Cost Rationalization

We deploy ML models to disaggregate GL data across chart-of-accounts, matching cost centers to activity nodes via unsupervised clustering. The output surfaces non-value-added spend that traditional finance systems obscure—enabling structural budget resets.

Using transformer-based time-series forecasting, we map micro-cost behavior against dynamic revenue drivers (covers, ADR, REVPAR) in real time. This reveals elastic vs inelastic cost zones, forming the blueprint for elasticity-weighted budget resets.

We construct a graph neural network (GNN) to map vendor relationships, contract interdependencies, and pricing variances across SKUs and geographies. The model auto-identifies arbitrage opportunities and high-cost anomalies invisible to human auditors.

We create a digital twin of the hotel operation at GL-level granularity. ZBB scenarios are simulated across peak, off-peak, and stress environments—allowing management to stress-test budget structures against occupancy shocks, rate drops, or input cost spikes.

Using NLP-trained auto-classifiers on invoice metadata and transactional logs, we identify cost leakages misclassified under CAPEX, marketing, or COGS. Reallocation improves ZBB clarity and flags non-strategic spend masked in bulk allocations.

We use computer vision models deployed on CCTV and kitchen cams to quantify real-time cost driver signals (e.g., prep time, idle labor, food wastage) and correlate them with cost nodes on the P&L. This bridges physical ops with budgetary precision.

We train RL (reinforcement learning) agents on historical labor and demand data to produce shift schedules that dynamically self-correct to forecasted occupancy, eliminating minimum staffing bias that distorts most legacy budgets.

We use multivariate regression across operational telemetry (energy, labor, throughput) and guest satisfaction indices (GSS, TripAdvisor, OTA feedback) to quantify which OPEX inputs directly impact revenue KPIs. Everything else is marked non-essential in ZBB.

For chains, we deploy federated Machine Learning across multiple units to build a cost normalization library—highlighting which unit spends deviate materially from learned “normative” behavior. This becomes a reference point for ZBB harmonization across properties