Emerging Workplace Management Solutions

PoE Networks Doing MORE Heavy Lifting Thanks to IoT Sensors and Machine Learning

The pandemic has significantly changed the workplace. As employees return to the office, they have high expectations for cleanliness, safety, and service. Today, more than ever before, there is a need for dynamic and predictive workplace management solutions. That is where things like Internet of Things (IoT)  sensors and machine learning come into the picture. 

Workplace Management Solution: IoT Sensors

The workplace can be pretty complicated. For instance, employees often do not have set work schedules and divide their work hours between home and office. In many workplaces, employees no longer have assigned seating or workstations; working in teams is also a common requirement. Add to all that the fact that COVID-19 is still with us, and cleaning becomes a challenge.

Sensors are a mighty tool for these challenges. They detect office activities and produce daily occupancy reports. The best sensors communicate directly with service request software that transmits occupancy data in real-time, which helps facility managers to develop relevant and efficient cleaning schedules, among other things.

There are four types of sensors needed in an office environment:

1. Occupancy data: Office density can be a problem. Sensors help monitor the comings-and-goings of employees. They also give insight into occupancy trends, such as how many people are in the office on Monday as opposed to Friday. 
2. Space utilization data: Sensor data regarding occupancy numbers is important, but beyond that, sensor data can help you see just how workers are using the office space that is available to them. With this valuable information, necessary changes or adjustments can be handled efficiently.
3. Environmental sensor data: COVID-19 has made it essential to monitor a workplace’s humidity levels. In a recent study led by Professor Michael Ward, an epidemiologist at the Sydney School of Veterinary Science at the University of Sydney, it was found that for every one percent decrease in relative humidity, COVID-19 cases could increase by as much as seven to eight percent. At a ten percent decrease, those percentages can double. IoT sensors can monitor humidity levels and temperatures throughout a building and automatically adjust the environment to ensure constantly desired humidity levels of 40 to 60 percent.
4. Asset utilization data: IoT sensors can send notifications when a restroom is low on soap or needs paper towels. Sensors can be attached to restroom doors and notify facility managers when the number of door swings reaches a certain threshold, indicating high usage and the need for cleaning. IoT sensors can also detect problems with furnaces or water heaters before they fail. This type of information leads to something called predictive workplace maintenance.

What is predictive workplace maintenance?

Predictive maintenance is a strategy that uses an asset’s actual utilization to decide when to perform maintenance. This tactic uses historical and current performance data provided by sensors, usually embedded in LED lighting systems, to determine when a malfunction is likely to happen. Armed with this information, a facilities manager can perform maintenance before the malfunction occurs.

For example, vibrations or temperatures beyond the normal range could indicate an impending disaster to an HVAC system. Sensor data of this type will trigger a work order within the facility maintenance software. As a result, technicians can be called out to inspect the HVAC unit and repair or replace any failing parts before a malfunction happens. This saves expensive and frustrating downtime and can often prevent more costly repairs or equipment replacement.

For a traditional office setting, predictive IoT sensors make the environment proactive. For example: Why wait until the air conditioning has stopped and employees are uncomfortable and grouchy? With the right sensors in place, a technician gets an immediate repair order on their smartphone.

Workplace Management Solutions: Machine Learning

Today’s top organizations use machine learning-based technology to optimize workplace processes, employee commitment, and customer satisfaction. Here are five ways machine learning adds value:

1. Personalizing customer service: Machine learning enhances customer service while lowering costs. By combining customer service data, natural language processing, and algorithms that learn from each interaction, customers can get high-quality, quick answers to their questions. As a result, chatbots are being accepted by consumers. Though they still prefer talking with humans, chatbots respond very quickly and can resolve simple requests while reducing service costs by up to 30 percent. Customer service reps can always step in to handle the more difficult problems—while algorithms peek over their shoulders to learn what to do next time.
2. Improving customer retention: Companies can collect data such as customer actions, transactions, and reactions to identify customers at high risk of leaving. This data helps when deciding what “next best step” procedures to take to retain unhappy customers. Businesses can also use machine learning to help anticipate customer behaviors that require customized offers to keep them from defecting to a competitor.
3. Hiring the best people: Corporations are known to get as many as 250 applications for each job opening. Therefore, one of the most time-consuming tasks of the hiring process is shortlisting candidates. Machine learning eliminates human bias and often finds great candidates who might have been overlooked because they do not fit traditional expectations. 
4. Automating financial processes: Machine learning can speed up financial processes and can even problem solve. For example, when a payment comes in without an order number,  an employee will have to determine which order the payment corresponds to. With machine learning, the system learns to recognize these types of situations and can often find ways to resolve the matter without employee input.
5. Detecting fraud: According to the Association of Certified Fraud Examiners (ACFE), the typical corporation loses five percent of its annual revenue due to fraud. This 2016 survey found “an average loss of $2.7 million per case, with $150,000 being the median loss.”  The report goes on to state: “Small organizations are particularly vulnerable to fraud because they have a significantly lower implementation of anti-fraud controls and have fewer resources to withstand losses. Small organizations also are much less likely to have anti-fraud controls in place than larger organizations.” Machine learning algorithms build models based on historical transactions, social network information, and other external data sources to develop pattern recognition that spots anomalies. As a result, suspicious patterns are detected in real-time.

Last Words

Cutting-edge workplace management solutions such as IoT sensors and machine learning are increasingly creating business environments that are clean, safe, and comfortable for the workforce while making the customer experience quick, customized, and efficient. Streamlining your company’s work processes and building maintenance is cost-effective in the long run and leads to employee satisfaction and customer retention. 

We hope you found this article helpful. Versa Technology is a global distributor of high-quality network technologies. Our chief goal is to provide first-rate support that enables our customers to attain their networking goals. To view our Power over Ethernet (PoE) products and services, visit the Versa Technology homepage.