Supply Chain Resilience: How an Energy Company Cut Stockouts by 40%

Stockouts in the energy sector aren't just an inventory inconvenience — a missing critical spare part can mean an extended outage affecting thousands of customers. This case study covers how we helped an energy utility rebuild its supply chain planning around SAP Integrated Business Planning (IBP), paired with a purpose-built AI add-on we call SupplyVision.

Client Overview

The client is a regional energy utility responsible for maintaining generation and distribution infrastructure across a wide geographic service area. Their spare parts inventory spans thousands of SKUs, from common consumables to long-lead-time specialized components sourced from a small number of global suppliers.

The Challenge

Prior to this engagement, demand planning was managed through a combination of SAP ECC's basic MRP functionality and offline spreadsheets maintained by regional planners. This created three compounding problems:

• Reactive replenishment — orders were frequently placed only after a shortage was already impacting field crews, rather than anticipating demand.
• No visibility across regions — surplus stock in one distribution center often sat unused while another region faced a stockout of the identical part.
• Long-lead-time parts caught planners off guard — specialized components with 90+ day lead times had no forward-looking demand signal to trigger early ordering.

The Solution: SAP IBP + SupplyVision AI

We proposed a two-part solution: implement SAP Integrated Business Planning to give the client a modern, statistical demand-planning foundation, and layer our SupplyVision AI add-on on top to address the utility-specific forecasting challenges that standard statistical models handle poorly — like weather-driven failure spikes and infrastructure-age-based replacement patterns.

• SAP IBP provided unified demand planning, inventory optimization, and supply planning across all distribution centers in a single connected model.
• SupplyVision AI ingested historical weather and asset-age data to adjust demand forecasts for weather-sensitive failure-prone components, and flagged long-lead-time parts for early procurement action automatically.

Implementation Approach

The engagement was structured to deliver value incrementally rather than waiting for a single big-bang rollout:

1. Months 1-2: SAP IBP core demand planning setup, historical data load, and statistical baseline forecasting across all regions.
2. Months 3-4: Inventory optimization configuration, safety stock policy redesign by part criticality tier, and initial SupplyVision AI model training on weather and asset-age data.
3. Months 5-6: Supply planning integration, planner training across all regional teams, and phased cutover with the legacy spreadsheet process running in parallel for validation.

Results

Within the first full quarter after go-live, the client saw measurable improvement across the metrics that mattered most to field operations:

• Stockouts on critical spare parts fell by 40% compared to the same quarter the prior year.
• Cross-region stock transfers increased significantly as planners gained visibility into surplus inventory elsewhere in the network.
• Long-lead-time part shortages — previously the most disruptive category — dropped to near zero as SupplyVision's early-warning flags gave procurement a 60-90 day head start.
• Regional planners reported spending significantly less time on manual spreadsheet reconciliation, redirecting that time toward exception management.

"For the first time, our planners could see the whole network instead of just their own region. That visibility alone changed how we make replenishment decisions." — Director of Supply Chain, Client Organization

Lessons Learned

A few themes from this engagement are broadly applicable to other asset-intensive organizations considering a similar modernization:

1. Standard statistical forecasting handles steady-state demand well, but industry-specific disruption patterns (weather, asset age, regulatory cycles) usually need a purpose-built layer on top.
2. Running the legacy process in parallel during cutover — rather than switching cold — gave planners confidence in the new system's outputs before fully committing.
3. Cross-region visibility is often the single highest-value outcome of a unified planning platform, even before AI-driven forecasting improvements are factored in.

If stockouts or fragmented regional planning are a recurring pain point in your own supply chain, we're happy to discuss what a similar assessment could look like for your organization.