All Foundries

Commodity Management & Trading

Transforming trading ecosystems through data-driven insights and secure, agile platforms that empower global commerce.

Customer & Marketing Strategy

Reimagining customer engagement with personalized, connected experiences that build trust and long-term value.

Digital Engineering

Turning ideas into scalable digital solutions—accelerating transformation through design, technology, and engineering excellence.

Station Studio

Redefining mobility hubs with digital innovation to create smarter, sustainable experiences for people and businesses on the move.

Supply Chain & Industry 5.0

Building resilient, intelligent supply networks that anticipate change and deliver efficiency with purpose.

Sustainability

Driving responsible innovation to help organizations achieve net-zero goals and create a positive impact for future generations.

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Next-Gen Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

Retail

From satisfaction surveys to EBITDA: closing the loop on customer pain points

A department store retailer in Mexico turned a biannual NPS programme into a closed-loop customer listening mechanism, connecting feedback to pain-point initiatives and incremental EBITDA.

From satisfaction surveys to EBITDA: closing the loop on customer pain points

Retail

Unified returns management

A retailer processing more than two million returns a year moved from channel-specific workflows toward one digital returns system. The initiative connects request creation, status tracking and routing across sales and return channels.

Unified returns management

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Hydropower Generation Forecasting & Scenario Simulation

For a renewable power generation business, medium-term hydropower planning depended on forecasts that needed to become more precise, repeatable and easier to update as hydrological conditions changed. The initiative brought together internal data, external information and key generation drivers, including precipitation, reservoir volume, temperature and accumulated snow, to support monthly forecasting, scenario simulation and energy-market decision support.

Hydropower planners review reservoir and rainfall information beside a hydroelectric dam in a mountain renewable energy setting.

Forecast Error

2.31 GWh one-month error

Forecast Improvement

4 GWh versus prior approach

Commercial Decisions

EUR 750k documented impact

Scenario Planning

Repeatable hydrological simulations

Energy market planners compare hydropower scenario materials in a utilities planning room connected to reservoir operations.

Hydropower Forecasting for Smarter Generation Planning

Monthly hydropower forecasting and scenario simulation helped turn changing weather, reservoir and snow conditions into a clearer planning layer for renewable power generation.

Hydropower generation planning becomes more difficult when operational forecasts cannot explain why conditions are changing. The client needed a more reliable way to connect hydrological signals with generation expectations and commercial decisions.

Objectives

The objective was to implement a capability for more accurate medium-term generation planning, giving planners a structured way to forecast aggregate monthly hydropower generation for the current and following year.

Opportunity

The documented approach creates an opportunity to extend forecasting and scenario simulation across hydroelectric generation portfolios, reinforcing support for energy purchase and sale negotiations while improving the repeatability of planning under hydrological uncertainty.

Planning under hydrological uncertainty

The challenge was not only to improve a model. The capability needed to fit the client’s operating context, connect the right explanatory variables and give business users outputs they could trust, refresh and reuse.

Key Challenges

Hydropower specialists inspect reservoir level and weather measurement equipment near a dam and mountain snowpack

1: Limited driver visibility
Existing forecasting did not give planners enough visibility into how precipitation, reservoir volume, ambient temperature and accumulated snow affected future generation.

A hydropower planner and engineer compare forecast curves and generation records inside a hydroelectric operations environment.

2: Difficult deviation analysis
When forecasts moved away from expectations, teams lacked a clear way to diagnose the drivers behind the deviation and update planning assumptions.

A utilities planning team organizes basin maps, reservoir diagrams and generation schedules for hydropower forecasting.

3: Complex planning horizons
The initiative needed to manage multiple explanatory variables, time horizons and aggregation levels without losing consistency between operational forecasting and business planning.

Hydropower planners update structured scenario materials and hydrological assumptions in an operations planning workspace.

4: Manual scenario effort
Scenario preparation depended too heavily on manual work, making it harder to reproduce, compare and refresh assumptions as new information became available.

Solution

A repeatable forecasting and scenario simulation capability

NTT DATA helped shape an end-to-end planning capability that connects data preparation, analytical forecasting, scenario simulation and decision support into a workflow business users can apply repeatedly.

Hydrological data integration

Integrated internal company data with external information and the most influential generation drivers, including precipitation, reservoir volume, temperature and accumulated snow.

Forecast explainability

Made forecast changes and their underlying assumptions visible, helping planners understand deviations and translate changing hydrological conditions into planning inputs.

Monthly generation forecasting

Built an aggregate monthly hydropower-generation forecast for the current and following year, combining historical information, explanatory variables and analytical models.

Scenario simulator

Created a simulator so users could refresh forecasts, test changes in the main drivers and compare alternative assumptions through a repeatable decision layer.

Impact

More confident planning and energy-market decisions

The initiative supported more accurate medium-term generation planning, faster forecast refresh when hydrological assumptions changed and a more structured understanding of forecast deviations. The reviewed source documents report a 2.31 GWh one-month forecast error, a 4 GWh improvement versus the prior approach and EUR 750k impact linked to energy purchase and sale decisions. Beyond model accuracy, the value lies in making planning more repeatable, scenario-based and useful for commercial decision-making.

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