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Representative solution

Availability & Scheduling Automation

A fictional example of a configuration-led workflow that repeatedly checks availability, stores the result in a structured history and turns a once-manual check into a useful capacity-reporting dataset.

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Scenario

The fictional client situation

Business context

NorthAxis Training & Assessment offers a range of service types requiring regular availability checks. Staff currently log into a platform, search different categories one by one and paste the earliest date into a tracker. By the next day, the previous result has been overwritten.

Typical problems

  • Repeated manual searching every day or week.
  • No proper history of how availability changed over time.
  • New service types require more manual admin.
  • Failures or empty results are easy to miss.
  • No lead-time trend or capacity dashboard for management.
Before & after

What changes

Before

Search, copy, overwrite

  • Users manually check availability for each service type.
  • Tracker only shows the current state, not the trend.
  • Different users may apply different search settings.
  • Capacity issues are seen late and reactively.
After

Configuration-led automation

  • A single flow loops through a maintained configuration list.
  • Each run records the earliest availability and related metrics.
  • Daily history supports trend reporting and warning bands.
  • Success, empty results and failures are visible in the data.
Workflow diagram

Example automation pattern

1. Read configuration

The flow starts by reading a list of active service types and their search settings.

2. Run checks

Each active configuration is processed using the relevant search window, duration or category rules.

3. Store result

The result is written into a daily history table with earliest date, lead time, count of slots and status.

4. Report trend

Power BI compares service types, lead-time movement and potential pressure points over time.

Synthetic screen example

Illustrative dashboard view

Availability & Capacity MonitorIllustrative only • Fictional data
Service types tracked46Active configurations
Average lead time38 daysAll services
Critical services6Above warning threshold
Run success97.8%Last 30 days
Lead-time by service groupCurrent snapshot
Availability healthGreen / amber / red
Example daily snapshotHistorical table style view
ServiceEarliest dateLead timeStatusSlots
Service A14 Sep12 daysHealthy5
Service C28 Oct56 daysWarning2
Service E15 Dec104 daysCritical1
Solution outline

What the delivered solution could include

Automation & data design

  • Configuration table for service types and search parameters.
  • Automated scheduled workflow to process every active configuration.
  • Historical result table rather than overwriting the latest record.
  • Search status and error logging for transparency.

Reporting & monitoring

  • Power BI trends for lead time, slot counts and service pressure.
  • Threshold bands to highlight deteriorating availability.
  • Operational views for current availability and management views for trends.
  • Simple process for adding or pausing service types in configuration.
Less repetitive adminHistorical evidenceCapacity visibilityScalable automation
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