From post-acquisition complexity to a margin-oriented network¶

After an acquisition cycle expanded its industrial and logistics footprint, Cimento Nacional needed a faster way to decide where to produce, how to serve each market, and which alternatives created economic value.

The company's planning team connected demand, supply-chain optimization, and Cost-to-Serve in OpsFactor to replace fragmented spreadsheet analyses with a reusable scenario model.

The inflection point¶

The network had grown from two plants to five plants, two grinding units, and six distribution centers. Different production costs, product portfolios, regional demand profiles, and partially overlapping service areas created close to 7,000 plant-to-destination combinations.

At that scale, the critical question was no longer whether a spreadsheet could calculate a scenario. It was whether the planning team could consistently represent the same assumptions, compare many alternatives, and understand their contribution-margin impact.

Three criteria for the solution¶

The publication records three capabilities that had to exist at the same time and guided the platform choice:

A solution that delivered only speed would omit important assumptions. A detailed solution that required third parties for every run would not fit the planning cadence. Combining all three criteria turned the model into an operating capability, not only a study.

A model built around three decisions¶

The implementation connected three capabilities:

1. Demand Planning provided a statistical baseline and a structured way to incorporate commercial knowledge and alternative market scenarios.
2. Supply Chain Planning and Optimization allocated demand across the network while respecting capacity, productivity, lead-time, logistics, and operating constraints.
3. Cost-to-Serve traced scenario economics to customer-product level so footprint and service decisions could be discussed through contribution margin, not only total volume.

The model represented hundreds of thousands of variables and, according to the publication, ran strategic network scenarios in less than ten minutes in the cloud.

How the decisions connect¶

This sequence matters. Demand creates the requirement, the network determines what can be produced and moved, and Cost-to-Serve makes the financial consequence visible. Optimizing only one of the three can move cost or constraint elsewhere without improving the whole decision.

What a reusable scenario needs¶

The publication describes the business process; the product documentation shows the reusable data structure behind it:

• materials and locations define products, plants, grinding units, distribution centers, and markets;
• the transportation network defines possible origin-destination flows;
• production resources and routings and bills of material and production versions represent capacity, productivity, and product eligibility;
• the Demand Plan provides the market requirement, while the Supply Plan records the feasible network response;
• Supply Planning setup preserves scenario assumptions so alternatives can be compared on the same basis.
• the economic decision data model connects those physical outputs to valuation, propagated costs, and contribution.

Autonomy was part of the operating model¶

The company did not treat the model as a one-time consulting exercise. Its own planning team configured the network, validated the as-is baseline, challenged assumptions, and learned how to create and compare new scenarios.

The roadmap followed five logical stages:

• collect and validate data and model the current network;
• validate the as-is scenario;
• estimate logistics costs on new routes and identify open-network opportunities;
• evaluate and refine multiple feasible scenarios;
• hand the process over to planning and business teams for S&OP and S&OE cycles.

More than 200 simulations were evaluated with different network and cost assumptions.

From scenarios to actions¶

The model exposed four families of action:

• expand participation in markets with available capacity and competitive Cost-to-Serve;
• review plant product line-up to improve throughput and cost;
• revisit whether regions and channels should be served directly from plants or through distribution centers;
• redesign service territories where the current model did not produce adequate profitability.

The monitored indicators included net price per ton, logistics cost per ton, capacity utilization, and contribution margin by region.

The economic opportunity reported¶

The publication reports that the opportunities were validated by the company finance team and indicated:

The freight-cost value is reproduced with the sign printed by the publication and should not be described as a reduction. The most useful interpretation is the combined effect: the scenarios changed commercial, production, and logistics choices together and identified a potential +4.9% contribution-margin impact.

What other planning teams can learn¶

The transferable lesson is not a target percentage. It is a decision architecture:

• model the current network before optimizing it;
• make costs and constraints explicit;
• compare scenarios through both operational feasibility and economic value;
• keep the model inside the planning cycle so the team can update it as the market changes.

To apply the pattern, continue with Supply Network Planning and Design and Cost-to-Serve and P&L. For another planning trade-off, read Inventory policy: how much service is another day of coverage worth?.

Opportunity identified in scenarios and validated by the company finance team, as reported by Mundo Logística*. It is not a realized result promise.