Advanced Analytics
When the question doesn't fit a standard model: simulation, optimization, machine learning and large-batch forecasting, built in R and Python. The same methods run inside every service we offer.
Life Sciences
For supply, commercial and portfolio questions that don't fit a standard model. We build simulation, optimization and machine learning, and independently test the models you already rely on.
What we factor in
- Supply and manufacturing risk
- Portfolio uncertainty
- Allocation constraints
- NDC × account volume
- Patient-level behavior
- In-house and vendor models
Simulation
Monte Carlo on supply, program or portfolio risk
We simulate thousands of possible futures for supply, program spend or portfolio value, with distributions on the inputs that matter, such as yield, demand, timing and probability of success.
What it gives you: Make decisions against the range of outcomes, with the biggest risks quantified.
Optimization
Allocation and network optimization
Mathematical optimization finds the best allocation of constrained supply, field resources or budget across products, accounts and regions, subject to the business rules and capacity limits you set.
What it gives you: Replace spreadsheet trade-offs with the allocation that does the most within your limits.
Large-batch forecasting
Thousands of NDC × account series
We forecast every NDC, account and channel combination in one automated run on the Geneva engine, with best-fit method selection and accuracy reported per series, refreshed on your planning cadence.
What it gives you: Cover the full portfolio at account level without adding planners.
Patient-level machine learning
Discontinuation risk with SHAP explanations
Gradient-boosted models on de-identified patient-level data score the risk of discontinuation or switching, and SHAP values explain which factors drive each prediction, so results can be acted on and defended.
What it gives you: Target patient support where it prevents the most discontinuations.
Model review and validation
Independent audit of in-house or vendor forecasts
We review the assumptions, data, methods and accuracy of forecasts built in-house or by vendors, back-test them against actuals, and document what holds up and what should change.
What it gives you: Go into budget and board reviews with a forecast that has been independently tested.
Custom R and Python builds
Documented and handed off
When nothing off the shelf fits, we build the model in R or Python, document the code and assumptions, and hand it to your team with training, so it doesn't depend on us.
What it gives you: Own a model your analysts can run, explain and extend.
Consumer Goods & Retail
For trade, pricing and supply questions that outgrow standard tools. We optimize promotion calendars, simulate scenarios and forecast at scale, and stress-test the models your teams already use.
What we factor in
- Promotion calendars, deal costs and budgets
- NielsenIQ and Circana POS data
- SKU × store volume
- Product and location hierarchies
- Demand uncertainty
- In-house and vendor models
Promotion calendar optimization
What-if calendars on POS and shipment data
We combine NielsenIQ and Circana point-of-sale data with internal shipments, deal costs and trade performance, then run what-if scenarios to select which events to run, when, and at what depth across the year. We also support mid-course corrections when weather, tariffs or inflation change the picture.
What it gives you: Evaluate alternative calendars in minutes and get more incremental profit from your trade budget.
Batch forecasting
Geneva API at scale
The Geneva API forecasts thousands of SKU, store and channel series in one request, with automatic method selection, so large catalogs get a statistical baseline every cycle without manual work.
What it gives you: Refresh every item-location forecast on schedule, at a fraction of the manual effort.
ML demand models
Gradient boosting with SHAP explanations
Gradient-boosted models combine price, promotion, weather, events and store attributes in one demand model, and SHAP values show how much each factor contributed, so planners can see why the number moved.
What it gives you: Capture complex driver interactions without losing the explanation.
Hierarchical forecasting
Reconciled SKU, store and region totals
We forecast at every level of the product and location hierarchy and reconcile them, so SKU and store forecasts add up to the region and brand totals that finance and sales plan to.
What it gives you: Give every team numbers that add up, from store shelf to annual plan.
Model review and validation
Independent audit against a benchmark
We test in-house or vendor demand and promotion models against actuals, check the data and assumptions, and compare them with a statistical benchmark, so you know whether the model is earning its keep.
What it gives you: Decide whether to keep, fix or replace a model on evidence.
Scenario simulation
Monte Carlo on the plan
We run the annual plan thousands of times across plausible price, lift, competitor and supply outcomes, so you see the range of results instead of a single number.
What it gives you: Go into annual planning and retailer negotiations knowing the downside as well as the target.
Related case studies
Kraft Foods: trade promotion forecasting
Kraft Foods partnered with RoadMap Technologies to replace sales-driven overrides with statistical baseline forecasting, econometric promotional lift modeling, and retailer-level profit simulation across its trade planning process.
Multi-account retail · Trade promotionEnterprise Forecasting for Walmart
At the peak of the home entertainment market, RoadMap Technologies partnered with Walmart to optimize shelf space allocation and replenishment across the Home Entertainment division using cluster-based demand forecasting and economic/demographic store segmentation.
Cluster-based demand planning & shelf space optimizationMedia & entertainment: forecasting demand across release windows and subscriptions
RoadMap applied its launch and lifecycle forecasting methodology to media and entertainment businesses to handle demand that spikes on release, decays along reception curves, and spreads across physical, digital, licensing, and subscription channels.
Releases, subscribers & multi-window channels
Frequently asked questions
What is advanced analytics?
Advanced analytics is purpose-built modeling for questions a standard forecasting tool can't answer. RoadMap Technologies helps life science and consumer goods teams by building Monte Carlo simulations, optimization models, machine learning and large-batch forecasting in R and Python, using the Geneva engine where it fits and custom models where it doesn't.
When does a problem need advanced analytics instead of a standard forecast?
When the question involves constraints, uncertainty or scale that a standard forecasting tool wasn't built for, such as allocating short supply, valuing a portfolio under risk, or forecasting hundreds of thousands of series. If a standard model is enough, we'll say so.
Can you review a forecast model built by our team or a vendor?
Yes. We review the data, assumptions and methods, back-test the model against actuals, and compare it with a statistical benchmark. You get a written assessment of what holds up and what should change.
Which tools do your analysts build with?
Mostly R and Python, with the Geneva Forecasting™ Engine for statistical forecasting at scale. We build on your infrastructure or ours, and document the code so your team can run and maintain it.
How do you make machine learning models explainable?
We use SHAP values, which show how much each input, such as price, promotion or a patient attribute, pushed each prediction up or down. Planners and reviewers can see why the model gave the answer it did.
Start with a pilot
Measurable outcomes agreed upfront, a live model on your data in about two weeks, and the ROI proven before you scale.