answers to exams review 2

8/10/2019 Answers to Exams Review 2

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PART A

Question 1

1.1 Difference between Computer Simulation and System Modeling Computer Simulation

Computer simulation ispresented byKelton et al(2007) as the collection of methods andapplicationsmimicking the behavior of real systems over time using computer software.Simulationneeds to be usedprior toaltering any existing system or a non-existingone, this helps minimizechances of failure whenmeeting specifications and eliminatingunexpectedfailures. Results from asimulated process can numerically be evaluated and recommendation from the deduction can bemade.

System Modeling

System modeling is the study of the use of models to conceptualize and construct systems in business.A model is simpler than the system it represents. One sole function of a model is to facilitate

the users to predict the outcome of changes to a system. Also, a model showsa close estimate ofresults from the real systems being under studied.

1.2 Four skills required to model and simulate business processes. Technical expertise

This ability is needed to make out the core components and functions of business processesand postulate the factors that are most likely required to simulate any business process. Failure totechnically understand any business process before simulating will provide wrong results from the

process being mimicked.

Decision-making

This skill is required to help state the effect a change or idea can adversely have on theoperations of a business process. A careful decision helps to assign roles and properly associatethedifferent business components of the business environment before designed and simulated.

Problem-solving

This ability will suggeststhe distinguishing and capturingof logical connections betweenvarious business processes and provide appropriatesolutions from the sets of inputs and outputs

provided to simulate a business process.

Analytical

This ability is needed to help infer from the sets of inputs provided to simulate the businessoperation and interrelationship of the outputs represented.This skill is very critical in any simulation

process.

1.3 Why End Users Are Needed To Simulate and Model a Business Processes

Issues recognized by end users in a business simulation processvary considerably and havinga way to likely address these issues can be based on the different user satisfaction and technology


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acceptance. Conclusion from an array of studies (e.g. Robey and Farrow 1982) postulates thateffective participation of end users in simulation yields the following benefits

They help advice to avoid costly system features that may not be necessary or cannot be used. They help improve the quality of any business process, because they provide more accurate

user defined requirements for the simulation process. They provide better levels of acceptance of the business processes in a given environment. End users can help evaluate tactical decisions of the business process that is being simulated.

End users also help to identify operational problems which can hence be factored into thesimulation development process of the business operationsas and when they come out andrecommendation can be made.

1.4 Examples of Simulation Tools for solving complex problemsSimulation tools have made much important contributions to the great progress in

development of complex systems. These tools provide visual environment to seamlessly model and process many complex and decisions that are mutually dependent quickly and provide users with thelikely consequences of a given scenario, three of these tools include:

General Purpose Languages

They are basically high level programming languages used in simulation. An example isFOTRAN

General Simulation Packages

They are simulation tool which provides a drag and drop feature and makes modelingof symbolic computations easy. An example is ARENA.

Special Purpose Packages

The packages are specialized tools used in simulation. An example is MapleSim.

1.5 Causes and Effects of Process Management

Before we discuss process management, a brief descriptionof itis warranted. ProcessManagement in simulation is an extension of design experience, allowing users to study theirmodel's behavior and explore alternate designs. It delivers a simulation process thecapabilities to analysis business processes and design, providing early guidance during themost critical stages of innovation.

1.5.1 Causes of Process ManagementThe edgeto improve performance when changesare done and its benefits spreadacross the business consequently, this employs the use of process management.When the need to minimize operational cost and also help find solutions tobusinessoperational problems faster, using lesserandsmaller amountof resources, time andeffort results in the use of process management to support IT systems.Another is the thought to better measure the business performance accurately hasgiven rise to the implementation of process management.

1.5.2 Effects of Process Management


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The careful employ of process management drastically minimizes businessoperational and processing cost and other overheads.Process management in the long in turn help reduces proportionally the number oferror in any business operations.Process management also helps lessen the number of customer complaints to

business services.Lastly it also accurately increases theforecastand process throughput times of

business operations.

1.6 Reason why businesses engage in Business Process Modeling

Business process modeling is the process of using simulation to model business processes andactivities. It has emerged over the years as a very important tool for research and information systemdesigning.BPM has brought into play applications that have strategically been used to analyzeorganizations and made improvements.

Listed below are five reasons a business would employ modeling in their business processes: Modeling over the years as provided and presented a better understanding of financial and

management concepts of business operations. It has also proven its capacity by providing vivid understanding of how daily decisions impact the

company strategically and financially. It provides the core platform for the identificationand continual improvement of business

operational processes. Modeling of business processes has also helped to bring to light how businesses generate profits

and how it needs to maintain the generation of profits in the future. Lastly, it has provided better communication on key decisions during times of organizational

change.

1.7 Methods of Business Process Modeling in diagrammatic representationFlow chart (Cash Withdrawal from a bank)

Fig 1 shows a flowchart diagram of a withdrawal process at a bank

Start CustomerArrives

Teller verifiesdocuements

valid ?Check thebalance

Is bala nce valid fortransaction

Maketransaction

Yes

Processtransaction

RejectNo

Log transaction

Yes

Give cash tocustomer

End

No

Customer makesrequest

No


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Business Process Modeling Notation (Cash Withdrawal from a bank)

Fig 2 Shows a BPMN diagram of a withdrawal process at a bank

IDEF (Valuation of stock on a money market)

Fig 3 Shows an IDEF0 diagram of a stock valuation process at an investment bank

Data Flow Diagram (DFD) Supermarket Payment Data Flow

Yes

No

Yes

No

Start

End

Customer Arrives

Make transaction Is balance validfor transaction

valid ?

Customermakes

request

Tell er verifiesdocuments

Check the balance

Processtransaction

Reject

Log transaction Give cash to cli ent

Estimate the market value of a stock

1

Valuer information

Market information

Stock data analysis

Compnay information

T r a

d i n g

f o r m u

l a t i o n

S y s t e m s i n

f o r m a t i o n

C o m p a n y

f a c i

l i t y

C l i e n t s r e q u i r e m e n t s

P r o f e s s i o n a l s t a n d a r d s

Final opinion of stock

Valuation report


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Fig 3 Shows a DFD diagram of a payment flow at a supermarket

Key advantage of the above methods

Flowcharts provide a blueprint during the systems analysis and solution development

phase. A flowchart shows every step of a process in clear and explicit detail.BPMN provides the tool set to model and communicate your processes at a high level ofabstraction for business executives and solution architects.IDEF provides documented and robust standards that can be used without having todefend the technique.DFD mainly shows how information flows through an information system.

1.8 Elements of BPMN (BPMN 2.0)BPMN is used to describe the common states of the operations in a business

environment.BPMN usually presents a high level representation of business operations.

List below are the elements that are used to represent Business process modeling notation.

Flow Objects

Flow objects are the main graphic elements that define the behavior of the processes. Theattributes of flow objects are Events, Activities and Gateways.

Connecting Objects

Flow objects are connected to each other by means of connectors to create the basicframework of the business process structure. The attributes of connecting objects areSequence, Flow, Message Flow and Association.

Swimlane

Swim lanes are mechanisms to arrange activities in separate display categories to illustrate thedifferent functional areas or persons in charge. The attributes of Swimlane are Pools andLanes.

Artifacts

Artifacts are used to provide additional information about the process. They provide thenotation with flexibility to express different contexts properly. The attributes of Artifacts areData Object, Group and Annotation.

Customer

Pass itemsover scanner

1

Look up in fileand code

price

2

Calculate totalcost

3

Get moneyissue receipts

4

Description

Itemsand

prices

Amou

nt tobepaid

Barcode

BarcodeItem

descriptionand price

Itemand

prices

Item,prices,

subtotal

Cash registerreceipt

Cash, debitcard

CustomerDatabase Database


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Components of UML Used In BPM Actor

An actor is an external entity that interacts with the system

Component

It represents the replaceable part of any given system and provides the realization of a set ofinterfaces.

Class

It defines the attributes and the methods of a set of objects.

1.9 BPM Architectures, Frameworks and Reference ModelsBusiness process management (BPM) can be stated as the logical approach of making businessoperations more effectual, more efficient and more seemingly capable of adapting to an ever-changing environment.

Zachman framework

This logical structure is intended to provide a well comprehensive representation ofinformation systems in a business enterprise. It provides several perceptions on how business

processes and structures can be represented. The Zachman framework shows how the different perceptions from a modeled system interrelate.

ARIS (Architecture of Integrated Information Systems)

Aris is also another approach to business process modeling. It provides holistic view of howmodels can be represented in a in a business process design, management and work flow. ARIS doesnot just provide a generic methodology to define an enterprises framework but also aa business

process modeling tool.

GRAI

It is a form of modeling used to analyze the operation of all or part of a production process. Ittransforms user requirements into user specification in terms of function, information, decisions and

resources. The strength of the GRAI method is in its ability to provide modelers an effective modeleddecision-making platform.

CIMOSA (Computer Integrated manufacturing and Open System Architecture)

It represents a kind of modeling framework which supports the integration of computersystems and people in a business environment. It provides framework which changes userrequirements gathered into functional specifications.


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1.10 IDEF Family Technology. IDEF0 (IDEF Function Modelling)

IDEF3 (Process Description Model)

1.11 Difference between discrete, continuous and hybrid simulations.

Discrete Event SimulationA Discrete event simulation employs the use of mathematical models to simulate real life

systems. It helps to show the change of state at a given point in the system of study. Considering thestate change and the viable time changes happen, the analysis of the changes can serve as reference

point for take decisions and make recommendations. Discrete event simulation can for example beused to model the waiting in line of customer to determine the best rate they can be served.

Receive ATM cardfor processing

1

Account details

Process transaction

Account details

Verify accountbalanace

Customer

Print receipt

Give money

to cus tomerNotify

customer

Verifyaccountdetails

Yes

Evaluate Project

1

Evaluate taskremaining

2

Evaluate effortremaining

3

Evaluate progressrate

8

Evaluate number oftask complete

9

Determine level ofpersonnel

4

Evaluate the timerequired

5

Indicatecompletion date

6

Schedulecompletion date

7

Productivity Productivity


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Continuous Simulation

A Continuous simulation on the other handalso makes use of mathematical equationformodeling, but there is no exact precise relationship between the state and the time the simulationruns. The main objective to why continuous simulation is done, is to model real life system whereresults are not explicitly based on the interrelation between changes between the change in state of asystem and the time those changes happen.Continuous simulation can for example be employed inscience to study biological systems.

Hybrid SimulationHybrid simulation employs the use of the two mentioned above thus discrete and continuous

simulation.It is defined as an integration of both continuous and discrete event simulations, where both models symbiotically enhance each others capabilities and reduce the possible limitations thatcan come up by sharing information.

1.7.1 Poisson distributionIt is a type of probability distribution where random variables occur in a specific time interval. In a

random event, Poisson can easily be used to determine the independent occurrence of an event in a

specific time, the frequency at which those events occur and the count of those events. Poisson was basically derived from a binomial distribution.

For example a Poisson distribution can easily be used to determine a number of students accessingthe turnitin website to review their work.

Denoted by ( )!

x

P X x e x

These requirements must be met to satisfy a Poisson distribution:

1. The fixed length of time of the observation.

2. The average rate of time an event must occur.3. The independent number of event occurring.

The Poisson distribution has expected value [ ] E X and variance ( )Var X

1.7.2 Weibull DistributionThis distribution is commonly used when the reliability of an event is to be analyzed.

Any rate of failure determined in a Weibull distribution is represented by ( / ) x

Where

1 ( )( ) x f x x e

The Weibull distribution has expected value 1

E X

and a variance of

22

2

2 1 1( ) 2Var X


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1.12 The Elements of Discrete simulation modeling

EntitiesThese are objects that comprise the system.They move round, make changes and are also affected by

other entities. They are the dynamic element in any given simulation process. They are created, move aroundthe system and later get disposed as they leave. Some entities may never leave a system as long it runs.

Entities can be parts to be processed. They are created upon arrival, join the queue, and later getserved then finally get disposed as they leave. Entities usually exist in a simulation to represent real objects.

AttributeTo make an entity unique in its state, attributes are attached to them. Attribute represents the

common characteristic of all entities. They provide specified values to each entity in the system. They are basically defined to represent values to change the state of entities.

VariableThese elements are the piece of information that shows characteristic of a simulation process. A

variable is also represented uniquely in a system. In contrast to attributes, variables are not specificallyassociated to any entity in the system. They are usually accessible by all entities. Variables can also be usedto represent an occurrence in the simulation process.

ResourceEntities usually compete to be serviced by resources. In real life, they represent a personnel,

equipment or storage space. Entities are seized by resources, processed and released when completed.Resources are intended to be assigned to entities rather than entities being assigned to a resource. A resourcecan represent a group of servers each presented as a unit. The number of servers represented by a resourcecan always be changed during a simulation run.

QueuesWhen entities cannot move, it is because there are is a possibility another entity is being held up by a

resource, these entities need to wait and that purposefully forms a queue.

Statistical accumulators

This represents variables that monitor the change in states of a simulation process. They usually donot take part in the simulation process by statistical analyze rate at which states changes and presents that asresults for analysis. They can be used to deduce the following in any given simulation process.


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1.13 SCIENTIFIC REJOINDER ADVOCATING THE NEED FOR COMPUTER SIMULATIONAND MODELING IN TODAYS BUSNIESS PROCESSES.

Manfred Egan's commentary points to the fact that a computer simulated outcome is A theory thathas only the alternative of being right or wrong. A computer model has a third possibility; it may be right,but irrelevant . Hence the extent of any simulated environment could be overestimated and may becomeirrelevant.

Pierre Gallois on the other hand also provided his view on the subject matter by giving a commentarywhich includes further analysis and history as to the cause of the original disconnect as manifested in theirrelevant results to any simulation software due to the accuracy of the data and the instruments collectionthese data. His theory states that If you put tomfoolery into a computer, nothing comes out of it buttomfoolery. But this tomfoolery, having passed through a very expensive machine, is somehow ennobled andno-one dares criticize it.

Having both the perception of being a current practitioner of employing simulation software tomimic business operations and as an academician, Manfred and Gallios comments provide some unique

provocative views which may need debunking.

To rebut these commentaries, although computer simulations and other modeling tools have assumeda pivotal role in business processes, their value continues to be questioned. This rejoinder presents severaldebunking statements that illustrate rewards of a simulation approach.

Business processes are broken down into more simple activities when viewed holistically by a model.During their execution, activities have to be coordinated (Desel& Erwin, 2000). Usually resources will haveto be provided to the activities to enable their execution process. A process has to be described in a wayspecifying which activities have to be executed in what order and what resources are needed for theexecution of these activities.

Over the years, simulations can and have provided some tremendous formal frameworks necessary to bring in new ideas in business processes, they have been used extensively to explore the implications of verycomplex system models, and they have turn out to predict seemingly unreasonable findings and uncovered a

number of relationships hidden in systems.Another key benefit of Computer Simulation is also adverse provision of businesses with models that

has helped evaluate different alternative scenarios for improvement which are usually the driving factors ofany business process (Bosilj-Vuksic, Indihar Stemberger, Jaklic, &Kovacic, 2003).

Computer Simulation also provides the very techniques and tools that enable modeling and designingof any business processes, the careful evaluation of their performance and the indexes that affect them, alsoexperimenting with different configurations which are highly suitable for organizational design.

Computer Simulation creates in any business a reference model for understanding and analyzing its business processes. One sure thing computer simulation helps to do is also to enable the movement of thestatic real world event into a more dynamic one. (Aguilar, Rautert, & Alexander, 1999).

Computer based simulation models of business processes can help overcome the inherentcomplexities of studying and analyzing organizations and therefore contribute to a higher level ofunderstanding and designing organizational structures (Giaglis et al., 1999).

Simulation can also serve as a tool for deriving new knowledge on current business processes, suchas additional in-depth understanding of how the process is executed and the identification of the sources ofthe problems observed during the process execution (Bosilj-Vuksic et al., 2003).

Simulation offers a wide range of possibilities for analyzing time, cost and resources aspect of a

business process and can aid business decision makers in prioritizing improvement actions and resourceallocation decisions


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Finally, this paper has investigated and deduced the potential of the use of computer for simulatingand modeling business processes. Following a discussion related to business process and their definitions, a

brief overview of how computer simulation is important to businesses was presented. The usability ofsimulation modeling for evaluating alternative business process strategies was investigated.

To conclude, having refuted and debunked the commentaries stated by Egan and Gallois. There aremany reasons why computer simulation should be. For example, a new business process might take bolddecision about a capital investment maybe difficult to reverse. It is usually too expensive to experiment withthe real business processes.

Using simulation models can improve various skills, from technical to decision making. Goodsimulation models can led to replicable results where assumptions are explicit and can overcome limitationsin human reason (Darnton and Darnton, 1997). However, if models are not adequately developed andvalidated, then the assumptions made by Egan about the behavior of real processes are likely to be wrong. Itmight also be difficult to achieve a required precision for a model due to lack of date or to the highlyunpredictable behavior of some process resources.

Regardless of the problems associated with business process simulation, the argument for usingcomputer simulation in modeling business processes remains valid. A more use of computer simulation for

business process modeling and desi gn can drastically increase the rate of any businesss productivity.


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QUESTION 22a.

Solution : Mean arrival rate 10 cars per hour

Mean service rate 1

60 203

per hour

10 120 2

(i) Average number of cars in the system

1 21

(1 ) 1 1 2 s L

car.

(ii) Average time a car spends in the queue

Let q L

0.50.05 3 minutes .

10q L hour

(iii) Average queue length

22 1 2 1 40.5

(1 ) 1 1 2 1 1 2q L car

(iv) The probability that there will be two cars in the system.

n units of cars in the system (1 )n n P P If 2n ,2

21 1 12 2 8

P

2b

Solution : Mean arrival rate 30 trains per day

Mean service rate 1

60 24 4036

per day

300.75

40

(i) The probability that the yard is empty, queue size is zero.

0 1

11 N

P

Where 9 N

0 9 1

1 0.750.2649

1 (0.75) P


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(ii) The average queue length.

10

11

N n

s N n

L n

9

9 10

1 0.750.75

1 (0.75)n

sn

L n

0.28 9.58 3 trains

QUESTION 3a.

Solution :

Number of phases 3k

Service time per phase =1.5 minutes

Service per customer 1.5 3 4.5min utes 1

4.5 Customer/minute

1 4060

4.5 3 Customers/hour

6 Customers/hour

(i) the average time a customer spends waiting in cafeteria

1 1. .2q

k W k

3 1 6 3 1. .

402(3) 40 63

qW

9220

hours

9 2760 2.45min220 11 utes

(ii) Find the most probable time spent in getting the service

The most probable time spent 1 3 1 1

40 2033

k hour

k

160 3min20 utes


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3b MM/M/S: (N, FIFO) QUEUEMultiple Server Poisson Queue ModelIt is a queuing model where arrivals follow a Poisson process,

The first MM represent the multiple inputs that arrival in the system.The second M represents the single output processed in the systemS represents the servers in the system, where s is a positive number.

The number of potential customers is denoted by N in the arrival populationCustomers arrive in a Poisson distribution with a mean arrival rate of Each server provides service at an average rate of per time period, the actualservice rate follows an exponential distributionA FIFO queuerepresents a First In First Out method of queuing serviced by thefirst available server.

Operating CharacteristicsCharacteristics DescriptionU Determines the timeserver is busy.

0 P Probability that there are no customers in the system.

q L Average number of customers in queue

L Average of number of customers being servedqW Average time a customer spends in queue before

being servedW Average time a customer spends in the system

w P Probability that an arriving customer is has no waittime

Short notes on

Bayes Total Probability Rule Bayes rule, named after the English mathematician Thomas Bayes, is a rule for

computing conditional probabilities.The rule:Let A and B be two events.Denote their probabilities by P A and P B and suppose that both 0 P A and

0 P B .Denote by | P A B the conditional probability of Agiven B and by | P A B theconditional probability of B given A .

Bayes' rule states that: |

| P B A P A

P A B P B

Probability Distribution Function

A random variable is said to be discrete if the set of values it can take has either afinite or an infinite but countable number of elements. Its probability distribution can becharacterized through a function called probability mass function.


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A random variable is discrete if its support x R is countable and there exist a

function : 0,1 x P , called probability mass function of X , such that (x) (X ) x P x where P(X ) x is the probability that X will take the value x .

Cumulative Distributive Function

Distribution function at a given point is equal to the probability of observing arealization of the random variable below that point or equal to that point. The distributionfunction is also often called cumulative distribution function.

If is a random variable, its distribution function is a function : 0,1 x F suchthat (x) (X x) x F x

Where (X x) is the probability that X is less than or equal to x .

Random Variable

A random variable is a value whose outcome is determined by a probabilisticexperiment. Its value is a priori unknown, but it becomes known once the outcome of theexperiment is realized.

Denotedby . A random variable associates a real number to each element of , as stated bythe following:

A random variable is a function from the sample space to the set of realnumbers :

PART B

Experiments A B C D ECustomer arrival rate 1 2 3 4 5

Mean service time 1 2 3 4 5 Number of servers 2.7 3 3.3 3.6 3.9

Maximum allowed inqueue 3 4 5 6 7

Simulation run time 10 15 20 25 30

Simulation Outputs -Simulation times in

minutes A B C D E


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Time last customerleaves 0.81 4.73 9.18 10.92 14.63

Average time in queue per customer 0.00 2.10 5.83 6.30 9.30

Maximum time in queuefor any customer 0.00 5.15 11.07 11.53 12.92

Average number ofcustomers in queue 0.00 2.68 4.55 5.41 6.58

Maximum number inqueue 0 4 5 6 7

Fraction of time eachserver is busy 21.4% 86.3% 99.7% 94.8% 96.4%

Number of customers processed 12 22 17 24 21

Number of customersturned away 0 9 37 76 132

Fraction of customersturned away 0.0% 29.0% 68.5% 76.0% 86.3%

Probability distributionof number in queue

Number in queue % of time % of time % of time % of time % of time0 86.64% 17.13% 1.24% 1.08% 0.82%1 11.00% 0.39% 0.22% 0.08%2 17.25% 1.20% 0.10% 0.10%3 22.15% 7.85% 1.13% 0.21%4 31.91% 21.27% 4.77% 0.52%5 67.79% 18.21% 1.17%6 74.43% 13.16%7 82.83%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0

P e r c e n t o

f T i m e

Number in Queue

Distribution of Number in Queue of Experient A


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0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

0 1 2 3 4

P e r c e n t o

f T i m e

Number in Queue

Distribution of Number in Queue of Experient B

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

0 1 2 3 4 5

P e r c e n t o

f T i m e

Number in Queue

Distribution of Number in Queue of Experient C


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Based on observation deduced from the results above.

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

0 1 2 3 4 5 6

P e r c e n t o

f T i m e

Number in Queue

Distribution of Number in Queue of Experient D

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

0 1 2 3 4 5 6 7

P e r c e n t o

f T i m e

Number in Queue

Distribution of Number in Queue of Experient E


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The utilization of the server were at the peak per the different experiment results generated when themaximum allow in the queue increased steadily. This also affected the time a customer spends in a queue byincreasing it as the queue got longer. This in tend increase the rate at which customer lost patience and leftthe queue.

From the analysis, an advice will be made to the bank to make provision for more servers to attend tocustomers. This will mean a huge cost in employing more tellers and expanding banking hall to containmore customers.

An ATM can also be proposed out of the bank premise to help serve customers who wish to redraw smalleramount of cash to help decrease the queue lengths in the banking halls.

Part CPurposes of business process modeling in Information Systems Design

Business process modeling allows for detailed, dynamic analysis of current or proposed processes.By enabling analysts to test process ideas under simulated conditions before putting those ideas into practice,

business process modeling plays a key role in technology-assisted business process management (BPM).One key criterion for evaluating BPM platforms, then, is the degree to which they facilitate effective

business process modeling.

Application of business process modeling in Information Systems design provides useful tools thatcan used to address the following under listed section of business operations.

It provides concrete evidence on what type of Enterprise Resource Planning solution should be employed in any business operations.

Provide the architecture for system development in Software Engineering. Provides outlines for the modeling of workflows in business environments. Then also, greatly utilized in the simulation of any business process existing or non-existing.

BPM in the arena of selecting an ERP solution

ERP solutions have played a major role in enabling enterprises standardize the way information isrecorded, presented and used. They have enhanced process efficiency, speed of decision-making andoperational excellence.

Enterprises of all sizes are facing an ever growing need to meet the challenge of the changing world,including globalization, shorter times to market, and competition from business of all sizes, in addition torequirements for cost reduction and responsiveness to change and flexibility.

Business Process Modeling provides the platform to fill gaps in information that are caused becauseof data spread across disparate systems, and thus enhancing process efficiency.

Business Process Modeling provides the blueprint for innovation and enables new processes and ideato be seamlessly integrated into an ERP with systems designed to process newer types of information.


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Business Process Modeling provides ways to extend new functionalities easily and make these newadditions cost-effective to existing enterprise systems such as CRM.

Business Process Modeling in Workflow Management

Business Process Modeling help increase the efficiency of business processes by reducing time spenton mundane processes and task. By providing efficiency, exceptions that may come up as problems in thefuture of the business operations are curbed.

It also provides the visibility for management to see staff performance and also make planning andassignment of role to business operations easier.

Consistency is also a key factor provided by BPM solution in a business process. It makes it veryeasy to add on new staff and provide effective training.

Since efficiency in workflow is provided by BPM, the satisfaction in business operation of bothcustomers and staff are greatly maximized.

Business Process Modeling in Software Development

In software development the key factor in the success of any system is the ability to model the core processes in detail. BPM has provided software and system analyst the ability to seamlessly providestructures and procedures that a software system may require.

Business process modeling provides another technical support for building systems by providingmanuals and blueprint for the proper implementation of any software development solution. This helpsreduce cost in the development process where a solution is provided and it does not meet the specifiedrequirement of the end user.

In software development, Business Process Modeling provides a conceptual model of a businessoperation that maybe under development. A business model helps to define the static objects in any softwaredevelopment operation.

Another general feature BPM provides in software development is the idea to create live demo of thesoftware implementation process.BPM also help to describe the activities and states a software solution may

provide.

Ideally after a business process model is generate for a software development solution and tested,results of the outcome can be analyzed and recommendations given to support the system.

Business Process Modeling in Simulation

Once the processes have been documented and data quality has been ensured, it is valuable tosimulate the processes. Effective simulation modules offer multiple possibilities for simulating the existing

processes and comparing process alternatives.

In simulation, businesses running simulation can make some assumptions about how the business process and provide inputs and the related results these processes present.

Process simulation helps to identify cost-intensive activities hidden in the overall process. It providesinsights about the duration of the processes, as well as consumed resources.

Simulating business processes not only provide a preview into how your new processes will perform,it also offers the opportunity to validate changes to existing processes without disturbing current businessoperations.


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PART DArena Simulation Lab 1

Arena Simulation Lab 2

Arena Simulation Lab 3

SystemPart Arrives To Drilling Center System

Part Leaves

00

0

QUEUE LENGTH

1009080706050403020100

0

Crea t e P rodu c t A

C re a t e P r o d u c t B

C re a t e P r o d u c t C

C re a t e P r o d u c t D

Att rib ut es As s ig n Pro du c t A

Attr ib ut es As s ig n Pro du c t B

Attr ib ut es As s ig n Pro du c t C

Attr ib ut es As s ig n Pro du c t D

Pr oduc t A Prep

Produ c t B Prep

Produ c t C Prep

Produ c t D Pre p

P r o c e s sI n s p e c t i o n

3055

Else

Inspec t ion Dec i s ion

P ro c e s sR e f u r b i s h m e n t

P ro c e s sD i s m a n t l i n g

O ri ginal

Dupl ic a t e

C o m p o n e n t s

True

Fal se

Recovered Componen ts

R e f u r b i s h e dR e c o r d

C o m p o n e n t sR e c o r d R e c o v e r e d

R e c o rd R e c y c l e d

S e n d T o M a r k e t

R e m a n u f a c t u r i n gS e n d To

S e n d T o R e c y c l i n g

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0


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DCT Terminal Flowchart

Start Trucks arrive atterminal

BF Truck

F Truck

B Truck

checkdocuments

Processpaper Disapprove

Approve

SucceedQueue forcrane

End

Craneprocess

Fail

Storagge area

B TruckF Truck

BF Truck

Set paperrigth

Part A Arrive

Part B Arrive

Ti m eSealer and Arrival

As s ig n Pa rt A

Ti m eSealer and Arrival

As s ig n Pa rt B

Prep A Process

Prep B Process

Sea le r P roces s

Failed Sealer InspectionTrue

False

Rework Process Fa iled Rework InspectionTrue

False

Record Scrap Parts

PartsRecord Sa lvage

PartsRecord Sh ipped

Scrapped

Sa lvaged

Sh ipped

0

0

0

0

0

0

0 0

0

0

0

0

0


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Start

Finish

CraneProcess

Storagearea

TrucksArrive

B Truck

F Truck

BF Truck

CheckPaper

Processpaper

Setpaperight

Queuefor

crane

F Truck

BF TruckB Truck


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References

W. David Kelton, Randall P. Sadowski, Nancy B. Swet (2010). Simulation with Arena. Fifth Edition,International Edition, McGraw-Hill

van der Aalst W, van Hee K (2004) Workflow Management: Models, Methods, and Systems.Cooperative Information Systems Series, MIT Press

Becker J, Kugeler M, Rosemann M (2002) Process Management. A Guide for the Design of Business

Processes. Springer

Dumas M, van der Aalst W, terHofstede AH (eds) (2005) Process Aware Information Systems:Bridging People and Software through Process Technology. John Wiley & Sons

Robbins, S.P., Judge, T.A., Sanghi, S (2009).Organizational Behaviour, Pearson Education.

Stoner, R. James A.F., Edward Freeman Daniel R Gilbert Jr., Management 6TH Ed, .Prentice-Hall ofIndia.

Kelton WD, Sadowski RP, Sturrock DT (2009) Simulation with ARENA. 5/e edn. McGraw-Hill,New York

Adan I, Resing J (2002) Queueing theory.Technical report. Department of Maths and ComputerScience, Eindhoven University of Technology, The Netherlands

Harrington, J., 1991. Business Process Improvement: The Breakthrough Strategy for Total Quality,Productivity and Competitiveness. McGrawHill, New York, USA.

Dover, C. (2012). Worldwide enterprise resource management applications 2012 2016 forecast and2011 vendor shares.

Bingi, P., Sharma, M. K., &Godla, J. K. (1999).Critical issues affecting an ERP implementation.Information Systems Management, 16(3), 7-14.

Abugabah, A., &Sanzogni, L. (2010). Enterprise resource planning (ERP) system in highereducation: A literature review and implications. World Academy of Science, Engineering and Technology,71

Aiken, P. (2002). Enterprise resource planning (ERP) considerations. VCU/Institute for DataResearch,

answers to exams review 2

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