Asset Optimisation: Doing the Basics Well

Asset optimisation can also take many different forms – reducing energy consumption, ensuring food safety, maintaining occupant comfort and improving maintenance practices just to name a few. Often, businesses aim to achieve one or more of these outcomes simultaneously, however, to be effective, asset optimisation requires more than just deploying technology, it requires a clear and analytical approach to achieve the best intended outcomes.

In this post, we’ll explore two approaches to optimisation – the one size fits all method and the asset by asset strategy. We’ll also look at the importance of establishing a clear performance baseline and demonstrate how just doing the basics well can lead to significant improvements. Through some examples such as setpoint analysis and schedule optimisation, we can demonstrate how basic data analysis can help businesses make informed decisions and begin to optimise their assets in the best possible way.

Which Approach to Asset Optimisation?

There’s two common ways to approach asset optimisation:

1. One size fits all: This approach applies standardised settings across all assets of the same type, assuming that a uniform approach will on average yield consistent improvements. Whilst this is easy to implement at scale, it overlooks the variations of individual assets such as different usage patterns, operational conditions and equipment efficiency.
2. Asset by asset: This approach takes into account the unique characteristics of each asset, analysing data for the asset to determine the optimal settings for each. While it is more resource intensive to carry this out, if can yield greater efficiencies through tailoring the settings to the specific conditions in which the asset operates.

Both approaches clearly have their merits, but in many cases, its best to apply a hybrid approach where standardisation is applied as a foundation and then fine tuned on an asset by asset basis to deliver both short term and long term results.

Establishing Baseline Performance

Before any optimisation can take place, its critical to establish a baseline of current asset performance as without an accurate starting point, it becomes difficult to measure any improvements or identify any inefficiencies.

Some key steps to establishing a baseline include:

• Collecting and centralising data from the connected assets, including but not limited to telemetry, energy consumption and maintenance logs.
• Defining any key performance indicators relevant to the optimisation goals such as the baseline energy consumption from measurement and verification in line with the IPMVP, asset uptime, or closeness to setpoint as some examples.
• Identifying existing inefficiencies through analysing historical data from the assets.

Doing the Basics Well

Many times businesses jump straight into advanced analytics and AI/ML solutions, when in reality, the most significant gains can often be made by simply ensuring that fundamental practices are adhered to. Some of the most effective improvements come from:

• Checking setpoints: Ensuring that assets are operating within their intended parameters which is a simple yet powerful way to optimise performance.
• Reviewing schedules: Ensuring that asset operational schedules align with either actual demand or trading/operating hours to prevent unnecessary asset operation.
• Identifying anomalies: Spotting the outliers in asset behaviour which can highlight potential maintenance issues before they become costly failures. For example, how close assets operate to their setpoint can indicate either poor or good performance through simple visualisation.

Example 1: Analysing Asset Setpoints
Setpoints play a critical role in asset efficiency. For example, in Refrigeration and HVAC systems, setpoints have to balance both efficiency and performance. Across estates, a boxplot analysis, which summarises data using five key figures (minimum, first quartile, median, third quartile and maximum), can reveal patterns and inconsistencies in setpoints across multiple assets.

Through analysing a boxplot of setpoint variations, businesses can:

• Identify assets operating outside of expected ranges.
• Detect inconsistencies in setpoint settings across same asset types.
• Determine whether deviations are justified by operational needs or are simply just inefficiencies.

From the plot below as an example, we see the following:

Minimum Setpoint: 1.0 C

Maximum Setpoint: 2.9 C

Median Setpoint: 1.9 C

First Quartile: 1.6 C

Third Quartile: 2.2 C

These values indicate that most of the assets are operating within a reasonable range, but there is clearly some variation. The fact that the minimum setpoint is 1.0oC suggests that some assets may be running colder than necessary, increasing energy consumption. Similarly, the maximum setpoint of 2.9oC could indicate that some assets are operating above or close to their limits for food safety.

Based on this analysis, we could take several actions to optimise the assets:

• Standardise Setpoints: If food safety allows, align all units closer to the median setpoint to reduce unnecessary cooling and energy consumption.
• Investigate Low Setpoints: The assets operating below the first quartile threshold of 1.6oC could be cooling too much. Increasing these setpoints could result in significant savings without compromising food safety.
• Examine High Setpoints: The assets operating above the third quartile threshold 2.2oC should be checked for potential issues such as faulty probes or maintenance issues.

Example 2: Analysing Asset Schedules
Schedules dictate when assets should and shouldn’t be operating, yet over time, they often drift due to human intervention, system overrides or changes in operational practices. This drift, where assets run longer than necessary, can lead to excessive and wasted energy usage and increase asset wear.

A simple visual comparison audit and realignment with actual requirements can often achieve significant savings with minimal investment.

Summary

As seen, asset optimisation isn’t always about deploying the latest in Artificial Intelligence and Machine Learning. Sometimes, just by doing the basics well can lead to substantial improvements. Through taking an analytics driven approach in establishing a clear baseline, ensuring setpoints are correctly configured and keeping schedules aligned, business can optimise their connected assets efficiently with little cost if any.

In the long run, focussing on these fundamentals not only optimises asset performance, but also serves as a solid foundation for future works, where advanced analytics, artificial intelligence and machine learning can really thrive because the basics have been done well.