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Big Data, Better Budgeting: Machine Learning for Facilities Management

Big Data, Better Budgeting: Machine Learning for Facilities Management

Faster decisions, more efficient operations, and consistent performance: sounds like an ideal employee, doesn’t it?

SwissCognitiveWhile the Internet of Things becomes increasingly nimble, facilities management stands to win big: incorporating equipment and tools with smart tech brings higher quality, superior organization, and better performance — all with easier operation.

What is Machine Learning?

Machine learning is a particular subset that deals in data. It’s not all that new — computer scientists have been developing tech since 1959, primarily to focus on system performance improvements in computers. The system relies on algorithms and analysis; it doesn’t require a pre-programmed answer to reach the best available solution.

Two key branches of are hitting the facilities industry in a big way:

  1. Supervised , which uses labeled responses to help solve problems. Supervised can develop an algorithm to help predict when replacement parts should be budgeted and installed based on operating costs and patterns.
  2. Unsupervised , which evaluates raw datasets to find patterns: think traffic, usage, or demand. This type of data usage generally relies on sensors, cameras, and metrics like energy consumption to optimize facility operations.

How is Machine Learning Relevant to Facilities Management?

Pattern recognition is a key part of : delving deep into data sets to identify recurring patterns and adapting to suit them. Facilities managers can’t just dig into those numbers on their own — in most operations, the information sets are just too vast. However, the patterns they display are real, and extremely relevant in the case of say, optimizing energy usage.

Predicting Problems

When a facility manager tracks operations with , the disruptions in normal patterns are just as relevant as the consistencies. For example, if a process suddenly fails to meet its routine performance levels, it’s possible to isolate and replace a failing part — before it becomes a problem.

Smart technology can be programmed to set alerts when the facility displays inconsistencies, all through self-monitoring data collection.

Tracking Usage

By establishing operational patterns, it’s easy for facilities managers to develop proactive system scheduling: parts ordering, cleaning, routine shutdowns, and equipment replacement can all be arranged at the most cost-effective and efficient times.

Optimizing Energy Efficiency

A recent study from IIC Testbed investigated through an IoT platform for asset optimization throughout a regular office building — a Toshiba facility in Kawasaki, Japan. The vast system of sensors tracked 35,000 measured data points per minute and drew insights on everything from prioritized elevator scheduling to kitchen odors, automated temperature adjustments, and lighting controls.[…]

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