CHICAGO — The American Society of Quality (ASQ) defines benchmarking as the process of measuring products, services and processes against those of organizations known to be leaders in one or more aspects of their operations.
“Understanding how to find, analyze and apply this data can be a game-changer for improving your operation’s processes and efficiencies,” says W. Kirby Wagg, a senior business adviser with Performance Matters out of Sarasota, Florida, who spent 45 years at Wagg’s Linen & Uniform and is a member of the American Laundry News Panel of Experts.
ANALYZING, UNDERSTANDING THE DATA
According to Cliff Beiser, principle of Champions Touch, a consulting service in Kissimmee, Florida, the most important component to data analysis is to thoroughly understand the basic data that is being produced. It is both science and art due to endless varieties of laundries throughout the world.
“You do not have to know how to do the math, but knowing the math means nothing if you do not understand the inner workings of running a laundry,” he shares. “If someone cannot explain what they are doing, remain skeptical until they can.
“If you are a fantasy sports enthusiast or maybe trade stocks, the process that is used by the professionals to analyze and attempt to predict the future results of a player or stock are quite like the methods used to improve laundry pounds per operator hour (PPOH) as well as chemical usage and preventive and predictive maintenance to your equipment.
“When there are many different brands of equipment, and the flow is disjointed the analysis should be completed in segments and then combined for improvement of both quality and productivity.”
More and more equipment manufacturers are including data analysis tools with their equipment in both industrial and laundromat areas, says Beiser.
“Third-party software companies are growing in number and artificial intelligence and machine learning (AI/ML) are being used by these companies and in-house laundries.
“There are other terms to become familiar with such as Linear Regression, K Means Clustering, IOT + AI/ML, RFID tracking, Just in Time and Lean. These are and will have far-reaching applications to both extend the reach of a laundries revenue and understand the costs and even predict the maintenance and staffing issues that will likely occur.”
Beiser points out that internationally, where there is a greater prevalence of off-site laundry cleaning, automated sorting tools are being developed, used and improved to reduce rewashing and sending defective laundry back to the customer. The software itself tracks and provides analysis of all stations of a laundry operation.
“As a specific example, consider that you manage a 5 million pound per year operation and your average wage is $20 per hour including benefits,” he posits. “If you improve 10 PPOH, you will save $90,000 in labor for the year.”
As mentioned, more manufacturers and third-party software companies are building this capability into their products.
There are subscription-based data analysis tools and Excel can be used effectively, and they have a Power BI program, which is excellent, and Beiser says he has used Tableau for many of his analysis and predictive projects.
“My favorite quote for forecasting and data analysis is, ‘Know the data going into the analysis so well that the answer seems obvious,’” he shares.
Ken Koepper, director of member and industry relations for TRSA, says that most respondents to its Industry Performance Report (IPR) survey find the best indicators for improvement by comparing income statements to those of the most profitable companies in the report.
They benefit from benchmarking with all respondents as well as those similar to their operations in:
- Market mix (primarily F&B, healthcare or industrial, or a balance of all three).
- Number of locations (plants, depots).
- Total sales volume.
“Income statement line-item comparisons prompt executives to consider if their companies’ spending is generating an industry-leading or -lagging return,” shares Koepper.
“Regarding balance sheets, the IPR calculates financial ratios and return-on-investment paths for facilitating additional comparisons to assess business health. Key examples: profit margin, asset turnover and financial leverage, which drive return on assets and return on net worth.”
For linen and uniform service companies, Koepper says the IPR helps prevent business from becoming a maze with no recognizable end.
“Operators want increased sales, profitability and return on investment,” he points out. “Without a realistic idea of what to achieve, they might get lost in the maze. Drawing conclusions from the IPR helps quantify goals and identify guideposts to reach them.”
Wagg’s recommendations for analyzing and better understanding data include:
- Identify Key Metrics: Focus on the KPIs most relevant to your operation, such as labor cost per pound, pounds per operator hour (PPOH), energy consumption, or turnaround time. Merchandise costs are a good indicator of a well-run operation. Understanding which metrics impact your bottom line will help narrow your focus.
- Compare Against Industry Standards: Use the data to see how your operation stacks up against industry averages. Reach out to your friends in our great industry: they will be extremely helpful. Are you lagging in certain areas, or are you outperforming in others? This comparison helps you identify strengths and weaknesses.
- Trend Analysis: Look at how your operation’s metrics have evolved over time; the historical perspective is critical for planning for the future. Are there patterns or trends that can indicate areas for improvement or highlight successful strategies?
- Break Down the Data: Analyze the data by different segments of your operation, such as by department, shift or type of service. This granular analysis can reveal inefficiencies or opportunities for improvement that might not be apparent when looking at the operation.
“Start slow and make sure you understand the factors involved in the metric you are benchmarking against,” says Michael Dodge, continuous improvement manager for CITY Healthcare in Minnesota. “For example, the major benchmark is PPOH. Ensure the pounds you are using for this benchmark are accurate and consistent with what you are benchmarking against.
“Is there a scale establishing this weight? Are you using soil or clean pounds? Are the hours we are comparing against our benchmark the same? Do we include management, janitorial, or anybody not touching our processed products? When I see a high PPOH, I like to look into all of the data going into this calculation.”
Click HERE to read Part 1 with ideas for finding laundry operation information. And come back Thursday for the conclusion with ways to implement laundry improvements based on the data.
Have a question or comment? E-mail our editor Matt Poe at [email protected].
