The Benefits of Evaluating the Jordanian Social Security ... · THE BENEFITS OF EVALUATING THE JORDANIAN SOCIAL SECURITY USING DECISION MODELING AND NOTATION Ahmad Zyad Alghzawi,

International Journal “Information Theories and Applications”, Vol. 25, Number 4, © 2018

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THE BENEFITS OF EVALUATING THE JORDANIAN SOCIAL SECURITY USING

DECISION MODELING AND NOTATION

Ahmad Zyad Alghzawi, Frank Vanhoenshoven, George Sammour, Koen Vanhoof

Abstract: The main objective of this paper is to analyze the situation of the pensions in the Jordanian

Social Security (JSS) system in order to evaluate it .We will elaborate on Business Process Model and

Notation (BPMN) and Decision Model and Notation (DMN) and the association that exists between the

two. Next we show how BPMN and DMN are able to (1) enhance clarity of JSS procedures and (2)

improve decision making.

Keywords: Business process (BP), Decision Management, Business Process Modeling Notation

(BPMN), Decision Model And Notation (DMN).

Introduction

Today business process models are important for business organizations as they provide an expressive

means to represent their operational model. Work activities, their logical ordering, data, and

organizational responsibilities provide the basic inputs for business process modeling. Using these

models, organizations can improve, control, automatize, and measure their processes effectively [1]. In

our study of business process modeling and decision models and their application to the JSS system,

we will explain the pension presses and decision making, as they exist in this JSS system.

In this paper, we apply process modelling and decision modelling on the pension procedures of the

Jordanian Social Security. We demonstrate how the models can be used to identify gaps,

inconsistencies or ambiguities. Furthermore, we show how decision modeling can help to create

simulations that can be used to support decision making of applicants as well as policy makers. The

information used in this paper is publicly available via the official website of the JSS system.

Business process (BP)

A business process is a structured and coordinated flow of activities. These are carried out by

participating people, who work with and decide on data and knowledge to achieve certain business

objectives(Debevoise, Taylor et al. 2014).

A business process (BP) is a set of one or more linked procedures or activities executed following a

predefined order which collectively realize a business objective or policy goal, normally within the


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context of an organizational structure defining functional roles or relationships (Chinosi and Trombetta

2012).

A business process can be entirely contained within a single organizational unit as well as it can span

several different organizations (WfMC April 2011). Business process collaboration across enterprise

boundaries is a complex task due to the lack of a unique semantics for the terminology of their BP

models and to the use of various standards in BP modeling and execution.

Business process management (BPM) tries to identify, understand and manage business processes

that support the organization's business model. Ideally, these processes are capable of supporting

adaptations in the business model due to changes in the economy or in customer preferences. Business

practices might be another reason for these changes. The same process model or logic must adapt to

the changes within the rules of decision models (Debevoise, Taylor et al. 2014). It provides governance

of a business's process environment to improve the agility and operational performance of the process.

It is thus a systematic approach to improve any organization's business processes (Chinosi and

Trombetta 2012). BPM is not a technology and it is not related to diagram creation or systems

architecture. Business Process Modeling, instead, is defined as the time period when manual and/or

automated (workflow) descriptions of a process are defined and/or modified electronically (WfMC April

2011). Business Process Modeling is as such the activity of “representing processes of an enterprise,

so that the current (“as is”) process may be analyzed and improved in future (“to be”)” (M. zur Muehlen

2008). Business Process Modeling is typically performed by business analysts and managers who are

trying to improve process efficiency and quality.

Decision Definitions

Business processes can often only be completed if decisions are taken, especially in the decision-

making pattern. Decisions in business processes are made by applying business knowledge to the data

processing. This knowledge may take the form of business principles or any other decision logic

(Debevoise, Taylor et al. 2014, Metsemakers, Morgenstern et al. 2017).

From the BPM perspective, a decision defines a set of terms or business concepts that selects a

particular answer in terms of value for a certain set of possibilities (Debevoise, Taylor et al. 2014,

Metsemakers, Morgenstern et al. 2017).

Decision Management

Decision management allows an organization to control, manage and automate repetitive decisions at

the heart of its business. It does so by effectively applying business rules and by using analytics and

optimization technology (Debevoise, Taylor et al. 2014). Decision Management enables the

development of simpler, smarter and more agile business processes (Debevoise, Taylor et al. 2014,

Hilmi, Safa et al. 2017).


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Thus business rules are central to decision management. Decision management maximizes the ROI of

these business rules by applying them effectivity. Similarly, data mining and predictive analytics provide

insight management (Debevoise, Taylor et al. 2014). Decision management multiplies the value of

analytics to the business result by focusing mainly on numerous repetitive day-to-day decisions

(Debevoise, Taylor et al. 2014, Hilmi, Safa et al. 2017).

From the perspective of BPM, decision management achieves three end goals (Debevoise, Taylor et al.

2014) :

1. Identifying decisions within business processes, whether these are automated or manual;

2. Precisely and unambiguously representing and populating a decision model to specify how the decision should be made who should do it without adding this information to the process model itself; and

3. Implementing, reporting and updating processes in order to continually refine the effectiveness of decision making as well as improving the efficiency of the processes.

Business Process Modeling and Notation (BPMN)

The primary goal of BPMN is to provide a notation that is readily understandable by business users,

ranging from the business analysts who sketch the initial drafts of the processes to the technical

developers responsible for actually implementing them, and finally to the business staff deploying and

monitoring such processes. BPMN was originally published in 2004 by the Business Process Modeling

Initiative as a graphical notation (partially inspired by UML Activity Diagrams) to represent the graphical

layout of business processes. The ever increasing number of adoptions by companies and the growing

interest for this notation caused the adoption of BPMN as Objective Management Group (OMG)

standard in 2006 (Chinosi and Trombetta 2012, von Rosing, White et al. 2015).

BPMN provides a graphical notation in order to represent a business process as a Business Process

Diagram (BPD). The development of a BPMN is based on a flowcharting technique tailored to creating

graphical models of business process operations. A Business Process Model, then, is a network of

graphical objects, which are active ties (i.e., work) and the flow controls that define their order of

performance. Activities are represented as rectangles and diamonds represent alternative workflow

paths (von Rosing, White et al. 2015).

It should be emphasized that one of the drivers for the development of BPMN is to create a simple

mechanism for creating business process models, while at the same time being able to handle the

complexity inherent to business processes (A.white July, 2004). The approach taken to handle these

two conflicting requirements was to organize the graphical aspects of the notation into specific

categories. This provides a small set of notation categories so that the reader of a BPD can easily

recognize the basic types of elements and understand the diagram. Within the basic categories of

elements, additional variation and information can be added to support the requirements for complexity


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without dramatically changing the basic look-and-feel of the diagram (Aguilar-Savén 2004, Gabryelczyk

and Jurczuk 2017).

BPMN has several uses. It is used to communicate a wide variety of information to different audiences.

BPMN is designed to cover many types of modeling and allows the creation of process segments as

well as end-to-end business processes, at different levels of fidelity. Within the variety of process

modeling objectives, there are two basic types of models that can be created with a BPD (Gabryelczyk

and Jurczuk 2017):

• Collaborative (Public) B2B Processes and

• Internal (Private) Business Processes

Decision Model and Notation:

In the last few years, the OMG (Object Management Group) proposed a new innovation, which is called

“Decision Model and Notation” (DMN) specification. In February 2014, version 1.0 of the DMN

specification was approved by OMG’s board (OMG: ). Since March 2016, version 1.2 has been

available via their website.

DMN Overview

DMN stands for Decision Model and Notation. The essential goals of DMN is to support a common

notation for decision login that is understandable for business users, business analysts and developers

alike. DMN supplies the constructs for the decision-making process itself and allows to model decision

rules.

A DMN model defines two levels: the decision requirements graph (DRG) and the decision logic. Where

the required information is coming from and how it can be depicted in one or more decision

requirements diagrams (DRDs) is the object of the former, whereas the latter describes the logic behind

the decision, depicted in the Decision Table [15]. The upper half of a decision table indicates all

potential combinations of conditions that may lead to certain actions, while the bottom half describes the

actions to be taken (i.e., outcomes). A minimal scope is specified for the standardization by OMG

because the goal of DMN is to offer support to other decision logic notations (e.g., decision trees) and to

allow for references to other types of models (e.g., SBVR).

Connecting between DMN and BPMN :

Decision Management aims to separate decision logic from the process logic. Large sequences of

gateways and checks, signalling decision logic, will all be removed from the BPMN model and captured

within a single decision step. A decision-driven process then uses the outcome of the evaluation of this

decision logic in several possible ways, including (Debevoise, Taylor et al. 2014):

Changing the sequence of activities that are taken after a decision, including what the next activity or process that is required to meet the directive of the process.


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Selecting between the paths on the diverging or the splitting side of a gateway.

Deciding who or what participant should perform the needed activity.

Creating data values that will be consumed later in the process.

inputs into the decision include (Debevoise, Taylor et al. 2014):

Data that can identify events or process-relevant conditions.

Data that must be validated for correctness.

Data used in calculations.

The Association Principle between BPMN and DMN (Mertens, Gailly et al. 2015)

BPMN diagrams that display long sequences of gateways often express decision logic instead of

process logic. A better approach would be to focus on process logic in BPMN and move all decision

logic to a single decision task. “Make a decision” is defined as a business rule task which is indicated in

the diagram by a small table symbol in the top left corner of it (Debevoise, Taylor et al. 2014)(see Figure

2.4.1) A DMN diagram will be linked with each decision task.

Fig.4 Business rule task(BPMN)

BPMN + DMN = Separation of concerns

Having two different BPMN and DMN Models presents the advantage of decoupling the decision making

from the process. This is called the separation of concerns (SoC). SoC is an old (1974) best practice

coming from computer science (Dijkstra 1982). Each one of several coupled models can indeed be

developed independently and this process can be done with the support of different stakeholders,

according to their needs and skills ( Business Process or IT people for instance). Each one of these

models (Business Decision or Process) can consider the other one as a black box, exchanging data

between eachother (Biard, Le Mauff et al. 2015).

The two mentioned models respect the main property required for a good Separation of Concerns.

They have their own consistency. In order to understand one model, it is not necessary to know the

other one. Moreover the Decision-making context can be detailed explicitly into the DMN diagram. Sub-

decisions can be reused into several decisions as well. This capitalization can lead to Knowledge

Management (KM) (Biard, Le Mauff et al. 2015, Mertens, Gailly et al. 2015).

Application of BPMN and DMN in JSS

In this section we will introduce the JSS System, the problems it faces and the processes present in the

system. We will describe these elements using BPMN Diagrams.


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In 1980, the Jordanian government determined to organize the JSS system as a government

organization and system in order to insure Jordanian people after retirement in terms of old-age and in

to give a mourning family some compensation after a loss. There are also provisions for disability,

maternity costs, and health insurance. Furthermore, the JSS system insures against work-related

accidents and job losses. We will focus particularly on retirements that have to be paid by the social

security.

The information for creating the model in Fig. 5 is retrieved from a document written by JSS (Security

2014). The model provides an easy and understandable means of clarifying the process logic to be

followed when applying for JSS. Moreover, the model forces the modeler to think critically about the

process, which is not always enforced by a written text. For example: what exceptions could occur in the

process and what do we need to do when a certain check fails?

The BPMN of the main pension system of the JSS looks as in (Figure 5) (Security 2014) :

The BPMN shows that the main pension process (Fig.5) starts by receiving a pension application from

the applicant. If all the required documents are available, the type of request has to be determined.

There are four different types of pension, which will be presented later on. The JSS process

subsequently verifies the accuracy and reliability of all data and executes the pension calculation. Here,

one possible inefficiency can already be identified. There are separate steps for verifying data. Are both

steps necessary or could they be merged? Would merging these steps yield an improvement in terms of

quality or throughput time? Another remarkable feature is that all types of pensions are treated

differently. It would seem that there is some room to exploit synergies or at least streamline the process

to reduce variations.

Cases of incompleteness can be seen by the absence of alternative paths. The model in Fig. 5 does not

stipulate what should happen when all documents are not confirmed Similarly, there is no indication of

what would happen when the type of request cannot be determined. Furthermore, given the lack of

alternative paths, the model leads to believe that in all cases, a pension will be calculated after it is

confirmed that no documents are missing. This is very likely to be the case in most applications, but it is

doubtful that this process can be followed for every single applicant.

It has to be stated that the answer to these questions cannot be readily derived from the process model.

However, the model provides a convenient starting point for identifying potential issues that could trigger

discussions to ultimately improve the process.

As mentioned above there are four different types of pension in Jordan: the natural death pension, the

permanent physical disability and permanent partial disability pension, the work related death pension

and the retirement pension. In the next Figures BPMN graphs for each of these pension types are

developed.


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Fig.5 The BPMN for main pension system of the JSS

The Determine Natural Death Pension Eligibility BPMN is shown in (Figure 6) (Security 2014):

Fig.6 BPMN for the natural death pension


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The natural death pension process (Fig.6) starts by taking the signatures from the heirs who ask for the

pension. Next, all the required documents are attached. Then the JSS system extracts the applicant

data from the system. After that the JSS system verifies the conditions of entitlement in terms of years

of contribution. Then the reliability and accuracy of the data are verified after all necessary data are

collected. Next, it is verified if the applicant has had more than one previous job. Finally, the process

also checks if the person eventually incurred work injuries during their lifetime.

The Determine Disability Pension Eligibility BPMN is shown in (Figure 7) (Security 2014):

Fig.7 BPMN for the Permanent natural partial disability illness pension

The BPMN for the Permanent natural partial disability illness pension process (Fig.7) starts by extracting

the applicant’s data from the system. Next the JSS system verifies whether in terms of years of

contribution the applicant is entitled to the pension. After all these necessary data are collected, the

system will again verify the accuracy and reliability of these data. Next, the system checks whether the

applicant has had more than one previous job. Finally, the system also checks if the person incurred a

work related injury during his life.

The Determine Injury Death Pension Eligibility BPMN is shown in (Figure 8) (Security 2014):

Fig.8 The BPMN for the Work related Death process

The Injury death pension process (Fig. 8) is somewhat smaller. It starts again by extracting the

applicant’s data from the system. After all the necessary data are collected, the JSS system will verify

the accuracy and reliability of these data. Next, the JSS system looks whether the applicant has had

more than one previous job and finally whether the conditions of entitlement are met. This is the last

step in the verification process.


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The Determine Retirement Eligibility Pension BPMN is shown in (Figure 9) (Security 2014):

Fig.9 The normal Pension BPMN

The Retirement Pension BPMN (Fig.9) is applicable for people who reach an eligible retirement age,

which can be an early retirement as well as a normal retirement. This process starts by the registration

of the file from the applicant. Then the JSS checks if the applicant deserves a pension from the system.

If this is not the case, the process ends. If the retired person however may get the pension, the process

continues by extracting the applicant’s data from the system. Then the conditions of entitlement are

checked. This is mainly a verification in terms of the number of years of contribution. After all the

necessary data are collected, the JSS verifies the accuracy and reliability of them. Next, it is verified if

the applicant has had more than one previous job. Then the JSS process checks whether it has to

compensate for one payment only. This involves checking if the retired person has incurred a work

injury before asking for the pension. Finally, there is also a check whether a compensation for disabled

work is needed.

After one of these processes is finished, the JSS has to calculate the amount of the pension of the

person applying for it (see Fig. 9. Above).

As in the main process, the subprocesses all concentrate on the happy path and provide little or no

support when exceptions occur. These are gaps that should be clarified in the official procedures. In

particular the process in Fig. 9 stipulates an alternative path that is not taken into account in the main

process. Should the process really end in case of early retirement or does this need to be handled

differently?


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When analyzing the efficiency of the proposed process models, some apparent characteristics are easy

to derive from the process models. Firstly, there are a lot of steps that involve data extraction or data

validation. It triggers the question why those validations are executed in such a fragmented sequence.

Do some of these steps represent double work or, if not, could they also be merged into a single

activity? A deeper analysis on task-level rather than process-level could reveal some opportunities to

make the process more efficient.

Furthermore, as can be seen in the Table below the subprocesses seem to have some common

activities; It would be beneficial to examine to what extend these tasks really are similar to each other

and whether the four different processes could actually be collapsed into a single model.

Natural death Partial disability Work-related RetirementCollect signature of heirs X Attach documents X Extract data X X X X Verify entitlement X X X X Verify reliability X X X X Verify intervention periods X X X X Verify employment injury X X X Confirm early retirement X Confirm former one-time payment X Confirm former unemployment allowance

X

The BPMN for the calculation of the pension is mentioned in (Figure 10) (Security 2014):

Fig.10 The BPMN for the pension calculation


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The pension calculation process (Fig.10) starts by choosing the right pension calculation. There are

three types of decisions possible in the JSS system according to the type of pension the applicant asks

for. They are shown in Fig.10. We will explain these three decisions later in this section. Finally, JSS

send the pension calculation to the applicant.

This DMN of the Current and new Early retirement Eligibility looks as follows (Figure 11) (office 2015) :

Fig.11 This DMN of the Current and new Early Eligibility

Overview of decisions and sub-decisions (Security 2014)

Decision Question Answer Eligibility If the retirements can have the pension according to JSS condition Yes or No

The main decision in the model above (Fig 11) the Current and new Early Eligibility is decided based on

the input data Person age at 1 March 2014, gender, age and years of contribution.(office 2015)

Table 1a: Retirement age U Gender Minimum retirement age {Male,Female} [0-∞] 1 Male 60 2 Female 55

Normal retirement age for men is 60, while it is 55 for women.

Table 1b: Minimum early retirement age F Person age at 1 March

2014 (Number) Gender {Male,Female}

Year of contribution [15-30]

Minimum early retirement age [0-∞]

1 ≤41 Male ≥21 50 2 =42 Male ≥21 48


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3 =43 Male ≥20 47 4 =44 Male ≥19 46 5 ≥45 Male ≥19 45 6 ≤41 Female ≥19 50 7 =42 Female ≥18 48 8 =43 Female ≥17 47 9 =44 Female ≥16 46 10 ≥45 Female ≥16 45 11 - - ≥25 0 12 - - - = Retirement age

Depending on your age in March 2014, your gender and your years of contribution, you are allowed to

retire early from a certain age. People with more than 25 years of contribution are always allowed to

have early retirement. All other people can only retire when they reach the normal retirement age.

Table 1c: Current and new early retirement eligibility F Minimum retirement age [0-∞] Eligibility [Yes, No] 1 = Retirement age No 3 ≤ Age Yes 4 - No

Whenever the minimum early retirement age is determined as being equal to normal retirement, the

person is not eligible for early retirement. In all other cases, the applicant is eligible as soon as they

reach the minimum early retirement age.

The current and new early retirement calculation is shown below in Fig. 12., using DMN.

This DMN of the Current and new Early retirement looks as follows (Figure 12) (office 2015) :

Fig.12 This DMN of the Current and new Early

The main decision in the model above (Fig 12) the Current and new Early Pension. Pension is decided

based on average salary, dependent and reduction, which are all sub-decisions. So are gender and

years of contribution, which are both considered as input data for the Pension. The first sub-decision is


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reduction. Reduction is decided based on the sub-decisions average salary, also age and gender which

are both considered as input data. The second sub-decision is average salary based on the salary

which is considered as input data. The Final sub-decision is the dependent based on the sub-decisions

average salary and the number of dependent which the retirements have and the minima is (0) and the

maximum is (3) (office 2015).

Overview of decisions and sub-decisions (Security 2014)

Decision Question Answer Pension Calculate the pension salary that will be paid out to the

applicant on a monthly basis (Non-negative) Monetary value in JD.

Average salary

Calculate the average salary according to the last 2 years salary the retirements had

(Non-negative) Monetary value in JD.

Dependent how many dependent the retirements have (Non-negative) Number. Redaction How much discount the JSS will do according to the law (Non-negative) Monetary

value in JD. Table 2 explains the pension decision (office 2015).

Pension salary =Average of salary + Dependent-Redaction + 20

Table 2 explains the main decisions shown in the model above (Fig 12). We can see the calculation of

the pension according to this type of decision from the table. The sub-decisions average salary, number

of dependent people and conditions for reduction, which we will explain later in this section. The input

data are gender and years of contribution.

In the left column of the table, three different pension decisions can be found. Also the eligibility,

average salary, dependent and redaction are sub-decision and gender, years of contribution input data

to have the output pension salary.

In our case for the calculation of the DMN of the Current and new Early pension, the decision has more

than one sub-decision In Table 3. we will explain the first sub-decision, namely the average salary

(office 2015).

Table 3: Average salary sub-decision

F Salary (Number) Average Salary (Currency) 1 ≤ 50 JD 50 JD 2 ≤ 1500 JD = 0.025 * Years of contribution * Salary 3 > 1500 JD = 0.025 * Years of contribution * 1500 + Years of contribution * (Salary – 1500)

In this sub-decision as we can see that there are only three conditions, namely the salary before

retirement and the years of contribution. If the salary is either (≤50) or (≤1500) or (≥1500) and years of

contribution are between 15 and 30, the JSS does the average of the pension calculation as we can see

in table (3).

In this Table(4) we will explain the second sub-decision, namely the number of Dependent people (office

2015).

Table 4: Dependent sub-decision


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U Number of Dependents

Dependent

Number Number 1 = 0 0 2 = 1 = Average Salary * 0.12 3 = 2 = Average Salary * (0.02 + 0.06) 4 ≥ 3 = Average Salary * (0.02 + 0.06 + 0.06)

As we mentioned before and as we can see from Table (4) in the JSS the minimum number of

dependent people is (0) and the maximum is (3). In this case there is only one input data number of

dependent people and based on the other sub-decision, the average salary, retiring people will

according to the number of dependent people get extra’s on their pension.

In some cases, the retirements get some reduction on their pension and in Table (5) we will present the

cases. That is the last sub-decision for the main pension decision (office 2015).

Table 5: Reduction sub-decision

F Age [45-∞] Gender {Male,Female} Reduction Currency(JD) 1 <46 Male =Average of salary*0.2 2 <47 Male =Average of salary*0.18 3 <48 Male =Average of salary*0.16 4 <49 Male =Average of salary*0.14 5 <50 Male =Average of salary*0.12 6 <51 Male =Average of salary*0.11 7 <52 Male =Average of salary*0.1 8 <53 Male =Average of salary*0.09 9 <54 Male =Average of salary*0.08 10 <55 Male =Average of salary*0.07 11 <56 Male =Average of salary*0.06 12 <57 Male =Average of salary*0.05 13 <58 Male =Average of salary*0.04 14 <59 Male =Average of salary*0.03 15 <60 Male =Average of salary*0.02 16 ≥60 Male 0 17 <46 Female =Average of salary*0.14 18 <47 Female =Average of salary*0.12 19 <48 Female =Average of salary*0.1 20 <49 Female =Average of salary*0.08 21 <50 Female =Average of salary*0.07 22 <51 Female =Average of salary*0.06 23 <52 Female =Average of salary*0.05 24 <53 Female =Average of salary*0.04 25 <54 Female =Average of salary*0.03 26 <55 Female =Average of salary*0.02 27 ≥55 Female 0

In this sub-decision we can see that there are three conditions. Two of them are the input data age and

gender and the third sub-decision is the average of salary. The table seems to be designed to


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discourage early retirements by deducting a percentage of the normal pension. As would be expected,

this deduction is larger the further a person is removed from the age of retirement. Generally speaking,

applicant gain 2% of their salary by postponing their retirement by one year. At a certain point in time,

however, this gain decreases. For men aged 49, an additional year in the workforce will only decrease

the reduction by 1% of the average salary. For women, this effect takes place from the age of 46. These

could be considered as the ages before which authorities attempt to discourage retirement.

Given that there is a clear mathematical relation between the age column and the factor in the

reduction, we could decompose this particular decision even further by using FEEL expressions. This

alternative way of modeling increases maintainability of the decision and is likely to be preferred by IT

developers. However, it is not self-evident whether this representation is also easy to understand by

non-technical users.

The alternative solution would be modeled as follows.

Table 5a: Retirement age

U Gender {Male,Female} Retirement age 1 Male 60 2 Female 55

Table 5b: Preferred early retirement age

F Gender {Male,Female} Preferred early retirement age1 Male 50 2 Female 47

Table 5c: Years until retirement

Pension salary =Retirement age - Age

Table 5d: Years until preferred early retirement


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Pension salary =Preferred early retirement age - Age

Table 5e: Reduction percentage

U Years until retirement

Years until preferred early retirement

Reduction percentage Percentage

1 > 0 > 0 =0.10 + 0.02 * Years until preferred early retirement 2 > 0 ≤ 0 =0.01 + 0.01 * Years until retirement 3 ≤ 0 - 0

Table 5f: Reduction

Reduction =Average of salary * Reduction percentage

This DMN of the Normal Old Age Eligibility (Figure 13) (office 2015):

Fig.13 the Normal Old Age decision Eligibility

Overview of Normal Old Age Eligibility decisions (office 2015).

Decision Question Answer

Eligibility If the retirements can have the pension according to JSS condition Yes or No

Table 6: Normal Old Age Eligibility decision F Age Gender Years of

contribution Eligibility

Number (Male, Female) Number (Yes, No) 1 ≥ 60 Male ≥ 15 Yes 2 ≥ 55 Female ≥ 15 Yes 3 - No

In this Table (6), we explain the Eligibility decision in the left column. We find two different conditions in

the table. For this decision, we have different input data, like his or her actual age, gender and years of

contribution. The decision in this case is the Eligibility.


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This DMN of the Normal Old Age (Figure 14) (office 2015):

Fig.14 the Normal Old Age decision

The main decision in the model above (Fig 14)is the Normal Old Age Pension. Pension is decided

based on average salary and dependent, which are all sub-decisions. So are gender is considered as

input data for the Pension. The sub-decision is average salary based on the salary which is considered

as input data. The second sub-decision is the dependent based on the sub-decisions average salary

and the number of dependent which the retirements have and the minima is (0) and the maximum is (3).

Overview of Normal Old Age decisions and sub-decisions (office 2015).

Decision Question Answer Pension Calculate the pension salary that will be paid out to the

applicant on a monthly basis (Non-negative) Monetary value in JD.

Average salary

Calculate the average salary according to the last 2 years salary the retirements had


Dependent how many dependent the retirements have (Non-negative) Number. Table 7: explain the Normal Old Age pension decision (office 2015).

Pension salary =Average of salary + Dependent + 40

Table (7) explains the main decisions for Normal Old Age pension shown in the model above table (8).

We can see the calculation of the pension according to this type of decision from the table. The sub-

decisions are Eligibility, Average salary and number of Dependent, which we will explain later in this

section and the gender as input data.


average salary and dependent are sub-decision and the gender as input data, to have the output

pension salary.


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Also, this decision like the previous decision have a combination of sub-decisions. In table (9) we

explain the Eligibility of one person for a pension according to different decision conditions. And this is

only one of the sub-decisions to be made (office 2015).

In the normal old age for the calculation of the pension, the decision has more than one sub-decision

(not only the Eligibility). In Fig. 18. we will explain the second sub-decision, namely the Average salary

(office 2015).

Table 8: Normal Old Age Average of salary sub-decision

F Age [0-65]

Gender {Male,Female}

Salary Currency (JD)

Years of contribution [15-30]

Average of salary Currency (JD)

1 =65 Male >1500 JD ≥15 0.03 * 'Years of contribution' * 1500 + 0.025 * 'Years of contribution' * (Salary - 1500)

2 =65 Male ≤1500 JD ≥15 0.03 * 'Years of contribution' * Salary 3 =60 Female >1500 JD ≥15 0.03 * 'Years of contribution' * 1500 + 0.025 * 'Years

of contribution' * (Salary - 1500) 4 =60 Female ≤1500 JD ≥15 0.03 * 'Years of contribution' * Salary 5 >64 Male >1500 JD ≥15 0.029 * 'Years of contribution' * 1500 + 0.0234 *

'Years of contribution' * (Salary - 1500) 6 >64 Male ≤1500 JD ≥15 0.029 * 'Years of contribution' * Salary 7 >59 Female >1500 JD ≥15 0.029 * 'Years of contribution' * 1500 + 0.0234 *

'Years of contribution' * (Salary - 1500) 8 >59 Female ≤1500 JD ≥15 0.029 * 'Years of contribution' * Salary 9 >63 Male >1500 JD ≥15 0.028 * 'Years of contribution' * 1500 + 0.0222 *


'Years of contribution' * (Salary - 1500) 12 >58 Female ≤1500 JD ≥15 0.028 * 'Years of contribution' * Salary 13 >62 Male >1500 JD ≥15 0.027 * 'Years of contribution' * 1500 + 0.0214 *


'Years of contribution' * (Salary - 1500) 16 >57 Female ≤1500 JD ≥15 0.027 * 'Years of contribution' * Salary 17 >61 Male >1500 JD ≥15 0.026 * 'Years of contribution' * 1500 + 0.021 * 'Years

of contribution' * (Salary - 1500) 18 >61 Male ≤1500 JD ≥15 0.026 * 'Years of contribution' * Salary 19 >56 Female >1500 JD ≥15 0.026 * 'Years of contribution' * 1500 + 0.021 * 'Years

of contribution' * (Salary - 1500) 20 >56 Female ≤1500 JD ≥15 0.026 * 'Years of contribution' * Salary 21 ≥60 Male >1500 JD ≥15 0.025 * 'Years of contribution' * 1500 + 0.02 * 'Years

of contribution' * (Salary - 1500) 22 ≥60 Male ≤1500 JD ≥15 0.025 * 'Years of contribution' * Salary 23 ≥55 Female >1500 JD ≥15 0.025 * 'Years of contribution' * 1500 + 0.02 * 'Years

of contribution' * (Salary - 1500)


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24 ≥55 Female ≤1500 JD ≥15 0.025 * 'Years of contribution' * Salary 25 - - - - oJD

In this sub-decision as we can see that there are four input data, his or her actual age, gender, namely

the salary and the years of contribution. the JSS does the average of the pension calculation in this

type of decision as we can see in Fig (14).

In this table (9) we will explain the third sub-decision, namely the number of Dependent people (office

2015).

Table 9: Normal Old Age Dependent sub-decision U Number of dependents Dependent Number Currency 1 = 0 0 JD 2 = 1 = Average of salary * 0.12 3 = 2 = Average of salary * (0.12 + 0.06) 4 ≥ 3 = Average of salary * (0.12 + 0.06 + 0.06)

As we mentioned before and as we can see from Fig (19) in the JSS the minimum number of dependent

people is (0) and the maximum is (3). In this case there is only one input data number of dependent

people and based on the other sub-decision, the average salary, retiring people will according to the

number of dependent people get extra’s on their pension.

This DMN of the OLD Pension Eligibility (Figure 15) (office 2015):

Fig.15 The OLD pension Eligibility

Overview of OLD Eligibility decisions(office 2015).

decision Question Answer Eligibility If the retirements can have the pension according to JSS condition Yes or No The main decision in the model above (Fig 15)is OLD Pension Eligibility. OLD Pension is decided based

on the input data age, gender and year of contribution.

In table (10) we explain the Eligibility of one person for a pension according to different decision

conditions(office 2015).


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Table 10: The OLD Eligibility decision F Age Gender Years of

contribution Eligibility

Number (Male, Female) Number (Yes, No) 1 ≥ 45 Male ≥ 18 Yes 2 ≥ 45 Female ≥ 18 Yes 3 - No

This DMN of the OLD Pension (Figure 16) (office 2015):

Fig.16 The OLD decision

The main decision in the model above (Fig 16)is OLD Pension. OLD Pension is decided based on

average salary, dependent and reduction, which are all sub-decisions. So gender considered as input

data for the OLD Pension. The first sub-decision is reduction. Reduction is decided based on the sub-

decisions average salary, also age and gender which are both considered as input data. The second

sub-decision is average salary based on the salary which is considered as input data. The Final sub-

decision is the dependent based on the sub-decisions average salary and the number of dependent

which the retirements have and the minima is (0) and the maximum is (3).

Overview of OLD decisions and sub-decisions (office 2015).

decision Question Answer Pension

Calculate the pension salary that will be paid out to the applicant on a monthly basis


Average salary Calculate the average salary according to the last 2 years salary the retirements had


Dependent how many dependent the retirements have (Non-negative) Number. Redaction How much discount the JSS will do according

to the law (Non-negative) Monetary value in JD.


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Table 11 explain the OLD pension decision (office 2015).

Pension salary = Average of salary + Dependent-Redaction + 20

Table 11 explains the main decisions for Normal Old Age pension shown in the model above (Fig 15).

We can see the calculation of the pension according to this type of decision from the table. The sub-

decisions are Eligibility, Average salary and number of Dependent, which we will explain later in this

section and the Gender as input data.


average salary and dependent are sub-decision and the gender as input data, to have the output

pension salary.

In the OLD pension calculation, the decision has more than one sub-decision. In table 12 we will explain

the second sub-decision, namely the Average salary (office 2015).

Table 12: OLD Average of salary sub-decision

F Salary Average salary Currency Currency 1 ≤ 50 JD 50 JD 2 > 1500 JD = 0.025 * Years of contribution * 1500 + 0.02 * Years of contribution * (Salary – 1500) 3 ≤ 1500 JD = 0.025 * Years of contribution * Salary

In this sub-decision as we can see that there are only two conditions, namely the salary before

retirement and the years of contribution. If the salary is either (≤50) or (≤1500) or (≥1500) and years of

contribution are between 15 and 30, the JSS does the average of salary calculation as we can see in

table (12).

In this Table (13) we will explain the third sub-decision, namely the number of Dependent people for

OLD decision (office 2015).

Table 13: OLD Dependent sub-decision

F Number of dependents DependentReduction Currency Currency 1 = 0 0 2 = 1 = Average Salary * 0.1 3 = 2 = Average Salary * 0.15 4 ≥ 3 = Average Salary * 0.2

As we can see from table (13) in this decision in the JSS the minimum number of dependent people is

(0) and the maximum is (3). In this case there is only one input data number of dependent people and

based on the other sub-decision, the average salary, retiring people will according to the number of

dependent people get extra’s on their pension.

In this cases, the retirements get some reduction on their pension and in table (14) we will present the

cases. That is the last sub-decision for the main decision (office 2015).

Table 14 OLD Redaction sub-decision


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F Gender Age OLD Reduction (Male, Female) Number Percentage 1 Male ≥ 59 0.00% 2 Male ≥ 45 =( (59 – Age)*0.01 + (49 – Age)

) * 0.02 * Average Salary 3 Female ≤ 54 5.00% 4 Female ≥ 54 0.00% 5 Female ≤ 50 10.00% 6 - - 0.00%

In this sub-decision, we can see that there are three conditions. Two of them are the input data age and

gender and the third sub-decision is the average of salary. There are five different cases two of them for

male and the rest for female which leads to a final redaction.

The BPMN for all pension calculation s mentioned in (Figure 17)

Fig 17 The BPMN for all pension calculation

The pension calculation process (Fig 17) starts by Sending the retirement treatment to take the decision

pension calculation. Here we merge the three decision in one decision and one Eligibility.. They are

shown in Fig.18. We will explain this new decision later in this section. Finally, JSS send the pension

calculation to the applicant.

This DMN of all pension decision (Figure 18)

Fig.18 The all pension decision


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The main decision in the model above (Fig 18) is for all pension design with one Eligibility. sub-

decisions and input data like the previous sub-decisions and input data.

Overview of all pension decision and sub-decisions (office 2015).

decision Question Answer Pension

Calculate the pension salary that will be paid out to the applicant on a monthly basis


Type Eligibility Determine which pension system to apply to the applicant

Enumeration None Early Retirement Normal Pension OLD Pension

OLD pension Calculate the OLD pension salary that will be paid out to the applicant on a monthly basis


Normal old age Calculate the Normal old age pension salary that will be paid out to the applicant on a monthly basis

(Non-negative) Monetary value in JD. .

Current and new early retirement

Calculate the Current and new early retirement pension salary that will be paid out to the applicant on a monthly basis


Table 15: Eligibility sub-decision

F Person age at 1 March 2014 Number

Age [45-∞]

Gender {Male,Female}

Years of contribution[15-30]

Applies for OLD pension Boolean

Eliigibilty {EARLY RETIREMENT, NORMAL RETIREMENT, OLD Pension, NO PENSION}

1 - - - ≥25 - EARLY RETIREMENT 2 ≤41 ≥50 Male ≥21 - EARLY RETIREMENT 3 =42 ≥48 Male ≥21 - EARLY RETIREMENT 4 =43 ≥47 Male ≥20 - EARLY RETIREMENT 5 =44 ≥46 Male ≥19 - EARLY RETIREMENT 6 ≤41 ≥50 Female ≥19 - EARLY RETIREMENT 7 =42 ≥48 Female ≥18 - EARLY RETIREMENT 8 =43 ≥47 Female ≥17 - EARLY RETIREMENT 9 =44 ≥46 Female ≥16 - EARLY RETIREMENT 10 ≥45 ≥45 Female ≥16 - EARLY RETIREMENT 11 - ≥60 Male ≥15 - NORMAL RETIREMENT 12 - ≥55 Female ≥15 - NORMAL RETIREMENT 13 - ≥45 Male ≥18 True OLD Pension 14 - ≥45 Female ≥15 True OLD Pension 15 - - - - - NO PENSION

In this table (15), we explain the Eligibility sub-decision in the left column. We find twenty five different

conditions in the table. For this sub-decision, we have different input data such as the person’s age on

March 1, 2014, his or her actual age, gender. OLD pension and years of contribution. The decision in

this case is the Eligibility for all pension decision.

Decision Analysis Simulation


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Being a structured and non-ambiguous language, DMN can be executed by a decision engine. This

allows for decisions to be simulated. Simulation gives decision-makers the ability to foresee and quantify

the effect of changing decisions. As such, several possible scenario can be compared and the best

scenario can be selected.

Apart from being executable, the visual representation of decisions in DMN allows for easy

understandability and transparency. Decision-makers can perform visual checks on completeness and

consistency of their decision models.

Table (16) shows the old-age pension simulations for people with varying age and gender with an

average salary of 400JD and maximum years of contribution. One can easily see that the age of

retirement has a positive effect on the pension. The effect is similar for both men and women. A noted

distinction is that women are allowed to go to old-age retirement earlier. It is notable that in this

situation, with one dependent, men and women both could get a pension that is actually higher than

their current salary. For men, this happens from age 63 onwards, woman already get a pension higher

than their salary from 58. It seems that there would be little incentive to remain in the work force at that

age. Note that when the number of dependents drop to 0, the pension will decrease as well and they will

not be earning as much as their salary anymore.

Table 16: Simulations of pension for people with varying age and gender for an average salary (400JD)

and maximum years of contribution (30)

Age Gender Number of dependents Salary Simulated Pension 60 Male 1 400 JD 376 JD 61 Male 1 400 JD 376 JD 62 Male 1 400 JD 389.44 JD 63 Male 1 400 JD 402.88 JD 63 Male 0 400 JD 364 JD 55 Female 1 400 JD 376 JD 56 Female 1 400 JD 376 JD 57 Female 1 400 JD 389.44 JD 58 Female 1 400 JD 402.88 JD 58 Female 0 400 JD 364 JD

Potential applicants could use these simulations to guide their own retirement decisions and could

weigh off the effects of staying employed for an additional year. Similarly, this could be an input to policy

makers who would be interested to trigger people to work longer. As we can see from the table, there is

no incentive for male applicants aged 60 to work for an additional year as their pension salary would

remain virtually the same. Working one additional year would yield an extra 14JD per month, while

working 5 more years until 65 delivers 24JD per month. It is up to the policy makers whether they

consider this a big enough incentive to stimulate late retirements.

Table (17) salary of 3500JD and maximum years of contribution. Here, if a 60-year old man decides to

remain in the work force, he would make a monthly gain of 174JD if he works an additional 3 years and


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an extra 351JD per month for an additional three years. Women can receive the same benefits, only five

years earlier.

Table 17: Simulations of pension for people with varying age and gender for an high salary (3500JD)

and maximum years of contribution (30)

Age Gender Number of dependent

Salary Year of contribution

Pension

60 Male 0 3500 JD 30 2365 JD 55 Female 0 3500 JD 30 2365 JD 63 Male 0 3500 JD 30 2539 JD 58 Female 0 3500 JD 30 2539 JD 65 Male 0 3500 JD 30 2890 JD 59 Female 0 3500 JD 30 2890 JD

Using these DMN models, we could make a simulation to support an hypothetical 45-year old man with

a salary of 600JD in making his retirement decision. Table (18) compares four different situations where

he would either retire immediately or wait until 50, 55 or 60 years of age. Based on age and years of

contribution. In general, if the retired go to the pension he will receive less pension early per month put

he will get more money from JSS in total. However, the applicant should consider that in case of early

retirement, he loses his current salary of 600JD, which amounts to 7200JD annually. Disregarding

factors such as stress or physical complaints, it seems that in the current system, the lost income would

be the main motivation for people to apply for a pension at a later stage.

Table 18: Different retirement scenarios for a 45 year old man, having currently 3 dependents and a

salary of 600JD.

Age of retirement 45years 50 years 55 years 60 years Years of contribution 25 30 30 30 Years having 3 dep 6 years 1 0 0 Yeas having 2 dep 10 years 10 years 5 years 0 Years Having 1 dep 15 years 15 years 15 years 15 years First pension 438.95 568.19 591.14 544 Total pension until 75 154289.4 140864.28 121364.4 97920 Average Yearly Pension 4977 5418 5779 6528 Salary earned from employer (since 45) 0 36000 72000 108000

Given the logic defined in DMN, we could make projections for an entire population if we have sufficient

information about the demographics. Table 5 displays a simplistic example of demographics in different

scenarios. Based on current numbers, 12188 new people go on retirement. In general, 61% of them

enjoy early retirement around the age of 50. The other 39% receives old-age retirement at an average

age of 60. Best case scenario is considered an optimistic 10% early retirees, the remainder receiving

old-age pension. In a worst case, one could see as much as 80% of applicants for early retirement.

Based on these estimated ratios, we can calculate the total number of applicants in each scenario. It

can be seen that the total number of applicants is lowest when the fewest people apply for early

retirement.


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Table 19: Demographics of realistic case, best case and worst case scenarios

Parameter Realistic Case Best Case Worst Case Estimated number of new applicants 12188 4754 14503 Age 50 years-61%

60 years-39% 45 years-10% 60 years-90%

45 years-80% 60 years-20%

Dependents 0:20% 1:50% 2:15% 3:15%

0:50% 1:40% 2:10% 3:0%

0:0% 1:10% 2:30% 3:60%

Based on the numbers in Table (19), we can make estimations Table (20) about the total expenditures

for retirements in different cases. These numbers can be used by policy makers to assess affordability

of the system and make eventual corrections.

Table 20 : Estimates of additional expenses for JSS for pension

Scenario Current rules Best case 2525562 JD/month Worst case 6139177 JD/month Realistic case 5506856 JD/month

Conclusion

As far as we know, this is the first time BPMN and DMN have been applied in science to analyze the

situation of the JSS.

The models present the pension calculation in a more transparent and understandable format compared

to a written text. The process models added clarity to the official text document and helps us to spot

exception paths and some ambiguities. Notable mention should be made of the different processes that

are followed to prepare for each application type (Fig 6, 7, 8, 9). Currently, JSS follows a different

process for each, but it can be seen from the models that that is a significant similarity between them.

The models could be used by JSS to create a single, integrated process to cover all cases.

Simultaneously, it seems that merging or rearranging some similar steps may bring some gains in

efficiency.

The entire pension calculation can be expressed in a single decision diagram, where all individual

pension systems (early retirement, normal pension, OLD pension) have their own specific

implementation and a dedicated Eligibility decision routes the applicant towards the correct decision.

The main benefit of DMN is that it forms a means of communication to stakeholders, not only between

policy makers themselves but also towards the people responsible for implementing the JSS systems

and the Jordanian people in general. Apart from communication, DMN provides JSS with a tool that

helps to simulate, modify and improve the pension calculation. Also, the decision models in this paper

can be used to analyse the effect of possible changes that they would like to apply. while also using the

models


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We have shown how simulations can help the Jordanian people decide on their retirement age. This

knowledge can be used by policy makers to change rules so that desired behavior can be awarded and

stimulated.

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Authors' Information

Ahmad Zyad Alghzawi – Department of Business Informatics, Hasselt University, Belgium; e-mail: [email protected]

Frank Vanhoenshoven – Department of Business Informatics, Hasselt University, Belgium; e-mail: [email protected]

George Sammour – Department of Management Information system, Princess Sumaya University for Technology, Jordan; e-mail: [email protected]

Koen Vanhoof – Department of Business Informatics, Hasselt University, Belgium; e-mail: [email protected]

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The Benefits of Evaluating the Jordanian Social Security ... · THE BENEFITS OF EVALUATING THE JORDANIAN SOCIAL SECURITY USING DECISION MODELING AND NOTATION Ahmad Zyad Alghzawi,