Turn your data into decisions – fast.
At Andymus Consulting, we help leaders use data to make confident, measurable decisions. From quick analytics sprints to production-grade machine learning, we deliver practical data science that improves how you operate today—while building the capability you’ll need tomorrow.
Perfect for: SMEs and mid-market organisations that want outcomes (not buzzwords), transparent delivery, and skills uplift for their teams.
Get started: Contact Us to set up a discovery call.
Where Data Science Helps Most
- Revenue & demand: Forecast sales, optimise pricing, understand churn, and personalise offers.
- Operations & supply chain: Predict delays, reduce stockouts, optimise routing, and right-size inventory.
- Finance: Driver-based forecasts, margin analysis, cash flow scenario modelling.
- Workforce: Capacity planning, rostering optimisation, skills and utilisation modelling.
- Quality & risk: Anomaly detection, predictive maintenance, alerting and triage.
- Customer & service: NPS drivers, service triage, text analysis of tickets and feedback.
Pair this with our simulation solutions to test “what-if” scenarios before you commit budget or change processes.
What We Deliver
1) Insight Sprint (2–4 weeks)
A rapid analytics engagement to answer a high‑value question with your existing data.
- Outcomes: A clear answer, a Power BI dashboard, and a short decision brief.
- Typical uses: Sales forecast, inventory right‑sizing, service demand forecast, cost driver analysis.
- Good for: Proving value quickly—with minimal disruption.
2) Production ML Pilot (6–12 weeks)
Operationalise a model that runs reliably in your environment and integrates with your workflow.
- Outcomes: Production-ready model, data pipeline, monitoring, and documentation.
- Typical uses: Churn prediction, demand forecasting, lead scoring, anomaly detection.
- Good for: Measurable impact with clear ROI and guardrails.
3) AI & Analytics Enablement (8–16 weeks)
Build the capability—people, process, and platform—to scale data science across functions.
- Outcomes: Data governance basics, MLOps patterns, training, playbooks, and reference dashboards.
- Good for: Organisations ready to embed data-driven decision making.
How We Work (Practical & Transparent)
- Frame the decision
Clarify the outcome, decision owner, and how value will be measured.
We also like to map the stakeholders, what the end user will experience – along with understanding their current challenges. - Assess data & feasibility
Identify data sources, gaps, and the simplest path to value.
Examine options to gather data better, IoT and sensing options. - Build & validate
Develop features and models; validate with real-business tests, not just accuracy metrics. - Operationalise
Deploy, integrate into workflows, set up monitoring, and train your team. - Measure & improve
Track uplift (e.g., stockouts ↓, forecast error ↓, margin ↑) and iterate.
Methods We Use (Fit-for-Purpose, Not Over-Engineered)
- Descriptive & diagnostic analytics: Cohorts, contribution analysis, drivers and sensitivities.
- Forecasting: Classical time series, gradient boosting, deep learning when warranted.
- Optimisation: Routing, rostering, pricing, and allocation—often paired with simulation.
- NLP: Classify support tickets, extract entities, summarise feedback, prioritise actions.
- Anomaly & risk: Outlier detection, early-warning signals for quality, fraud, or downtime.
- Computer vision (when relevant): Quality checks, counting, compliance detection.
- Reinforcement learning & digital twins: When combined with simulation to explore policies safely.
Technology We Prefer (But We’re Tool-Agnostic)
- Data & ML: Python (pandas, scikit‑learn, XGBoost, PyTorch), R (where preferred), SQL
- Cloud: Azure, AWS, GCP on request
- BI & apps: Power BI, Excel, Power Platform for light-weight app & workflow integration
- MLOps basics: Versioning, experiment tracking, CI/CD, monitoring, and rollback patterns
We’ll work within your existing stack where possible and keep architecture simple and supportable at an appropriate scale to fit your business – from micro to enterprise.
Data Science + Simulation (Better Together)
Data science finds patterns in historical data; simulation safely tests future scenarios.
Together, they let you predict, stress-test, and optimise before you commit:
- Use a forecasting model to estimate demand, then simulate warehouse operations to size labour and inventory.
- Use churn predictions to target at-risk customers, then simulate campaign strategies to find the best ROI.
- Use anomaly detection for early warnings, then simulate maintenance schedules to minimise downtime.
Explore more on our simulation solutions page.
Example Outcomes (Illustrative)
- Inventory costs ↓ 12–18% while maintaining service levels via demand forecasting + reorder tuning.
- On‑time delivery ↑ 8–15% from route optimisation and service time forecasting.
- Call handling time ↓ 10–20% by triaging tickets with NLP and surfacing next-best actions.
- Forecast error ↓ 25–40% with a repeatable forecasting pipeline and bias correction.
Note: results depend on your context and data; we agree success metrics upfront.
Deliverables You Can Expect
- Decision brief (plain-language) and prioritised recommendations
- Cleaned datasets and feature pipelines
- Reproducible notebooks or scripts
- Power BI dashboards and/or Excel models
- Deployed model/service with monitoring approach
- Runbook, documentation, and handover
- Optional training for your team
Industries We Serve
Resources & mining, energy & utilities, manufacturing, logistics & supply chain, agriculture & food, healthcare & community services, professional services, and the public sector.
We especially enjoy helping SMEs build practical, right-sized data capability.
Engagement & Pricing
- Fixed-price Insight Sprint (from $5,000)
- Pilot & production (milestone-based; fixed or T&M)
- Retainer (fractional data science leader/coach)
- Training & capability uplift (tailored for your team)
We’ll propose a clear scope, outcomes, timeline, and price—no surprises.
FAQs
Q: We don’t have perfect data. Can we still start?
A: Yes. We design around the data you have, quantify gaps, and deliver value quickly while building foundations.
Q: How do you ensure models are reliable in production?
A: We use lightweight MLOps: versioning, testable pipelines, monitoring for drift, and a rollback plan.
Q: Do we need a data lake or big platform first?
A: Not necessarily. Many wins start with your current systems (ERP/CRM/CSV/Excel) and scale from there.
Q: Can you work with our team and upskill them?
A: Absolutely. Pairing and capability transfer are built into every engagement.
Q: How is this different from BI?
A: BI explains what happened; data science predicts what will happen and what to do about it—and pairs with simulation to test strategies before you act.
Next Steps
Contact us to discuss the challenges you feel data science approaches could assist.
Also see: simulation solutions to explore how we de‑risk decisions using digital twins and scenario testing.