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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Electricity Pool Price & Bid Optimization

Electricity market bidding depends on decisions made under volatility, time pressure and imperfect visibility. For an anonymized energy retailer utility, NTT DATA structured a repeatable analytical process that connects pool price forecasting, scenario analysis and bid preparation, giving trading and energy-management teams a more consistent basis for comparing alternatives before execution.

Energy trading team reviewing electricity market scenarios with grid infrastructure visible in the background.

Market data use

More consistent bidding inputs

Scenario comparison

Alternative bid positions compared

Exposure visibility

Forecast uncertainty made clearer

Optimization foundation

Repeatable process for automation

Utility decision-makers comparing electricity bid scenarios and market exposure in a control and trading environment.

Connecting pool-price forecasting with smarter electricity bidding

A more disciplined decision layer for wholesale electricity bidding, built around forecasts, scenarios and reusable assumptions.

When pool prices move quickly, bidding decisions need more than intuition. They need a repeatable way to read market signals, test assumptions and understand exposure before committing positions.

Objectives

The initiative aimed to establish a repeatable analytical process that combines electricity pool-price forecasting with scenario-based bid decision support. The objective was to help trading and energy-management teams use historical market, operational and explanatory data more consistently when preparing and reviewing bids.

Opportunity

The documented approach creates a foundation that can be extended across wholesale electricity bidding and energy management workflows. Over time, the same analytical base may support broader optimization and automation of bidding processes, provided future capabilities and measured outcomes are validated in the client context.

Making bidding decisions more consistent in volatile markets

The case required more than a forecasting model. The capability needed to fit the utility operating context, connect the right information and provide outputs that business users could trust, compare and reuse.

Key Challenges

Energy analysts monitoring changing electricity market conditions near visible grid and substation infrastructure.

1: Volatile pool prices
Energy-market bidding decisions depended on changing pool prices and market conditions, creating a need for structured analysis before execution.

Electricity trading desk with separated planning materials and screens representing fragmented bid assumptions.

2: Disconnected assumptions
Forecasts, assumptions and bid scenarios risked being handled in separate tools, making it harder for teams to assess exposure and compare alternatives consistently.

Utility analytics team reviewing electricity demand, weather and grid signals for pool-price forecasting.

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

Energy management desk with bid planning materials used to compare and update electricity market scenarios.

4: Manual scenario effort
The initiative needed to reduce dependence on manual scenario preparation and make assumptions easier to reproduce, compare and update as new information became available.

Solution

A repeatable forecasting and scenario workflow for electricity bidding

NTT DATA combined data preparation, forecasting logic, scenario analysis and decision-support outputs into an end-to-end workflow designed for trading and energy-management use.

Prepared market and operational data

Historical market, operational and explanatory data were prepared and consolidated to support price modelling and consistent analytical use.

Built forecasting logic

Forecasting logic was developed for pool-price evolution and relevant bidding horizons, connecting market expectations to bid preparation.

Enabled scenario analysis

Scenario analysis allowed teams to compare alternative assumptions and bid positions, linking forecast outputs to business decision workflows.

Structured a reusable decision layer

Outputs were organized for use in trading and energy-management workflows, supporting ongoing model review and a repeatable foundation for future optimization.

Impact

A stronger basis for electricity-market decisions

The case supports more consistent use of market data in bidding decisions, a clearer ability to compare price and bid scenarios before execution and better visibility of market exposure and forecast uncertainty. Because no validated project-specific quantitative improvement is published in the reviewed source, the impact should be presented as qualitative and scope-based rather than as measured performance uplift.

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