WO2018135995A1

WO2018135995A1 - Method for making data comparable

Info

Publication number: WO2018135995A1
Application number: PCT/SE2018/050040
Authority: WO
Inventors: Dan MATTSSON
Original assignee: Reforce International AB
Current assignee: Reforce International AB
Priority date: 2017-01-18
Filing date: 2018-01-18
Publication date: 2018-07-26
Anticipated expiration: 2019-07-18
Also published as: SE1750038A1

Abstract

The present invention relates to a method for presenting comparable and consistent feedback and data in terms of change and performance in a similar fashion regardless of the type of process being run and the amount of variables measured. The present invention specifically teaches that metrics are collected in a number of well-defined data types, and that metrics of different type are normalized into comparable metrics.

Description

METHOD FOR MAKING DATA COMPARABLE

Field of invention

The present invention relates to a method for making feedback and data in terms of change and performance comparable and consistent in a similar fashion regardless of the type of process being run and the amount of variables measured.

The present invention also relates to a computer program product and a computer readable medium whereby an inventive method can be realised. Description of background art

It is known to evaluate strategy execution and assist in accelerating strategy execution by enforcing methods of practice, expressed as unified processes, which processes can be deployed and executed in different software solutions. Known solutions provides support for process execution and lets business decision makers lead and follow up on the performance of the

organization.

An execution environment can be modelled for arbitrary processes according to customer needs, where assistance is provided in process execution, steering and visualization of results. As such, it is required that comparable metrics are provided regardless of the type and extent of the processes run. The problem and solution described below is related to a method of providing such metrics. This generalized solution is henceforth called 'the system'.

The system runs an arbitrary amount of processes modeling real-world actions. These processes correspond to actual, human-led initiatives where the actions of individuals are recorded and documented. The end result of these processes - positive as well as negative - is well defined.

The output and performance of the organization is reflected in a number of parameters:

• Process status

This mirrors the amount of progress a certain process has reached. The status consists of one of several possible, sequential statuses. There is a desired Optimal path' represented by a subset of these statuses that represents process execution according to a desired progress.

• Events The system records certain predefined events inside and outside of process execution. These are recorded individually and can occur several times.

• Calendar time

The calendar time elapsed while performing a certain task or between status updates.

• Effective time

The amount of time spent completing a certain task or between status updates.

These parameters are used to present insights in two dimensions;

change and performance.

Many of the processes run within the system require adaptation on the part of the user; the user needs to change the way he or she performs tasks. This change is normally not trivial and takes time to complete.

The performance of the users is the other dimension, expressed in terms of actual output.

Summary of the present invention

Problems

It is a problem to present comparable and consistent feedback and data in terms of change and performance. This data needs to be presented in a similar fashion regardless of the type of process being run and the amount of variables measured.

Another problem is that the analysis needs to be presented as in several forms:

• Snapshot

Process performance at a certain point in time

• Comparison

Process performance compared to a certain point in time

· Progress

Execution progress in terms of status, output or value from one point in time to another

• Goal fulfillment Solution

With the purpose of solving one or more of the above mentioned problems, and from the standpoint of a method according to the field of invention, it is proposed that metrics are collected in a number of well-defined data types. The definition of metrics vary but they all produce similar, quantified results. The types of metrics which can be used include, but are not limited to, the following:

• Temporal metrics

Measuring instances of duration, for instance regarding work effort or calendar time. Temporal metrics specify duration in relation to process execution (events or status changes).

• Event metrics

Represents events which can occur an arbitrary amount of times.

• Subjective metrics

As indicated by the designation, subjective metrics are not quantified by the execution engine but rather external observations. It could relate to the quality of result (a relative term) or a binary assessment (true/false).

• Status metrics

By looking at the status of a process, the system can measure how far execution has come.

Other possible types are, for instance, scalar metrics representing externally collected measurements which could be entered into the system, manually or via an externally accessible Application Programming Interface (API).

The metrics listed above, once set up, represents the body of information extracted from the execution of processes in the system.

All metrics produce the same kind of normalized data; either as a quota, binary or absolute.

The base metrics in themselves rarely present any insight in process performance and are specific to the actual type of process being executed. In order to make the observations generic and comparable between contexts, metrics need to be normalized. In practice, that means several metrics need to be composed into relative measurements.

Relative measurements are created using composite metrics, which use one or more underlying metrics to extract data from the execution. For instance, the system could employ Ratio Metrics to find mean values for process characteristics. Finding the ratio between time required to reach different states makes certain steps comparable between contexts although they differ in scale.

In order for the metrics produced by the system to be evaluated, they are set in relation to other, predefined data. This data represents existing baseline data from the same context as the processes have been run or from a body of knowledge representing comparable process execution. The context in which this body of knowledge has been produced may or may not be identical to the current context.

Advantages

The advantages of a method according to the present invention is that it provides a possibility to make observations from different contexts generic and comparable between contexts.

The inventive method allows the use of information from different banks of data that have never before been available for mutual use before, due to incompatibility between the data in the different banks, to be used in one mutual bank of data, thereby providing larger databanks to increase interpolation and prediction accuracy.

Brief description of the drawings

A method, a computer program product and a computer readable medium according to the present invention will now be described in detail with reference to the accompanying drawings, in which:

Figure 1 is an illustration of configuration of metric types,

Figure 2 is an illustration of a first example of composed metrics, Figure 3 is an illustration of a second example of composed metrics, Figure 4 is an illustration of a first process,

Figure 5 is an illustration of a second process,

Figures 6a to 6e are illustrations of exemplifying embodiments showing that different kinds of data from different kinds of industries can be made comparable through the invention, Figures 7a and 7b are illustrations of comparisons between teams working in mutually different kinds of industries, and

Figure 8 is an illustration of an inventive computer program and a

computer readable medium.

Description of embodiments as presently preferred

The present invention will now be described in more detail with reference to figure 1 showing an example configuration of metric types, or a composition of metrics.

The present invention teaches that relative measurements are created using composite metrics, which use one or more underlying metrics to extract data from the execution.

Figure 1 shows examples of types of metrics which can be used including, but not limited to, the following:

· Temporal metrics

• Event metrics

Represents events which can occur an arbitrary amount of times.

• Subjective metrics

· Status metrics

By looking at the status of a process, the system can measure how far execution has come. Other possible types of metrics are, for instance, scalar metrics

representing externally collected measurements which could be entered into the system, manually or via an externally accessible API.

All metrics produce the same kind of normalized data; either as a quota [0..1 ], binary [0, 1 ] or absolute [-∞..∞].

Relative measurements are created using composite metrics, which use one or more underlying metrics to extract data from the execution.

For instance, the system could employ Ratio Metrics to find mean values for process characteristics. Finding the ratio between time required to reach different states makes certain steps comparable between contexts although they differ in scale.

Figure 2 shows a first examples of composed metrics where the metric is speed. The figure shows:

Time passed: Y (Temporal metric) -> Summary:∑Y (Numerator) Status: X (Status Metric) -> Count: Status X (Descriminator) Average Metric: ηχ/ΣΥ (Composite Metric)

The result is how many times per time unit status X occur, in other words with what speed does status X occur. The result is an average metric, which by its nature is a ratio metric.

Figure 3 shows a second examples of composed metrics where the metric is efficiency. The figure shows:

Hours of work: B (Temporal metric) -> Summary:∑B (Numerator) Status: A (Status Metric) -> Count: Status A (Descriminator)

Ratio Metric: n_A/∑B

The result is how many hours of work are required to reach a number of status B, in other words, how efficient is the work made to achieve a set number of B. The result is a ratio metric. In the examples speed and efficiency are normally not comparable, however, the produced ratio metrics can be compared since they have the same unit [number of something /time].

Another example to illustrate one of the primary benefits of the inventive method is to extract similar properties from disparate processes, here illustrated with two different processes, deployed at two different companies, where the objective, the extent of the process and the amount of people involved differ. Nonetheless, it is desired to compare certain aspects of these processes to each other and to an established baseline.

Process A describes a sales initiative where representatives approach prospective customers to finalize a sale of a service that the company offers. Process B, on the other hand, models a way of capturing and managing proactive suggestions in order to improve the efficiency of the company's operations.

Common for these processes and the successful introduction of them into respective company is the fact that they involve behavior change. Typically, introduction of new processes like the ones above require some behavior to be altered to change the status quo and achieve the expected result. For this example, a conversion rate from one status to another is used.

Figure 4 illustrates process A, which process defines the statuses Ai to

An, representing a certain progress in the customer's journey. For each step, a conversion rate can be calculated by solving RATIO(COUNT(nAn+i),COUNT( ΠΑΠ)) for each n < (m-1 ). Given that the number of processes with a status A_n+i can never exceed that of the previous A_n a conversion rate between 0 and 100% will be received for each status transition.

The same holds true for process B, illustrated in Figure 5, providing two sets of conversion rates. These can be compared between process types and organizations and there would eventually be a large body of knowledge created which would assist in evaluating the results. Similar libraries of results would be built for other types of metrics such as temporal and event based metrics. Typically, they would be used to prioritize coaching and training efforts, indicate where process development is needed or serve as a decision basis for employee assessment.

As an example A can represent a customer support process within waste management in the waste management sector and B being a proactive sales process for the same company. In any process, there are bottlenecks and as a service provider, it is desired to identify those and prioritize the correct measures to improve performance.

A, being a transactional process, has a rather low expected value of 60.

That is, a successful completion (status A10) of the process represents a value of 60 to the company. Since every 6th initiative that reaches status A6 result in a successful closure, the value of A6 is 60/6 = 10. As shown in the table below, the financial potential of the transition A5→A6 is the throughput of 7,5 times the value of 10, equaling 75.

In comparison, B is a high-value, high-complexity process which aims at finding and signing new customers for the company. A new customer is, on average, worth 5 000.

Using the same calculations as for process A, the value of the transition chosen in this particular example, B7→B8, is worth 4 / 8 ^* 5 000 = 2 500. With a throughput of 0,075, the potential is 2 500 ^* 0,075 = 187,5. Conversion Average Conver- Hourly

Status Description Occurrences

rate effort sions/h potential

BO Initiative started 70 - - - -

B7 Offer completed 16 - - - -

Accepted by

B8 8 15% 2 h 0,075 187,5 client

B12 Initiative closed 4 - - - -

Clearly, even though there are far fewer initiatives of type B, their potential is bigger. Naturally, in a real case all transitions would be evaluated but in this simplified example the examination is limited to two transitions. Suffice it to say that given that a similar effort would be required to improve the performance, process B would be the best subject.

Based on the different characteristics of these processes, a library of metrics is being built up over time and further improves the hit rate and quality of the analysis, leading to an increasing precision of the model.

Figures 6a to 6e illustrates that the invention enables a comparison between changes and progress in different industries and between different kinds of organizations, where

- figure 6a shows a comparison of change between any number of

organizations cross any field,

- figure 6b shows a comparison of change between any number of departments cross any fields and cross organizations,

- figure 6c shows a comparison of change between any number of teams cross departments and/or organizations and fields,

- figure 6d shows a comparison of change between any number of roles cross departments, organizations and fields, and

- figure 6e shows a comparison of change between any combination of

organizations, departments, teams or roles cross departments and/or organizations and/or fields.

It is thus clear that the invention provides a possibility to:

- compare(organization_1 , ... , organization_N)

- compare(team_1 , ... , team_N) - compare(organization_1 (team_X), organization_N (team_Y))

- compare(organization_1 , organization_N (team_Y))

- compare(organization_1 (team_X (role_a)), organization_N (team_Y (role_a))

where:

- organization_1 = w_1 ^* team_1 , w_N ^* team_N

- w = weighting

- team_1 = member_1 , member_N

- role_a = anonym ized member

- membe = anonymized quantifiable progress

due to:

1 . weighting teams in organization after

a. business impact

b. calendar time

c. time spent

d. deadlines

e. quantifiable goals

2. data is quantifiable performance

3. organization and/or team delta data is compared

Figures 7a illustrates that the invention enables the comparison of change between any number of teams cross departments and/or organizations and industries. Even when only small amount of data is provided together with historic, goal, from date, to date and the weight is set on all levels it can be calculated.

Even when only small amount of actual outcome is provided, together with:

• historic

• goal

• from date

· to date

• weight

it can be calculated and compared with no regard to physical quantity.

Change = actual / historic ^* weight

Performance = 1 - (actual / goal) ^* weight Figure 7b shows the data without physical quantity, which can now be compared, over time or at an instance of time.

The invention has been tested and proved in several industries: construction, IT services, financial services, business products, health, logistic, consumer products, human resources, energy, government services, software, retail, telecommunication, security, insurance, media, environmental services. , and it has been theoretically proved to work in any industry.

Figure 8 illustrates that the present invention also relates to a computer program product 1 1 comprising computer program code 1 1 a, which, when executed by a computing unit 1 , enables the computing unit 1 to perform the steps of the inventive method.

The present invention also relates to a computer readable medium 12 upon which computer program code 1 1 a according to the inventive computer program product 1 1 is stored.

In figure 8, the inventive computer readable medium 12 is exemplified by a non-volatile memory, in this case a compact disc.

It will be understood that the invention is not restricted to the aforede- scribed and illustrated exemplifying embodiments thereof and that modifications can be made within the scope of the invention as defined by the accompanying Claims.

Claims

1 . A method for making feedback and data in terms of change and performance comparable and consistent in a similar fashion regardless of the type of process being run and the amount of variables measured, characterised in, that metrics are collected in a number of well-defined data types and that metrics of different type are normalized into comparable metrics.

2. A method according to claim 1 , characterised in, that metrics of different type are composed into relative measurements.

3. A method according to claim 2, characterised in, that relative

measurements are created using composite metrics, which use one or more underlying metrics to extract data from an execution.

4. A method according to claim 3, characterised in, that Ratio Metrics is employed to find mean values for process characteristics.

5. A method according to claim 4, characterised in, making certain steps comparable between contexts although they differ in scale by finding the ratio between time required to reach different states.

6. A computer program product, characterised in, that said computer program product comprises computer program code, which, when executed by a computing unit, enables said computing unit to perform the steps of a method according to any one of Claims 1 to 5.

7. A computer readable medium, characterised in, that computer program code according to claim 6 is stored on said computer readable medium.

8. A computer readable medium according to claim 7, characterised in, that said computer readable medium is a non-volatile memory.