Services
One discipline, several domains.
Every engagement draws on the same combination: modern analytics, with Lean Six Sigma process discipline behind it. The difference between these practices is only whether the system you are improving is made of people or of processes.
Where a problem calls for more than process improvement, the toolkit goes well past Lean Six Sigma — into predictive models that forecast what is coming, prescriptive models and optimization that recommend what to do about it, and simulation that tests a change before it is made.
Understanding why your organization behaves as it does
Survey and engagement data, workforce modeling, attrition and retention, and equity analysis — including novel metrics developed on the Gallup and Great Place to Work frameworks.
- Survey diagnostics — which few questions actually drive the outcome, which parts of the business most need attention, and what to do there first
- Bottom-up, prescriptive workforce models — how many people to hire, and when, so FTE supply tracks FTE demand
- Attrition and retention diagnostics, linked to survey results where the data allow, so you can see which experiences precede people leaving
- Pay equity and fair-treatment review across demographic groups
Typical engagementA survey diagnostic — four to six weeks, from raw responses to the drivers worth acting on, the areas of the business to start with, and how.
Modern analytics, with Lean discipline behind it
Detailed analysis and modeling across laboratories, manufacturing, supply chain, warehousing, and logistics — on the full data an operation now generates, with Master Black Belt process method behind it. Capacity forecasting here feeds directly into long-range financial planning.
- Equipment and labor capacity forecasting, tied to your volume forecast and long-range financial plan
- Shift structure optimization — coverage matched to when demand actually arrives
- Throughput, turnaround, and bottleneck diagnostics, with relief sequenced in the order that raises output
- Inventory risk modeling — service levels held with less capital tied up
- Quality and automation performance, including automated statistical process control
- Discrete event simulation and ROI modeling to test a change before committing capital to it
Typical engagementA capacity or staffing diagnostic — two to three months, ending in an equipment or hiring plan tied to your volume forecast. Both together run longer.
Building the function, not just the analysis
For teams standing up an analytics function, or trying to make an existing one matter more to the business — including the data strategy and modeling that often has to come first. Grounded in having built seven teams across two continents — six of them analytics functions — and in formal study of strategic leadership and team effectiveness.
- Data strategy — what must be captured and modeled before a question can be answered
- Data modeling from raw tables into production layers, and scoping the extraction work that has to come first
- Analytics roadmaps and prioritization — sequencing what gets built, and choosing what is worth answering at all
- Analytics team design, roles, and operating model
- Review of existing reporting: what to keep, what to retire
Typical engagementAn analytics roadmap — four to six weeks, ending in a sequenced plan for what to build, in what order, and with whom.
Using AI to make an analytics team faster, more consistent, and more trusted
I build with AI daily and led a team that put it into production practice — not as a pilot, but as how the work got done. Speed is the headline: work that took weeks takes days. What makes the speed safe to rely on is everything built around it — review, automation, and consistent definitions.
- Accelerated analysis — weeks compressed into days, so decisions stop waiting on the work
- Automating recurring analytical work so analysts spend their time on judgment
- Consistent metrics and definitions — one meaning for a term across an organization
- Review discipline that keeps a human accountable for every published number
Typical engagementAn AI readiness review of your analytics function — where it will genuinely help, where it will not, and how to adopt it safely.
Engagements
What working together looks like
Fixed-fee projects
A defined question, a fixed deliverable, a fixed price. You know the cost before the work starts. Most run four to twelve weeks. Best when the decision is clear and the data exist.
Phased engagements
Priced by phase or billed hourly, with approval before each phase so scope and budget stay visible. Best when the first look will reshape what comes next.
Advisory
Ongoing counsel for leaders building or steering an analytics function — a standing call rather than a project.
How it starts
A 30-minute call, no charge. You describe the decision you are facing; I tell you whether analysis will help, what it would take, and what it would cost — as a fixed project price where the scope is clear, or by phase where it is not. If the answer is that you do not need me, I will say so.
What I need from you
Access to the relevant data and one person who can answer questions about how the operation actually works. Most delays come from access, not analysis — so we settle that first.
Confidentiality and your data
I work under NDA as a matter of course and am comfortable with legal-privilege and protected-class constraints, having run equity analyses on demographic data. Client data stay in your environment wherever the work allows it. Where AI tools are used, they run under commercial terms that contractually exclude your data from model training — and I will tell you in advance where AI is used in an engagement and under what controls. If you would prefer no AI tooling touch your data at all, say so and that is how the engagement runs.
How I use AI
As an accelerant and a second reader, never as a substitute for judgment. I frame the questions, review everything, and stand behind conclusions on the reasoning rather than on the tool that helped produce them. If a finding cannot be explained and defended without reference to AI, it does not reach you. I hold formal training and certification in prompt engineering and AI-assisted analysis.
What you get
A readout that leads with the decision, the evidence behind it, and a proportionate next step — plus the models and documentation, which are yours. The goal is that your team can carry the work forward without me.
Not sure which of these you need?
That is the normal place to start. Describe the situation on a 30-minute call and we will work out the shape of it together — including whether it is a project, a phase, or nothing at all.
Email me to set up a call → Or book a time directly →