Deep Dive

Traditional Capacity Management

Conventionally, in most sectors, capacity planning is undertaken at a site level, often in isolation, using static spreadsheets or enterprise resource planning (ERP) reports. Global coordination is limited, leading to:

• Reactive Infrastructure Decisions: Sites added or closed only after capacity issues have already caused delays or service degradation.
• Suboptimal Investments: Without a clear global picture, expansions are often misaligned with actual demand growth patterns or long-term market shifts.
• Missed Risk Signals: Latent capacity risks, such as simultaneous product launches, regulatory changes, or contract manufacturing loss, can go undetected.

This approach hinders the ability to proactively plan for shifting market dynamics, regulatory constraints, and manufacturing lead times that often span years.

Modern Supply Network Capacity Modelling

Today’s modeling tools allow supply chain planners to simulate and visualize the relationship between long-term demand (typically 5–10 years) and current or planned capacity across internal and external sites. These models typically include:

• Production and packaging capacities pulled from Enterprise Resource Management systems (volume, SKU-level granularity, campaign constraints)
• Warehousing capacities pulled from Warehouse Management Systems (WMS) (depending on scope)
• Demand scenarios (baseline, upside, downside)
• Site-level parameters (ramp-up time, flexibility, tech capabilities)
• Strategic initiatives (portfolio changes, tech transfers, policy shifts)
• Model outputs highlight where and when gaps are expected—enabling scenario planning that supports:
• Site Consolidation: Identify underutilized sites that are no longer needed.
• Capacity Expansion: Justify CAPEX for expanding lines or building new plants.
• Make-vs-Buy Decisions: Balance internal vs. external manufacturing footprints.
• Tech Transfer Planning: Preemptively transfer production to alleviate future bottlenecks.

Key Benefits of using this type of technology include:

• Strategic Visibility: Multi-year, network-wide projections offer decision-makers a panoramic view of the supply base’s ability to meet global needs which are critical in pharma, chemicals, and complex manufacturing environments.
• Proactive Infrastructure Investment: Rather than reacting to crises, companies can invest ahead of need, aligning with market growth, regulatory lead times, and sustainability goals.
• Risk Mitigation: Modeling highlights constraints well in advance, such as geographic over-reliance or aging facilities, supporting resilience strategies like dual-sourcing or regional diversification.
• Cross-Functional Alignment: Capacity models serve as a single source of truth for Finance, Supply Chain, Manufacturing, and Strategy teams to align on key investment decisions.

Despite these benefits, utilizing these methods remains manual and time consuming, requiring supply chain leaders to select the scenarios they want to model, comparing the resulting simulation results, and attempting to find an optimal network design.

An Enabling Technology

Advanced AI platforms like HASH can integrate with capacity modeling systems to add intelligence and automation, or create advanced capabilities from the ground up:

• Pattern Recognition: AI can detect emerging mismatches earlier by recognizing subtle trends in forecast shifts, cycle time erosion, or aging infrastructure.
• Scenario Optimization: Machine learning can simulate thousands of capacity-demand scenarios to recommend optimal configurations of sites, shifts, and investments.
• Autonomous Alerts: AI agents can monitor real-time updates to demand or supply assumptions and flag when key thresholds are crossed (e.g. when capacity saturation exceeds 85% for multiple years).
• Digital Twin Integration: Capacity models can feed digital twin simulations that assess how network decisions affect KPIs such as service level, cost-to-serve, or carbon footprint.

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