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.

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.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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.

[0012] 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;

[0013] FIG. 2A is a block diagram that illustrates an example of a plurality of washing machines at one of the facilities in FIG. 1;

[0014] FIG. 2B is a block diagram that illustrates an example of a plurality of washing machines at another of the facilities in FIG. 1;

[0015] 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;

[0016] FIG. 4 is a flow chart that illustrates an example of a method for monitoring washing machine usage at a plurality of facilities;

[0017] 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;

[0018] 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;

[0019] 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;

[0020] FIG. 6 is a flow chart that illustrates an example of a method for monitoring washing machine usage at a plurality of facilities;

[0021] FIG. 7A is an image that illustrates an example of a graphical user interface for entry of facility data;

[0022] FIG. 7B is an image that illustrates an example of a graphical user interface for entry of washing machine data at a facility;

[0023] FIG. 7C is an image that illustrates an example of a graphical user interface for entry of cycle data for a washing machine;

[0024] FIG. 7D is an image that illustrates an example of a display output that indicates one or more suggested corrective action steps; and

[0025] FIG. 8 is a block diagram that illustrates a computer system useful for the methods of the present disclosure.

[0026] 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

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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

[0038] 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.

[0039] 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-US-00001 TABLE 1 Account Definition Location ID Location Name Group Name Postcode Business City Business Country Number of Residents

[0040] 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.

[0041] 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.).

[0042] 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.

[0043] 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-US-00002 TABLE 2 Washing Machine Information Location ID Washing Machine ID Washing Machine Model Washing Machine Brand Washing Machine Capacity (kg) Number of Washing Machines

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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-US-00003 TABLE 3 Cycle & Product Usage Location ID Washing Machine ID Report from Report to Cycle Name Number of Cycles Product Name Product Consumption_L

[0048] 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).

[0049] 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.

[0050] 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.

[0051] 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).

[0052] 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.

[0053] 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.

[0054] 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).

[0055] 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).

[0056] 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.

[0057] 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-US-00004 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

[0058] 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).

[0059] 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

[0060] 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-US-00005 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

[0061] 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.

[0062] 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-US-00006 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

[0063] 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.

[0064] 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.

[0065] 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:

[00001]$\begin{array}{cc}\mathrm{Deviation}{\mathrm{cycle}}_j=.Math.\mathrm{Use}{\mathrm{Percentage}}_{i,j}-\mathrm{Target}{\mathrm{value}}_j.Math.& \left(1\right)\end{array}$

[0066] Table 7 below provides an example of the deviation computed using equation 1 for each cycle at each facility 104:

TABLE-US-00007 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

[0067] 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.

[0068] 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:

[00002]$\begin{array}{cc}{.Math.}_{{\mathrm{cycle}}_j=1}^{\mathrm{cycle}j=n}.Math.\mathrm{Use}{\mathrm{percentage}}_{i,j}-\mathrm{Target}{\mathrm{value}}_j.Math.& \left(2\right)\end{array}$

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-US-00008 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

[0069] 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.

[0070] 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.

[0071] 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-US-00009 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

[0072] 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.

[0073] 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

[0074] 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).

[0075] 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.

[0076] 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-US-00010 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

[0077] 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:

[00003]$\begin{array}{cc}\mathrm{Total}{\mathrm{Cycles}}_j\mathrm{PDPR}=\underset{\mathrm{Cycle}i=1}{\overset{\mathrm{Cycle}i=n}{.Math.}}{\mathrm{Cycle}}_{j,i}\mathrm{PDPR}& \left(3\right)\end{array}$

[0078] 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.

[0079] 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:

[00004]$\begin{array}{cc}\mathrm{Deviation}\mathrm{Cycles}\mathrm{PDPR}\left(\%\right)=.Math.1-\frac{\mathrm{Total}{\mathrm{Cycles}}_j\mathrm{PDPR}}{{\mathrm{Cycles}}_{\mathrm{target}}\mathrm{PDPR}}.Math..Math.100& \left(4\right)\end{array}$

[0080] 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-US-00011 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

[0081] In another example, in step 210 a deviation index is computed for each facility of index j based on equation 5:

[00005]$\begin{array}{cc}\mathrm{Deviation}{\mathrm{index}}_j=.Math.\frac{{\mathrm{Cycles}}_j\mathrm{PDPR}}{{\mathrm{Cycles}}_{\mathrm{target}}\mathrm{PDPR}}.Math..Math.100& \left(5\right)\end{array}$

[0082] 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-US-00012 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

[0083] 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

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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 0.67100=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

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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).

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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

[0100] 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.

[0101] 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.

[0102] 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

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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

[0111] 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. [0112] 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. [0113] 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. [0114] 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. [0115] 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. [0116] 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. [0117] 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. [0118] 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. [0119] 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. [0120] 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. [0121] 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. [0122] 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. [0123] 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. [0124] 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. [0125] 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. [0126] 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. [0127] C. A method for monitoring an operation of one or more washing machines each located at a plurality of facilities, said method comprising: [0128] 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; [0129] 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; [0130] 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; [0131] comparing, with the processor, the value of the normalized parameter for each facility with a target value of the normalized parameter; and [0132] outputting, on a display, an output based on the comparing step.

Further Definitions and Cross-References

[0133] 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.

[0134] 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.

[0135] 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.