Advanced Analytics
Advanced Analytics
Predictive Modeling
Predictive Modeling
Scenario Simulation
Scenario Simulation
Nuno Pedro
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