The Potential of Predictive Analytics in Labor Industries

By Devin Partida, Editor-in-Chief of ReHack.

com People often think of predictive analytics tools as primarily resourceful for white-collar work.

However, these five examples show that it offers plenty of potential in blue-collar jobs, too.

   Most professionals who address problems with cars only see those vehicles once someone notices symptoms.

By that time, the issue may be so extensive that it costs the vehicle owner hundreds or thousands of dollars to fix, plus increases the manual labor required by the mechanic.

Theres been a more recent move towards getting diagnostic information throughout the whole lifecycle of a vehicle.

That approach lets technicians see problems earlier, often before they cause symptoms.

With the help of data processing in the cloud and wireless networks, mechanics can get real-time status updates for individual vehicles or entire fleets and become proactive about keeping them at peak performance.

Knowing about a problem before a vehicle arrives in a shop also aids in better planning.

For example, if a technician feels nearly certain about the cause of an issue, they could order a part and have it ready to install before ever seeing the vehicle.

   Predictive maintenance also assists the teams of people who oversee building maintenance.

Facility managers often connect sensors on equipment used for building climate control.

They can then see notifications about faulty components or other problems before breakdowns occur.

People can collect more than 350,000 data points annually by analyzing 10 metrics every 15 minutes.

Moreover, this use of data science can show users the likely effects of specific tweaks before they make them.

For example, if a building maintenance manager wants to focus on energy savings, a dashboard could reveal what changes would give the biggest payoffs.

That knowledge removes costly and potentially frustrating guesswork.

Many customers know the importance of getting regular maintenance on their heating and air conditioning equipment.

However, problems can still occur between appointments.

Applying advanced predictions to the equation reduces the chances of service disruptions in commercial buildings.

   Along with the earlier discussion about relying on analytics to spot automotive malfunctions before they happen, people who manage or operate commercial fleets have another compelling reason to invest in predictive analytics.

They can increase driver safety by predicting which employees are most likely to have accidents.

The tools can also highlight the drivers with above-average safe driving practices.

Many of todays solutions that utilize data science examine factors both within and outside of a drivers control.

For example, a tool may measure a persons driving habits, as well as environmental elements such as traffic and weather.

This approach to risk management allows supervisors to intervene and provide coaching before mishaps occur.

People interested in using analytics this way have dozens of possible metrics to track.

However, GPS tracking is especially advantageous in this use case.

It allows showing the time spent in particular locations.

If a driver takes a break or needs to stop and pick up a delivery, managers can see how long those pauses take.

Similarly, they can monitor if drivers spend too much time in traffic and need to reroute.

   Construction projects can be difficult to complete on time and under budget.

However, predictive analytics could give an unprecedented level of transparency to such endeavors at all stages.

Applying data science to the preconstruction phase helps managers understand where costs could get out of control or which aspects might take longer than expected to complete.

Analyzing data at later stages also helps project managers take corrective action to keep things on track before problems happen.

This capability makes it easy to keep clients pleased and in the loop, too.

Making the most of analytics in construction requires taking the information out of silos.

People also need to ensure that the imported data is in the right format for the tool they want to use.

Taking care of those foundational necessities will help them have better overall outcomes.

   People are also increasingly interested in using data-based predictions to enhance warehouse management.

For example, a dashboard could calculate the possible number of hours saved if a company improves a process or reduces the distance a worker must travel when transporting goods around the facility.

An advanced tool could also determine how a facilitys labor needs will change as seasonal demands fluctuate.

Knowing those details helps company leaders know when to hire more team members to avoid overstretching the current workforce.

High-tech analytics platforms play a crucial role in injury reduction, too.

If a tool examines historical accident data, it could help decision-makers choose what to change so that people stay healthy and able to give their full contributions during a day at work.

   These five use cases illustrate why people should not immediately assume that there is no place for data science in blue-collar work.

Analyzing data in the right ways can improve effectiveness, productivity and safety, among other desirable aims.

  Bio: Devin Partida is a big data and technology writer, as well as the Editor-in-Chief of ReHack.


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