DATA / FDE / Planning brief / data study / 2026
Planning the flow from supply to demand
Study the data contracts behind supply/demand planning: input grain, product keys and a proposed Ontology-backed decision workflow.
ENGINEERING PERSPECTIVE
Inside the work
Study the data contracts behind a planning view: forecasts, production actuals and product definitions need compatible keys and grain before any variance or fill-rate indicator can be trusted.
- CSV / input inspection
- A labelled cleanup script checks local input shape and schema.
- Foundry planning
- The retained brief and demonstration notes describe the proposed Ontology-backed workflow, not a reproducible platform export.
Identify inputs
Separate forecast demand, production actuals and product master records.
- Grain and joins
- A product-period forecast and a production record cannot be joined safely without understanding their relationship.
- Metric definitions
- Fill rate and variance need an explicit denominator, time window and missing-data policy.
- Decision support
- A recommendation view needs traceable inputs; a demo narrative alone does not establish business impact.
What the demonstration establishes
This is an input-validation and workflow study. The local counts are supported; deployed dashboards, delivered reports and operational outcomes are not independently verified.
Evidence behind this page
Planning brief, local inputs and demonstration notes. Raw records remain private.
What it is
Manufacturing planning material combines forecast demand, production actuals and product master data. A retained demonstration script also describes dashboard KPIs, issue drilldowns, adjustment recommendations and weekly reporting.
Why it was built
A supplied planning brief connects manufacturing forecasts, production actuals and product master data.
My contribution
Retained brief, inputs and a planning demonstration narrative. The notes describe analytical views and weekly reporting, but no export independently establishes those implementations. The schema-inspection script is an explicit portfolio addition.
How it works
Document input grain and validate schemas before designing joins and variance/fill-rate indicators. The cleanup adds a clearly labelled input-inspection script.
- 01Forecast
- 02Production actuals
- 03Product master
- 04Validate keys
- 05Proposed planning view
Technical implementation
- Inspect row counts, required columns, product keys and input grain using a standard-library Python script.
- The proposed workflow would join product master data with forecast and production actuals for variance and fulfilment views.
- The supplied files contain 32 forecast rows, 40 production rows and four product records. Original CSVs are excluded while redistribution rights remain unclear.
Results & evidence
- Supplied inputs contain 32 forecast rows, 40 actuals rows and four product-master rows.
Engineering challenges
Separate a proposed data contract, a demonstration narrative and verified platform execution. Planning recommendations also require clearly defined units and trustworthy join keys.
Scope & limitations
The brief, local input study and demonstration narrative establish scope, not an independently reproduced Foundry application. No application export, report-delivery trace or business-impact measurement was recovered. Raw data remains private.
What I learned
Making data contracts explicit before proposing an analytical workflow.
What I would improve now
Confirm input provenance, create a shareable implementation with tests and distinguish generated sample data from any supplied business records.