A Monte Carlo resource modelling enables data-driven workforce planning by forecasting resource demands, identifying overallocation risks, and supporting strategic scenario planning for improved decision-making.

The Challenge: Reactive Resourcing and Hiring

Many organisations face significant challenges in accurately forecasting their resourcing needs. Traditional methods often rely on static assumptions or manual processes, leading to:

• Suboptimal Resource Utilisation: Teams and roles can be either over-allocated, leading to burnout and decreased productivity, or under-allocated, resulting in missed project opportunities.
• Inefficient Hiring: Without a clear, data-driven understanding of future demand, hiring decisions can be reactive, leading to rushed recruitment, higher costs, or a shortage of critical skills when needed.
• Lack of Objective Insights: Subjective assessments of project likelihood and resource availability can obscure the true picture of an organisation's capacity.
• Difficulty with Scenario Planning: It is challenging to assess the impact of changes to project timelines or new business opportunities on resource requirements.

These challenges collectively hinder an organisation's ability to scale effectively, deliver projects on time, and maintain a competitive edge.

The Solution: Monte Carlo Resource Modelling

To address these challenges, a comprehensive resource modelling application was developed, designed to provide a data- driven and objective method for assessing resourcing and hiring requirements. The core of the solution lies in its use of a Monte Carlo simulation, which accounts for the inherent uncertainty in future project wins.

Key Components and Functionality:

1. RUNN Data Collection

2. Probabilistic Project Win Simulation

It then uses a Monte Carlo method to simulate thousands of potential project outcomes. For each simulation, it randomly determines whether a tentative project is "won" based on its defined win probability, provided by our commercial team.

This iterative process creates a holistic view of how resource needs are affected by various upcoming win likelihoods, providing a more realistic forecast than deterministic models.

3. Resource Overallocation Analysis (Role and Team Level)

For each simulation, the model calculates potential resource overallocation.

• Role-Level Analysis: It compares the number of people assigned to a specific role against the required hours per day, accounting for time already allocated. This identifies specific skill gaps.
• Team-Level Analysis: Roles are mapped to their respective teams, and the same calculation is performed using team headcounts. This provides a broader view of team capacity and potential bottlenecks.

The model generates P50 (50th percentile) and P90 (90th percentile) views of overallocation, indicating expected and worst-case scenarios.

Interactive charts visualise this data, allowing users to quickly identify high-risk areas and sustained periods of overallocation.

4. Scenario Modelling

A built in scenario editor allows users to manipulate project parameters, such as start dates, to observe the immediate impact on resourcing requirements, enabling proactive "what-if" analysis for strategic planning.

The Results:  Enhanced Decision-Making and Strategic Workforce Planning 

The implementation of the probabilistic resource modelling application has delivered significant benefits, including:

• Objective Hiring Decisions: By providing a data-driven view of future resource needs, the app removes subjectivity from the equation, reducing recruitment costs and ensuring critical roles are filled in advance.
• Improved Scenario Planning: The scenario editor empowers leadership to quickly assess the impact of strategic decisions, such as taking on new projects or adjusting timelines. This allows for optimisation at a resource level by adjusting flexible project parameters.
• Enhanced Transparency: Clear visualisations and summarised data make complex resourcing insights accessible to all stakeholders, fostering better collaboration.

Looking Ahead

The resource modelling framework is a powerful tool that can be further enhanced. Future enhancements we are looking to develop include:

• Integration of Skill-Based Modelling: Expanding the current role-based analysis to incorporate skill-based resourcing, allowing for even more granular and flexible workforce planning.
• Advanced Optimisation Algorithms: Implementing algorithms to suggest optimal resource assignments or hiring plans based on defined objectives (e.g., minimise overallocation, maximise project profit).

This replicable framework can be tailored for any organisation looking to increase agility in analytics delivery, build internal data maturity over time, and avoid long-term resourcing commitments.