July 3, 2025

Advanced Analytics for Strategic Capacity Planning in Industrial Manufacturing

A leading cable manufacturer leveraged advanced analytics to optimize production capacity, test efficiency scenarios, and support strategic investment decisions — generating possible scenarios and roadmap to a projected 40% increase in volume and an 11% growth in margin in a 3-year horizon span.

Advanced Analytics for Strategic Capacity Planning in Industrial Manufacturing

At a glance

Challenge

A cable manufacturer needed to efficientlyalign production capacity with commercial forecasts, optimize resourceallocation, and evaluate the impact of efficiency improvements and newequipment investments.

Solution

We developed a scenario-based analyticsmodel integrating commercial forecasts, management control data, machineefficiencies, and labor costs to simulate and optimize future productionstrategies.

Results

The client gained a dynamic planningtool, for a rolling month exercise, enabling a 40% projected increase inproduction volume and an 11% margin growth in a 3-year horizon span, whilesupporting data-driven investment and operational decisions.

Challenge

A cable manufacturer needed to efficientlyalign production capacity with commercial forecasts, optimize resourceallocation, and evaluate the impact of efficiency improvements and newequipment investments.

Approach

Solution

We developed a scenario-based analyticsmodel integrating commercial forecasts, management control data, machineefficiencies, and labor costs to simulate and optimize future productionstrategies.

Results

The client gained a dynamic planningtool, for a rolling month exercise, enabling a 40% projected increase inproduction volume and an 11% margin growth in a 3-year horizon span, whilesupporting data-driven investment and operational decisions.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

A major player in the cablemanufacturing sector faced the challenge of aligning its production capacitywith ambitious commercial growth targets over a 3 to 5 year-horizon. Thecompany needed to:

  • Project future capacity requirements based on commercial forecasts.
  • Test the impact of efficiencyimprovements andcapacity increases, including the addition of new production equipment (andtaking into account alternative purchasing options).
  • Optimize resource allocation (machines, labor, and shifts) tomaximize margin and meet demand.
  • Integrate diverse data sources such as management control, machineefficiencies, and labor costs to support robust scenario analysis.

Key performance indicators (KPIs)included total production volume, machine occupancy rates, gross margin perton, and payback periods for new investments.

To address these challenges, weimplemented a comprehensive analytics solution structured around scenariomodeling and predictive forecasting:

  • Development of the model:
    A modular analytics platform was created to simulate production scenarios overa 3 to 5-year horizon, integrating commercial forecasts, machine data, and coststructures. The solution encompasses an analytical forecasting and astate-of-the-art optimization module.
  • Scenario Generation:
    Multiple scenarios were tested, including:

o   Baseline (AS-IS) and commercial planprojections.

o   Efficiency improvement scenarios (e.g.,+20% efficiency).

o   Capacity expansion through theacquisition of new equipment.

o   Optimization of shift regimes andresource allocation.

  •  Data Integration:
    The model consolidated inputs from management control systems, historicalproduction data, machine efficiency metrics, and labor cost structures.
  • Validation and Calibration:
    The model was validated against actual production data, ensuring high accuracyin forecasting and scenario outcomes.
  • Optimization Algorithms:
    Advanced optimization techniques were used to maximize margin, consideringconstraints such as equipment payback periods and operational feasibility.

The analytics-driven approach deliveredsignificant benefits:

  • Dynamic Planning Tool:
    The client now possesses a flexible model to rapidly update forecasts and testnew scenarios as market conditions evolve, enabling a monthly update exercisewith updated efficiency and budget execution data.
  • Production Volume Growth:
    In the following 3-years, the model projects a 40% increase in production volume, driven by efficiency gains andstrategic equipment investments.
  • Margin Improvement:
    Gross margin per ton is expected to grow by 11% in the same period, with the most notable gains in medium andhigh-tension cable segments.
  • Resource Optimization:
    Machine occupancy rates are projected surpass 90%, and the model supportsoptimal shift and equipment allocation, reducing unnecessary costs andmaximizing asset utilization. At the same time, the exercise allowed themanufacturer to uncover product lines with sparing capacity.
  • Investment Support:
    The scenario analysis provided clear payback calculations for new equipment,supporting confident capital investment decisions.
  • Strategic Foresight:
    Predictive modeling enables the anticipation of market trends and proactiveadjustment of production strategies.

Our AI-generated summary

Our AI-generated summary

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