Key Takeaways

  • Data-driven cleaning uses real-time data to improve efficiency and results.
  • Workloading software helps managers optimize staffing and control labor costs.
  • Smart sensors and cleaning robots improve service, tracking, and consistency.

By Keith Schneringer 

If you are a facility cleaning manager, there are probably some common questions you ask yourself each day regarding building cleanliness. 

“Has my facility been cleaned?”  

“Does my facility need to be cleaned?” 

“How clean is my facility?”  

“Is my facility clean enough?” 

“Are my restrooms stocked with enough supplies?” 

Sound familiar? 

Ultimately, these questions, amongst many others, are asked and answered by every facility across the country every day. 

For the most part, facility cleaning managers have always answered these types of questions and managed the cleaning process through an assorted collection of schedules, checklists, spreadsheets, inspection reports, and in some extreme cases, in response to building occupant complaints. 

While these approaches incorporate some of the best available tools facility cleaning managers have had at their disposal to manage the cleaning process, times have changed and now a combination of increasing building occupant expectations, rising operational costs, and ongoing labor shortages have highlighted the need for smarter and more efficient cleaning strategies. 

Fortunately, advances in technology have led to 21st century tools such as workloading programs, smart sensors and dispensers enabled with Internet of Things (IoT), and autonomous cleaning equipment that can empower facility cleaning managers to make data-driven decisions to transform the act of cleaning facilities from a reactive activity to a more strategic, measurable, and repeatable process tied to desired outcomes.   

Evolving Beyond Cleaning Schedules 

Historically, the schedules used to clean facilities have been based upon a series of assumptions of what someone thinks is going to happen as opposed to the actual cleanliness of a facility or conditions in a building at any given moment.  

And in many cases, that cleaning schedule and its associated scope of work have merely been inherited from someone else who might have occupied the facility cleaning manager position at some point in time previously. That pre-existing cleaning schedule may reflect how those assumptions have been perpetuated over the years, as opposed to reflecting more up to date information based upon current building conditions.  

For example, restrooms may have been earmarked to be serviced every two hours because supplies ran out that one time instead of being based upon current actual usage. Or floors may have been scheduled to be stripped out and refinished regardless of actual need.  

Unfortunately, these misinformed approaches can lead to inefficiencies for a facility cleaning manager who has used existing tools to create a cleaning plan that provides either “too little” and “too much” service.  

In some instances, high-traffic areas may become dirty, and supplies run out of stock between scheduled cleanings, while in other instances, low-traffic areas may receive unnecessary services at the expense of other more “used” spaces. The end results could be wasted labor, inconsistent cleaning levels, increased building occupant complaints, and higher operational costs instead of a clean, well-supplied building. 

Data-driven cleaning replaces these historical assumptions, inherited scopes of work, and guestimates with current and actionable information for today’s facility cleaning manager.  

By using current technology to gather and analyze data in real time, facility cleaning managers can gain visibility and insights into how building spaces are currently being used and where cleaning resources are needed the most. 

Optimizing Labor 

And when we think about the “cleaning resources” that are needed the most, labor is at the top of the list. As a matter of fact, labor is the single largest component of a cleaning budget, typically responsible for 90 to 95 percent of the total cost to clean a building. Since labor is the largest single part of the budget, having a firm understanding of how labor is being allocated is critical to a facility cleaning manager focused on managing costs. 

One of the first questions a facility cleaning manager or cleaning service provider must answer when establishing their cleaning budget is, “How many people do I need to clean this building?” The answer to this question will be a big portion of the foundation upon which the cleaning budget is built. 

That is where workloading can play a significant role. 

Workloading a building is the process of determining how much time, labor, and resources are required to complete a specific scope of work. Workloading involves establishing cleanable square footage for all of the building areas, determining the list of cleaning tasks for each area, assigning the desired frequencies for each of the cleaning tasks, applying cleaning time standards for each cleaning task, and then using all of that information for the purpose of calculating total labor hours and costs. 

Rather than relying on inherited historical staffing levels or subjective guestimates, workloading software provides objective data that supports staffing decisions and operational planning. 

In addition to providing defensible data and valuable documentation to help justify staffing levels and support budget requests, workloading programs can also offer facility cleaning managers the opportunity to manage productivity expectations, balance employee workloads, and deploy cleaning teams where they can deliver the greatest impact. 

And as facilities evolve and occupancy and building usage patterns change, workloading programs can be updated to better reflect current building conditions, ensuring labor resources remain best aligned with actual needs. 

Using a workloading program, a facility cleaning manager can answer many other questions that are critical to managing a cleaning operation such as: 

“How long should it take to perform a cleaning task according to industry standards?” 

“How does changing cleaning task frequency impact overall cleaning labor costs?” 

“How does adding or subtracting cleanable square footage impact overall cleaning labor costs?” 

“How does a change in cleaning process impact overall cleaning labor costs?” 

“How does mechanizing cleaning tasks impact overall cleaning labor costs?” 

For facility cleaning management professionals, workloading is the best available tool to strike a balance between cost and quality, and to ensure the cleaning plan fits current needs and doesn’t include “too much” or “too little” service. 

Optimizing Labor Through IoT 

If the first phase of the internet served to connect people to computing devices (think static webpages that communicate information but don’t provide the opportunity to interact), and if the second phase of the internet served to connect people to people (think social media sites), then the third phase of the internet has served to connect computing devices with other computing devices—think “smart” appliances that have the ability to communicate their status to other connected devices to provide updates and information. 

For the cleaning industry, some of those connected devices and systems include restroom dispensers, occupancy sensors, managed inventory supply, waste and recycling receptacles, and equipment fleet management. 

IoT-connected restroom dispensers such as paper towels, hand soap, and toilet paper dispensers report fullness levels in real-time to help prevent runouts. Using these tools, facility cleaning managers can look at the fullness status of all their dispensers, identify dispensers that need service soon, and receive alerts for dispensers that have run out. 

In addition, these smart dispensers also collect and display historical usage data to help identify trends for planning purposes. A facility cleaning manager can determine the average usage for each dispenser in each restroom and plan out supplies and associated service accordingly. 

IoT-connected occupancy sensors are frequently pared with dispenser sensors to be able to track and monitor building occupancy and traffic. Since it stands to reason that if the number of people visiting the restroom goes up, then the usage of supplies is also going to go up. 

Some sensors also track occupancy as it relates to indoor air quality, with a metric of CO2 levels being used to determine if a room has become overcrowded. These sensors can also measure particulate count and Volatile Organic Compounds (VOCs) in the air as well, which can alert a cleaning team that there is a need to dust and vacuum in the space. 

Some sensors can assist with inventory management and order control where product is scanned into inventory when it is received and then scanned out as it is leaving the supply room to be used. Usage patterns are tracked, and par levels are established to assist with demand forecasting and prevent having too much or too little inventory on hand. 

IoT-connected sensors can also be used for waste and recyclable collections, with some waste and recycling receptacles equipped with sensors to communicate fullness levels. In addition to providing real-time status of fullness levels for all receptacles, these systems also track historical fullness levels so that staff can be allocated to empty receptacles when they are almost full each time, as opposed to emptying partially full receptacles just because they are making their rounds. 

IoT-connected sensors can also be used for equipment fleet management, allowing a facility cleaning manager to ascertain the location, usage history, and current performance status of their equipment fleet. Some sensors are even able to alert the facility cleaning manager that a proactive repair is needed before the equipment goes out of service. 

The Future of Data-Driven Cleaning 

And speaking of “smart” data-driven cleaning management, there are now autonomous cleaning equipment or “cleaning robots” available to not only perform cleaning functions to complement the work of existing cleaning staff but also to provide performance data to verify cleaning tasks that have been completed for the facility cleaning manager. 

Of course, there is also the associated benefits of labor-savings, increased cleaning coverage, more consistent results, and the reliability of having autonomous cleaning equipment do the cleaning but imagine having a fleet of cleaning robots that not only completes the work of cleaning their assigned spaces as programmed but also provides a report confirming all of the areas that have been cleaned. 

Facility cleaning managers who choose to embrace elements of data-driven cleaning today are positioning themselves to more effectively meet the challenges of tomorrow. As technologies continue to evolve, facility cleaning managers will gain even greater capabilities to predict needs, allocate resources, and improve cleaning outcomes. 

By combining workloading tools, smart technology, and autonomous cleaning, facility cleaning managers can use data to achieve better results and hopefully work smarter, not harder.

Keith Schneringer has been in the sanitary supply industry since 1990 and is currently the Senior Director of Marketing Jan/San + Sustainability for Imperial Brady. In his current role, Keith is responsible for marketing to the jan/san and facility care industry, for developing vertical-market-specific programs to better assist customers, and for leading the company's sustainability initiatives. He is a LEED AP O+M, CIMS-GB ISSA Certification Expert, former President of San Diego Green Building Council, stakeholder in the standard development process for ISSA CIMS-GB as well as the Green Seal GS-1, GS-37, and GS-52 standards, and has been recognized as Advocate of the Year by ISSA. Before assuming his current responsibilities, he worked as an account consultant, sales manager, marketing manager, and director of channel marketing + sustainability for WAXIE Sanitary Supply.


posted on 6/25/2026