Supply Chain Portfolio & Projects

2 min read

A supply-chain portfolio should show how you make a decision from imperfect operational data. Decorative dashboards are weak evidence if they do not connect to inventory, service, supplier, forecast or logistics action.

What counts as evidence

A strong project contains:

  1. business problem;
  2. data structure;
  3. metric definitions;
  4. assumptions;
  5. analysis;
  6. decision;
  7. limitations;
  8. artifact.

Useful artifacts include:

  • Excel model;
  • SQL queries;
  • Power BI dashboard;
  • forecast comparison;
  • inventory policy;
  • supplier scorecard;
  • process map.

Demand forecast project

Build a forecast for multiple SKUs.

Include:

  • naïve baseline;
  • moving average or exponential smoothing;
  • seasonal treatment where justified;
  • MAE/WAPE or another suitable metric;
  • bias;
  • product segmentation;
  • override logic.

The project should answer:

Which items require planner attention and why?

Inventory optimization project

Use SKU-level demand, lead time and stock data.

Build:

  • ABC segmentation;
  • service assumptions;
  • reorder logic;
  • safety-stock logic;
  • excess/stockout flags;
  • scenario comparison.

Do not present one inventory formula as universally optimal.

Supplier and spend analysis

Create supplier/PO data.

Analyze:

  • spend by supplier/category;
  • price variation;
  • OTIF;
  • quality;
  • concentration;
  • potential consolidation.

Then recommend which supplier/category deserves sourcing attention.

Logistics project

Use shipment data to analyze:

  • lanes;
  • carriers;
  • service;
  • freight cost;
  • expedited freight;
  • accessorials.

A useful decision might be:

Which lanes should be renegotiated, consolidated or moved to a different service mode?

S&OP scenario

Build a simple monthly scenario with:

  • demand plan;
  • supply capacity;
  • inventory;
  • backlog;
  • financial impact.

Create two or three choices rather than one “perfect” plan.

Example:

  • protect service with overtime;
  • accept backorder;
  • build inventory early.

Explain trade-offs.

Dashboard project

A Power BI project should include:

  • clean data model;
  • metric definitions;
  • drilldowns;
  • exception logic;
  • decision-oriented views.

Do not make 15 charts just to show visual variety.

How to present work

Use a consistent structure:

Question

Data

Method

Result

Decision

Limitation

If data is synthetic, say so.

A portfolio becomes credible when an interviewer can challenge your assumptions and you can explain them.

Related content

Sources