Case Study: Rebuilding S&OP for a $400M Industrial Manufacturer

A $400M industrial manufacturer — a portfolio company of a mid-market private equity firm — was struggling with a Sales & Operations Planning process that had broken down over several years of rapid growth.

The symptoms were familiar: excess inventory in some SKUs, stockouts in others, a planning cycle that consumed two weeks of management time every month, and a finance team that had stopped trusting the demand forecast entirely.

The sponsor brought us in 18 months post-close, after an internal effort to fix the process had stalled.

Our diagnostic, completed in the first two weeks using APEX, identified four root causes:

1. Fragmented data. Demand signals were living in three separate systems — the ERP, a legacy spreadsheet model maintained by the sales team, and a third-party forecasting tool that hadn't been properly integrated. No one had a single version of the truth.

2. No accountability structure. The S&OP meeting existed on the calendar, but it had become a status update rather than a decision-making forum. There was no clear owner for the demand plan, and the supply planning team was making decisions in isolation.

3. Misaligned incentives. The sales team was compensated on bookings, not on forecast accuracy. This created a systematic bias toward over-forecasting — which the supply team had learned to discount, creating a different kind of error.

4. Insufficient granularity. The demand plan was being built at the product family level, which masked significant variation at the SKU level. The result was a plan that looked reasonable in aggregate but was consistently wrong at the level where purchasing and production decisions were actually made.

We used APEX to integrate the three data sources into a single demand signal, applying AI-assisted anomaly detection to identify and correct historical data quality issues. This gave the planning team a clean baseline for the first time in years.

We redesigned the S&OP process from the ground up, with a clear accountability structure, defined decision rights, and a meeting cadence that was built around decisions rather than status updates.

Key changes:

• Appointed a dedicated S&OP process owner (a role that didn't previously exist)
• Restructured the monthly cycle from a two-week marathon to a four-meeting cadence totaling less than six hours
• Introduced a formal demand review step with explicit sign-off from sales leadership
• Built a supply review process that surfaced constraints and trade-offs before the executive S&OP meeting

We deployed APEX's demand sensing capability to build a statistical baseline forecast at the SKU level, incorporating external signals (customer order patterns, seasonal indices, and leading indicators from the sales pipeline).

The statistical model was designed to be transparent — planners could see why the model was forecasting what it was forecasting, and override it with documented rationale. This was critical for adoption.

Working with the CFO and CHRO, we redesigned the sales compensation structure to include a forecast accuracy component. This was the most politically sensitive part of the engagement — but also one of the highest-leverage changes we made.

Ninety days after kickoff:

• Inventory reduced by 22% — from 94 days of supply to 73 days, freeing approximately $18M in working capital
• On-time delivery improved from 81% to 94% across the top 200 SKUs
• Planning cycle time reduced from 10 days to 4 days per month
• Forecast accuracy (MAPE) improved by 31% at the SKU level
• Finance re-engaged with the demand plan — the CFO described it as "the first forecast I've trusted in three years"

Three things made this engagement successful where the internal effort had failed:

Dedicated execution capacity. We embedded a team of three on-site for the full 90 days. This wasn't advisory — we were in the room, running the meetings, and driving the work.

APEX as the connective tissue. The data integration and AI-assisted forecasting that APEX provided would have taken 6–12 months to build internally. We had it running in four weeks.

Honest stakeholder management. The incentive alignment conversation was uncomfortable. The data quality conversation was uncomfortable. We had them anyway. The sponsor and the management team were ready to hear the truth — they just needed someone to say it clearly.

This case study has been anonymized to protect client confidentiality. Results are representative of actual engagement outcomes.