What I do

Six areas where better data quietly changes the business

Most supply chain decisions live in one of these six places. This is what each one covers, why it matters and what I'd actually build for you.

01

Inventory Management

Right stock, right place, right time — without the spreadsheets that nobody trusts.

What this covers

Most inventory problems aren't visible until they hurt: a stockout on your top-seller in week three of the quarter, $400K tied up in slow-movers nobody reviews, safety stock policies last set three years ago. The work here is making inventory positions, risks and policies visible enough that you can act on them before they become incidents.

  • Reorder point & safety stock policy setting
  • ABC / XYZ segmentation by value & variability
  • Slow-mover & excess stock identification
  • Stockout risk & cover ratio tracking
  • Inventory turns & days of stock by class
  • Working capital tied up in inventory
  • Multi-location stock balancing
  • Obsolete & aging stock policy
Examples of what I'd build
A weekly dashboard flagging which SKUs are below ROP, with cover ratio and the supplier they need to be reordered from.
An ABC-XYZ segmentation refreshing monthly with policy recommendations — which items deserve which service level.
A slow-mover report ranked by cash tied up, with last-sold dates and a recommended action: discount, return, or write off.
02

Demand Forecasting

A baseline you can trust, accuracy you can measure, and a feedback loop that improves both.

What this covers

Most teams have a forecast. Few teams measure how wrong it was last month, and fewer still feed that learning back into the next cycle. The work here is building a statistical baseline that's transparent, tracking forecast accuracy in a way that exposes bias rather than hides it, and giving planners a clean place to add their judgment on top of the math.

  • Statistical baseline forecasts (Holt-Winters, ARIMA, ML where warranted)
  • Seasonality & trend decomposition
  • Forecast accuracy: MAPE, WAPE, bias tracking
  • Confidence bands & demand variability
  • New product & intermittent demand handling
  • Promotional & event uplift modelling
  • S&OP forecast consensus support
  • Forecast vs. actuals exception alerts
Examples of what I'd build
A statistical baseline forecast at SKU-month level with seasonality, refreshing automatically each cycle.
A forecast accuracy scorecard showing MAPE and bias by category, planner and product family — so you know where the forecast is reliable and where it's not.
An exception report flagging SKUs where actuals deviated more than X% from forecast in the last 4 weeks, ranked by revenue impact.
03

Purchasing & Replenishment

Buy the right quantity, at the right cadence, from the right supplier — informed by math, not habit.

What this covers

Purchasing decisions are usually made by buyers under time pressure with old data. EOQs are set once and never revisited. MOQ trade-offs are taken on faith. Volume discounts get accepted without calculating whether the carrying cost outweighs the saving. The work here is putting the economics of each purchase decision in front of the buyer at the moment they need it.

  • EOQ & order cycle optimisation
  • MOQ & volume discount trade-off analysis
  • Container / load consolidation
  • Lead time–driven order timing
  • Purchase order coverage & pipeline reporting
  • Spend analysis & supplier rationalisation
  • Buyer workload distribution & alerts
  • Working capital impact of buying decisions
Examples of what I'd build
A replenishment view showing each SKU's days-of-cover, EOQ, and next order quantity recommended — so buyers spend their time on exceptions, not calculations.
An MOQ trade-off model: when a supplier offers a price break at 5,000 units, does the saving justify the extra carrying cost? Get a clear yes/no with the math behind it.
A PO pipeline dashboard showing inbound stock by week, supplier and SKU — so planners see what's coming before it lands.
04

Supplier Performance

Make the supplier conversation factual — and make sure the right suppliers are getting the right share of business.

What this covers

Supplier reviews often run on feeling: "Supplier X is unreliable, Supplier Y is great." The data usually tells a more specific story — Supplier X is on-time 92% of the time but their late deliveries cluster in one category that drives your worst stockouts. The work here is building objective supplier scorecards that surface those patterns, and giving you something concrete to bring to the next QBR.

  • On-time delivery & OTIF tracking
  • Lead time mean & variability by supplier
  • Order accuracy & defect / return rate
  • Stockouts attributable to supplier issues
  • Quarterly supplier scorecards for QBRs
  • Spend concentration & risk exposure
  • Performance trends over time, not single snapshots
  • Cost-to-serve by supplier
Examples of what I'd build
A quarterly supplier scorecard generator that produces a one-page PDF per supplier for QBR meetings — OTIF, lead time, defect rate, trend lines, action items.
A supplier risk dashboard ranking by spend concentration, lead time variability and stockout contribution.
A lead time variability report flagging suppliers whose published lead times don't match reality — and quantifying the safety stock you're carrying as a result.
05

Supply Chain Metrics

Measure the right things, define them properly, and stop arguing about what fill rate actually means.

What this covers

Fill rate. Service level. Perfect order. These terms get used loosely in most organisations — and when finance, operations and sales each calculate them slightly differently, the metrics stop driving decisions and start driving arguments. The work here is fixing the definitions, picking the metrics that genuinely move the business, and structuring them into a hierarchy that connects daily operations to strategic targets.

  • KPI hierarchy: strategic → tactical → operational
  • Fill rate & service level methodology
  • Days of stock by ABC class, not just overall
  • Perfect order & order-cycle metrics
  • Cash-to-cash cycle time
  • SCOR framework alignment where helpful
  • Industry benchmarking where data exists
  • Metric retirement: what you should stop tracking
Examples of what I'd build
A documented KPI definitions catalogue — the formula, the data source, who owns it, and what decision it drives. So fill rate means the same thing in three departments.
A monthly supply chain scorecard structured by tier (executive summary, operational detail) with 8–12 KPIs and traffic-light thresholds.
A benchmark comparison against industry norms where data is publicly available — so you know whether your 96% fill rate is best-in-class or actually below median for your sector.
06

Reporting & Dashboards

Built in your tools, refreshed automatically, designed for the people actually making decisions.

What this covers

The best dashboard isn't the prettiest — it's the one your team opens on Monday morning to plan their week. That means it loads fast, refreshes reliably, surfaces exceptions rather than data dumps, and lives in tools your team already uses. The work here is building dashboards that get used, in the BI tool you already have, with the documentation that makes them maintainable after I'm gone.

  • Apache Superset, Power BI, Tableau
  • Cloud data warehouse connectivity
  • Automated dashboard refresh & scheduling
  • Exception-based e-mail alerts
  • Role-based views (planner, manager, exec)
  • SQL data models & documentation
  • User training & adoption support
  • Migration from spreadsheet reports to BI
Examples of what I'd build
An inventory & replenishment dashboard for the planning team in your existing BI tool, refreshing nightly from your data warehouse, with exception alerts pushed to your e-mail.
An executive supply chain scorecard for the operations director — fill rate, days of stock, supplier OTIF, forecast accuracy — refreshed weekly, on one page.
A full data documentation set: source-to-dashboard lineage, KPI definitions, refresh schedules, owner contacts — so the system survives team changes.

Want to talk through which of these matters most for your team?

The free audit is a 45-minute conversation — no data review. Walk away with three prioritized observations and a directional read.

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