Promodel report

Manufacturing Simulation and Optimization

The Use of Simulation Modelling Software in the Design

and Analysis of Manufacturing Systems

Author: Joshua JonesModule: Manufacturing

E2Tutor: Dr.F Zahedi

Last updated/submitted:March 21, 2014

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Table Of Contents

1 Introduction 4

2 Aims and Objectives 4

3 Introduction to modeling and Simulation 6

4 Model Details 84.1 The Exponential Distribution . . . . . . . . . . . . . . . . . . 114.2 The Normal Distribution . . . . . . . . . . . . . . . . . . . . . 124.3 The Triangular Distribution . . . . . . . . . . . . . . . . . . . 13

5 Experiment 1 13

6 Experiment: 2 17

7 Experiment: 3 197.1 warm up initilization . . . . . . . . . . . . . . . . . . . . . . . 20

8 Experiment: 4 208.1 replications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

9 Experiment: 5 22

10 Experiment: 6 2310.1 Iteration Testing . . . . . . . . . . . . . . . . . . . . . . . . . 26

11 Performance and Six Sigma analysis 2711.1 What is Six Sigma 6 . . . . . . . . . . . . . . . . . . . . . . 2811.2 Other Manufacturing Tools . . . . . . . . . . . . . . . . . . . 31

11.2.1 The Toyota Way . . . . . . . . . . . . . . . . . . . . . 3111.2.2 Ishikawa diagrams . . . . . . . . . . . . . . . . . . . . 31

12 Comparison to real world methods 32

13 Comparison to alternate computational methods 3313.1 Why use Simulation? . . . . . . . . . . . . . . . . . . . . . . . 3313.2 Promodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

13.2.1 Promodel in Project Management . . . . . . . . . . . . 3413.3 Simul8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

14 Benefits and limitations of simulation 36

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15 Project review and conclusions 3715.1 Cumulative statistics for all experiments . . . . . . . . . . . . 37

16 References 39

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Abstract and Executive Summary

The aim of this report is to explore the performance and capabil-ity of the descrete event modeling software Promodel and simulationsoftware in general.This was carried out by simlulating a manufacturing process and per-forming iterative changes to produce a highly optimized theoreticalrevision of the process.The aim of the project was to learn the software, and gain experi-ence in simulating industrial processes, in this way the student canappreciate the benifits and limits of the simulation, and elect to usesimulation in a proffessional setting if deemed suitable, furthermorelearning the software enhances the skill set, and problem solving ap-proach.Understanding the descrete event approach allows for step by stepproblem solving to be fostered.

In carrying out this project the student was encouraged to experi-ment with changing situations and scenarios by conducting a range of6 experiments, the varying nature of the experiments are designed togive the student an understanding of each aspect of the manufacturingprocess at the highest level: planning and optimization.

Where possible, the system has been modeled completely, and ac-curately, the scenarios have been implemented and thouroughly testedoften for extended periods to asses underlying trends.

Efforts to produce a more complete description of the system byassuming the type of product to be made and then researching thepotential costing of the system has been carried out, in this way thestudent can assess the profit and cost, including initial setup and anapproximate overhead.

Statistical process control has been highlighted as important and arange of methodologies have been discussed, with the Six Sigma defectrate being calculated for each experiment.

This project has explored the nature of descrete event simulationand looked at the options available in terms of software packages, fora comparison of suitability.

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1 Introduction

In the fields of mechanical and industrial engineering, an appreciation of in-dustrial processes is key to producing a viable product, both correctly andeffciently in both financial and environmental arenas. In todays economy op-erating a manufacturing process must be focused on using the latest Processcontrol tools to ensure the product is produced as cheaply and quickly aspossible, with the fewest number of defects.

Many companies have developed systems to use when manufacturing,such as The Toyota way obviously by Toyota, Six Sigma by Motorola,Lean manufacturing, Lean Six Sigma, and many others. These methodsform the ethos and methods of reducing waste, defects and costs, and in-creasing profitability of a system, thus providing value to stakeholders andcustomers. The overarching field is known as statistical process control. Thisreport is aimed at exploring an industrial process via the use of descrete eventsimulation, using the package solidworks.

The reccommended reading book for this course was given asDescrete-event-Simulation by J.Banks, and J.S.Carson, et al.[5]and will be used to form the basis of theoretical ideas and points.

The goal of the carrying out the simulation and the optimized, iterativesimulation with a number of constraints, is aimed at producing the maxi-mum improvement in production rate, while simultaneously using the leastamount of resources, and least amount of resources added to the base model.

Using promodel the project has explored the viability of the simulationby testing integrity and repeatability of the simulation, that is the simula-tion should be predictable to an extent. The project has also explored thebenefits and limitations of a purely simulated analysis of the manufacturingsystem.

2 Aims and Objectives

The aim of the coursework is to build a model of a manufacturing processusing the promodel simulation package, using this model to carry out testsand experiments to understand how the system behaves is a further aim, Interms of the Six Sigma development Cycle see figure 1 this would be car-

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ried out during the measure and analyze stage, also in Design for six sigmaDFSS, see figure 2, this experimentation will occur in the new product (inthis case manufacturing process) development stage. However in practise thesimulation could be used at all stages to validate and explore how making aninterative change may effect the system in an unforseen way, especially forsystems with a high level of process dependancy, that is many processes needto succeed for a certain process to be possible. These dependancies shouldbe carefully designed in the system to reduce the number of root causes forproblems (see root cause analysis )

Figure 1: DMAIC Six Sigma Process

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Figure 2: Design for Six Sigma (DFSS)

3 Introduction to modeling and Simulation

A simulation is the imitation of the operation of a real world processor system over time.[5]

Simulation involves the generation of an artificial history of a system, thethe observation of that system to draw inferences concerning the operatingcharacteristics of the real system.[5]. In the scope of promodel, observing andinterpreting the statistics produced by the output viewer can allow certaintrends to be picked out, and gather an undersanding of the system and howchanges effect it.

When simulation is suitable:

Simulation is suiable when the pervieved potential saving outweighs thecost of conducting the simulation, in practise this is usually the case as thesimulation is a one time purchase, however continued saving only increaseswith time.

Simulation enables the study of, and experimentation with, the internalinteractions of a complex system[5], and highlights the dependancies andconsequences of changing any small aspect of the system, and the effect thishas on the whole.

Simulation is suitable when changing the inputs and interpreting the out-

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puts is possible, in this way the simulation can show how sensitive the outputis to a particular input, and this may allow running at a minimum for oneinput thus eliminating waste without any performance decrease, if issues al-ready exist, simulation can highlight the root causes of problems, and showwhich variables and inputs are the most important (most critical).

Simulation can be used to verify analysical solutions, determine require-ments for a certain machine to operate at peak efficiency, and experimentwith new designs[5]

Manufacturing represents one of the most important applications of Sim-ulation. This technique represents a valuable tool used by engineers whenevaluating the effect of capital investment in equipments and physical facili-ties like factory plants, warehouses, and distribution centers. Simulation canbe used to predict the performance of an existing or planned system and tocompare alternative solutions for a particular design problem.[4]

Simulation should not be used when the problem can be solved usingcommon sense (Banks and gibson, 1997).

Simulation should not be used when the problem can be solved analyti-cally, in practise for complex systems this may be possible however easier tojust simulate.

Simulation should not be used when the problem can be tested with di-rect experiments, this is not possible for the scenario in this project however.

Simulation should not be used when the cost to do so exceeds the savings[5]

Disadvantages have been offset in some simulation packages by improvingthe functionality and reusabillity of models, for example some softwares areimplementing system sections, and templates, to reduce the time it takes tomodel a system [5]. Some simulation packages are offsetting their costs byincluding very detailed, thorough analysis. As technology improves so doesthe capability of the software and simulations. Simulation packages can han-dle very complex systems, which closed form models can not.[5]

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Applications:

1. Manufacturing

2. Semiconductor manufacturing

3. Construction engineering

4. Military applications

5. Logistics, transport, and distribution

6. Business operations and processes

7. Human systems, Air traffic control, Parking, population movement

4 Model Details

The simulation specification was the real world model of an assembly linethat produced two different products, using a range of engineering opera-tions, at multiple stations, both parts share a common raw material, giventhe nature of the description it will be assumed that the parts produced aresome kind of container, and the variation exists in that one type (Type II)also requires the addition of a cover or lid. It is envisioned that the product isperhaps some kind of barrel with the variation being, open/closed top, at in-put the initial loading bay for incoming parts can hold 5 units of raw material.

Initial thoughts on this system are that given the fact the products usea common raw material, sourcing this raw material is a critical node in theprocess life cycle, indeed it is the first one. Furthermore, the fact both partsshare an common first node is cause for potential bottleneck, and supplyissues, ideally separation of the systems would be prefferable is favouringthe most simplistic approach, however costing of producing two systems isprohibitive, when similar production rates may be possible with one system.

The initial specification specified, that all machines were single capacity,which is standard for real world machines such as CNC and turning/lathes,which can only accomodate one work piece at a time.

The specification calls for a initial split between Type I and Type IIproducts, of 50/50, given that only 5 raw units can be supplied at once thismay give some difference in the split between products.

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Type I Type IITurning MillingMilling DrillingDrilling Assembly (Cover From Storage)Inspection(Scrap(if Defective)) InspectionPacking Scrap if Defective

Packing

Table 1: Type I and Type II Process

Between each process there will be conveyor belts used to transport WIP(Work in progess), and a number of personnel:

Job Title RoleSkilled Worker Responsible for Turning, Milling, and

Drilling OperationsSemi-Skilled Responsible for Assembly, Inspection, and

PackingTransporter Responsible for transporting WIP to stations

and conveyors

Table 2: Employees

All conveyors are designed to be 10m long, holding 3 parts, and a speedof 50meters per minute, however this is something that could realistically bechanged.

The system is intended to give no priority to any WIP and process on afirst come first process method.

Fail rates for both type I and type II products are 0.05, or 5% defectrate at inspection, note that this occurs after the aluminum cover is addedto type II product, and therefore a registered defect also contributes as awasted aluminium cover.

The transport activities are Transfer for next process, and transfer toscrap from inspection (having failed inspection).

The method of processing parts varies and uses differring distributionmethods:

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Operation Distribution Value(Minutes)Arrivals Exponential Mean =20Turning Triangular (6,8,10)Milling Triangular (9,12,14)Drilling Normal Mean=5 Standard Deviation = 1Assembly Normal Mean=7 Standard Deviation = 1Inspection Normal Mean=5 Standard Deviation = 2Packing Normal Mean=3 Standard Deviation = 0.5

Table 3: Distribution types

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4.1 The Exponential Distribution

How much time will elapse before an earthquake occurs in a given region?How long do we need to wait before a customer enters our shop? How longwill it take before a call center receives the next phone call? How long will apiece of machinery work without breaking down? Questions such as these areoften answered in probabilistic terms using the exponential distribution. Allthese questions concern the time we need to wait before a given event occurs.If this waiting time is unknown, it is often appropriate to think of it as arandom variable having an exponential distribution. Roughly speaking, thetime X we need to wait before an event occurs has an exponential distributionif the probability that the event occurs during a certain time interval isproportional to the length of that time interval.[1]

Figure 3: Exponential Distribution

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4.2 The Normal Distribution

The normal distribution is the most commonly used statistical distributionused to model a range of events, its use in this projecthas been extensive,for example the Six Sigma statistical process control methadology employsthe concepts of standard deviation to measure performance of a process. Byplotting the mean and then the sample values around it either side () wecan see how much the system varies, and also if the system is performingwithin certain limits, the USL and LSL upper and lower specified limits,respectively.

The Normal Distribution has:mean = median = mode symmetry about the centre 50% of values less

than the mean and 50% greater than the mean

Figure 4: Normal Distribution

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4.3 The Triangular Distribution

The Triangular distribution is used when there are a small number samplesand only the upper, lower, and modal expected limits are known, in this waythe algorithm for the distribution can set iterative and recursive limits onthe systems performance, also known as the lack of knowledge distribution,

A triangular distribution is a continuous probability distribution with aprobability density function shaped like a triangle. It is defined by threevalues: the minimum value a, the maximum value b, and the peak value c.

Figure 5: Triangular Distribution

5 Experiment 1

The first experiment was to simply implement the model in Promodel asaccurately as possible, and assess the performance. From figure 19 we cansee that the turning spends a long time blocked, and the drilling and assemblystations are lightly blocked, this may be due to convey availability in termsof speed and capacity, and also an inccorectly selected turning machine that

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is unable to perform the operations quick enough.The system was simulated for 40hrs, this was interpreted as a one weekperiod, assuming continual operation for 5, 8 hours shifts, with one shiftoccuring per day. This system ran with 100% uptime.

Figure 6: Experiment 1 Outcomes

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Experiment 1Type 1 41Type 2 57Total 98Reject 1 1Reject 2 2Reject Total 3% defective 2.97%% Sucessful 97.03%Process Sigma 3.39Aluminium covers 62Aluminum Covers Wasted 5% Aluminum waste 8.06%Aluminum Process sigma 2.90DPMO 29703ThroughputPer second 0.00068Per minute 0.040833333Per hour 2.45

Table 4: Experiment 1 Results (DPMO = Defective parts per million Oper-ations)

Figure 7: Experiment 1 initial layout

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Figure 8: Experiment 1 finished layout

Figure 9: Experiment 1 output

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6 Experiment: 2

Experiment 2 was aimed at optimizing experiment 1, throughput increasedby 0.3 parts per hour, leading to a volume increase of 13 parts, howeverwhile overall volume has increased, so has the number of defects, and thenumber of aluminum covers both used and wasted. The sigma performanceindicator has dropped from 3.39 in experiment 1 to 3.22 in experiment 2,this drop in quality is unfortunate, however in terms of overall productivitythis experiment has been a positive impact, accruing 13 more parts for only 2more defects. One vital statistic is the Defective parts per million operations,initially only 29703 defects would occur on average, however this is nearlydoubled in experiment 2 to 43478, however the time take to produce a millionparts is reduced from 139.78 years, to 123.4 years assuming an 8 hour shiftconstitutes a day and a year is 365 days of continuous operation.

In experiment 2 only the number of staff changes, with one more skilledworker being recruited, and the lengths, both of the conveyor and the rout-ing for the staff being optimized and balanced. As can be seen from the twographs figure 10 and figure 9 the bottle neck in the turning operation hasbeen eliminated, this was done by balancing the conveyor speeds either sideof the station.

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Experiment 2Type 1 52Type 2 59Total 111Reject 1 1Reject 2 4Reject Total 5% defective 4.21%% Sucessful 95.69%Process Sigma 3.22Aluminium covers 66Aluminum Covers Wasted 7% Aluminum waste 10.61%Aluminum Process sigma 2.75DPMO 43478.00ThroughputPer second 0.00077Per minute 0.04625Per hour 2.775



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7 Experiment: 3

Experiment 3 is concerned with the time taken to achieve steady state output,or the utilization of a warm-up period, at each time interval (hours), theproductivity rate is erratic and differing, however for simulation with onlyuser specified inputs there should be a linearity to the results, by discountingthe erratic stage, these results can be found, by taking an iterative approachto solving this problem the system warm up time was found to be 96 hours.



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7.1 warm up initilization

8 Experiment: 4

The number of replications to provide a steady state value of output forthesimulation was determined iteratively to be around 20 replications.

Figure 12: Experiment 4 output replications

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8.1 replications

9 Experiment: 5

Experiment 5 was concerned with the possibility of changing the mix betweenTypeI and Type II, turning was utilized more than previously, and the generalsplit was consistent with a 25% : 75% mix as seen in figure 14, due to the lowutilizaton of aluminum this may represent some cost saving, and the systemwas quite efficient, running at 3.21 sigma, and 3.32 sigma for the aluminumoperation. The throughput increased to 2.775 parts per hour.

Figure 14: Experiment 5 output for mix



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10 Experiment: 6

In experiment 6 the aim was to double the speed and optimize the system,the previous values were from experiment 4 and the aim was to double theperformance. Initially total throughput was 116 parts at a rate of 2.9 partsper hour, running at 3.15 sigma, Small incremental changes to map the be-haviour of the simulation meant that by the 16th iteration the system wasproducing 204 parts at a rate of 5.1 parts per hour, running at 3.18 sigma, inreal terms this means 88 more products and 4118 fewer defective parts permillion.

In model 6 another assembly station was added, along with having 2 ofeach worker. The conveyor was further optimized and balanced by regulatingthe speeds and having some sections move faster than others to allow for thevariation in completion time at each station.

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10.1 Iteration Testing

Figure 19: Experiment 6 iteration results

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11 Performance and Six Sigma analysis

Figure 20: Six Sigma overview

Sigma 1.5 shift DPMO Defective Yield Short-term Cpk Long-term Cpk1 -0.5 691,462 69% 31% 0.33 0.172 0.5 308,538 31% 69% 0.67 0.173 1.5 66,807 6.7% 93.3% 1.00 0.54 2.5 6,210 0.62% 99.38% 1.33 0.835 3.5 233 0.023% 99.977% 1.67 1.176 4.5 3.4 0.00034% 99.99966% 2.00 1.57 5.5 0.019 0.0000019% 99.9999981% 2.33 1.83

Table 10: Sigma levels used to quantify process performance, Other manu-facturing tools

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The 1.5 shift is indicative of defects over extended periods, and is beyondthe scope of this simulation for all but the extreme time scale cases.

11.1 What is Six Sigma 6

Six Sigma at many organizations simply means a measure of quality thatstrives for near perfection. Six Sigma is a disciplined, data-driven approachand methodology for eliminating defects (driving toward six standard devia-tions between the mean and the nearest specification limit) in any process from manufacturing to transactional and from product to service.[2] The sta-tistical representation of Six Sigma describes quantitatively how a process isperforming. To achieve Six Sigma, a process must not produce more than 3.4defects per million opportunities. A Six Sigma defect is defined as anythingoutside of customer specifications. A Six Sigma opportunity is then the totalquantity of chances for a defect. [2] The fundamental objective of the SixSigma methodology is the implementation of a measurement-based strat-egy that focuses on process improvement and variation reduction throughthe application of Six Sigma improvement projects. This is accomplishedthrough the use of two Six Sigma sub-methodologies: DMAIC and DMADV.The Six Sigma DMAIC process (define, measure, analyze, improve, control)is an improvement system for existing processes falling below specificationand looking for incremental improvement. The Six Sigma DMADV process(define, measure, analyze, design, verify) is an improvement system used todevelop new processes or products at Six Sigma quality levels. It can also beemployed if a current process requires more than just incremental improve-ment. Both Six Sigma processes are executed by Six Sigma Green Belts andSix Sigma Black Belts, and are overseen by Six Sigma Master Black Belts.[2]

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Figure 21: Six Sigma overview

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Figure 22: Six Sigma overview perspective

According to the Six Sigma Academy, Black Belts save companies ap-proximately $230,000 per project and can complete four to 6 projects peryear. (Given that the average Black Belt salary is $80,000 in the UnitedStates, that is a fantastic return on investment.) General Electric, one of themost successful companies implementing Six Sigma, has estimated benefitson the order of $10 billion during the first five years of implementation. GEfirst began Six Sigma in 1995 after Motorola and Allied Signal blazed theSix Sigma trail. Since then, thousands of companies around the world havediscovered the far reaching benefits of Six Sigma.Many frameworks exist for implementing the Six Sigma methodology. SixSigma Consultants all over the world have developed proprietary methodolo-gies for implementing Six Sigma quality, based on the similar change man-agement philosophies and applications of tools.[2]

Six Sigma mostly finds application in large organizations. An importantfactor in the spread of Six Sigma was GEs 1998 announcement of $350million in savings thanks to Six Sigma, a figure that later grew to morethan $1 billion. According to industry consultants like Thomas Pyzdek andJohn Kullmann, companies with fewer than 500 employees are less suited to

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Six Sigma implementation, or need to adapt the standard approach to makeit work for them.[3] Six Sigma however contains a large number of toolsand techniques that work well in small to mid-size organizations. The factthat an organization is not big enough to be able to afford Black Belts doesnot diminish its abilities to make improvements using this set of tools andtechniques. The infrastructure described as necessary to support Six Sigmais a result of the size of the organization rather than a requirement of SixSigma itself.[3]

11.2 Other Manufacturing Tools

11.2.1 The Toyota Way

The 6 Ms (used in manufacturing industry)1.Machine (technology)2.Method (process)3.Material (Includes Raw Material, Consumables and Information.)4.Man Power (physical work)/Mind Power (brain work): Kaizens, Sugges-tions5.Measurement (Inspection)6.Milieu/Mother Nature (Environment)The original 6Ms used by the Toyota Production System have been expandedby some to include the following and are referred to as the 8Ms. However,this is not globally recognized. It has been suggested to return to the rootsof the tools and to keep the teaching simple while recognizing the originalintent; most programs do not address the 8Ms.7.Management/Money Power8.Maintenance

11.2.2 Ishikawa diagrams

Ishikawa diagrams see figure 23 (also called fishbone diagrams, herringbonediagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams cre-ated by Kaoru Ishikawa (1968) that show the causes of a specific event.[1][2]Common uses of the Ishikawa diagram are product design and quality de-fect prevention, to identify potential factors causing an overall effect. Eachcause or reason for imperfection is a source of variation. Causes are usuallygrouped into major categories to identify these sources of variation.The categories typically include:People: Anyone involved with the process

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Methods: How the process is performed and the specific requirements fordoing it, such as policies, procedures, rules, regulations and lawsMachines: Any equipment, computers, tools, etc. required to accomplish thejobMaterials: Raw materials, parts, pens, paper, etc. used to produce the finalproductMeasurements: Data generated from the process that are used to evaluateits qualityEnvironment: The conditions, such as location, time, temperature, and cul-ture in which the process operates

Figure 23: Cause effect diagram - Ishikawa diagrams

12 Comparison to real world methods

Simulation can never completely emulate a real world system with 100%accuracy because the world is not in descrete values, precision is only limitedby the level of precision possible by measurement, there will always be moreinformation, and more decimal places in a

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13 Comparison to alternate computational meth-

ods

13.1 Why use Simulation?

Accurate Depiction of Reality Anyone can perform a simple analysismanually. However, as the complexity of the analysis increases, so does theneed to employ computer-based tools.While spreadsheets can perform many calculations to help determine the op-erational status of simple systems, they use averages to represent schedules,activity times, and resource availability.[6]

This does not allow them to accurately reflect the randomness and inter-dependence present in reality with resources and other system elements. Sim-ulation, however, does take into account the randomness and interdependencewhich characterize the behavior of your real-life business environment.[6]

Using simulation, you can include randomness through properly identi-fied probability distributions taken directly from study data. For example,while the time needed to perform an assembly may average 10 minutes, spe-cial orders may take as many as 45 minutes to complete. A spreadsheet willforce you to use the average time, and will not be able to accurately capturethe variability that exists in reality.[6]

Simulation also allows interdependence through arrival and service events,and tracks them individually. For example, while order arrivals may placeitems in two locations, a worker can handle only one item at a time. Simula-tion accounts for that reality, while a spreadsheet must assume the operatorto be available simultaneously at both locations.

13.2 Promodel

Simulation is the Cornerstone for Decision Support With more than 4,000companies using this technology including 42 of the Fortune 100, ProModelis recognized as the industry leader tool for rapid and accurate simulation-based decision support. The bottom line savings are realized in the followingareas:[6]

Hard-dollar savings Lower capital expenditure.Increased existing facility utilization reduces net cost.

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Proper labor assignments prevent unnecessary new hires.Accurate and insightful facility planning eliminates unnecessary rework costs.[6]

Soft-dollar savings Facility rearrangement or reassignment of duties in-creases productivity.Reduced wait time improves customer satisfaction.Accurate system depiction ensures valid decision-making information.[6]

Labor savings Rapid development establishes time and cost data quicklyand accurately.[6]

Intangible benefits Increased understanding of the actual process im-proves employee education.[6]Coordinated simulation projects improve teamwork and communication andfocus resources in areas which will provide biggest benefit.[6]

The ProModel Optimization Suite is a powerful yet easy to use simulationtool for modeling all types of manufacturing systems ranging from small jobshops and machining cells to large mass production, flexible manufacturingsystems, and supply chain systems.

ProModel is a Windows based system with an intuitive graphical interfaceand object-oriented modeling constructs that eliminate the need for program-ming.It combines the flexibility of a general purpose simulation language with theconvenience of a data-driven simulator.In addition, ProModel utilizes an optimization tool called SimRunner thatperforms sophisticated what-if analysis by running automatic factorial de-sign of experiments on the model, providing the best answer possible.[6]

13.2.1 Promodel in Project Management

The major challenge in project management is being able to ensure thatprojects are delivered within defined constraints such as scope, time, andcost. Some of the most common problems faced by project managers are:

Outsourcing decisions

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Inability to accurately predict resource requirements and cost Communicating a solution across an Vrganization Varying task times Mitigating unperceived risks Missed deadlines Bottlenecks Insufficient and shared resources Inability to align resources Multiple conflicting goals i.e. - fastest Completion time at lowest cost Accelerated schedules

Pfizer, ITT, Laureate Pharma, Merck, Hot Topic and others are using Pro-Models PPM (Project and Portfolio Management) solutions to address these,and even more issues, in order to improve their project management and port-folio planning results. ProModel Simulation Solutions for Project Managersand Portfolio Planners ProModels Project Management solutions allow youto Visualize, Analyze, and Optimize the execution of a project or portfolio ofprojects by taking into account variability, resource contention, and complexinterdependencies. Unlike typical static analysis programs such as spread-sheets and project or portfolio management software, ProModels technologyexpresses information in ranges of answers, with confidence levels and depen-dencies, which more accurately reflect how a project will actually perform.[7]

13.3 Simul8

SIMUL8 is a computer package for Discrete Event Simulation. It allows theuser to create a visual model of the system being investigated by drawingobjects directly on the screen. Typical objects may be queues or servicepoints. The characteristics of the objects can be defined in terms of, forexample, capacity or speed. When the system has been modelled then a

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simulation can be undertaken. The flow of work items around the system isshown by animation on the screen so that the appropriateness of the modelcan be assessed. When the structure of the model has been confirmed, thena number of trials can be run and the performance of the system describedstatistically. Statistics of interest may be average waiting times, utilisationof work centres or resources, etc.[8]

14 Benefits and limitations of simulation

Advantages DisadvantagesDoes not disrupt real process Requires special training

New processes can be tested for free/littlecost

Difficult to interpret

Reasons for process performance can betested for feasibility

Time consuming

Time sensitive tests can be sped up

Expensive

Insight to variable interaction can be found

Analytical models may becheaper and better

Bottleneck analysis

Understand system more completely

Aids Design and answers what-if questionsand iterative approaches

Table 11: Pros and cons of simulation

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15 Project review and conclusions

15.1 Cumulative statistics for all experiments

Figure 24: All Results

Figure 25: All Results

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Figure 26: All Results Sigma analysis

Figure 27: Long time period testing

Figure 28: Time to produce a million parts

In conclusion the system has been optimized to a high level, and considera-tions of the system have been fully mapped out, moving on from this project,

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a large veriety of other projects may be tackled in a similar way, howeverPromodel did lack features in the outputing of data, and using excel wasjust as powerful, in order to incorporate the output viewer as part of ourresults, more powerful interpretation must be included. Overall promodelwas easy to use, however the outputs often gave little indication as to theperformance of the system, and an iterative approach was taken to ensureincremental improvement.

In a real world project Promodel would be useful however the systemquickly becomes unwieldly when dealing with larger sections, it is reccomendedto break the system down into sub systems to gain an intricate look at therelationship between variables and to establish variable input.output sense-tivity before continuing on to producing a finished system.

16 References

[1] Unknown, (2014), Exponential distribution, Available:http://www.statlect.com/ucdexp1.htm, Last accessed 21.03.14.

[2] http://www.isixsigma.com/new-to-six-sigma/getting-started/what-six-sigma/ ,

[3] Dusharme, Dirk. Six Sigma Survey: Breaking Through the Six SigmaHype. Quality Digest.

[4] Benedettini, O., Tjahjono, B. (2008). Towards an improved tool to facil-itate simulation modeling of complex manufacturing systems. Interna-tional Journal of Advanced Manufacturing Technology 43 (1/2): 1919.doi:10.1007/s00170-008-1686-z.

[5] Discrete event system simulation 3rd ed. , banks, carson, et al, prenticehall international series in systems and industrial engineering

[6] SIMULATION MODELING AND OPTIMIZATION USING PRO-MODEL, Deborah Benson , PROMODEL Corporation, Proceedings ofthe 1997 Winter Simulation Conference, ed. S. Andradottir, K. J. Healy,D. H. Withers, and B. L. Nelson

[7] ProModel Simulation Improves Project and Portfolio Management, pro-model.com

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[8] Jim Shalliker & Chris Ricketts, (2002), Intro to SImulate, Available:http://www.wirtschaft.fh-dortmund.de/eurompm/bilbao/S8intro.pdf,Last accessed 21.3.14.

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IntroductionAims and ObjectivesIntroduction to modeling and SimulationModel DetailsThe Exponential DistributionThe Normal DistributionThe Triangular Distribution

Experiment 1Experiment: 2Experiment: 3warm up initilization

Experiment: 4replications

Experiment: 5Experiment: 6Iteration Testing

Performance and Six Sigma analysisWhat is Six Sigma 6Other Manufacturing ToolsThe Toyota WayIshikawa diagrams

Comparison to real world methodsComparison to alternate computational methodsWhy use Simulation?PromodelPromodel in Project Management

Simul8

Benefits and limitations of simulationProject review and conclusionsCumulative statistics for all experiments

References

Documents

Promodel report