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Artificial Intelligence Strategies for Laundry Operations

Using laundry machine information to improve efficiency, PPOH, retention

KISSIMMEE, Fla. — Consider a quote from J.W. “Bill” Marriott Jr., chairman of Marriott International Hotels and Resorts: “I have never been satisfied with anything we have ever built. I have felt that dissatisfaction is the basis of progress. When we become satisfied in business, we become obsolete.” 

Having worked for Marriott both as an employee and contractor, I can personally attest to Marriott’s never-ending pursuit of quality.

What are the three biggest enemies of innovation? Resistance to change, justifying the cost and lack of impartial information to educate all the stakeholders. 

How important is change today? Consider the eagle as an example of how important it is to change. An eagle can identify a rabbit a mile away, thus, eagles must know where their prey will be and not just where they are currently to be successful in the hunt. 

Artificial intelligence/machine learning (AI/ML) can and is already providing this same ability to laundries worldwide. 

This article investigates using AI (machine learning) by using the machine’s information to improve efficiency and pounds per operator hour (PPOH) while improving the retention quality of processed linens and personnel that flow throughout the entire laundry. 

How to choose and use the best tool and use it correctly is the key to harnessing this success.

DATA AND SCHEDULING

Where there is data there is opportunity (“ML Learns from data to predict outcomes,” Eric Siegel, Forbes, 3/25/24).

We all love benchmarks and want “The Answer.” This is impossible due to each laundry being unique in both layout and equipment. What we can know is how close to peak efficiency we are for our laundry at present. 

Whatever type of equipment we use, we need to know the maximum number of pounds we can produce in an eight-hour shift. AI/ML can provide an on-demand status report and where possible bottlenecks are currently and can even predict, with high confidence, what station likely will show a bottleneck soon.

Putting time, attendance and PPOH goals into the peak efficiency formula provides a continuous monitoring of the flow required to meet production and financial requirements. 

Shane Suda, the vice president of operations and supply chain at Bay Towel was asked, knowing his interest in Lean Six Sigma, where he sees the intersection of AI and Lean in day-to-day operational life managing the laundry operation.

“Scheduling is a key component that will be helped by AI,” he answers. “More analytics will help laundry companies operate more efficiently. We use a third-party system to track results currently.”

We can see this illustrated in a labor vs. budget calculation. 

What does one minute of lost time equal for a plant processing 10 million pounds per year? At 14 cents per pound labor and 10 million pounds per year working an eight-hour shift five days per week, this equals $11.22 per minute or $673 per hour.

A key ingredient to increasing PPOH in laundry is more efficient scheduling.

Just imagine what AI can accomplish in not just scheduling how many people are necessary but the exact point in time they should arrive at the station. This will improve the flow of the laundry (flow of staff and flow of linen).

Laundry machines have been linked together for some time. Marien van Bezooijen of Gotli Labs explains in a 2022 CINET article, “The focus is currently mainly on the lean side, machine learning real-time links, and information available just in time.” 

Retrieving data acquisition, either manually or by writing a small program, directly from equipment has become a key component of using AI/ML in industrial laundries in Europe and more recently in the United States. It is applicable with all sizes of laundry and laundromats that are performing commercial/resort laundry.

“In general, the more data a machine learning algorithm is given, the better it performs,” (“IFR Shares Top Five Robot Trends of 2024,” Automation.com). 

A simple example of this is flipping a “fair” coin. There is a 50% chance you will get a head with each coin flip. 

However, after five flips you might end up with four heads, yet, as you flip the coin 1,000 times, you will end up at 50% or very close to it. 

Just imagine the information your equipment provides such as batch, customer, time started and completed, utility usage, chemical usage, etc. There is so much opportunity as stated at the start of the article.

A current tool being used to increase PPOH and improve employee efficiency is to stagger employee start times which softens the production backlog between stations (Theory of Constraints) (Lean Six Sigma). 

Using linear regression taken from the machine’s usage times to pinpoint backlogs that inhibit the flow through the plant or laundry space (“Achieving PPOH Increase Goals in 2024 (Part 1)”). 

I asked Seth Twarek, a laundry consultant at Western State Design what roadblocks, if any, are holding back AI adoption in commercial/industrial laundry. 

“Affordability of technology in an already stressed financial environment,” he says. “In addition, workforce education will be vital to widespread adoption of AI.” 

Due to his vast experience in current equipment specifications and the latest innovations, I followed up with this question where he sees the most opportunity to use the equipment data that can be accessed to provide ROI info for the plants using AI/ML as their tool. 

“Ability to reduce labor costs and improve quality control,” Twarek shares. “It will reduce rewashes as well due to immediate feedback of data from the machine.” 

COMMERCIAL LAUNDRY CHANGE

One of the founding fathers of efficiency and quality, Edwards Demming, summed up why we should be open to change: “It is not necessary to change, survival is not mandatory.” 

The point is clear, we must see quality not as it is, but as what it can become. Every laundry, no matter the size, can supervise the production flow in real-time, tracking batches of linen and finding bottlenecks before they occur. 

Linen loss is estimated between 17-33% of the total laundry costs, depending on what source you use. During my decades of managing resort laundries, this line item was vital to ensuring profitability for the entire operation.  

Labor efficiency was paramount, and limiting linen loss was the topping on the dessert. Whether you own the linen and terry or are processing it for a client, there are two absolutes. 

First, they want their laundry returned and not someone else’s. Second, the customer expects you to take great care of the linen and minimize loss. 

Regarding this vital subject, I asked Don Ward, CEO and founder of Laundris what roadblocks, if any, are holding back AI adoption in commercial laundry. 

“Education is vital to improve depletion rates along with identifying proper circulation patterns of linen,” he says. “New and emerging technologies, such as Laundris’ patented Precision (IoT + AI/ML) Tech Platform, allow you to see relevant business data in real-time. 

“In most cases leveraging an automation inventory and asset tracking platform such as Laundris dramatically reduces costs associated with loss by up to 30%. Especially as it relates to older generations of technology that were utilized in the marketplace.”  

Using machine data and applying ML, we gain vital information on the number of times linen and terry are processed and can track new and in-process linen and terry life-cycle costing of each piece. 

A small example is a 30% gain on a $250,000 purchase. The bottom line is savings of $75,000. Stephen Few, a leading data visualization expert and visionary wrote the following: “Numbers have an important story to tell. They rely on you to give them a voice.” 

In conclusion, “Two engineers were watching a flock of birds. They watched the formation and decided to study why birds flew in a particular V formation. 

“What they found was extraordinary because due to the momentum and system of flight as a flock, they gain something close to 70% greater flying range than if they were flying alone,” (Speakers Library of Business Stories, Anecdotes, and Humor, Joe Griffith). 

Imagine your laundry operation using AI/ML to flow with ease and accomplish what the birds know instinctively. 

It is possible today. 

Artificial Intelligence Strategies for Laundry Operations

(Image licensed by Ingram Image)

Have a question or comment? E-mail our editor Matt Poe at [email protected].