METHOD FOR MONITORING WASHING MACHINE USAGE

Abstract

A method is provided for monitoring an operation of a plurality of washing machines that is each located at a plurality of facilities. The method includes receiving, at a processor, first data indicating a value of one or more parameters related to usage of the plurality of washing machines at each facility of the plurality of facilities. The method also includes determining, with the processor, a value of a normalized parameter for the usage of the plurality of washing machines at each facility based on the first data. The method also includes comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter. The method also includes outputting, on a display, an output based on the comparing step.

Description

FIELD

The present disclosure relates generally to washing machine usage and more specifically to a method for monitoring the usage of washing machines at a plurality of facilities.

BACKGROUND

Conventional methods are available for monitoring washing machine usage. Such conventional methods include technical models which are used to track and evaluate washing machine usage. Additionally, other conventional methods are available that are used for tracking the usage of an individual washing machine.

The discussion of shortcomings and needs existing in the field prior to the present disclosure is in no way an admission that such shortcomings and needs were recognized by those skilled in the art prior to the present disclosure.

SUMMARY

The inventors of the present invention recognized various drawbacks with conventional methods used to track and monitor washing machine usage. For example, the inventors recognized that the technical models of such conventional methods are limited in their ability to determine optimal washing machine usage for comparison with actual washing machine usage. The inventors realized that an improved method would collect usage data from a population of washing machines to determine a model for optimal washing machine usage. This improved model of optimal washing machine usage would then provide more effective monitoring of one or more washing machines.

The inventors of the present invention also recognized that conventional methods for monitoring washing machine usage only monitor the usage of an individual washing machine. The inventors developed the improved method herein that can be used to simultaneously monitor a plurality of washing machines, such as a plurality of washing machines each located at a plurality of similar facilities.

Various aspects of the disclosure solve the above-mentioned problems and provide methods and devices useful for simultaneously monitoring usage of one or more washing machines each located at a plurality of facilities based on a model that combines usage data from a plurality of washing machines.

In one example, a method is provided for monitoring an operation of a plurality of washing machines that is each located at a plurality of facilities. The method includes receiving, at a processor, first data indicating a value of one or more parameters related to usage of the plurality of washing machines at each facility of the plurality of facilities. The method also includes determining, with the processor, a value of a normalized parameter for the usage of the plurality of washing machines at each facility based on the first data. The method also includes comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter. The method also includes outputting, on a display, an output based on the comparing step.

In another example, a method is provided for monitoring an operation of a plurality of washing machines each located at a plurality of facilities. The method includes receiving, at a processor, first data indicating one or more characteristics of each facility of the plurality of facilities. The method also includes receiving, at the processor, second data indicating one or more characteristics of the plurality of washing machines at each facility of the plurality of facilities. The method also includes receiving, at the processor, third data indicating a value of one or more parameters related to usage of the plurality of washing machines at each facility of the plurality of facilities. The method also includes determining, with the processor, a value of a normalized parameter for the usage of the plurality of washing machines at each facility based on the third data. The method also includes comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter. The method also includes outputting, on a display, fourth data based on the comparing step.

In yet another example, a method is provided for monitoring an operation of a plurality of washing machines each located at a plurality of facilities. The method includes providing, for each of the plurality of washing machines at each facility, a composition delivery system in fluid communication with each washing machine, where the composition delivery system and each washing machine are separate. The method also includes receiving, at a processor, first data from each composition delivery system in fluid communication with each of the plurality of washing machines at each facility. The first data indicates a value of one or more parameters related to usage of the plurality of washing machines at each facility of the plurality of facilities. The method also includes determining, with the processor, a value of a normalized parameter for the usage of the plurality of washing machines at each facility based on the first data. The method also includes comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter. The method also includes outputting, on a display, an output based on the comparing step.

These and other features, aspects, and advantages will become better understood with reference to the following description, figures, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of this disclosure can be better understood with reference to the following figures, which illustrate examples according to various aspects of the disclosure.

FIG. 1 is a block diagram that illustrates an example of a plurality of facilities and a system for monitoring washing machine usage at the plurality of facilities;

FIG. 2A is a block diagram that illustrates an example of a plurality of washing machines at one of the facilities in FIG. 1;

FIG. 2B is a block diagram that illustrates an example of a plurality of washing machines at another of the facilities in FIG. 1;

FIG. 3 is a block diagram that illustrates an example of the washing machine at each facility and a composition delivery system in fluid communication with each washing machine;

FIG. 4 is a flow chart that illustrates an example of a method for monitoring washing machine usage at a plurality of facilities;

FIG. 5A is a histogram that illustrates an example of target values of a normalized parameter of washing machine usage at the plurality of facilities;

FIG. 5B is a histogram that illustrates an example of target values and actual values of a normalized parameter of washing machine usage at one of the facilities;

FIG. 5C is a histogram that illustrates an example of target values and actual values of a normalized parameter of washing machine usage at one of the facilities;

FIG. 6 is a flow chart that illustrates an example of a method for monitoring washing machine usage at a plurality of facilities;

FIG. 7A is an image that illustrates an example of a graphical user interface for entry of facility data;

FIG. 7B is an image that illustrates an example of a graphical user interface for entry of washing machine data at a facility;

FIG. 7C is an image that illustrates an example of a graphical user interface for entry of cycle data for a washing machine;

FIG. 7D is an image that illustrates an example of a display output that indicates one or more suggested corrective action steps; and

FIG. 8 is a block diagram that illustrates a computer system useful for the methods of the present disclosure.

It should be understood that the various examples of the methods of the present disclosure are not limited to that which is illustrated in the figures.

DETAILED DESCRIPTION

Introduction and Definitions

This disclosure is written to describe the invention to a person having ordinary skill in the art, who will understand that this disclosure is not limited to the specific examples or aspects described. The examples and aspects are single instances of the invention which will make a much larger scope apparent to the person having ordinary skill in the art. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by the person having ordinary skill in the art. It is also to be understood that the terminology used herein is for the purpose of describing examples and aspects only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

All the features disclosed in this specification (including any accompanying claims, abstract, and drawings) may be replaced by alternative features serving the same, equivalent, or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. The examples and aspects described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to the person having ordinary skill in the art and are to be included within the spirit and purview of this application. Many variations and modifications may be made to the aspects of the disclosure without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure. For example, unless otherwise indicated, the present disclosure is not limited to particular materials, reagents, reaction materials, manufacturing processes, or the like, as such can vary. It is also to be understood that the terminology used herein is for purposes of describing particular aspects only and is not intended to be limiting. It is also possible in the present disclosure that steps can be executed in different sequence where this is logically possible.

All numeric values are herein assumed to be modified by the term “about,” whether or not explicitly indicated. The term “about” generally refers to a range of numbers that one of skill in the art would consider equivalent to the recited value (for example, having the same function or result). In many instances, the term “about” may include numbers that are rounded to the nearest significant figure.

In everyday usage, indefinite articles (like “a” or “an”) precede countable nouns and noncountable nouns almost never take indefinite articles. It must be noted, therefore, that, as used in this specification and in the claims that follow, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a support” includes a plurality of supports. Particularly when a single countable noun is listed as an element in a claim, this specification will generally use a phrase such as “a single.” For example, “a single support.”

Unless otherwise specified, all percentages indicating the amount of a component in a composition represent a percent by weight of the component based on the total weight of the composition. The term “mol percent” or “mole percent” generally refers to the percentage that the moles of a particular component are of the total moles that are in a mixture. The sum of the mole fractions for each component in a solution is equal to 1.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit (unless the context clearly dictates otherwise), between the upper and lower limit of that range, and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

In this specification and in the claims that follow, reference will be made to a number of terms that shall be defined to have the following meanings unless a contrary intention is apparent.

“Washing machine” refers to an appliance comprising a receptacle, e.g., a drum, into which items which are desired to be laundered are placed. In accordance with one or more preprogrammed cycles, the receptacle is filled with water to at least some extent and the receptacle, or portion thereof, is rotated and/or agitated to clean the items within the receptacle.

It is worth noting that the method of the present disclosure can be utilized with appliances that are used to dry items from the washing machine.

“Composition delivery system” refers to a device which can be discrete or integrated with a washing machine which includes one or more containers to respectively hold one or more different chemical compositions, e.g., detergents, bleach, fabric softener, hueing dyes, enzymes, whiteness additives, anti-microbial, scent boosters, degreasers, pre-treatment compositions, and the like, which collectively can be used to launder textiles, e.g., bed sheets, pillowcases, blankets, towels, hospital gowns, and the like, such that a volume of one or more compositions can be supplied by a pump along a conduit to one or more washing machines at one or more phases of a cycle of the one or more washing machines. Composition delivery systems of the present disclosure preferably comprise a pump.

“Facility” refers to an institution or business that utilizes one or more washing machines to clean laundry for one or more residents who reside at each institution or business including but not limited to a hospital, a nursing home, a prison, hotels, motels, event centers, assisted living facilities, and the like, (excluding a private residence).

Gathering Facility Data and Washing Machine Usage Data

FIG. 1 is a block diagram that illustrates an example of a plurality of facilities and a system 100 for monitoring washing machine usage at the plurality of facilities. A controller 110 is provided with a memory 109 for storing data thereon and a washing machine facility monitoring module 111 that includes a set of one or more instructions that cause the controller 110 to perform one or more steps of the method, such as the method 200 of the flowchart depicted in FIG. 4. The processor or controller 110 can be any computer or computer system (e.g. standard computer, cloud, or a mobile device such as a smartphone or table, etc.) appreciated by one skilled in the art. In one example aspects, the controller 110 is a computer system as described below with reference to FIG. 8. It is worth noting that the one controller may be provided for each facility and comprise a washing machine monitoring module for each machine at a particular facility.

A method step is now discussed where the controller 110 selects which facilities to monitor washing machine usage. This method step involves the controller 110 selecting a group 101 of the facilities shown in FIG. 1 based on certain selection criteria (e.g. same facility type). In this method step, the controller 110 receives data from each of the facilities 104, 105 that indicate one or more characteristics of each facility 104, 105. In one example, the controller 110 is communicatively coupled with a data controller or processor at each facility 104, 105 and receives signals from the data controller or processor at each facility 104, 105 that indicate the one or more characteristics of each facility 104, 105. In an example, the one or more characteristics of each facility 104, 105 include but are not limited to an identifier for each facility (e.g. number identifier), a name of each facility, a type of each facility (e.g. hospital, nursing home, prison, etc.), geographical information (e.g. postcode, city, country, etc.) and a number of residents at each facility. Table 1 below depicts an example of one or more characteristics of each facility 104, 105 communicated to the controller 110.

TABLE 1
Account Definition
Location ID
Location Name
Group Name
Postcode
Business City
Business Country
Number of Residents

As shown in FIG. 1, the controller 110 can receive data from the facilities 104a through 104d that indicate a first characteristic (e.g. first facility type, such as hospital) and received data from the facilities 105a and 105b that indicate a second characteristic (e.g. second facility type, such as prison). In this example, the controller 110 selects the group 101 of the facilities 104a through 104d to monitor washing machine usage, since they share a same characteristic (e.g. hospital facility type). The inventors recognized that since facilities having a same characteristic (e.g. same facility type) will typically use similar laundry with their residents, they will have similar washing machine usage (e.g. similar cycles run by the washing machines). Thus, the inventors of the present invention recognized that it would be advantageous that the method first select a group of facilities that share common characteristics since such facilities are reasonably expected to have similar washing machine usage for their residents.

After selecting the group 101 of facilities at which to monitor the washing machine usage, the method receives data regarding the washing machines at each facility in the selected group. FIG. 2A is a block diagram that illustrates an example of a plurality of washing machines 102a through 102c at one of the facilities 104a in the group 101 of FIG. 1. FIG. 2B is a block diagram that illustrates an example of a plurality of washing machines 102a and 102b at another of the facilities 104b in the group 101 of FIG. 1. As shown in FIGS. 2A and 2B, the facilities 104a, 104b within the group 101 can have a different number of washing machines and still have their usage monitored by the method disclosed herein. Similarly, the washing machines at each facility 104 can have different capacity and still have their usage monitored by the method disclosed herein. This was achieved by the inventors realizing that facilities having different number of washing machines could have their usage monitored by using a normalized parameter to measure usage which is independent of the total machine load of a facility (e.g. % of each cycle that is run by the machines, amount of detergent/composition or energy or water used per cycle, amount of detergent/composition or energy or water used per pound of laundry, etc.).

As shown in FIGS. 2A and 2B, each washing machine 102 at each facility 104 can have a composition delivery system 120 that is separate from the washing machine 102 and connected to the washing machine 102 by a conduit 128 that supplies chemical composition from the composition delivery system 120 to the washing machine 102. However, the method disclosed herein can still monitor washing machines 102 at the one or more facilities 104 without the composition delivery system 120 attached to each washing machine 102. For example, the methods of the present disclosure can be utilized where detergents and/or other laundry compositions are provided to the one or more washing machines manually.

The controller 110 receives data that indicates one or more characteristics of the washing machines at each facility among the group 101 of facilities. In an example, the one or more characteristics of the washing machines include but are not limited to an identifier of the facility 104 where the washing machine is used, an identifier for the washing machine 102, a model of the washing machine 102, a brand of the washing machine 102, a capacity of the washing machine 102 and a number of washing machines 102 at the facility 104. Table 2 below depicts an example of the different washing machine characteristics received by the controller 110.

TABLE 2
Washing Machine Information
Location ID
Washing Machine ID
Washing Machine Model
Washing Machine Brand
Washing Machine Capacity (kg)
Number of Washing Machines

The controller 110 can use the received one or more characteristics of the washing machines at each facility to further select which facilities are to have their washing machine usage monitored. For example, if the number of washing machines at a facility is not greater than a threshold number (e.g. 2), then the facility may be excluded from the group of facilities whose washing machine usage is monitored.

The controller 110 can next receive data that indicates one or more characteristics of the usage of the washing machines 102 at each facility 104 in the group 101. This usage data can include a start time and a finish time over which the usage data was recorded. FIG. 3 is a block diagram that illustrates an example of the washing machine 102 at each facility 104 and the composition delivery system 120 in fluid communication with each washing machine 102. The controller 110 may be communicatively coupled with a processor 121 of each composition delivery system 120 and/or a processor 103 of each washing machine 102 and can receive the data indicating the one or more characteristics of the usage from the processor 121 of the composition delivery system 120 and/or the processor 103 of the washing machine 102.

Any suitable communication protocol may be utilized between the controller 110 and processor 103 and/or processor 121. Some examples, include advanced message queuing protocol, constrained application protocol, data distribution service, extensible messaging and presence protocol, lightweight machine to machine, Zigbee, LoRaWAN, cellular, MQTT, near field communication, infrared, Wifi, Bluetooth, wired connections and the like.

The one or more characteristics of the usage of the washing machine 102 can include but are not limited to the identifier of the facility 104, the identifier of the washing machine 102, and other characteristics of the usage from the composition delivery system 120 including but not limited to a start date/time for the usage data, a finish date/time of the usage data, a name of a cycle, a number of each cycle run between the start and finish date/time, a name of a chemical composition supplied by the composition delivery system 120, an amount of each chemical composition supplied by the composition delivery system 120 and consumed between the start and finish date/time, a volume of water consumed between the start and finish date/time and an amount of energy consumed between the start and finish date/time, such as electrical power, consumed between the start and finish date/time. Table 3 below depicts one example of the characteristics of the usage data.

TABLE 3
Cycle & Product Usage
Location ID
Washing Machine ID
Report from
Report to
Cycle Name
Number of Cycles
Product Name
Product Consumption_L

The operation of the composition delivery system 120 in conjunction with the washing machine 102 is now discussed herein. As shown in FIG. 3, the composition delivery system 120 can include a pump 122 and a plurality of containers 124a through 124c to respectively hold a plurality of chemical compositions. As appreciated by one skilled in the art, a washing cycle typically involves a plurality of phases (e.g. pre-wash, main wash, rinse, spin, etc.) during which a different volume and/or type of chemical composition may be used in the washing machine 102. During operation, the processor 121 of the composition delivery system 120 receives a signal from the processor 103 of the washing machine 102 that indicates a next phase of the washing cycle. The processor 121 of the composition delivery system 120 can transmit a signal to the pump 122 to cause the pump 122 to move a predetermined volume of one of the chemical compositions in one of the respective containers 124a through 124c through the conduit 128 to the washing machine 102. This can be repeated for each phase of the washing cycle, so that the composition delivery system 120 transmits a predetermined volume of one or more chemical compositions to the washing machine 102 for each phase of the washing cycle. To facilitate the operation, the processor 121 of the composition delivery system 120 can have a memory (not shown) which stores the predetermined volume and type of chemical compositions that are to be transferred to the washing machine 102 at each phase of each cycle. As further shown in FIG. 3, the composition delivery system 120 may include a display 112 which may be used to output data depending on the outcome of the method herein (e.g. suggestive corrective actions based on the monitoring of the washing machine usage).

The processor 121 of each composition delivery system 120 can transmit the data to the controller 110 that indicates the one or more characteristics of each washing machine 102 usage. In such configurations, the processor 121 receives this data based on the signals received from the processor 103 of the washing machine 102 that indicate the type of cycle and the number of each cycle performed by the washing machine 102 over time. Similarly, the processor 103 of each washing machine 102 can transmit data indicating one or more other characteristics of each washing machine usage (e.g. amount of water consumed over the time period, amount of energy consumed over the time period, etc.). The memory 107 of the processor 103 of the washing machine 102 and/or the memory 109 of the controller 110 can be preprogrammed with predetermined data for each preprogrammed cycle (e.g., predetermined amount of water, predetermined amount of chemical compositions and predetermined amount of energy consumed by the washing machine 102). Thus, in an example, the controller 110 can determine each of the amount of water, the amount and type of composition and/or the amount of energy consumed by the washing machine 102 over the time period using this stored data coupled with the cycle data (e.g. number of each cycle performed over the time period) provided by the composition delivery system 120.

It is worth noting that a single composition delivery system 120 may be utilized to supply chemical composition to a plurality of washing machines. For example, the composition delivery system 120 may be connected to a manifold which has a plurality of conduits which go to a plurality of washing machines. In such configurations, it may be beneficial to have in line flow meters installed in each of the conduits such that information regarding the amount of chemical composition supplied can be obtained and analyzed as described herein.

As further shown in FIG. 3, the washing machine 102 features a hot water conduit 130 that is connected with a hot water source/drain 142 in order to receive hot water through the conduit 130 from the source 142 and/or discharge used water through the conduit 130 to the drain 142. The washing machine 102 similarly features a cold water conduit 132 that is connected with a cold water source/drain 144 in order to receive cold water through the conduit 132 from the source 144 and/or discharge used water through the conduit 132 to the drain 144. The washing machine 102 is also electrically coupled with the power source 140 (e.g. electrical power source).

A flowchart that shows one or more steps of the method will now be discussed herein. FIG. 4 is a flow chart that illustrates an example of a method 200 for monitoring washing machine 102 usage at a plurality of facilities 104. Although the flow diagram of FIG. 4 is depicted as integral steps in a particular order for purposes of illustration, one or more steps, or portions thereof, may be performed in a different order, or overlapping in time, in series or in parallel, or are deleted, or one or more other steps are added, or the method is changed in some combination of ways.

In step 201, one or more washing machines are provided at each facility among a plurality of facilities 104, 105. As shown, in step 201 a separate composition delivery system 120 is connected in fluid communication with each washing machine 102. In step 201 the separate composition delivery system 120 can be connected with each washing machine 102 by the chemical composition conduit 128 such that a predetermined volume of a predetermined type of chemical composition is provided to the washing machine 102 along the conduit 128 at one or more phases of a preprogrammed cycle.

In step 202, first data is received at the controller 110 from the plurality of facilities 104, 105 that indicate one or more characteristics of each facility 104, 105. For example, in step 202 the controller 110 can receive data indicating a type of each facility 104, 105. In an example, in step 202 the controller 110 selects the group 201 of facilities 104a through 104d from among the plurality of facilities 104, 105 based on the selected group of facilities 104a through 104d having a common characteristic (e.g., same facility type, such as hospital).

In step 204, second data is received at the controller 110 from each facility 104 in the selected group 101 that indicates one or more characteristics of the washing machines 102 at each facility 104. In an example, in step 204 the controller 110 can select the group 201 of facilities 104a through 104d based on the selected group of facilities having a common characteristic of the washing machines at each facility (e.g., greater than a threshold number).

In step 206, third data is received at the controller 110 that indicates a value of one or more parameters related to usage of the washing machines 102 at each facility 104 in the selected group 101 over a time period. For example, in step 206 the data can be received at the controller 110 from each composition delivery system 120 of each washing machine 102 for each facility 104. In this example, the data provided by each composition delivery system 120 can provide usage parameter values including but not limited to a start date, a finish date, a type of each cycle run by the washing machine 102, a number of each cycle type run by the washing machine 102 between the start and finish dates, a type of each chemical composition consumed by the washing machine 102 and an amount or volume of each chemical composition consumed by the washing machine 102 between the start and finish dates. The controller 110 can use stored data in the memory 109 of preprogrammed data (e.g., preprogrammed amount of water, composition and/or energy consumed for each preprogrammed cycle) coupled with the type and number of each cycle to determine an amount of water, an amount of composition and/or an amount of energy consumed by each washing machine 102 between the start and finish dates. In other aspects, in step 206 the controller 110 receives data from the processor 103 of each washing machine 102 of each facility 104. In this example, the data received at the controller 110 from each washing machine 102 can provide usage parameter values including but not limited to an amount of water, an amount and type of each chemical composition and an amount of energy consumed between the start and finish dates.

Table 4 below indicates an example of various parameter values of the third data received at the controller 110 in step 206. The Account ID is an identifier for the facility 104, the washing machine ID is an identifier for each washing machine 102 at each facility 104, the “report from” date is the start date, the “report to” date is the finish date, the cycle name is the type of each cycle and the number cycles is the number of each cycle type run by the washing machines 102 at the facility 104 over the predetermined time period (between the “report from” and the “report to” dates).

TABLE 4
	Washing	Report	Report	Cycle	Number
Account id	Machine id	From	To	Name	Cycles
17593	514	15 May 2023	15 Jun. 2023	Table Linen	16
17593	513	15 May 2023	15 Jun. 2023	Delicates	13
17593	514	15 May 2023	15 Jun. 2023	Delicates	11
17593	513	15 May 2023	15 Jun. 2023	Red Bags (60° C.)	55
17593	514	15 May 2023	15 Jun. 2023	Red Bags (60° C.)	30
17593	513	15 May 2023	15 Jun. 2023	Bedding/Towels	22
17593	514	15 May 2023	15 Jun. 2023	Bedding/Towels	35
17593	513	15 May 2023	15 Jun. 2023	Clothes normal	40
17593	514	15 May 2023	15 Jun. 2023	Clothes normal	50
17593	513	15 May 2023	15 Jun. 2023	Mops/Kylies/Cloths	8
17593	514	15 May 2023	15 Jun. 2023	Mops/Kylies/Cloths	4
28917	613	24 Jul. 2023	24 Oct. 2023	Delicates	43
28917	614	24 Jul. 2023	24 Oct. 2023	Delicates	73
28917	613	24 Jul. 2023	24 Oct. 2023	Red Bags (60° C.)	166
28917	614	24 Jul. 2023	24 Oct. 2023	Red Bags (60° C.)	223
28917	613	24 Jul. 2023	24 Oct. 2023	Bedding/Towels	51
28917	614	24 Jul. 2023	24 Oct. 2023	Bedding/Towels	71
28917	613	24 Jul. 2023	24 Oct. 2023	Mops/Kylies/Cloths	19
28917	614	24 Jul. 2023	24 Oct. 2023	Mops/Kylies/Cloths	14
28917	613	24 Jul. 2023	24 Oct. 2023	Red Bags (40° C.)	4
28917	614	24 Jul. 2023	24 Oct. 2023	Red Bags (40° C.)	0
28917	613	24 Jul. 2023	24 Oct. 2023	NoProducts	0
28917	614	24 Jul. 2023	24 Oct. 2023	NoProducts	0

Thus, for example, Table 4 above indicates that the facility 104 with Account ID 17593 has two washing machines 102 with Washing Machine ID 513 and 514. Those washing machines 102 run six different cycles (Table Linen, Delicates, Red Bags, Bedding/Towels, Clothes normal and Mops/Kylies/Cloths).

A normalized parameter for washing machine usage is now introduced, which is used to assess washing machine usage at each facility. As previously discussed herein, the selected group 101 of facilities 104 whose washing machine usage is monitored may have different features, such as a different number of washing machines (FIG. 1B) and/or a same number of washing machines but where some of the washing machines have a different capacity or load. Thus, the inventors of the present invention recognized that in order to properly compare the washing machine usage of the facilities to each other and/or to a target usage, a normalized parameter for washing machine usage was developed. The inventors developed this normalized parameter for washing machine usage so that facilities with different washing machine load capacity (e.g. different number of washing machines and/or a same number of washing machines with different capacities, etc.) could have their washing machine usage fairly compared with each other and/or to a target usage. The normalized parameter for washing machine usage is independent of the load capacity of a facility (e.g., sum of the loads of each washing machine at the facility).

Normalized Parameter Based on a Percentage Use of Each Cycle

A first normalized parameter is now discussed that is based on a ratio of a number of each cycle run at the facility 104 to a total number of all cycles run at the facility 104. In step 208, a value of a normalized parameter is determined for each facility 104a through 104d based on the third data received in step 206. The normalized parameter can be determined based on the number of each cycle run by the washing machines 102 at each facility 104 and a total number of all cycles run by the washing machines 102 at each facility over a predetermined time period (e.g. between the start and finish dates of the third data). In an example, the normalized parameter can be determined based on a ratio of the number of each cycle run by the washing machines 102 at each facility 104 over the predetermined time period to the total number of all cycles run by the washing machines 102 at each facility 104. In one example, the ratio is a percentage value of the number of each cycle run by the washing machines 102 at each facility 104 to the total number of all cycles run by the washing machines 102 at each facility 104 over the predetermined time period. Table 5 below indicates an example of values of the ratio (e.g. percentage value) of the number of each cycle run by the machines 102 at each facility 104 to the total number of cycles run by the machines 102 at each facility 104. The “percentage of cycles” in Table 5 indicates the value of the ratio as a percentage value. Thus, for example, Table 5 below indicates that the facility 104 with Account ID 9227 has four cycles run by their washing machines 104, where the Bedding/Towels cycle is 67.8% (using the nearest tenth of a decimal place) of all cycles run; where the Delicates cycle is 1.38% of all cycles run; where the Mops/Kylies/Cloths cycle is 7.1% of all cycles run and where the Red Bags cycle is 23.7% of all cycles run over the predetermined time period (e.g. between the “Report From” date and the “Report To” date in Table 5).

TABLE 5
	Report	Report	Cycle	Percentage
Account id	From	To	Name	of Cycles
9227	24 Apr. 2023	24 Jul. 2023	Bedding/Towels	67.8141136
9227	24 Apr. 2023	24 Jul. 2023	Delicates	1.376936317
9227	24 Apr. 2023	24 Jul. 2023	Mops/Kylies/Cloths	7.142857143
9227	24 Apr. 2023	24 Jul. 2023	Red Bags (60° C.)	23.66609294
9244	28 Mar. 2023	28 Jun. 2023	Bedding/Towels	58.36680054
9244	28 Mar. 2023	28 Jun. 2023	Delicates	2.409638554
9244	28 Mar. 2023	28 Jun. 2023	Mops/Kylies/Cloths	2.945113788
9244	28 Mar. 2023	28 Jun. 2023	Red Bags (60° C.)	36.27844712
9452	24 Apr. 2023	24 Jul. 2023	Bedding/Towels	63.10832025
9452	24 Apr. 2023	24 Jul. 2023	Delicates	4.788069074
9452	24 Apr. 2023	24 Jul. 2023	Mops/Kylies/Cloths	0.549450549
9452	24 Apr. 2023	24 Jul. 2023	Red Bags (60° C.)	31.55416013
9468	15 Jun. 2023	15 Sep. 2023	Bedding/Towels	9.968186638
9468	15 Jun. 2023	15 Sep. 2023	Clothes normal	8.589607635
9468	15 Jun. 2023	15 Sep. 2023	ClothesHeavy	27.25344645
9468	15 Jun. 2023	15 Sep. 2023	Delicates	3.499469777
9468	15 Jun. 2023	15 Sep. 2023	Mops/Kylies/Cloths	12.30116649
9468	15 Jun. 2023	15 Sep. 2023	NoProducts	0.106044539
9468	15 Jun. 2023	15 Sep. 2023	Red Bags (60° C.)	38.28207847
13544	11 May 2023	11 Aug. 2023	Bedding/Towels	28.91791045
13544	11 May 2023	11 Aug. 2023	Clothes normal	31.21890547
13544	11 May 2023	11 Aug. 2023	ClothesHeavy	21.33084577
13544	11 May 2023	11 Aug. 2023	Delicates	5.534825871
13544	11 May 2023	11 Aug. 2023	Mops/Kylies/Cloths	1.927860697
13544	11 May 2023	11 Aug. 2023	NoProducts	0
13544	11 May 2023	11 Aug. 2023	Red Bags (60° C.)	11.06965174
17593	15 May 2023	15 Jun. 2023	Bedding/Towels	20.07042254
17593	15 May 2023	15 Jun. 2023	Clothes normal	31.69014085
17593	15 May 2023	15 Jun. 2023	Delicates	8.450704225
17593	15 May 2023	15 Jun. 2023	Mops/Kylies/Cloths	4.225352113
17593	15 May 2023	15 Jun. 2023	Red Bags (60° C.)	29.92957746
17593	15 May 2023	15 Jun. 2023	Table Linen	5.633802817

In the next step 209 of the method 200, a target value for the normalized parameter can be determined, which can be used to compare with the values of the normalized parameter for each facility 104 obtained in step 208. This comparison is performed in order to assess the washing machine usage at each facility. In order to determine the target value for the normalized parameter, the method uses the third data received in step 206 from multiple facilities, such as all facilities 104a through 104d in the group 101 and/or from multiple facilities beyond those in the group 101 but which share one or more common characteristics (e.g. same facility type) with the facilities 104 in the group 101. For example, the method can utilize third data from step 206 from a large plurality of facilities over a large geographical area (e.g. over a certain state, province, country or global) to determine the target value of the normalized parameter in step 209.

The target value of the normalized parameter determined in step 209 can be a target value of the percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles. Table 6 below provides an example of target values of the percentage of each cycle to the total number of cycles run by the washing machines at each facility. For example, Table 6 below lists ten different cycles run by the washing machines including a NoProducts cycle that has a target percentage value of 0.2% among all cycles; a Table Linen cycle that has a target percentage value of 2.5% among all cycles; a Kitchen Cloths cycle that has a target percentage value of 4.4% among all cycles; a ClothesHeavy cycle that has a target percentage value of 10.3% among all cycles; a Red Bags 60° C. cycle that has a target percentage value of 20.4% among all cycles; a Clothes normal cycle that has a target percentage value of 23.2% among all cycles; a Bedding/Towels cycle that has a target percentage value of 22.1% among all cycles; a Delicates cycle that has a target percentage value of 10.7% among all cycles; a Mops/Kylies/Cloths cycle that has a target percentage value of 4.2% among all cycles and a Red Bags (40° C.) cycle that has a target percentage value of 2.0% among all cycles. These percentage values are cited herein to the nearest tenth of a decimal point.

TABLE 6
Cycle Name         Percentage, %
NoProducts         0.195578959
Table Linen        2.511442255
Kitchen Cloths     4.410914217
ClothesHeavy       10.30057837
Red Bags (60° C.)  20.44239531
Clothes normal     23.19270188
Bedding/Towels     22.11063453
Delicates          10.68019124
Mops/Kylies/Cloths 4.183534865
Red Bags (40° C.)  1.972028365

FIG. 5A shows a histogram 160 that depicts these example target values of the normalized parameter in Table 6. The horizontal axis 162 is the cycle name and the vertical axis 164 is percentage of the number of each cycle among the total number of all cycles run by the washing machines at a facility.

In step 210, after obtaining a first value of the normalized parameter for each facility in step 208 and a second target value for the normalized parameter in step 209, the method compares these values from steps 208 and 209 in order to assess the washing machine usage at each facility 104. In step 210 the value of the normalized parameter from step 208 can be compared with the target value of the normalized parameter in step 209.

Where the normalized parameter is the percentage of each cycle among all cycles that is run by the washing machines 102 at each facility 104, step 210 can involve computing a deviation between the percentage value of each cycle run by the washing machines 102 at each facility and the target percentage value for the respective cycle. For a given facility i and cycle j, an absolute value of this deviation is computed using equation 1 below:

$\begin{array}{cc}\mathrm{Deviation}{\mathrm{cycle}}_j=❘\mathrm{Use}{\mathrm{Percentage}}_{i,j}-\mathrm{Target}{\mathrm{value}}_j❘& \left(1\right)\end{array}$

Table 7 below provides an example of the deviation computed using equation 1 for each cycle at each facility 104:

TABLE 7
Account		Percentage
id	Cycle Name	Use	Deviation
9227	Mops/Kylies/Cloths	7.142857143	3.204049888
9227	Red Bags (60° C.)	23.66609294	6.621823118
9227	Delicates	1.376936317	7.375622551
9227	Bedding/Towels	67.8141136	43.95898152
9244	Mops/Kylies/Cloths	2.945113788	0.993693466
9244	Delicates	2.409638554	6.342920314
9244	Red Bags (60° C.)	36.27844712	19.2341773
9244	Bedding/Towels	58.36680054	34.51166846
9452	Mops/Kylies/Cloths	0.549450549	3.389356705
9452	Delicates	4.788069074	3.964489794
9452	Red Bags (60° C.)	31.55416013	14.5098903
9452	Bedding/Towels	63.10832025	39.25318818
9468	NoProducts	0.106044539	0.094062982
9468	Delicates	3.499469777	5.25308909
9468	Mops/Kylies/Cloths	12.30116649	8.362359235
9468	Bedding/Towels	9.968186638	13.88694543
9468	ClothesHeavy	27.25344645	15.32620923
9468	Clothes normal	8.589607635	18.27379111
9468	Red Bags (60° C.)	38.28207847	21.23780865
13544	NoProducts	0	0.20010752
13544	Mops/Kylies/Cloths	1.927860697	2.010946558
13544	Delicates	5.534825871	3.217732997
13544	Clothes normal	31.21890547	4.355506732
13544	Bedding/Towels	28.91791045	5.062778375
13544	Red Bags (60° C.)	11.06965174	5.974618084
13544	ClothesHeavy	21.33084577	9.403608549
17593	Table Linen	5.633802817	0.68706931
17593	Mops/Kylies/Cloths	5.897079726	1.958272471
17593	Delicates	5.945535599	2.807023269
17593	ClothesHeavy	5.963302752	5.96393447
17593	Clothes normal	19.62947409	7.233924649
17593	Bedding/Towels	32.62695439	8.771822314
17593	Red Bags (60° C.)	30.10240341	13.05813359

Thus, in one example, the percentage value of the Mops/Kylies/Cloths cycle at the facility 104 corresponding to Account ID 9227 deviates by 3.2% from the target percentage value for the Mops/Kylies/Cloths cycle.

In step 210, in addition to computing the deviation for each cycle using equation, an absolute value of a sum of the deviations among all cycles can be computed using equation 2:

$\begin{array}{cc}{\sum }_{{\mathrm{cycle}}_j=1}^{\mathrm{cycle}j=n}❘\mathrm{Use}{\mathrm{percentage}}_{i,j}-\mathrm{Target}{\mathrm{value}}_j❘& \left(2\right)\end{array}$

Table 8 below shows an example of a sum of the deviations computed using equation 2 for all cycles at one of the facilities 104.

TABLE 8
                   Abs. Deviation
Cycle Name         from Target
NoProducts         0.20
Table Linen        2.51
Kitchen Cloths     4.41
ClothesHeavy       2.88
Red Bags (60° C.)  4.56
Clothes normal     4.34
Bedding/Towels     3.06
Delicates          0.88
Mops/Kylies/Cloths 4.26
Red Bags (40° C.)  1.97
Average Deviation  3.14
Total Deviation    29.07

As shown in Table 8 above, in this example the sum of the deviations among all cycles is about 29%. In one example, in step 210 the sum of the deviations among all cycles at a facility 104 is compared with a target value (e.g. 40%). In this example, the sum of the deviations (29%) for the facility 104 is less than the target value (40%) of the sum of the deviations. Thus, as shown in FIG. 4, the outcome of the decision in step 210 is no (the deviation does not exceed the threshold) and thus the method 200 moves back to step 206. Hence, no corrective action is needed, since the deviation in step 210 does not exceed the target value or threshold. It should be noted that this is based on the example where the target value of the deviation sum is 40% and thus the outcome of step 210 may differ depending on a different target value of the deviation sum in step 210.

FIG. 5B shows a histogram 165 that depicts the percentage values for each cycle and the target percentage values of each cycle for the facility 104 whose deviation data is listed in Table 8 above. As shown in FIG. 5B, the percentage values for each cycle at the facility 104 is relatively close to the target percentage values for each cycle (e.g. Table 8 above indicates that the average deviation for each cycle is only 3.1%). Thus, in this example, the method 200 can determine that the washing machine usage at the facility 104 is not outside an acceptable range and consequently does not take correction action by moving to step 212.

Another example will now be discussed where a facility indicates washing machine usage that is not within an acceptable range and thus correction action is taken in step 212. Table 9 below shows an example of a sum of the deviations computed using equation 2 for all cycles at one of the facilities 104.

TABLE 9
                   Abs. Deviation
Cycle Name         from Target
NoProducts         0.20
Table Linen        2.51
Kitchen Cloths     4.41
ClothesHeavy       10.30
Red Bags (60° C.)  50.56
Clothes normal     23.19
Bedding/Towels     11.89
Delicates          6.68
Mops/Kylies/Cloths 10.59
Red Bags (40° C.)  1.97
Average Deviation  12.23
Total Deviation    115.19

As shown in Table 9 above, in this example the sum of the deviations among all cycles is about 115.2%. In one example, in step 210 the sum of the deviations among all cycles at a facility 104 may be compared with a target value (e.g. 40%). In this example, the sum of the deviations (115.2%) for the facility 104 is greater than the target value (40%) of the sum of the deviations. Thus, as shown in FIG. 4, the outcome of the decision in step 210 is yes (the deviation does exceed the threshold) and thus the method 200 moves to step 212. Thus, corrective action is taken, since the deviation in step 210 exceeds the target value or threshold. It should be noted that this is based on the example where the target value of the deviation sum is 40% and thus the outcome of step 210 may differ depending on a different target value of the deviation sum in step 210.

FIG. 5C shows a histogram 170 that depicts the percentage values for each cycle and the target percentage values of each cycle for the facility 104 whose deviation data is listed in Table 9 above. As shown in FIG. 5C, the percentage values for at least one cycle (e.g. Red Bags 60° C.) at the facility 104 is relatively far from the target percentage values for those cycles (e.g. Table 8 above indicates that the deviation for the Red Bags 60° C. cycle is 50.5% and the average deviation for each cycle is 12.2%). Thus, in this example, the method 200 determines that the washing machine usage at the facility 104 is outside an acceptable range and consequently takes corrective action by moving to step 212.

Normalized Parameter Based on Number of Residents and Time Period

A second normalized parameter is now discussed that is based on a number of residents at each facility and/or the predetermined time period (e.g. between the start date and finish date) over which the usage data is collected. For the second normalized parameter, in step 208 the first values of the normalized value can be determined for each facility 104 based on the third data received in step 206 and the second data received in step 204 (e.g. number of residents at the facility 104).

For example, in step 208 the normalized parameter is a ratio of the total number of cycles run by the machines 102 at each facility 104 (from the third data obtained in step 206) to the number of residents at the facility 104 (from the second data obtained in step 204). In another example, in step 208 the normalized parameter is a ratio of the total number of cycles run by the machines 104 at each facility 104 to both the number of residents at the facility 104 and a number of days over which the usage data was obtained (e.g. based on the start date and the finish date of the usage data obtained in step 206). The normalized parameter can be a number of total cycles performed by the washing machines 102 at each facility 104 per day per resident (PDPR). The inventors of the present invention developed this normalized parameter as another parameter that can be used to effectively compare washing machine usage between different facilities that have different load capacity of the washing machines.

The normalized parameter can be a ratio of the number of each cycle performed at the facility 104 to both the number of residents and the number of days of the predetermined period. Table 10 below provides an example of a number of each cycle PDPR for multiple facilities. As shown in Table 10, in one example the washing machines 102 at the facility 104 having the Account ID 9227 run about 0.1 Red Bags (60° C.) cycles per day and per resident (PDPR).

TABLE 10
	Cycle	Cycles
Account id	Name	PDPR
9227	Bedding/Towels	0.028114742
9227	Delicates	0.001141715
9227	Mops/Kylies/Cloths	0.002961324
9227	Red Bags (60° C.)	0.009811617
9244	Bedding/Towels	0.010970209
9244	Delicates	0.000452899
9244	Mops/Kylies/Cloths	0.000553543
9244	Red Bags (60° C.)	0.006818639
9452	Bedding/Towels	0.035060178
9452	Delicates	0.005320077
9452	Mops/Kylies/Cloths	0.000610501
9452	Red Bags (60° C.)	0.017530089
9468	Bedding/Towels	0.006230117
9468	Clothes normal	0.005368505
9468	ClothesHeavy	0.017033404
9468	Delicates	0.004374337
9468	Mops/Kylies/Cloths	0.007688229
9468	NoProducts	0.000132556
9468	Red Bags (60° C.)	0.023926299
13544	Bedding/Towels	0.008774909
13544	Clothes normal	0.009473128
13544	ClothesHeavy	0.006472675
13544	Delicates	0.003358998
13544	Mops/Kylies/Cloths	0.000584994
13544	NoProducts	0
13544	Red Bags (60° C.)	0.003358998
17593	Bedding/Towels	0.011700566
17593	Clothes normal	0.009168934
17593	ClothesHeavy	0.001740295
17593	Delicates	0.002652546
17593	Mops/Kylies/Cloths	0.002179686
17593	Red Bags (60° C.)	0.012034158
17593	Table Linen	0.005734767

In step 208, for a given facility with index j, the number of each cycle PDPR can be summed over all cycles with index i to obtain a total number of cycles PDPR as provided by equation 3 below:

$\begin{array}{cc}\mathrm{Total}{\mathrm{Cycles}}_j\mathrm{PDPR}=\sum _{\mathrm{Cycle}i=1}^{\mathrm{Cycle}i=n}{\mathrm{Cycle}}_{j,i}\mathrm{PDPR}& \left(3\right)\end{array}$

A target value for the percentage value of each cycle PDPR or the total cycles PDPR is obtained in step 209 using a similar process as for the normalized parameter of the percentage of each cycle as previously discussed herein. In this example, in step 209 a target value for each cycle PDPR and/or a target value of the total cycles PDPR is obtained.

In step 210 the value of each cycle PDPR or the value of the total cycles PDPR is compared with the respective target value thereof. In one example, a deviation between the value of the total cycles PDPR from a target value of the total cycles PDPR as indicated in equation 4 below:

$\begin{array}{cc}\mathrm{Deviation}\mathrm{Cycles}\mathrm{PDPR}\left(\%\right)=❘1-\frac{\mathrm{Total}{\mathrm{Cycles}}_j\mathrm{PDPR}}{{\mathrm{Cycles}}_{\mathrm{target}}\mathrm{PDPR}}❘·100& \left(4\right)\end{array}$

Table 11 below provides an example of the value of the total cycle PDPR for each facility 104 determined in step 208 and the percentage deviation that was computed in step 210 for each value of the total cycle PDPR for each facility 104 using equation 4. In one example, in step 210 the threshold deviation is 25%. Thus, for those facilities 104 having a total cycle PDPR value less than this threshold deviation, the washing machine usage is not outside an acceptable range and the method 200 moves from step 210 back to step 206 to continue monitoring the washing machine suage without taking corrective action. However, for those facilities 104 having a total cycle PDPR value greater than this threshold deviation, the washing machine usage is outside an acceptable range and the method 200 moves from step 210 to step 212 to take corrective action.

TABLE 11
	Total	Percentage
id	Cycles/WM PDPR	Deviation
9057	0.11	23.59550562
17647	0.093	4.494382022
35271	0.079	11.23595506
9468	0.065	26.96629213
9665	0.049	44.94382022
9664	0.064	35.08988764
9134	0.007	92.13483146
17715	0.05	43.82022472
7283	0.216	142.6966292
13537	0.036	59.5505618
9693	0.059	33.70786517
9452	0.059	33.70786517
8828	0.118	32.58426966
9659	0.02	77.52808989
1583	0.014	84.26966232
9857	0.03	66.29213483
8257	0.085	4.494382022
17593	0.045	49.43820225
7138	0.074	16.85393258
35485	0.043	51.68539326
35442	0.051	42.69662921
9246	0.099	11.23595506
8834	0.054	39.3258427
8955	0.069	22.47191011
35374	0.057	35.95505618
35484	0.025	71.91011236
8636	0.035	60.6741573
17722	0.066	25.84269663
3176	0.092	3.370786517
17596	0.128	43.82022472
28721	0.1	12.35955056
9241	0.085	4.494382022
8354	0.106	19.1011236
7493	0.11	23.59550562
9661	0.089	0
8420	0.083	6.741573034
7302	0.104	16.85393258
9013	0.212	138.2022472
13544	0.032	64.04494382
35349	0.088	1.123595506
9038	0.069	22.47191011
17643	0.03	66.29213483
7184	0.008	91.01123596
9244	0.019	78.65168539
17720	0.07	21.34831461
33200	0.011	87.64044944
8633	0.084	5.617977528
8854	0.082	7.865168539
8835	0.073	17.97752809
8957	0.056	37.07865169
7108	0.057	35.95505618
7114	0.036	59.5505618
28917	0.065	26.96629213
35192	0.089	0

In another example, in step 210 a deviation index is computed for each facility of index j based on equation 5:

$\begin{array}{cc}\mathrm{Deviation}{\mathrm{index}}_j=❘\frac{{\mathrm{Cycles}}_j\mathrm{PDPR}}{{\mathrm{Cycles}}_{\mathrm{target}}\mathrm{PDPR}}❘·100& \left(5\right)\end{array}$

In this example, in step 210 the value of the deviation index for each facility 104 computed in equation 5 is compared with a threshold value (e.g. 100). In this example, if the facility deviation index value is greater than the threshold value, then this indicates that the facility 104 is running too many cycles. In this same example, if the facility deviation index is less than the threshold value, then this indicates that the facility 104 is running too few cycles. Table 12 below provides an example of the deviation index values for different facilities.

	TABLE 12
		# Cycles	Percentage	Deviation
	id	PDPR	Deviation	index
	9665	0.049	44.94382022	55.05617978
	9134	0.007	92.13483146	7.865168539
	17715	0.05	43.82022472	56.17977528
	7283	0.216	142.6966292	242.6966292
	13537	0.036	59.5505618	40.4494382
	9693	0.059	33.70786517	66.29213483
	9452	0.059	33.70786517	66.29213483
	8828	0.118	32.58426966	132.5842697
	9659	0.02	7.52808989	22.47191011
	1583	0.014	84.26966292	15.73033708
	9857	0.03	66.29213483	33.70786517
	17593	0.045	49.43820225	50.56179775
	35485	0.043	51.68539326	48.51460674
	35442	0.051	42.69662921	57.30337079
	8834	0.054	39.3258427	60.6741573

In this example, the facility 104 with the ID 9665 is running too few cycles (deviation index value is <100) whereas the facility 104 with the ID 8828 is running too many cycles (deviation index value is >100).

Normalized Parameter Based on Net Weight of Laundry

A third normalized parameter is now discussed that is based on a net weight of laundry produced at the facility 104 during the predetermined time period (e.g. between the start date and finish date) over which the usage data is collected. The third normalized parameter can be a ratio with a numerator that is one of a total number of cycles, a total amount of energy, a total volume of water and/or a total amount of composition utilized over the predetermined time period and the denominator is the net weight of laundry cleaned over the predetermined time period. The inventors of the present invention developed this normalized parameter as yet another parameter that is independent of the total load capacity of each facility and thus can be used to effectively compare washing machine usage between facilities and with a same target value.

For the third normalized parameter, in step 206 in addition to the previously discussed usage data that is communicated to the controller 110, a total weight of all laundry that is cleaned during the predetermined time period (e.g. between the start date and finish date) is communicated to the controller 110. For example, in step 208 the normalized parameter can be a ratio that includes the net weight of laundry in the denominator of the ratio and a value of a usage parameter (e.g. one of the total amount of energy, the total volume of water, the total volume of composition, etc.) consumed during the predetermined time period in the ratio numerator. In this example, the normalized parameter indicates a value of the usage parameter (e.g. amount of energy, volume of water, volume of composition, etc.) consumed per pound of cleaned laundry. In this example, in step 209 a target value is obtained for this ratio that indicates a maximum value for the ratio (e.g. an amount of consumed energy, water or composition per pound of cleaned laundry) not to be exceeded by the ratio value for the facility 104 obtained in step 208. It is worth nothing that quite a number of washing machines are capable of measuring the weight of each load via a pressure sensor.

As another example, in step 208 the normalized parameter can be a ratio that includes the net weight of laundry in the numerator of the ratio and a value of a usage parameter (e.g. one of the total amount of energy, the total volume of water, the total volume of composition, etc.) consumed during the predetermined time period in the ratio denominator. In this example, the normalized parameter indicates a number of pounds of cleaned laundry produced per unit of the usage parameter (e.g., amount of energy, volume of water, volume of composition, etc.). In this example, in step 209 a target value can be obtained for this ratio that indicates a minimum value of weight of cleaned laundry that should be produced per unit of the usage parameter (e.g. an amount of consumed energy, water or composition per pound of cleaned laundry).

In another example, where the normalized parameter can be an amount of energy consumed per pound of laundry produced, the target value of the normalized parameter is obtained by determining the value of the normalized parameter for each facility 104 and selecting the lowest value. The inventors selected the lowest value of the normalized parameter for each facility 104 as it would indicate an optimal energy efficiency (e.g. minimum amount of energy consumed in generating each pound of laundry). Thus, in this example, in step 210 the deviation is measured by a ratio where the numerator is the target value of the normalized parameter and the denominator is the target value for each facility 104 (e.g. the amount of energy consumed per pound of laundry). Thus, for example if the target value is 0.2 kW/pound and the determined normalized parameter value in step 208 is 0.3 kW/pound for a facility 104, the ratio determined in step 210 is 0.67×100=67% which indicates that the facility 104 has 67% efficiency in terms of the amount of energy consumed per pound of laundry. In steps 208 through 210, a similar ratio or percentage value can be used for other normalized parameters that involve the ratio with the value of the usage parameter (e.g. amount of energy, volume of water and/or volume of chemical composition consumed) in the numerator and the net weight of laundry cleaned in the denominator.

Corrective Action

Various types of corrective actions will now be discussed, that can be exercised based on a positive determination in step 210. These corrective actions are aimed to communicate to one or more users that the washing machine usage at the facility 104 is not within an acceptable range. Additionally, these corrective actions can be aimed to counteract the washing machine usage being outside an acceptable range.

FIG. 6 illustrates a flowchart that depicts additional examples of the method previously discussed herein. The method 200′ of FIG. 6 is similar to the method 200 of FIG. 4 with the exception of the features discussed herein. The prime notation in FIG. 6 indicates that each step is similar to the equivalent numbered step in the method 400 but differs in one or more ways.

As shown in step 212′ of the flowchart of FIG. 6, the corrective action step can involve one or more of conducting preventive maintenance at one or more of the washing machines 102 at the facility 104 where the deviation exceeds the threshold value in step 210. Such preventive maintenance may involve repairing one or more components of the washing machines 102. For example, in step 212′ if step 210 indicates that the consumed energy by the washing machines 102 at a facility 104 per pound of laundry cleaned exceeds a target value, this may indicate one or more problems with the power source 140 (FIG. 3), electrical components of the washing machine 102 to be checked and/or sensors which measure electrical usage to be checked, the pump, the washing machine itself, delivery system.

In step 212′ the method can also provide feedback to the user of the method, such as the owner or operator of the facility 104. In such configurations, the controller 110 transmits a signal to a display (e.g., display 112 of the composition delivery system 120 and/or display 314 of the computer system 300) that indicates that the washing machine usage at the facility 104 is outside an acceptable range. The display can indicate not only that the washing machine usage at the facility 104 is outside the acceptable range but further provides additional data (e.g., value of the normalized parameter for the facility 104 and the target value of the normalized parameter). In some examples, in step 212′ the corrective action further includes outputting an alert (e.g. visual or audible) to the user to communicate that the washing machine usage at the facility 104 is outside an acceptable range.

In another example, in step 212 the correction action performed can involve automatically adjusting one or more parameters of the preprogrammed laundry cycles of the washing machines 102 at the facility 104. In this example, the controller 110 transmits a signal to the processor 103 of one or more washing machines 102 at the facility 104 with data to automatically change or adjust one or more parameter values stored in the memory 107 for one or more preprogrammed cycles. In another example, in step 212 the controller 110 outputs on the display one or more recommended changes or adjustments to the one or more parameters of the preprogrammed laundry cycles of the washing machines 102 at the facility 104. An operator at the facility 104 can then view these recommended changes on the display at the facility and choose whether to manually adjust the parameter values of the preprogrammed cycles in the memory 107 of each washing machine 102.

FIG. 7D illustrates an image that lists one or more of such adjustments to the parameters of the preprogrammed laundry cycles. In one example, as listed in FIG. 7D, where the Red Bags cycle is 40% of the total cycles at the facility 104, the corrective action recommends reducing the temperature of the water from 90° C. to 60° C. In this example, reducing the water temperature of the Red Bags cycle may be recommended in order to reduce energy consumption (e.g. in the event that the amount of energy consumed per pound of laundry cleaned for the Red Bags cycle exceeds the target value).

In another example, as listed in FIG. 7D, where the Towels/Bedding/Underwear cycle is 30% of the total cycles at the facility 104, the corrective action recommends eliminating the pre-wash phase of the cycle. In this example, eliminating the pre-wash phase of the cycle may be recommended in order to reduce water consumption (e.g. in the event that the volume of water consumed per pound of laundry cleaned for the Towels/Bedding/Underwear cycle exceeds the target value).

As another example, as listed in FIG. 7D, where the Wools/Delicates cycle is 30% of the total cycles at the facility 104, the corrective action recommends reducing the temperature of the water from 70° C. to 30° C. and also to eliminate the pre-wash phase of the cycle. In this example, reducing the water temperature of the Wools/Delicates cycle may be recommended in order to reduce energy consumption (e.g. in the event that the amount of energy consumed per pound of laundry cleaned for the Wools/Delicates cycle exceeds the target value) and eliminating the pre-wash phase of the cycle may be recommended in order to reduce water consumption (e.g. in the event that the volume of water consumed per pound of laundry cleaned for the Wools/Delicates cycle exceeds the target value).

In other examples, other corrective actions that can be taken in step 212 include correcting the percentage of cycles running at each facility and/or washing machine. For this example, the methods of the present invention can identify facilities and/or washing machines where one cycle is being used more or less than its facility or other washing machine counterparts. This can be indicative of underloading washing machine(s) particularly for certain cycles, in the case of percentages above average, or overfilling washing machines in the case of percentages above the average.

As yet another example, the corrective action taken in step 212 may be based upon the number of cycles per day per resident. The corrective action may be to review the loading practices/use practices of the facility as an increase above average may be an indication of underfilling of one or more washing machines at one or more facilities. Similarly, a lower number of overall cycles may indicate overloading of one or more washing machines at one or more facilities.

It is worth noting that the method of the present disclosure can similarly be applied to a single facility with a plurality of washing machines. In such configurations, data may be obtained and analyzed as described herein for each of the plurality of washing machines.

As an example, after analysis of the data, there may be an indication of one washing machine utilizing more detergent composition(s) than its counterparts. This may indicate a problem with the washing machine and/or a problem with composition delivery system and trigger a corrective action as described herein. Similarly, the data may also provide insight as to the frequency of use of a particular washing machine. For example, if one washing machine is preferentially utilized over others, this may trigger an investigation into the use of the lesser preferred machine and trigger corrective maintenance as needed. Additionally, such data may trigger more frequent preventative maintenance/servicing of the preferred washing machine. As yet another example, the data may indicate higher power usage, higher hot water usage, higher water usage of one washing machine over its counterparts for the same types of loads and/or same number of loads. Such analysis may trigger a maintenance action for the one washing machine.

Graphical User Interfaces

Various graphical user interfaces (GUIs) will now be discussed that can be output on a display and used to facilitate user input of data to be used by the method 200. The GUIs discussed herein can be output on a display of the controller 110 (e.g. display 314 of the computer system 300). For example, the GUIs can be output on a display at each of the facilities 104 (e.g. display 112 of the composition delivery system 120 at each facility 104). FIG. 7A is an image that illustrates an example of a graphical user interface 180 for entry of facility data. The inputted data in the GUI 180 can be similar to the first data received in step 202. As shown in FIG. 7A the GUI 180 includes an active area for the user to input data regarding the facility 104 such as the facility type (e.g. hospital, prison, etc.). Although the GUI 180 in FIG. 7A lists certain data that can be input regarding the facility 104, the GUI is not limited to facilitating input of this data and include facilitating input of additional data such as the number of residents, the geographical location, an identifier to be used by the method 200, etc.

FIG. 7B is an image that illustrates an example of a graphical user interface 182 for entry of washing machine 102 data at each facility 104. For example, the inputted data in the GUI 182 can be similar to the second data received in step 204. As shown in FIG. 7B the GUI 182 includes an active area for the user to input data regarding the washing machines 102 such as the machine brand, the machine model, the capacity and an identifier (number). Although the GUI 182 facilitates input of certain data regarding the washing machines 102 at the facility 104, the GUI can facilitate input of additional data regarding the washing machines 102.

FIG. 7C is an image that illustrates an example of a graphical user interface 184 for entry of cycle data. For example, the inputted data in the GUI 184 can be similar to the third data received in step 206. As shown in FIG. 7C the GUI 184 includes an active area for the user to input data regarding each preprogrammed cycle (e.g. cycle name, main item type, water temperature, pre wash and use split). Although the GUI 184 in FIG. 7C lists certain data that can be input regarding the preprogrammed cycles, the GUI is not limited to facilitating input of this data and include facilitating input of additional data regarding the cycle such as the amount of consumed energy per cycle. Upon entry of the cycle data in the GUI 184, this inputted data can be automatically stored in the memory 107 of the washing machine 102 and/or the memory 109 of the controller 110.

Hardware

FIG. 8 is a block diagram that illustrates a computer system 300 upon which the methods of the present disclosure may be implemented. Computer system 300 includes a communication mechanism such as a bus 310 for passing information between other internal and external components of the computer system 300. Information is represented as physical signals of a measurable phenomenon Computer system 300, or a portion thereof, constitutes a means for performing one or more steps of one or more methods described herein. In some aspects, the computer system 300 is a standard computer (e.g. PC desktop or laptop computer). In other aspects, the computer system 300 is a mobile device, such as a smartphone or tablet, for example.

One or more processors 302 for processing information are coupled with the bus 310. A processor 302 performs a set of operations on information. The set of operations include bringing information in from the bus 310 and placing information on the bus 310. The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication. A sequence of operations to be executed by the processor 302 constitutes computer instructions.

Computer system 300 also includes a memory 304 coupled to bus 310. The memory 304, such as a random access memory (RAM) or other dynamic storage device, stores information including computer instructions. Dynamic memory allows information stored therein to be changed by the computer system 300. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory 304 is also used by the processor 302 to store temporary values during execution of computer instructions. The computer system 300 also includes a read only memory (ROM) 306 or other static storage device coupled to the bus 310 for storing static information, including instructions, that is not changed by the computer system 300. Also coupled to bus 310 is a non-volatile (persistent) storage device 308, such as a magnetic disk or optical disk, for storing information, including instructions, that persists even when the computer system 300 is turned off or otherwise loses power.

Information, including instructions, is provided to the bus 310 for use by the processor from an input device 312, such as a keyboard or keypad or touchscreen containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into signals compatible with the signals used to represent information in computer system 300. Other input devices coupled to bus 310, used primarily for interacting with humans, include a display device 314, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), for presenting images, and a pointing device, such as a mouse or a trackball or cursor direction keys, for controlling a position of a small cursor image presented on the display 314 and issuing commands associated with graphical elements presented on the display 314. In some aspects, the input devices 312 are external to the computer system 300 (e.g. display or mouse for a standard PC computer). In other aspects, the input devices 312 are integral with or form a part of the computer system 300 (e.g. display or touchscreen keypad on a smartphone or tablet).

Computer system 300 also includes one or more instances of a communications interface 370 coupled to bus 310. Communication interface 370 provides a two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general, the coupling is with a network link 378 that is connected to a local network 380 to which a variety of external devices with their own processors are connected.

The term computer-readable medium is used herein to refer to any medium that participates in providing information to processor 302, including instructions for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 308. Volatile media include, for example, dynamic memory 304. Transmission media include, for example, coaxial cables, copper wire, fiber optic cables, and waves that travel through space without wires or cables, such as acoustic waves and electromagnetic waves, including radio, optical and infrared waves. The term computer-readable storage medium is used herein to refer to any medium that participates in providing information to processor 302, except for transmission media.

Network link 378 typically provides information communication through one or more networks to other devices that use or process the information. For example, network link 378 may provide a connection through local network 380 to equipment 384 operated by an Internet Service Provider (ISP). ISP equipment 384 in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet 390. A computer called a server 392 connected to the Internet provides a service in response to information received over the Internet. For example, server 392 provides information representing video data for presentation at display 314.

The invention is related to the use of computer system 300 for implementing the techniques described herein. According to one example of the invention, those techniques are performed by computer system 300 in response to processor 302 executing one or more sequences of one or more instructions contained in memory 304. Such instructions, also called software and program code, may be read into memory 304 from another computer-readable medium such as storage device 308. Execution of the sequences of instructions contained in memory 304 causes processor 302 to perform the method steps described herein.

Combinations

• • A: A method for monitoring an operation of one or more washing machines each located at a plurality of facilities, said method comprising: receiving, at a processor, first data indicating one or more characteristics of each facility of the plurality of facilities; receiving, at the processor, second data indicating one or more characteristics of the one or more washing machines at each facility of the plurality of facilities; receiving, at the processor, third data indicating a value of one or more parameters related to usage of the one or more washing machines at each facility of the plurality of facilities; determining, with the processor, a value of a normalized parameter for the usage of the one or more washing machines at each facility based on the third data; comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter; and outputting, on a display, fourth data based on the comparing step.
  • A1: The method of Paragraph A, wherein the value of the one or more parameters of the first data include a number of each cycle of a plurality of cycles performed by the one or more washing machines at each facility over a predetermined time period and a total number of the plurality of cycles performed by the one or more washing machines at each facility over the predetermined time period; and wherein the value of the normalized parameter comprises a value of a ratio of the number of each cycle performed by the one or more washing machines at each facility to the total number of the plurality of cycles performed by the one or more washing machines at each facility.
  • A2: The method of any of Paragraphs A to A1, wherein the value of the normalized parameter is a value of a percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles performed by the plurality of washing machines at each facility over the predetermined time period; wherein the target value of the normalized parameter is a target value of the percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles performed by the one or more washing machines at each facility over the predetermined time period; and wherein the comparing step comprises computing a difference between the value of the percentage of each cycle of the plurality of cycles and the target value of the percentage of each cycle of the plurality of cycles.
  • A3. The method of any of Paragraphs A to A2, wherein the comparing step further comprises computing an absolute value of the difference between the value of the percentage of each cycle of the plurality of cycles and the target value of the percentage of each cycle of the plurality of cycles and further computing a sum of the absolute value of the difference for the plurality of cycles; and wherein the comparing step comprises computing a difference between the sum of the absolute value of the difference for the plurality of cycles with a target value of the sum of the absolute value of the difference.
  • B. A method for monitoring an operation of a one or more washing machines, said method comprising: receiving, at a processor, first data indicating a value of one or more parameters related to usage of the one or more washing machines; determining, with the processor, a value of a normalized parameter for the usage of the one or more washing machines based on the first data; comparing, with the processor, the value of the normalized parameter for each of the one or more washing machines with a target value of the normalized parameter; and outputting, on a display, an output based on the comparing step.
  • B1. The method of Paragraph B, further comprising: providing, for the one or more washing machines, a composition delivery system in fluid communication with each washing machine, wherein the composition delivery system and each washing machine, preferably wherein a composition delivery system is provided for each of the one or more washing machines, preferably wherein the composition delivery system(s) and the one or more washing machines are separate; and wherein the receiving step comprises receiving, at the processor, the first data from each composition delivery system in fluid communication with each of the plurality of washing machines at each facility.
  • B2. The method of Paragraph B1, wherein the composition delivery system comprises a plurality of containers to respectively hold a plurality of chemical compositions, wherein the method further comprises: receiving, at the composition delivery system for each washing machine, a signal from the washing machine indicating data related to one or more phases of a washing cycle among a plurality of cycles of the washing machine; pumping, with a pump, a volume of chemical composition from one of the plurality of containers to the washing machine based on the received signal indicating the one or more phases of the washing cycle; and transmitting, from the composition delivery system for each washing machine, the first data to the processor based on the received signal indicating data related to the one or more phases of the washing cycle.
  • B3. The method of any of Paragraphs B1 to B2, further comprising: determining, with the processor, the value of the one or more parameters of the first data including a number of each cycle of the plurality of cycles and a total number of the plurality of cycles performed by the one or more washing machines over a predetermined time period based on the received first data from the composition delivery system for each washing machine over the predetermined time period; and wherein the value of the normalized parameter comprises a value of a ratio of the number of each cycle performed by the one or more washing machines to the total number of the plurality of cycles performed by the one or more washing machines over the predetermined time period.
  • B4. The method of any of Paragraphs B to B3, wherein the value of the normalized parameter is a value of a percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles performed by the one or more washing machines the predetermined time period; wherein the target value of the normalized parameter is a target value of the percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles; and wherein the comparing step comprises computing a difference between the value of the percentage of each cycle of the plurality of cycles and the target value of the percentage of each cycle of the plurality of cycles.
  • B5. The method of any of Paragraphs B to B4, wherein the comparing step comprises computing an absolute value of the difference between the value of the percentage of each cycle of the plurality of cycles and the target value of the percentage of each cycle of the plurality of cycles and further computing a sum of the absolute value of the difference for the plurality of cycles; and wherein the comparing step comprises comparing the sum of the absolute value of the difference for the plurality of cycles with a target value of the sum of the absolute value of the difference.
  • B6. The method of any of Paragraphs B to B5, further comprising: receiving, at the processor, second data indicating one or more characteristics of each facility of the plurality of facilities; wherein the determining of the value of the normalized parameter is further based on the second data.
  • B7. The method of any of Paragraphs B to B6, further comprising: determining, with the processor, the value of the one or more parameters of the first data including a number of each cycle of the plurality of cycles and a total number of the plurality of cycles performed by the one or more washing machines over a predetermined time period based on the received first data from the composition delivery system for each washing machine over the predetermined time period; wherein the second data comprises a number of residents at a facility; and wherein the value of the normalized parameter comprises a value of a ratio of the number of each cycle performed by the one or more washing machines to at least one of a number of time units in the predetermined time period and the number of residents at the facility.
  • B8. The method of any of Paragraphs B to B7, further comprising: determining, with the processor, the value of the one or more parameters of the first data including a value of a first parameter comprising a net weight of laundry cleaned by the one or more washing machines over a predetermined time period and a value of a second parameter comprising at least one of a volume of water, a value of an amount of electrical power and a value of an amount of chemical composition utilized by the one or more washing machines over the predetermined time period based on the received first data from the composition delivery system for each washing machine over the predetermined time period; wherein the value of the normalized parameter comprises a value of a ratio of the value of the second parameter to the value of the first parameter.
  • B9. The method of any of Paragraphs B to B8, further comprising determining, with the processor, the value of the second parameter based on: a number of each cycle of the plurality of cycles performed by the one or more washing machines over the predetermined time period based on the received first data from the composition delivery system for each washing machine; and stored data in a memory of the processor including at least one of a volume of water, an amount of electrical power and a value of an amount of chemical detergent associated with each respective cycle of the plurality of cycles.
  • B10. The method of any of Paragraphs B6 to B9, further comprising: receiving, at the processor, third data indicating one or more characteristics of the one or more washing machines at each facility of the plurality of facilities; and wherein the plurality of facilities are further selected among the second plurality of facilities based on each facility of the plurality of facilities having the same third data.
  • B11. The method of any of Paragraphs B to B10, wherein the comparing step comprises determining a deviation between the value of the normalized parameter and the target value of the normalized parameter; wherein the method further comprises performing corrective action based on the determined deviation exceeding a threshold value, wherein the corrective action comprises one or more of; performing maintenance at one or more of the washing machines where the determined deviation exceeds the threshold value; providing feedback where the determined deviation exceeds the threshold value, said providing step comprising the outputting the output on the display, and adjusting a value of one or more parameters of a plurality of washing cycles of the one or more washing machines stored in a memory of each respective washing machine where the determined deviation exceeds the threshold value.
  • C. A method for monitoring an operation of one or more washing machines each located at a plurality of facilities, said method comprising:
    • providing, for the one or more washing machines at each facility, a composition delivery system in fluid communication with each washing machine, preferably wherein each of the one or more washing machines comprises a composition delivery system, preferably wherein the composition delivery system is separate from each of the one or more washing machines;
    • receiving, at a processor, first data from each composition delivery system in fluid communication with each of the one or more washing machines at each facility, said first data indicating a value of one or more parameters related to usage of the plurality of washing machines at each facility of the plurality of facilities;
    • determining, with the processor, a value of a normalized parameter for the usage of the plurality of washing machines at each facility based on the first data;
    • comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter; and
    • outputting, on a display, an output based on the comparing step.

Further Definitions and Cross-References

The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as “40 mm” is intended to mean “about 40 mm.”

Every document cited herein, including any cross referenced or related patent or application and any patent application or patent to which this application claims priority or benefit thereof, is hereby incorporated herein by reference in its entirety unless expressly excluded or otherwise limited. The citation of any document is not an admission that it is prior art with respect to any invention disclosed or claimed herein or that it alone, or in any combination with any other reference or references, teaches, suggests or discloses any such invention. Further, to the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition assigned to that term in this document shall govern.

While particular aspects of the present disclosure have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims

1. A method for monitoring, over a plurality of facilities, an operation of one or more washing machines at each facility of the plurality of facilities, the method comprising:

receiving, at a processor, first data indicating a value of one or more parameters related to usage of the one or more washing machines at each facility of the plurality of facilities;

determining, with the processor, a value of a normalized parameter for the usage of the one or more washing machines at each facility based on the first data;

comparing, with the processor, the value of the normalized parameter for the usage of the one or more washing machines at each facility with a target value of the normalized parameter; and

outputting, on a display, an output based on the comparing step.

2. The method of claim 1, further comprising:

providing, for the one or more washing machines at each facility, a composition delivery system in fluid communication with each of the one or more washing machines, wherein the composition delivery system and each of the one or more washing machine are separate; and

wherein the receiving step comprises receiving, at the processor, the first data from each composition delivery system in fluid communication with each of the one or more washing machines at each facility.

3. The method of claim 2, wherein a composition delivery system is provided for each of the one or more washing machines.

4. The method of claim 2, wherein the composition delivery system comprises a plurality of containers to respectively hold a plurality of chemical compositions, wherein the method further comprises:

receiving, at the composition delivery system for each washing machine, a signal from a respective washing machine indicating data related to one or more phases of a washing cycle among a plurality of cycles of the respective washing machine;

pumping, with a pump, a volume of a respective chemical composition from one of the plurality of containers to the respective washing machine based on the received signal indicating the one or more phases of the washing cycle; and

transmitting, from the composition delivery system for each washing machine, the first data to the processor based on the received signal indicating data related to the one or more phases of the washing cycle.

5. The method of claim 2, further comprising:

determining, with the processor, the value of the one or more parameters of the first data including a number of each cycle of a plurality of cycles and a total number of the plurality of cycles performed by the one or more washing machines at each facility over a predetermined time period based on the received first data from the composition delivery system for each of the one or more washing machines at each facility over the predetermined time period; and

wherein the value of the normalized parameter comprises a value of a ratio of the number of each cycle performed by the one or more washing machines at each facility to the total number of the plurality of cycles performed by the one or more washing machines at each facility over the predetermined time period.

6. The method of claim 5, wherein:

the value of the normalized parameter is a value of a percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles performed by the one or more washing machines at each facility over the predetermined time period;

the target value of the normalized parameter is a target value of the percentage of each cycle of the plurality of cycles to the total number of the plurality of cycles; and

the comparing step comprises computing a difference between the value of the percentage of each cycle of the plurality of cycles and the target value of the percentage of each cycle of the plurality of cycles.

7. The method of claim 6, wherein:

the comparing step comprises computing an absolute value of the difference between the value of the percentage of each cycle of the plurality of cycles and the target value of the percentage of each cycle of the plurality of cycles and further computing a sum of the absolute value of the difference for the plurality of cycles; and

the comparing step comprises comparing the sum of the absolute value of the difference for the plurality of cycles with a target value of the sum of the absolute value of the difference.

8. The method of claim 1, further comprising:

receiving, at the processor, second data indicating one or more characteristics of each facility of the plurality of facilities;

wherein the determining of the value of the normalized parameter is further based on the second data.

9. The method of claim 8, further comprising:

determining, with the processor, the value of the one or more parameters of the first data including a number of each cycle of a plurality of cycles and a total number of the plurality of cycles performed by the one or more washing machines at each facility over a predetermined time period based on the received first data from the composition delivery system for each of the one or more washing machines at each facility over the predetermined time period;

wherein the second data comprises a number of residents at each facility of the plurality of facilities; and

wherein the value of the normalized parameter comprises a value of a ratio of the number of each cycle performed by the one or more washing machines at each facility to at least one of a number of time units in the predetermined time period and the number of residents at each facility.

10. The method of claim 1, further comprising:

determining, with the processor, the value of the one or more parameters of the first data including a value of a first parameter comprising a net weight of laundry cleaned by the one or more washing machines at each facility over a predetermined time period and a value of a second parameter comprising at least one of a volume of water, a value of an amount of electrical power, or a value of an amount of chemical detergent utilized by the one or more washing machines over the predetermined time period based on the received first data from the composition delivery system for each of the one or more washing machines at each facility over the predetermined time period;

wherein the value of the normalized parameter comprises a value of a ratio of the value of the second parameter to the value of the first parameter.

11. The method of claim 10, further comprising determining, with the processor, the value of the second parameter based on:

a number of each cycle of the plurality of cycles performed by the one or more washing machines at each facility over the predetermined time period based on the received first data from the composition delivery system for each washing machine at each facility; and

stored data in a memory of the processor including at least one of a volume of water, an amount of electrical power, or a value of an amount of chemical composition associated with each respective cycle of the plurality of cycles.

12. The method of claim 8, wherein:

the receiving the second data comprises receiving the one or more characteristics from each facility of a second plurality of facilities;

the one or more characteristics of each facility of the plurality of facilities comprise a type of facility; and

the plurality of facilities are selected among the second plurality of facilities based on the second data of the plurality of facilities indicating a same type of facility.

13. The method of claim 12, further comprising:

receiving, at the processor, third data indicating one or more characteristics of the one or more washing machines at each facility of the plurality of facilities; and

wherein the plurality of facilities are further selected among the second plurality of facilities based on each facility of the plurality of facilities having the same third data.

14. The method of claim 1, wherein the target value of the normalized parameter is determined based on:

receiving, from each facility of a second plurality of facilities, the first data indicating the value of the one or more parameters related to usage of the one or more washing machines at each facility of the second plurality of facilities;

determining, with the processor, the target value of the normalized parameter based on the received first data from each facility of the second plurality of facilities.

15. The method of claim 14, wherein the target value of the normalized parameter is one of:

a mean or an average of the value of the one or more parameters of the first data from each facility of the second plurality of facilities; and

the value of the one or more parameters of the first data for one facility of the second plurality of facilities.

16. The method of any claim 1, wherein:

the comparing step comprises determining a deviation between the value of the normalized parameter for each facility and the target value of the normalized parameter; and

the method further comprises performing corrective action based on the determined deviation exceeding a threshold value for a respective facility, wherein the corrective action comprises one or more of: performing maintenance at a washing machine of the one or more washing machines at the respective facility where the determined deviation exceeds the threshold value, providing feedback to the respective facility where the determined deviation exceeds the threshold value, said providing step comprising outputting the output on the display, or adjusting a value of one or more parameters of a plurality of washing cycles of the one or more washing machines stored in a memory of each respective washing machine at the respective facility where the determined deviation exceeds the threshold value.