inventory a data-quality business case actually moves. Then choose an architecture by where the company competes, and name the hidden tax you are accepting.

Chapters (4)

Open any chapter to see its objectives, reading, worked example, and practice questions.

  1. Chapter 1: Choosing and Validating a Forecast Model

    • Design a holdout backtest that gives an honest estimate of forward accuracy rather than in-sample fit.
    • Benchmark a candidate model against a naive forecast using MASE and interpret the result.
    • Use a tracking signal to detect a live forecast that has drifted off-centre.

    3 sections · ~9 min read · 3 practice questions

  2. Chapter 2: Sizing Buffers From Forecast Error at Portfolio Scale

    • Convert a forecast-error metric into the standard deviation an inventory policy actually requires.
    • Segment a catalogue by volume and variability so that policy, not attention, is what gets assigned.
    • Allocate a fixed safety-stock budget across items to maximise demand-weighted service per dollar.

    3 sections · ~9 min read · 3 practice questions

  3. Chapter 3: Optimization Models for Network and Transportation Decisions

    • Formulate a balanced transportation problem as a minimum-cost flow linear program.
    • Verify that an allocation is optimal using reduced costs, and recognise alternative optima.
    • Choose between exact optimization, heuristics, and simulation for a given class of decision.

    3 sections · ~9 min read · 3 practice questions

  4. Chapter 4: Digital Supply Chain Stack and Data Quality

    • Identify the four layers of a typical supply chain technology stack: data, planning, execution, and visibility.
    • Quantify the inventory cost of poor data quality on a planning decision, and state the assumptions the estimate rests on.
    • Explain the tradeoffs between monolithic ERP, best-of-breed, and hybrid architectures.

    3 sections · ~9 min read · 3 practice questions