Advance Analytics - Forecast dashboard

The Forecast dashboard provides a detailed comparison between forecasted demand and actual demand, giving operational teams clear insight into forecast accuracy, variable-level performance, and predictive confidence ranges. It supports both historical forecast analysis and upcoming forecast projections, enabling data-driven staffing and planning decisions.

The dashboard is divided into two main modes:

• Historical forecast – Compare past forecasts versus actual performance

• Upcoming forecast – Review demand predictions with confidence ranges

How it works

The Forecast dashboard contains two main sections, each with multiple components:

1. Historical Forecast

   • Forecast vs Actual performance

   • Trends over time

   • Breakdown by variables

   • Hierarchy comparisons (district → unit → section)

2. Upcoming Forecast

   • Total forecast demand

   • Confidence range (90% likelihood)

   • Forecast by variable

   • Forecast by hierarchy (district → unit → section)

Each section includes filters for District, Unit, Section, Variable, Variable Type, and custom date range selection.

Historical forecast tab

This view compares forecasted demand against realised demand for a selected historical period.

Top KPIs

The header cards provide quick insight into historical forecasting performance:

• Forecast demand – Expected demand during the selected period

• Actual demand – Real demand recorded

• Performance vs Forecast (%)
  (actual − forecast) ÷ forecast
  Positive = actual demand was higher

• Forecast accuracy (%)
  (forecast − actual) ÷ actual
  Negative = forecast underestimated actuals

• Performance vs Forecast (value)
  Difference in absolute values

• Forecast miss (value)
  (forecast − actual)

These KPIs allow rapid identification of under- or over-forecasting trends.

Forecast vs Actual demand trend

A time-series chart showing a daily comparison between forecast and actual demand.

Use this chart to:

• Identify days with significant deviation

• Spot recurring patterns or spikes

• Validate forecast model behaviour

Forecast vs Actual by variable

Each demand variable (A, B, C, D, E, F, G) is evaluated across:

A colour-coded bar/stacked chart visualises variable contribution and deviation.

Forecast vs Actual demand by variable

A breakdown of forecast accuracy per variable.

A bar graph and line trend show how each variable contributes to total demand and how the actual values compare to the forecast across days.

Forecast vs Actual demand – District

Table and scatter-plot showing:

• Forecast demand

• Actual demand

• Performance vs forecast (%)

• Forecast accuracy (%)

Each district appears as a plotted point where:

• Green = actual > forecast

• Red = forecast > actual

       

Forecast vs Actual demand – Unit

Same format, scoped to units inside the selected district.

Forecast vs Actual demand – Section

Same format, scoped to units inside the selected district/ unit.

Upcoming forecast

The Upcoming Forecast view predicts future demand and provides confidence intervals to support staffing and labour planning.

Total forecast demand

A single KPI expressing the total forecast demand for the upcoming period.

Forecast confidence range

A table showing upper and lower bounds for predicted demand:

• Lowest scenario (90%)

• Highest scenario (90%)

This indicates a 90% likelihood that real demand will fall within the given range.

Daily forecast demand with confidence range

Three lines illustrate:

• Forecast Lower 90

• Forecast Demand

• Forecast Upper 90

Use this for:

• Anticipating variability

• Risk-based staffing decisions

• Identifying high-uncertainty days

Forecast demand by variable

A horizontal bar chart ranking variables by forecasted contribution. This helps identify demand drivers for the selected period.

Daily forecast demand by variables

A multi-line trend showing forecasted demand per variable over time.

Ideal for:

• Monitoring expected workload drivers

• Understanding which variables cause peaks

• Planning role-specific staffing

Forecast Demand – District

Includes:

• Forecast demand

• Lower 90% bound

• Upper 90% bound

Plus a graph plotting these values by district.

Forecast Demand – Unit

Same values plotted for specific units (e.g., Park Road North).

Forecast Demand – Section

Breakdown at section level within units.

These comparisons support:t

• Demand planning by region

• Load balancing across the organisation

• Staffing allocation before schedule creation

Practical Use Cases

1. Measure Forecast Accuracy for Workforce Planning

Use Historical Forecast to validate whether over- or under-staffing occurred.

2. Identify Demand Drivers

Variable-level charts reveal which operational factors contribute most to demand.

3. Anticipate High- and Low-Demand Days

Confidence interval charts help plan staffing buffers and risk contingencies.

4. Regional or Store-Level Planning

Hierarchy views show which districts, units, or sections consistently overperform or underperform against the forecast.

5. Improve Predictive Modelling

Performance metrics help analysts refine variable selection and model weighting.

6. Communicate Trends to Stakeholders

Export visual charts and tables to share insights with management or schedule planners.

Conclusion

The Forecast Dashboard provides a comprehensive view of both historical performance and upcoming demand. With insights across variables, time trends, and organisational hierarchy, it empowers teams to optimise labour planning, increase forecast accuracy, and make informed operational decisions.