SCM320

Supply Chain Analytics

Use data to compare supply chain decisions. Eight chapters cover service measures, forecasts, inventory, supplier risk, transport, simulation, and emissions, ending with a network design case.

  • 8 chapters
  • ~69 min of reading
  • 24 learning objectives
  • 24 practice questions

Course overview

The course runs to 8 chapters and about 69 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. Supply Chain Data and Performance Measurement

    State the grain, population, and time window before computing any supply chain metric, and always report an order-level metric such as OTIF or perfect order rate alongside volume-weighted fill rates, because the gap between the true perfect order rate and the independence estimate tells you whether your failures are dispersed or clustered.

  2. Forecast Validation and Error Diagnosis

    Validate out of sample at the horizon the decision actually uses, always score a naive baseline over the identical window so the error has an interpretable scale, and split the remaining error into bias, which you correct arithmetically, and dispersion, which you buffer with inventory.

  3. Inventory Optimization Under Uncertainty

    Order quantity is a flat, forgiving optimization that can be rounded to practical handling units, while safety stock is where the money and the judgment live: decompose the lead time demand variance into its demand and lead time components, because the larger component tells you whether to fix your forecast or fix your supplier.

  4. Supplier Risk Scoring and Expected Loss

    A weighted risk index is an ordinal triage tool, not a price; convert the suppliers it flags into expected annual loss using contribution margin net of existing cover, and because the probability input is always the weakest assumption, present the breakeven probability alongside the point estimate so the decision rests on a threshold judgment rather than a false precision.

  5. Transportation and Network Optimization

    Network design totals three cost families that scale differently with facility count, and because the resulting curve is shallow, the analyst's job is to report how thin the winning margin is and which assumption would flip it, then let service, capacity balance, and disruption exposure settle a decision the arithmetic leaves genuinely close.

  6. Discrete-Event Simulation and Service Risk

    Capacity plans built on averages hide the variability that actually creates queues, so trace a small model by hand to verify its logic, then run independent replications and report the confidence interval rather than the point estimate, because a simulation result you cannot bound is a number you cannot act on.

  7. Sustainability Measurement in Logistics

    Emissions estimates are activity data times a factor, so write the units on every line and record the factor's provenance; report intensity and absolute totals together, use a vehicle-kilometre basis whenever load factor or consolidation is the point of the analysis, and always price abatement against the option you would otherwise have chosen.

  8. Integrated Capstone: Designing a Network End to End

    An integrated comparison earns its value not from the totals it produces but from the flip points it exposes: express every component as an annual dollar figure so the pieces are addable, test each uncertain assumption for whether it reverses the ranking, and when the gap is a fraction of a percent, report the trade-off and its omissions rather than manufacturing a verdict the arithmetic does not support.

Chapters (8)

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

  1. Chapter 1: Supply Chain Data and Performance Measurement

    • Define the transactional records that supply chain analytics is built on and state the grain of each one
    • Compute line fill rate, unit fill rate, on-time delivery, OTIF, and perfect order rate from raw order data
    • Explain why aggregate KPI averages can hide the operational problem they are meant to expose

    3 sections · ~7 min read · 3 practice questions

  2. Chapter 2: Forecast Validation and Error Diagnosis

    • Design an honest holdout test that does not leak future information into the model
    • Compute and interpret MAE, RMSE, MAPE, mean error, and a scaled error ratio against a naive baseline
    • Separate bias from dispersion and choose the corresponding corrective action

    3 sections · ~8 min read · 3 practice questions

  3. Chapter 3: Inventory Optimization Under Uncertainty

    • Derive an economic order quantity and explain the shape of the total cost curve around it
    • Size safety stock when both demand and lead time vary, and state the assumptions this requires
    • Set a reorder point and translate a service level target into units and dollars

    3 sections · ~8 min read · 3 practice questions

  4. Chapter 4: Supplier Risk Scoring and Expected Loss

    • Build a weighted multi-criteria risk index and state the normalization and weighting assumptions behind it
    • Convert a qualitative risk rating into an expected annual loss in dollars
    • Evaluate a mitigation investment against the loss it removes and compute the breakeven condition

    3 sections · ~8 min read · 3 practice questions

  5. Chapter 5: Transportation and Network Optimization

    • Assign demand regions to distribution centers on a least-cost basis and total the resulting outbound cost
    • Trade fixed facility cost and inventory pooling against outbound transportation savings
    • Compute a truckload versus less-than-truckload breakeven weight and interpret it as a shipment policy

    3 sections · ~9 min read · 3 practice questions

  6. Chapter 6: Discrete-Event Simulation and Service Risk

    • Advance a single-server queue by hand through an event trace and compute waiting time, utilization, and idle time
    • Explain why average-based capacity calculations understate congestion and delay
    • Use independent replications to build a confidence interval and size the number of runs needed for a target precision

    3 sections · ~10 min read · 3 practice questions

  7. Chapter 7: Sustainability Measurement in Logistics

    • Estimate freight emissions from activity data using tonne-kilometres and an emission factor, and state the units at every step
    • Distinguish an intensity metric from an absolute total and explain when each one misleads
    • Compute a marginal abatement cost per tonne of carbon dioxide equivalent against the correct baseline

    3 sections · ~9 min read · 3 practice questions

  8. Chapter 8: Integrated Capstone: Designing a Network End to End

    • Combine forecast quality, inventory, transportation, disruption exposure, and emissions into one comparable annual cost
    • Identify which assumption the recommendation is most sensitive to and quantify the flip point
    • Communicate a close decision honestly, separating what the model shows from what it cannot

    3 sections · ~10 min read · 3 practice questions