Tag: dailyprompt-2897

The Electricity Bill Arrives Too Late: Why Operational Visibility Matters

There is something interesting about electricity bills. They are very good at telling us what has already happened.

They can tell us how much electricity we consumed and how much we need to pay. For commercial facilities, they can also reveal the financial impact of Maximum Demand. The problem is that by the time we see the bill, the event that caused the cost may have happened weeks earlier.

That is a good example of what I call operational blindness.

The Problem Is Not Lack of Data

I recently looked at an IoT project developed to monitor Maximum Demand in real time. The problem it addressed was surprisingly common.

A large facility can have air-conditioning systems, computers, laboratory equipment, motors and other electrical loads operating simultaneously. At certain times, these loads can push electrical demand sharply upward.

Without continuous monitoring, the people managing the facility may not realise that a demand spike is happening.

The spike occurs. The cost is incurred. The bill arrives later.

Only then does somebody ask, “What happened?”

The organisation had data, but it did not have visibility when the information could still influence an operational decision.

Seeing the Problem While It Is Happening

The project approached the problem differently. Electrical parameters such as voltage, current, power factor and demand were continuously measured and transmitted through an IoT architecture.

Operators could then observe demand behaviour as it happened rather than depending entirely on periodic readings and historical bills.

During testing, the system captured a live demand peak of 664.8 kW, compared with 680 kW recorded for billing purposes.

That difference tells an interesting story. Real-time IoT monitoring can provide a sufficiently close picture of what is happening operationally to help people recognise potentially costly conditions earlier.

The Dashboard Is Not the Real Value

We sometimes become too fascinated by dashboards. We talk about graphs, gauges, sensors, protocols and cloud systems.

But nobody wakes up in the morning saying, “I need another dashboard.”

What the operations team really needs is an answer to practical questions:

  1. Are we approaching a costly demand threshold?
  2. When do our highest demand periods normally occur?
  3. What activities coincide with those peaks?
  4. Is something behaving differently today?
  5. Should somebody act now?

That changes the purpose of IoT. We are no longer collecting data simply because we can. We are giving people enough visibility to make a decision while there is still time to act.

From Seeing to Predicting

The next step becomes even more interesting.

Once months or years of operational data have been collected, machine learning can begin looking for patterns that humans may miss. Instead of only showing that demand is high, the system could eventually warn that current conditions resemble patterns that previously led to a demand spike.

This moves us from monitoring towards early warning.

For me, that is where AIoT becomes meaningful. IoT allows us to see the physical world. AI helps us understand what those observations may mean.

Technology creates value when it shortens the distance between something happening and somebody knowing about it.

Because when the electricity bill finally arrives, the opportunity to change what happened last month is already gone.

[Note: This is one of Favoriot‘s Use Case – Energy Monitoring]

The Pump Was Running. But Could Anyone See What Was Really Happening?

Many IoT projects begin with technology. What sensor should we use? Which controller should we install? How will we connect the equipment to the cloud? After spending many years working with IoT projects, I have learned that these should not be the first questions.

I prefer to begin with something much simpler: What operational problem are we trying to solve? A recent airport water pump monitoring project illustrates why this question matters.

The Problem Was Not the Pump

Water pump motors are the kind of assets most people rarely think about until something goes wrong. In facilities such as airports, factories, commercial buildings and utilities, pumps may operate quietly for years while supporting critical operations behind the scenes.

The real problem was not whether the pumps could operate. The problem was that the people responsible for maintaining them did not have continuous visibility into their operating condition. Between routine inspections, a motor could begin behaving differently without anyone immediately knowing.

That creates what I call operational blindness. The equipment is running somewhere in the physical world, but the people responsible for it cannot continuously see what is happening.

The objective was therefore straightforward: monitor the behaviour of the pump motors remotely and provide enough visibility for the maintenance team to recognise abnormal conditions earlier.

This is where Connect → See → Act becomes useful as a way of thinking about IoT.

CONNECT: Bring the Physical Asset Into the Digital World

The first step was to capture electrical current from the pump motors using current sensors. The readings were collected through an industrial controller before being transmitted through an industrial communications gateway to a cloud-based monitoring platform.

This created a digital connection to equipment that previously depended heavily on local observation and periodic inspection. Data from the physical asset could now travel continuously from the field to people who needed to monitor it.

But connectivity alone does not solve the operational problem. Sending thousands of readings to the cloud only creates more data unless someone can understand what those readings mean.

SEE: Understand What Is Happening

Once the data reached the monitoring platform, engineers could observe pump behaviour remotely. They could view current readings, examine operating patterns and compare historical behaviour without physically visiting the equipment.

This changes the maintenance question. Instead of simply asking, “Is the pump running?”, the team can begin asking, “Is the pump behaving normally?”

That distinction matters. A machine can still be operating while its behaviour is beginning to change. Continuous visibility gives maintenance teams an opportunity to notice those changes earlier.

ACT: Respond When Something Changes

The final stage is where monitoring becomes operationally useful. Thresholds can be established so that unusual conditions generate alerts, allowing responsible personnel to investigate rather than waiting for the next scheduled inspection or an obvious equipment failure.

The complete process becomes simple to understand: 

Connect the equipment and collect its condition data. See its behaviour remotely and recognise changes. Act when the information indicates that attention may be required.

That is Connect → See → Act.

The lesson from this project is not really about pumps, sensors or cloud platforms. It is about removing an operational blind spot. The best IoT projects are often not those with the most technology. They are the ones that help people discover what is happening in their operations early enough to do something about it.

[Note: This is one of Favoriot‘s Commercial Use Case – Pump Monitoring]