[Project] UK Nursery School - Business Analysis Case Study

This project started from a simple question: how do nurseries actually make day-to-day decisions when attendance, staffing and income don’t always go hand in hand?

Using a realistic UK nursery context, I explored how a Business Analyst could support better operational decision-making by looking at attendance patterns, capacity utilisation and staffing alignment.

The focus of this case study is not on complex tools, but on understanding the problem, asking the right questions and turning insights into practical recommendations.

Business Context

This project is based on a medium-sized private nursery in the UK, caring for approximately 70 children across multiple age groups.

Like many nurseries operating in the UK, the organisation balances a mix of private fees and funded hours, while needing to meet strict staffing requirements and maintain high-quality childcare standards. Day-to-day operational decisions are often made based on experience and intuition rather than structured analysis.

The nursery faces typical challenges related to capacity planning, staff allocation, attendance variability and income predictability. While data exists in different forms (registrations, attendance records, staffing schedules), it is not consistently used to support decision-making.

This case study explores how a Business Analyst could use a simplified, realistic dataset to better understand nursery operations and support more informed business decisions.

Note: This case study is loosely inspired by real nursery operations in the UK. All data has been anonymised and simulated for analytical purposes.

Problem Statement

Despite having a fixed number of places and regular demand from parents, the nursery experiences fluctuations in attendance and occupancy across age groups and time periods. These fluctuations make it difficult to:

• plan staff levels efficiently

• maximise occupancy without compromising quality of care

• predict monthly income with confidence

As a result, decisions related to staffing and capacity are often reactive rather than proactive. This can lead to underutilised places, unnecessary staffing costs, or increased pressure on staff during busier periods.

The core business problem is therefore a lack of clear visibility into how attendance, capacity and staffing interact, and how these factors impact overall operational efficiency and financial performance.

The purpose of this analysis is to identify key operational insights and provide practical, data-informed recommendations that could help nursery management make more confident and sustainable decisions.

Key Stakeholders

Understanding the needs of different stakeholders is essential to ensure that any analysis leads to practical and usable recommendations.

The key stakeholders in this case include:

• Nursery Owner / Manager

  Responsible for overall performance, financial sustainability and compliance. Their main concern is maintaining high-quality childcare while keeping the nursery financially viable.

• Room Leaders and Childcare Staff

  Directly affected by staffing decisions and workload planning. They need clear schedules and appropriate staff-to-child ratios to ensure safe and effective care.

• Finance / Administration

  Focused on income, costs and budgeting. Reliable forecasts and clearer visibility of income fluctuations would support better financial planning.

• Parents (Indirect Stakeholders)

  While not involved in operational decisions, parents are impacted by availability of places, consistency of care and overall quality of service.

  
  

Business Questions

Before analysing any data, the following business questions need to be addressed. These questions focus on decision-making, rather than technical analysis.

• How does actual attendance compare to available capacity across different age groups?

• Which age groups contribute most consistently to nursery income?

• Where do attendance fluctuations create inefficiencies in staffing?

• Are there specific time periods where capacity is regularly underutilised?

• How could staffing be planned more proactively based on attendance patterns?

• What operational changes could improve income predictability without reducing quality of care?

  
  

These questions guide the analysis and ensure that insights remain aligned with real business needs, rather than producing data outputs with limited practical value.

Data Overview

For the purpose of this analysis, a simplified and realistic dataset has been assumed to reflect typical operational data available in a UK private nursery.

The dataset focuses on core operational and financial metrics that are most relevant to decision-making, rather than attempting to capture every possible detail.

The key data areas include:

• Child enrolment data

  Number of registered children by age group.

• Attendance data

  Actual daily attendance compared to available places.

• Capacity information

  Maximum number of places available per age group.

• Staffing data

  Number of staff scheduled per day and approximate staff costs.

• Income data

  Estimated income based on attendance and fee structure.

  
  

This data provides sufficient visibility to explore how capacity, attendance and staffing interact, and how these factors influence operational efficiency and income predictability.

Assumptions

As this is a business-focused case study, several assumptions have been made to allow meaningful analysis while keeping the scope realistic.

• All data used in the analysis is simulated and anonymised, but designed to reflect common nursery operations in the UK.

• Attendance patterns vary by age group and time period.

• Staffing levels follow standard staff-to-child ratios, but may not always align perfectly with daily attendance.

• Fees are assumed to be consistent within age groups for simplicity.

• External factors such as illness, holidays and funded hours contribute to attendance variability.

These assumptions enable the analysis to focus on patterns and trends, rather than precise operational figures.

Data Limitations

It is important to acknowledge the limitations of the available data.

• The dataset does not include detailed individual child or staff records.

• External influences (e.g. seasonal illness spikes or policy changes) are not modelled in detail.

• Financial figures are indicative rather than exact.

Despite these limitations, the data is sufficient to support high-level operational insights and practical recommendations, which is the primary goal of this analysis.

Analysis Approach

The analysis focuses on understanding how attendance, capacity and staffing interact in day-to-day nursery operations. Rather than exploring complex models, the approach prioritises clarity, relevance and decision support.

The analysis is structured around a small number of focused views, each designed to answer a specific business question identified earlier. This ensures that insights remain aligned with operational and financial decision-making.

The overall approach can be summarised as:

• start with current capacity and attendance patterns

• identify gaps between planned capacity and actual usage

• assess how staffing aligns with real attendance

• translate findings into practical recommendations

  
  

Key Metrics

To support this approach, the following key metrics are used throughout the analysis:

1. Capacity Utilisation

• Percentage of available places that are actually filled.

• Helps identify underutilised capacity and pressure points across age groups.

2. Average Attendance Rate

• Actual attendance compared to registered children.

• Highlights variability and potential over- or under-planning.

3. Staff-to-Child Ratio (Operational View)

• Comparison between scheduled staff levels and real attendance.

• Used to assess whether staffing is consistently aligned with demand.

4. Income per Age Group

• Estimated income generated by each age group.

• Supports decisions around capacity allocation and long-term planning.

5. Attendance Variability

• Degree of fluctuation over time.

• Helps explain unpredictability in staffing needs and income.

These metrics provide a balanced view of operational efficiency, financial impact and service quality, without introducing unnecessary complexity.

Key Insights

Based on the assumed data and analysis approach, several high-level insights emerge that highlight opportunities for improved operational decision-making.

1. Capacity Is Not Used Consistently Across Age Groups

While overall enrolment appears healthy, capacity utilisation varies significantly between age groups. Some groups operate close to full capacity, while others show regular underutilisation.

This suggests that available places are not always aligned with demand, which can limit income potential and create inefficiencies in staffing allocation.

2. Attendance Fluctuations Create Operational Pressure

Actual attendance often differs from registered numbers, particularly during certain periods. These fluctuations make it difficult to plan staffing efficiently and can result in either:

• overstaffing during quieter periods, or

• increased pressure on staff when attendance is higher than expected.

This variability reduces predictability and increases operational risk.

3. Staffing Decisions Are Not Always Demand-Led

Staffing levels are typically planned in advance to ensure compliance with required ratios. However, when attendance does not match expectations, staff resources may not be used as efficiently as possible.

This highlights a gap between planned staffing and actual demand, with potential cost implications.

4. Income Predictability Is Impacted by Attendance Variability

Income is closely linked to attendance rather than enrolment alone. Variability in attendance therefore has a direct impact on monthly income predictability, making financial planning more challenging.

Even small changes in attendance patterns can have a noticeable effect on overall revenue.

Summary of Findings

Overall, the findings indicate that the nursery would benefit from:

• improved visibility into attendance patterns

• better alignment between capacity, attendance and staffing

• more proactive, data-informed planning

These insights form a strong foundation for developing practical and achievable recommendations that support both operational efficiency and financial sustainability.

Recommendations

Based on the insights identified in the analysis, the following recommendations are proposed to support more informed and proactive decision-making.

1. Improve Capacity Planning by Age Group

Review the allocation of places across age groups to better reflect actual demand patterns. Where possible, adjusting capacity between groups could help reduce underutilised places and improve overall occupancy.

This change could increase income without impacting service quality or staffing requirements.

2. Use Attendance Patterns to Support Staffing Decisions

Introduce a simple review of historical attendance patterns to support staffing planning. While maintaining required staff-to-child ratios, this approach could help reduce consistent overstaffing during quieter periods.

Even small adjustments informed by data could lead to more efficient use of staff resources.

3. Introduce Basic Attendance Monitoring for Forecasting

Develop a simple attendance tracking view that highlights recurring patterns, such as quieter days or seasonal trends. This would support better short-term forecasting and improve confidence in monthly income planning.

The focus should remain on visibility and usability, rather than complex forecasting models.

4. Align Operational Decisions with Financial Impact

Link operational metrics (attendance, capacity, staffing) more clearly to their financial impact. This would help management better understand how day-to-day decisions influence overall nursery performance.

Clear visibility into these relationships supports more confident and transparent decision-making.

Next Steps

To build on this initial analysis, the following next steps are recommended:

• validate assumptions with real operational data where available

• refine attendance and staffing views over a longer time period

• explore simple forecasting approaches to support planning

• review recommendations regularly as operational patterns evolve

These steps would allow the nursery to gradually move towards more data-informed decision-making, without introducing unnecessary complexity or operational burden.

This case study reflects the way I like to work as an analyst — starting with the business context, understanding real operational challenges and focusing on decisions rather than tools.

Although the data used here is simulated, the questions, constraints and trade-offs are very real and closely reflect how many UK nurseries operate on a daily basis.

Role: Business Analyst Context: UK childcare sector | Simulated & anonymised data