Automated Multi-Plant Demand Loading

To streamline production demand balancing across three global manufacturing facilities, I developed a dynamic Excel Solver model that automates weekly transfer decisions while respecting stock norms and plant constraints. This tool saves hours of manual work and drives smarter, data-backed operations.

Situation

A global manufacturing company operating across three major production sites faced recurring challenges in balancing inventory and production loads. Each facility had its own constraints such as stock norms, lead times, buffer requirements, and production flexibility, yet there was a need for a standardized process for distributing excess or deficit volumes across locations.

Without automation, planning teams manually allocated production transfers each month using static spreadsheets, often resorting to rough guesswork or outdated assumptions. This reactive approach led to stock imbalances, overburdened sites, and underutilized capacity—particularly as long-term demand projections for 2026 pointed to growing strain.

The lack of a reliable, unified method for load balancing left the network vulnerable to last-minute shifts, wasted resources, and costly misalignments between supply and demand.

Task

My objective was to:

Automate the monthly production load / demand transfer process
Maintain stock norms across all three sites
Account for each facility’s minimum/maximum thresholds and production constraints
Enable flexible “what-if” scenario planning based on forecast volatility
Build trust in the tool’s recommendations to support strategic planning decisions

Action

I designed a comprehensive Excel optimization model using the Solver Add-In to automate weekly transfer decisions across three manufacturing facilities.

By calculating projected stock based on forecasted production and demand, the tool replaced manual guesswork with a data-driven algorithm that delivered the most efficient load balancing solution for each site.

Here’s how I approached it:

Constraint Modeling: I mapped out each plant’s operational norms, including stock targets, capacity ceilings, and transfer limits.
Solver Integration: I embedded Solver into a user-friendly Excel interface, allowing planners to input projected demand, current stock, and available surplus or deficit by site.
Scenario Flexibility: The tool could simulate different demand curves or “high/low” forecast cases, empowering planners to test risk thresholds before committing.
Visual Dashboards: I built dynamic charts to show transfer flows, capacity violations, and visual alerts, making it easier for decision-makers to spot issues at a glance.
Strategic Forecasting Outputs: I added year-end projections to expose upcoming stock shortfalls. This gave leadership the data they needed to justify future investments.

Result

This tool didn’t just eliminate manual effort, it fundamentally changed how the business approached capacity planning. By replacing manual processes with a Solver-powered optimization model, the team saved over 15 hours of labor each month previously spent on time-consuming balancing tasks. The tool provided clear, data-backed visibility into capacity constraints, and insights that helped reinforce the strategic case for plant expansion in 2026. More importantly, it created alignment between planners, operations, and leadership through a shared, transparent model that everyone could trust.

Production loads were proactively balanced, reducing the number of emergency transfers, minimizing backorder accumulation, and improving overall resource utilization. The model also introduced a repeatable process, replacing guesswork with data-driven decisions.

Key Skills

Excel Solver Optimization Modeling
Multi-Site Load Transfer Logic
Constraint-Based Planning
Scenario Simulation & Forecast Analysis
Visual Communication of Supply Chain Risk
Stakeholder Buy-In & Change Adoption
Strategic Forecasting for Capacity
Supply Chain Resilience Engineering

Disclaimer: The company name and proprietary business information have been intentionally omitted from this case study. All details are presented strictly for academic, professional, and educational purposes only. The solutions, data representations, and outcomes described are shared to illustrate process improvement methodologies, technical skills, and strategic problem-solving, not to disclose or represent any confidential or proprietary information.