Enhancing Property & Facilities Management: An Insight into AICA’s Solution

Efficiently overseeing multiple locations in property and facilities management remains an intricate challenge. At the core of this challenge lie three primary drivers: Labour, Materials and Equipment. AICA’s innovative approach seeks to address these complexities, not by offering a one-size-fits-all solution but by deeply integrating data-driven processes, including product data cleansing and enrichment, into the management matrix.

Understanding Multi-Location Management

To grasp the complexity of multi-location management, it’s essential to break down its three core components:

Labour: Labour costs can vary dramatically depending on the location. Accurately categorising and understanding these costs is pivotal for streamlined operations.

Materials: The breadth of materials used in property and facilities management, from basic supplies to highly specialised equipment, can introduce variability in procurement and management costs.

Plant and Equipment: Keeping track of equipment, whether owned or rented, is essential for ensuring operational continuity and managing expenses.

The Role of Data in Our Approach

Clean data is the cornerstone of our methodology. But raw data, especially when sourced from multiple vendors and locations, can be messy and inconsistent. This is where the process of product data cleansing comes into play.

Product Data Cleansing

This process involves removing, correcting or handling corrupted, incorrectly formatted, or duplicate data from a dataset. By ensuring data integrity, we enable facilities managers to make decisions based on accurate, trustworthy data.

Data Enrichment

Once cleansed, data can often benefit from enrichment. This involves enhancing, refining, and improving raw data to make it more meaningful and useful. 

Together, these processes ensure that property management companies have a rich, reliable dataset at their fingertips.

Classification and Customised Taxonomy

Beyond mere cost analysis, our approach involves classifying labour data. We employ advanced machine learning algorithms to meticulously categorise labour costs. 

Additionally, we extract historical data records from Labor, Materials and Plant and Equipment, which forms the basis for building a customised taxonomy. This taxonomy is tailored to your specific needs, offering a clear and precise view of your labour expenditures.

The Benefits of Our Solution


Leveraging machine learning, we facilitate precise categorisation and tracking of labour, materials and equipment. This isn’t about fancy tech but about harnessing the right tools to improve accuracy and efficiency.


Through standardised data formats, we ensure that when you’re communicating, everyone is on the same page.

Risk Mitigation

Clean, enriched data reduces ambiguities, and well-categorised data reduces chances of oversight, together helping in managing operational risks.

Informed Decision-making

With a comprehensive understanding of labour, materials, and equipment, management can make informed decisions, resulting in better operational outcomes.

In Conclusion

By prioritising the integrity, clarity and richness of data, we provide an underpinning that is both robust and adaptable. Our solution fundamentally rethinks how multi-location management can be approached in an information-driven era. 
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