Predictive analytics solutions that end in a decision, not a probability
We build forecasting and optimization systems for demand, maintenance and inventory that tell planners what to do next: order this much, service that pump this week, staff this shift. Forecasts are backtested against your history, optimization respects your real constraints, and every recommendation is explained. Where a problem is large enough, quantum-inspired optimization is an advanced option we benchmark against your current solver.
Why forecasts sit in dashboards while planners still use spreadsheets
Most organizations already have a forecast somewhere. It lives in a BI tool, it is roughly right, and nobody acts on it, because the hard part was never the prediction. The hard part is turning "demand will rise" into an order quantity that respects lead times, warehouse capacity and budget, or turning "this motor looks unhealthy" into a maintenance slot that does not stop the line.
So planners keep their spreadsheets. Maintenance stays calendar-based. Inventory is padded with safety stock that ties up cash.
Our approach to forecasting and AI optimization
We pair every predictive model with an optimization engine and a decision owner. The forecast estimates demand, failure risk or lead time with uncertainty bands. The optimizer takes those estimates, your constraints and your costs and produces a plan: a replenishment order, a maintenance schedule, a staffing roster. The planner reviews, adjusts and approves, and the system learns from what they changed.
Models are chosen by backtest, not fashion: gradient-boosted and statistical forecasters, survival models for equipment, mixed-integer and constraint solvers for planning. For routing, scheduling and allocation problems that outgrow classical solvers, we offer quantum-inspired optimisation as an advanced track: annealing-style and QUBO formulations on classical hardware today, benchmarked against your incumbent solver before anyone commits.
What the solution includes
Six components, from the data audit that decides feasibility to the monitoring that keeps the model honest.
Data readiness and feature engineering
An audit of your sales, sensor, maintenance and master data. We tell you what it can support before modelling, then build features and pipelines on a governed lakehouse.
Forecast models with backtesting
Candidate models trained and scored on rolling historical windows, so the accuracy you see is what the planner would have seen at the time.
Optimization objectives and constraints
Costs, service targets, capacities, lead times and business rules written as an explicit model the solver respects and your planners can read and challenge.
Scenario simulation
What-if runs for a supplier delay, a demand spike or a plant outage, so the plan is stress-tested before it is approved rather than after.
Decision dashboards and approval flow
Recommendations shown with reasons and uncertainty, with one-click approve, adjust or reject, and every adjustment recorded for learning.
Performance and drift monitoring
Forecast error, plan adherence and realized cost tracked in production, with alerts when the data or the business shifts away from what the model learned.
High-value use cases for predictive maintenance AI and forecasting
Six problems where a forecast tied to an optimizer pays back quickly, each with a clear baseline in your current practice.
Demand forecasting and replenishment
SKU-location forecasts with uncertainty, turned into order quantities that respect lead times, minimum order sizes and budget, for retail, distribution and manufacturing.
Predictive maintenance for rotating equipment
Vibration, temperature and run-hour signals from pumps, motors and compressors turned into failure-risk estimates and a maintenance schedule that avoids production windows.
Inventory optimization and safety stock
Service-level targets translated into stock positions per item and site, releasing cash from over-padded buffers without raising stock-outs.
Staffing and capacity planning
Call, order or footfall forecasts converted into shift rosters that meet service levels under labor rules and contracted hours.
Facility and energy forecasting
Sensor-driven predictions of facility issues before they become critical alarms, the pattern behind our Facility AI Platform, extended to energy load and HVAC scheduling.
Route, fleet and schedule optimization
Vehicle routing, delivery slotting and crew scheduling under time windows and vehicle constraints, the class of problem where quantum-inspired methods are worth benchmarking.
How does a forecast become an approved plan?
Six steps run nightly or weekly, with the planner in the loop at step five.
- 01
Ingest and validate data
Transactions, sensor readings and master data land in the lakehouse and are checked for gaps and anomalies.
- 02
Generate forecasts with bands
Models produce point forecasts and prediction intervals per item, asset or site.
- 03
Solve the optimization
The solver combines forecasts, constraints and costs into a candidate plan and runs the agreed what-if scenarios.
- 04
Explain the recommendation
Each line of the plan carries its drivers: the forecast, the binding constraint, the cost trade-off.
- 05
Planner reviews and approves
Adjustments are made in the dashboard and recorded, so the model learns what the planner knew that it did not.
- 06
Execute and measure
Orders, work orders or rosters post into the ERP or CMMS. Realized outcomes feed the next cycle.
Deployment, data platforms and integration
Pipelines and models run on your Databricks, Azure, AWS or Google Cloud estate, or on-premises where OT data cannot leave the plant. Plans post into SAP, Dynamics 365, Oracle or your CMMS through their APIs, and sensor data arrives over MQTT or historian connectors. Our data engineering practice builds the lakehouse so the model and the finance report use the same numbers.
Where we deliver: in Saudi Arabia, Vision 2030 industrial programs are driving predictive maintenance in energy and manufacturing under SDAIA PDPL and NCA ECC-2:2024; in UAE, logistics and retail operators need forecasting that handles Ramadan and tourism seasonality; in India, manufacturers run on fragmented ERP and spreadsheet data, so the readiness audit matters most; in United States, plants want models that integrate with existing CMMS and planning tools rather than replace them. Our offices support each region.
Benefits and success criteria
We benchmark against the plan you make today, on the same periods, before any model goes live. The pilot reports the difference.
Forecast error against incumbent
MAPE or WAPE per item and horizon compared with your current forecast or planner judgement on the same backtest windows.
Plan adherence
Share of recommendations approved unchanged, and the pattern in what planners override.
Realized cost and service
Inventory holding, expediting, downtime or overtime cost alongside fill rate or availability, measured after execution.
Planning effort
Hours per planning cycle before and after, and the time from data arrival to approved plan.
Implementation approach
A first engagement covers one decision, one site or product family, and one planning cadence.
- 01
Pick the decision and its baseline
We agree the single decision to improve, who owns it, and how it is made today. That is the baseline.
- 02
Audit data readiness
History depth, sensor coverage and master-data quality are assessed. If the data cannot support the decision yet, we say so.
- 03
Backtest candidate models
Forecasters and optimizers run on rolling historical windows against the baseline. The full comparison, including any quantum-inspired track, is shared.
- 04
Build the decision flow
Dashboard, approval steps and ERP or CMMS posting are built and tested with planners in a sandbox.
- 05
Run in parallel
The system produces plans alongside the existing process for several cycles and differences are reviewed with planners.
- 06
Go live and extend
The decision moves to the system with planner approval retained. The next decision, site or product family joins the platform.
Why Constient for predictive analytics and optimization
We build the data platform, the models and the ERP integration with one team, so a forecast does not stall at the hand-off to IT. Our backtests run the way a planner would have experienced them, with the incumbent's numbers next to ours.
We are also one of the few engineering firms in the region running quantum-inspired optimization research with classical benchmarking. We say plainly when it will not beat your solver this year. For plant and asset problems, our manufacturing and energy and utilities teams bring the OT data experience.
Frequently asked questions
How does a predictive analytics engagement with Constient start?
With one decision and a data audit. You name the decision you want improved, such as weekly replenishment for one product family, and share its history under NDA. We assess whether the data can support it, run a first backtest against your current practice and return a scoped pilot proposal. If the data is not ready, we say so.
What data do we need for predictive maintenance?
Maintenance and failure history per asset, plus the operating signals already collected by your PLCs, historian or condition-monitoring sensors. Two to three years of history is a comfortable start; less is workable for high-volume asset classes. We connect over MQTT or historian APIs, and the audit tells you which assets are predictable today and which need more instrumentation.
How do you measure forecast accuracy plainly?
By rolling backtests. Models are trained only on data available before each historical period and scored on what followed, so the error you see is what a planner would have experienced. We report it per item and horizon against your incumbent forecast on the same windows. Single headline percentages do not appear in our reports.
When is quantum-inspired optimisation worth considering?
When a routing, scheduling or allocation problem is large and combinatorial enough that your classical solver times out or needs heavy simplification. We formulate the problem as QUBO or annealing-style, run it on classical hardware and benchmark against your incumbent on your instances. If classical wins, the report says so. Read more on our quantum page.
Where does the data live and can it stay in-country?
Yes. Pipelines and models run in your own Databricks or cloud tenancy in the region you choose, or on-premises where OT data may not leave the plant. We design the residency to SDAIA PDPL, UAE PDPL or the DPDP Act 2023 as applicable and document the data flows before the first pipeline runs.
How long does a first pilot take and how is it priced?
One decision for one site or product family typically reaches parallel running in 10 to 14 weeks, depending on data readiness. The audit and pilot are a fixed fee. Production and later decisions are priced as a fixed scope per decision or as a dedicated data science and engineering team working through your planning roadmap.
Name the decision you want to improve
Give us one planning decision and its history. We will audit the data, backtest against how you plan today, and propose a pilot only if the numbers justify it.