Turn raw ERP exports into a bulletproof spend cube, no coding background needed
For procurement and supply-chain analysts who already have spend data and need it to hold up under scrutiny, this hands-on cookbook builds every advanced analytics skill a hiring manager will pay for.
11 chapters, 11 lessons
11
lessons, yours to run
Easy to pick up, built to get you the next job
No coding background needed, and you write code with Claude as you go.
Every section below turns into something a hiring manager recognises, not just notes.
Pull spend from every ERP at once, not one at a time
- You will configure an MCP filesystem server so Claude Code reads your EBS, Fusion, SAP and shadow-spend CSVs in a single turn, no copy-paste, no truncation
- You will run parallel subagents that normalise each ERP schema simultaneously, mapping category_ebs dot notation, Fusion arrow format and SAP material groups to one common schema
- No prior coding background needed: every step is a plain-English prompt or a small config file, and you will be doing it hands-on from the first lesson
Clean supplier names and classify every PO line automatically
- You will build a deduplication pipeline that catches IBM, I.B.M. and International Business Machines as the same supplier across sources
- You will auto-classify PO lines into UNSPSC using a taxonomy lookup agent, including freetext shadow-spend descriptions like 'survey platform'
- These are exactly the skills a spend-analytics or category-management role asks for at interview: structured supplier data and consistent classification
Produce global spend reporting that actually reconciles
- You will normalise multi-currency spend across USD, EUR, GBP, JPY, CNY, INR and BRL into a single reporting currency using a slash command you can re-run any time
- You will wire in automatic schema-validation hooks so Claude cannot write a spend file that is missing required columns, catching errors before they reach a stakeholder
- Hiring managers see this as owning data quality end-to-end, not just running a pivot table
Find savings and flag risk in the spend cube you have built
- You will identify tail spend worth consolidating, detect maverick and off-contract buying, and benchmark commodity lines against external indices
- You will drill from a spend anomaly straight to its root cause using a structured investigation workflow
- Every output is a working artifact you can demo in an interview or present to a CPO the week you finish the course
Finish this course and you can do all of this, no prior background required:
- You will be able to configure an MCP filesystem server that gives Claude Code read access to ERP extract CSVs without exposing anything else on your machine, a data-governance practice hiring managers
- You will be able to run parallel normalisation agents that map EBS, Fusion and SAP schemas to a single unified spend schema in one prompt, cutting multi-day manual consolidation down to a single comma
- You will be able to deduplicate supplier names across ERP sources using fuzzy-match logic, producing a clean supplier master that underpins every downstream spend report
- You will be able to auto-classify PO lines and shadow-spend freetext descriptions into UNSPSC taxonomy with confidence scores, the exact output category managers need for Kraljic positioning and sourc
- You will be able to detect maverick and off-contract buying programmatically and surface tail-spend consolidation opportunities, two findings that demonstrate direct savings impact in any procurement
- You will be able to benchmark commodity spend lines against external indices and trace a spend anomaly to its root cause, giving you the analytical depth that separates a senior analyst from a data-en
11 lessons, 11 chapters
Built by practitioners, not influencers
Every agent, skill file, and dataset is drawn from 25+ years and 50+ Oracle ERP implementations across pharma, manufacturing, semiconductor, and distribution.
Kept current with the stack
Claude Code, skills, and MCP move fast. Your licence includes every system update for as long as you're subscribed. Your system gets better, not obsolete.
You have the ERP extracts, but three systems means three schemas, three supplier naming conventions and three currency formats that never quite line up. Deduplication is a manual weekend job, classification is inconsistent, and by the time you have a spend cube you trust, the business has moved on. Job postings for senior spend analysts and category managers now ask for automation skills alongside Excel, and if your CV still says 'proficient in VLOOKUP' you are invisible to the shortlist. The painful part is that the data work itself is not hard conceptually, it is just relentless and repetitive, exactly the kind of work that Claude Code can take off your plate permanently once someone shows you how.
What you'll be able to do, module by module
Every module leaves you able to build and run something real. Here is the syllabus, in the order you work through it.
Curriculum
11 chapters · 11 lessons
Advanced Spend Analytics with Claude Code
You can consolidate spend from multiple ERPs using MCP, parallel subagents, slash commands and hooks; dedupe and normalise supplier names; auto-classify PO lines into UNSPSC; normalise multi-currency spend; identify tail spend and maverick buying; benchmark against commodity indices; and drill from
Data Files1 item
- Data Files🔒
Consolidate Spend Data from Multiple ERPs1 item
- Consolidate Spend Data from Multiple ERPs🔒
Dedupe and Normalize Supplier Names1 item
- Dedupe and Normalize Supplier Names🔒
Auto-Classify PO Lines into UNSPSC Taxonomy1 item
- Auto-Classify PO Lines into UNSPSC Taxonomy🔒
Near-Real-Time Spend Dashboard1 item
- Near-Real-Time Spend Dashboard🔒
Identify Tail Spend Consolidation Opportunities1 item
- Identify Tail Spend Consolidation Opportunities🔒
Detect Maverick and Off-Contract Spend1 item
- Detect Maverick and Off-Contract Spend🔒
Automate Monthly Spend Cube Generation1 item
- Automate Monthly Spend Cube Generation🔒
Normalize Multi-Currency Spend for Global Reporting1 item
- Normalize Multi-Currency Spend for Global Reporting🔒
Benchmark Spend Against Market Commodity Indices1 item
- Benchmark Spend Against Market Commodity Indices🔒
Enable Drill-Down from Spend Anomaly to Root Cause1 item
- Enable Drill-Down from Spend Anomaly to Root Cause🔒
Procurement roles that used to ask only for Excel and SAP experience now consistently ask for automation and data-engineering capability alongside category knowledge. A candidate who can demo a working multi-ERP consolidation pipeline, complete with deduplication, UNSPSC classification and anomaly detection, is the operator most hiring managers have not seen before and will not forget.
From subscribing to running your first artifact
Subscribe and access the course environment
After subscribing you get immediate access to the course platform, the full curriculum, and the datasets folder containing EBS, Fusion, SAP and shadow-spend CSV extracts. Everything is ready to open.
Open the first lesson and follow the plain steps
Lesson one walks you through installing the MCP filesystem server with one terminal command and adding a single config block to settings.json. No coding background assumed. You read the explanation, copy the config, run the prompt, and see Claude read all four ERP files in one turn.
Run your first consolidation and keep the artifact
By the end of the consolidation chapter you type /consolidate-spend in Claude Code and watch 750 rows across four sources, seven currencies, and three ERP schemas merge into one validated spend mart. That file is yours to keep as a portfolio piece and a template for your real data.
Catching a cross-ERP duplicate before it inflates your spend report
Claude reads all four sources via MCP
You type one prompt asking Claude to read po_lines_ebs.csv, po_lines_fusion.csv and po_lines_sap.csv via the filesystem server. All three files load in a single turn. No copy-paste, no row limit.
Parallel agents normalise each schema
The erp-normalizer subagent runs once for EBS, once for Fusion and once for SAP simultaneously. Each agent maps its source columns to the common schema: po_number, supplier_name, amount_usd, currency, po_date, erp_source. Main Claude receives three compact JSON summaries, not 650 raw rows.
The duplicate-flagger agent scans across sources
The duplicate-flagger subagent receives the merged normalised data and checks for pairs where supplier names fuzzy-match, PO amounts are within one percent, dates are within seven days, and erp_source differs. It returns a JSON list of flagged pairs. One flag reads: EBS PO-EBS-4500002 IBM EUR 1,486,118.88 and Fusion PO-FUS-7000008 I.B.M. EUR 1,411,251.60, same invoice, two systems.
The schema-validation hook confirms the output is clean
Before the consolidated mart is written to disk, the pre-write hook runs validate_spend_schema.py. It confirms all eight required columns are present. If any column is missing, Claude receives an exit-code-1 signal and corrects the file before saving. The hook runs automatically every time, on every write.
You present a reconciled spend cube with flagged duplicates removed
The final mart is 750 rows, seven currencies, four sources, with duplicate pairs clearly flagged for your finance counterpart. You ran the whole pipeline with /consolidate-spend. That is the artifact you show in your next interview or present to the CPO.
The toolkit you'll build and run in this program
You leave with working assets, not notes.
Real things you can demo in an interview.
A working MCP filesystem server config (.claude/settings.json) you can drop into any future project
Three normalisation subagent files (erp-normalizer.md, shadow-classifier.md, duplicate-flagger.md) ready to adapt to your own ERP schemas
A library of slash commands (/consolidate-spend, /spend-reconcile, /shadow-audit) that encode your methodology in git, not in your head
A Python schema-validation hook that automatically checks every spend file Claude writes against your required column list
A validated multi-ERP spend mart covering 750 rows across four sources and seven currencies, ready to use as a portfolio demo
A root-cause investigation workflow that drills from a flagged spend anomaly through PO lines to the underlying category or supplier issue
You'll get the most from this if
Spend analysts who already pull ERP extracts manually and want to automate the consolidation, deduplication and classification work that eats their week
Category managers who need a clean, classified spend cube to run Kraljic analysis, identify tail spend and build a sourcing strategy
Procurement professionals preparing for a senior analyst, spend intelligence or category lead role and who need practical automation skills to put on their CV
Supply-chain and finance analysts responsible for multi-entity or multi-currency spend reporting who are tired of reconciling the same mismatches every quarter
Yearly plan: 14-day keep-the-system guarantee
14-day refund on the yearly plan. Finish the first two modules, and if you haven't run your first live pipeline, take a full refund.
Straight answers before you subscribe
I have never written code. Will I actually be able to do this?
Yes. The course is designed explicitly for analysts with no coding background. Every lesson gives you plain-English prompts to type into Claude Code and small config files to copy. The first hands-on task is adding one block to a settings.json file and running one terminal command. There are no programming exercises, no syntax to memorise, and a mentor review is available if you get stuck.
Does it work with my ERP setup? I am not on EBS or SAP.
The course uses EBS, Fusion and SAP extracts as the training datasets, but the MCP configuration, subagent pattern and slash-command structure work with any CSV export from any system. The methodology you learn is schema-agnostic: you map your column names to the common schema the same way the course maps EBS column names. The skills transfer directly.
What is the refund policy if it is not right for me?
There is a 14-day refund period. If you work through the early lessons and decide the course is not the right fit, you can request a full refund within 14 days of purchase, no questions asked.
How does the course stay current if Claude Code changes?
The course is updated whenever a material change to Claude Code or the MCP specification affects the lessons. As a subscriber you receive those updates automatically. The daily live sessions are also a channel for surfacing anything that has shifted since the last recorded lesson.
Can I use the artifacts I build commercially, for example in my employer's spend reporting?
The config files, subagent markdown files, slash commands and scripts you build during the course are yours to use in your own work, including in a professional or commercial context. The course datasets are provided for learning and should not be used in production reports, but your own ERP extracts work with exactly the same pipeline.