A Case Study in Software Enhancements as Six Sigma Process Improvements: Simulating Productivity Savings
|
|
- Harry Williamson
- 8 years ago
- Views:
Transcription
1 A Case Study in Software Enhancements as Six Sigma Process Improvements: Simulating Productivity Savings Dan Houston, Ph.D. Automation and Control Solutions Honeywell, Inc. Abstract This case study illustrates the application of Six Sigma process improvement to software upgrades justification. Specifically, software use was simulated based on business process maps and data provided by users. The simulation results were combined with upgrade investment estimates to calculate the return on investment and justify the upgrades. 1. Introduction Information technology (IT) has become recognized as an essential component of business, providing the platform for a wide array of business processes. When considered in this context, IT enhancements may result from process improvement projects. IT software upgrades can often be justified in terms of savings due to increased productivity. However, in a Six Sigma organization, the DMAIC method and a host of tools can be brought to bear on business process improvement through software enhancement. This paper presents such a case study, the Configurator ROI project, relating the application of Monte Carlo simulation as a Six Sigma tool to identifying productivity savings in the deployment phase of a software lifecycle. Information technology software enhancements are performed for many reasons, including better customer service, better decision support, higher productivity, and so forth. Return on investment (ROI) is ordinarily calculated in terms of a focused objective for savings. Many of these enhancements either provide support for, or implement improved business processes, but the ROI is typically produced solely from a financial analysis, and not necessarily in the context of a process analysis. 2. Six Sigma DMAIC Six Sigma is a quality improvement program that looks at processes with a view to analyzing process steps, determining what process elements most need improvement, developing alternatives for improvement, then selecting and implementing one. It relies on a variety of qualitative and quantitative tools, emphasizing the use of data and statistical analysis within a method called DMAIC, an acronym for the names of its five phases (Define, Measure, Analyze, Improve, and Control) (Table 1). Six Sigma projects are typically selected for their potential savings in improving any process, whether it is in production, administration, engineering, or services. A Six Sigma project typically begins with a high level definition of a process, using a diagram to specify the process boundaries, inputs, outputs, customers, and requirements. In the measure phase, a process metric is selected and used to baseline the current performance of the process. In the analysis phase, the process is analyzed, usually with a process map and a failure modes and effects analysis (FMEA), but may include other types of analysis. The process map shows each process step with its inputs and outputs and provides the basis for either a FMEA or a quantitative, usually statistical, analysis. Areas for improvement are pinpointed and alternatives are generated and evaluated. Once an improvement option is selected Phase Define Measure Analyze Improve Control Table 1. DMAIC Method for Process Improvement Steps Identify an opportunity and define a project to address it. Analyze the current process and specify the desired outcome. Identify root causes and proposed solutions. Prioritize solutions; select, plan, validate, and implement a solution. Develop a plan for measuring progress and maintaining gains.
2 and implemented, the project enters the control phase. In this phase, a plan is established for monitoring and controlling the process to ensure that gains are maintained. The use of the DMAIC method may vary between projects. For example, the Measure and Analyze phases of this project ran concurrently rather than sequentially. Also, a proposed solution may emerge early in the Measure and Analysis phase, leading to an emphasis on planning and implementation in the Improve phase. Such was the case in the Configurator ROI project. Consequently, this paper focuses on the Measure and Analyze phases in which a simulation based on a process map provided the justification for an IT enhancement. 3. Configurators and the Configurator ROI Project Honeywell Industry Solutions, a division of Honeywell International, produces and services control systems for industrial process such as refining, power generation, and paper production. These complex control systems consist of a multitude of components controllers, sensors, monitoring devices, network cabling, signal converters, and so forth that must be configured to each customer s specifications. Over the years a number of software packages have been developed for configuring the various Honeywell control systems, each configuration package supporting a control system serving a particular market. A product configurator is essentially a rule-based package that specifies all the necessary components for a proposed control system, prices the components, then provides a set of reports, including a bill of materials. Salespersons and estimators use the configurators to develop proposals for customers. Each configurator was predicated on different business processes. With corporate globalization, the inefficiency of using multiple processes and software programs for system configuration became evident. In April 2001, a division-wide study of configurator requirements was undertaken with a Voice of the Customer (VOC) process. This process solicited input from sales, marketing, manufacturing, engineering, and information technology departments, analyzed it, and produced a set of high-level requirements for a comprehensive product configurator. This case study discusses the follow-on project, Configurator ROI, in which detailed requirements were specified and used as the basis for estimating ROI for the proposed upgrades to the TotalPlant Configurator (TPC), the primary existing product configuration system. Figure 1 illustrates the Measure and Analyze phases of the Configurator ROI project. Calculation of the ROI was broken down into two parts, the investment calculation and the productivity savings calculation. The investment calculation was performed by (1) identifying differences between the current configurator tools and the configurator requirements derived from the VOC process, (2) proposing engineering solutions to close the gaps, (3) using a quality function deployment matrix to evaluate the solutions against the requirements, and (4) estimating the cost of each solution. As the Configurator ROI project began, the solution was undecided. Two alternatives were available. These involved upgrading either one of the two leading Current Tools System Requirements Identify Gaps Estimate Upgrade Costs of Each Alternative Calculate ROI To-Be Process Map Survey System Users for Productivity Data Simulate To-Be Process Forecast Productivity Savings As-Is Process Map Simulate As-Is Process Fig. 1. Configurator ROI Project Diagram
3 Configuration and Proposal Order Entry Bill of Materials Fig. 2. Primary Functions of a Complete Configurator configurators, TPC or ebob, to address all of the requirements elicited during the VOC process. These two systems addressed, to varying degrees, the requirements of a complete system for configuration and proposal, order entry and Bill of Materials (BOM) for manufacturing (Figure 2). Though ebob provided much more flexibility in delivering a BOM and its data was automatically transferred into the order processing system, its configuration capabilities were largely manual because it lacked a rule base and expert system engine. TPC, on the other hand, had a well-designed and up-to-date rules base and expert engine, but needed more BOM flexibility and order entry capability. During the process of estimating the cost of each solution, it became clear that upgrading ebob would cost roughly twice the upgrade cost of TPC. From that point on, the project focused on the expected ROI for upgrading TPC. The productivity savings calculations were produced using a Monte Carlo simulation of existing productivity and comparing these results with those of a simulation of expected productivity after the upgrades. The remainder of this paper dwells on the details of data collection and simulation. 4. Simulation Approach Although many groups benefit from a product configurator, the primary beneficiaries are the estimators who develop proposals and provide quotes to salespersons and customers. This group of configurator users has defined a complex process for developing proposals. The paths of the proposal development process depend on the size of the proposed system, whether engineering services are to be provided, and the degree of additional consulting required. The Configurator ROI team obtained the process map for the proposal development process (fourteen 11 x17 pages) and used it to identify the process flow directly affected by configurator usage. The elements of the proposal development process specific to configurator usage were extracted into the process map of Figure 2. The process shown in Figure 3 branches depending on the number of personnel required for developing a proposal. The top branch is taken when a Account Manager develops a proposal without assistance. When Sales Cost Consultants are used, Engineering and Installation (E&I) services may or may not be proposed. When E&I are not being proposed, a Cost Team may or may not be required. If E&I services are being proposed, other services may or may not be required. In addition to the branches shown in Figure 3, the simulation had to account for various proposal sizes. Three proposal sizes were distinguished: small (< $100K), medium ($100K to $1M), and large (>$1M). A Monte Carlo approach to simulation was chosen for two reasons. First, the feedback loops in the process map are only around single or paired steps in series. This suggests that the combination of process and feedback loop can be simplified to only the process steps by treating the duration of each process step as an aggregate duration rather than discrete multiple durations. Secondly, the process steps of interest lie on independent paths. The lack of path dependencies means the process has no timing requirements and a simulation of it does not require a calendar. A simple Monte Carlo approach to simulating this process requires only two types of random variables: discretely distributed branching for proposal size, and continuously distributed effort applied in each process step simulated. Although the Configurator ROI team had access to simulation tools, other groups interested in the method and results do not. Accessibility to Monte Carlo simulation examples is highly desirable in the Industry Solutions business because simulation is not widely used for modeling business and development processes, but its value is recognized by the Six Sigma organization. Taking advantage of the simplicity of this simulation problem, Microsoft Excel, which is standard for desktops in the division, was chosen as the simulation vehicle. The additional effort required for programming the simulation in Excel rather than in a simulation program was justified by its accessibility to other teams and its usefulness for teaching simulation in Six Sigma courses.
4 SALES PURSUIT AM Only or Use SCC? AM Only AM prepares proposal using TPC/eBOB to obtain standard TPS hardware and software model numbers and list pricing. AM provides the TPS prj file / e.bob index number to SCCA for placement in job file and on server. Use SSC. of jobs. of jobs Is all required info included? SCC finalizes TPC and/or e.bob, creates block drawings, researches new products and secures other cost items. SCC obtains Honeywell hardware costs through HIET Data Report for TPS hardware or e.bob for PlantScape hardware. Modify proposal?. of jobs. of jobs Is this an E&I opportunity? Is Costing Team required? SCC reviews and completes TPC/eBOB file and saves the following reports: Bill of Material, Calculated Values, Service Data, HIET Data.. of jobs Other services required? SCC finalizes initial TPC/e.BOB file submitted by TPAM, creates block drawings, researches new products and generates other costing information. Modify proposal? Add'l SCC resources required?. of jobs Customer clarifications? Finish AM = Acount Manager SCC = Sales Cost Consultant E&I = Engineering & Installation Fig. 3. System Proposal Process Map for Configurator Usage 5. Data Collection Data had to be collected for two configurators: TPC and ebob. TPC was used for configuring TPS systems and ebob was used for configuring PlantScape systems. The project sought to eliminate one of the configurators and consolidate support for both TPS and PlantScape in the other. By the time the simulation was being developed, TPC appeared to be the better configurator for upgrading, and the decision was made to estimate the productivity savings and ROI for this choice. Data for the simulation was produced from three sources (Table 2), a proposal manager questionnaire, a survey of estimators, and the 2001 PlantScape order entry data. Because proposal size is a significant factor in the effort required to produce a proposal, the simulation model had to provide branches for proposal size and the probability of branching was based on the Table 2. Sources of Model Data TPS Proposals PlantScape Proposals. of Proposals in 2001 Proposal Manager Questionnaire 2001 PlantScape Order Entry Data As-Is Effort Estimator Survey: TPC data Estimator Survey: ebob data To-Be Effort Estimator Survey: TPC data Estimator Survey: TPC data
5 Table 3. Number of TPS Proposals (last 12 months) Job Size Personnel Involved Small Medium Large AM only AM+SCC, n-e&i AM+SCC+CT, n-e&i AM+SCC+OS All fraction of each proposal size produced in For TPS proposals, these fractions were calculated from the numbers of TPS proposals of each size passing through each configurator usage path (Figure 3), as provided by the proposal group manager in response to a questionnaire (Table 3). Because PlantScape is a much smaller system, proposals for it are entered by many people, so the 2001 PlantScape Order Entry datafile was used to determine the number of PlantScape proposals of each size. The process map of Figure 3 was validated after the results of the proposal manager questionnaire were received. It was found that the Account Manager-only branch was used very infrequently and only for small proposals. Therefore, this branch could be eliminated from the simulation model with an insignificant effect on the results. The validation exercise also revealed that, despite what the proposal process map indicated, the use of a Costing Team made no difference with regard to configurator usage effort. Therefore, the numbers of proposals for these two paths were merged. These simplifications left only three steps to be simulated for each proposal size. Table 4. Estimator Data Form for As-Is Effort Job Size Process Step Configurator Small Medium Large SCC finalizes TPC and/or ebob, creates block drawings, researches new products, secures other cost items, and saves the following reports: Bill of Material, Calculated Values, Service Data, HIET Data. TPC ebob SCC obtains Honeywell hardware costs through HIET Data Report for TPS hardware or ebob for PlantScape hardware. TPC ebob SCC finalizes initial TPC/eBOB file submitted by TPAM, creates block drawings, researches new products and generates other costing information. TPC ebob Table 5. Estimator Data Form for To-Be Effort Job Size Process Step Small Medium Large SCC finalizes TPC, creates block drawings, researches new products, secures other cost items, and saves the following reports: Bill of Material, Calculated Values, Service Data, HIET Data. SCC obtains Honeywell hardware costs through HIET Data Report for TPS or PlantScape hardware. SCC finalizes initial TPC file submitted by TPAM, creates block drawings, researches new products and generates other costing information.
6 The second survey solicited data from thirteen estimators about the amount of effort actually spent on each of the three steps for proposals (Table 4) and about the amount of effort that could be expected after the TPC upgrades described in the survey (Table 5). The proposed upgrades included enhancements for facilitating the use of TPC, as well as providing support for systems other than TPS, such as PlantScape. Continuous distributions were derived from the effort data obtained in the estimator survey using an input analyzer. Distributions having closed form inverse transforms were used: discrete, triangular, Weibull, exponential, and uniform. Excel formulas were written for inverse transforms of the effort distributions. 6. Random Number Generation Using Excel introduced a special problem for simulation because the quality of Excel s random number generator (RNG) is debatable [1]. However, the quality of any RNG s random number streams may be highly dependent on the choice of seeds. Use of seeds is desirable not only for common random numbers in reducing variance when comparing scenarios [2], but for choosing those seeds that produce the best pseudorandom streams. The Excel RNG was tested with 10,000 seeds using empirical tests for both uniformity (Chi-square and serial tests in two and three dimensions) and independence (runs-up) on streams of three different lengths, 600, 6000, and numbers. These stream lengths were chosen based on the stream lengths needed for the simulation model. The best seeds were selected using composite rankings for the tests across the four empirical tests and across the three stream lengths. Seed Rank = R ij R ij = rank for the i th empirical test and the j th stream length Each selected seed produced the three different streams for which each empirical test null hypothesis could not be rejected α=.05. A different random number stream was employed for each random variable because a high number of simulation runs was expected, making independent streams desirable. Since Excel s RNG lacks the capacity to sample multiple random number streams alternatively, streams were generated by a Visual Basic script and stored, one stream per spreadsheet. Fully synchronized common random numbers were used to reduce variance in the difference between the two cases, allowing calculation of variance from the difference of each pair of As-Is and To-Be runs. 7. Simulation Model An Excel worksheet was allocated for each of the four simulation models: TPC As-Is, TPC To-Be, ebob As-Is, and ebob To-Be. The first two columns of Table 6 are an excerpt from TPC As-Is, showing how the model works for a single process step and one proposal. The Description column in Table 6 explains the numbers in the second column. Table 6. Sample of Simulation Model: As-Is Proposal 1 in the AM & SCC <& OS>, E&I Path Path: AM & SCC <& OS>, E&I Description Proposal. 1 Row contains consecutive numbers. Fraction medium proposals (FracMed).186 Constant for this row. Fraction large proposals (FracLar).814 Constant for this row. Random number for branching proposal size (RN6) VB script reads a stored random number stream and enters this number. Step 3: configuration Random Number for proposal prep effort (RN7) VB script reads a stored random number Effort distributions by proposal size stream and enters this number. Medium 0 =IF(RN6<FracMed, InverseTransformForMedium(RN7), 0) Large =IF(RN6>=FracMed, InverseTransformForLarge(RN7), 0) Total Effort for All Proposals on this Path 4692 Sum values in Medium and Large rows. Total Effort for all paths Sum of Total Effort values for all paths.
7 Proposals are numbered sequentially in the Proposal. row. The number of proposals for a path is number of proposals reported for that path by the proposal manager. The fraction of each size proposal (FracMed and FracLar in Table 6) is also calculated from the proposal manager s report (this path did not have any small proposals). For each process step, two random numbers were used: one for branching on proposal size and the other for calculating the effort in the process step. A row is allocated for the effort distribution corresponding to each process step in the path. The path shown in Table 6 had medium and large proposals, so a row was allocated for each of the effort distributions, one for medium proposals and one for large proposals. The branching pseudorandom number determines which of the effort distributions is used. Subject to this condition, the proposal effort is calculated using the effort pseudorandom number in an inverse transform. The effort figures for each path are summed and the effort sums across paths are totaled. The simulation is run by a Visual Basic (VB) script. The script populates each model, calculates the worksheet, and stores the total effort on a Results worksheet. Each year represents all the proposals for The scripts produce 1000 runs on each of the four models. 8. Results The As-Is and To-Be runs for TPC were paired through the use of synchronized common random numbers, so the difference between each pair of runs was taken to obtain the expected savings in the To-Be scenario due to TPC enhancements that would facilitate its use. The same was done with the As-Is and To-Be runs for ebob, however the To-Be ebob runs used TPC effort distributions since ebob would be upgraded to support PlantScape proposals. The outputs and savings are normally distributed. Figure 4 shows the normal probability plots for TPS proposals made in TPC and Figure 5 shows the PlantScape proposals produce now in ebob and later in TPC. 99 Savings Percent To-Be As-Is Person-Hours Annually Fig. 5. rmal Probability Plots for TPC (TPS Proposals) Effort: As-Is, To-Be, and Savings
8 Percent Savings To-Be As-Is Person-Hours Annually Fig. 6. rmal Probability Plots for ebob (PlantScape Proposals) Effort: As-Is, To-Be, and Savings Taking the two savings together TPC upgrades to facilitate its use and upgrades to support PlantScape proposals the mean projected savings is 6600 person-hours annually with a standard deviation of 757 person-hours. Five of the proposed upgrades would save one to two person-years of effort in producing product proposals (95% confidence interval on range). The cost of the upgrades would be reclaimed in 1.3 to 2.0 years due only to increased proposal productivity. In addition, variation in the configuration effort required for proposals would be decreased 65% (indicated by the slope of the plots). References [1] B.D. McCullough and B. Wilson, On the accuracy of statistical procedures in Microsoft Excel 97, Computational Statistics and Data Analysis, 31 (1999), pp [2] Averill M. Law and W. David Kelton, Simulation Modeling and Analysis, 2 nd ed. McGraw-Hill, New York, Conclusions and Next Steps Simulation of software system usage, based on data elicited from regular system users, provided a sound, quantitative basis for estimating productivity increases and justifying upgrade investments. The next stage of the project calls for measurement of actual savings and use of a control plan for monitoring variation in improved steps of the process.
Unit 1: Introduction to Quality Management
Unit 1: Introduction to Quality Management Definition & Dimensions of Quality Quality Control vs Quality Assurance Small-Q vs Big-Q & Evolution of Quality Movement Total Quality Management (TQM) & its
More informationLearning Objectives Lean Six Sigma Black Belt Course
Learning Objectives Lean Six Sigma Black Belt Course The overarching learning objective of this course is to develop a comprehensive set of skills that will allow you to function effectively as a Six Sigma
More informationWebSphere Business Modeler
Discovering the Value of SOA WebSphere Process Integration WebSphere Business Modeler Workshop SOA on your terms and our expertise Soudabeh Javadi Consulting Technical Sales Support WebSphere Process Integration
More informationCourse Overview Lean Six Sigma Green Belt
Course Overview Lean Six Sigma Green Belt Summary and Objectives This Six Sigma Green Belt course is comprised of 11 separate sessions. Each session is a collection of related lessons and includes an interactive
More informationPerformance Testing Process A Whitepaper
Process A Whitepaper Copyright 2006. Technologies Pvt. Ltd. All Rights Reserved. is a registered trademark of, Inc. All other trademarks are owned by the respective owners. Proprietary Table of Contents
More informationSimulation and Lean Six Sigma
Hilary Emmett, 22 August 2007 Improve the quality of your critical business decisions Agenda Simulation and Lean Six Sigma What is Monte Carlo Simulation? Loan Process Example Inventory Optimization Example
More informationBest Practices Statement Project Management. Best Practices for Managing State Information Technology Projects
State of Arkansas Office of Information Technology 124 W. Capitol Ave. Suite 990 Little Rock, AR 72201 501.682.4300 Voice 501.682.4020 Fax http://www.cio.arkansas.gov/techarch Best Practices Statement
More informationBowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition
Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application
More informationInformation Technology Project Oversight Framework
i This Page Intentionally Left Blank i Table of Contents SECTION 1: INTRODUCTION AND OVERVIEW...1 SECTION 2: PROJECT CLASSIFICATION FOR OVERSIGHT...7 SECTION 3: DEPARTMENT PROJECT MANAGEMENT REQUIREMENTS...11
More informationBusiness Process Optimization w/ Innovative Results
Business Process Optimization w/ Innovative Results Sam DiSalvatore Introduction The principle of continuous process improvement is based on the belief that even excellent products and services can be
More informationBusiness Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing
More informationLean Six Sigma Black Belt-EngineRoom
Lean Six Sigma Black Belt-EngineRoom Course Content and Outline Total Estimated Hours: 140.65 *Course includes choice of software: EngineRoom (included for free), Minitab (must purchase separately) or
More informationQuantitative Risk Analysis with Microsoft Project
Copyright Notice: Materials published by Intaver Institute Inc. may not be published elsewhere without prior written consent of Intaver Institute Inc. Requests for permission to reproduce published materials
More informationCurriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different
More informationbusiness statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
More informationIBM SPSS Statistics 20 Part 4: Chi-Square and ANOVA
CALIFORNIA STATE UNIVERSITY, LOS ANGELES INFORMATION TECHNOLOGY SERVICES IBM SPSS Statistics 20 Part 4: Chi-Square and ANOVA Summer 2013, Version 2.0 Table of Contents Introduction...2 Downloading the
More informationPBS Professional Job Scheduler at TCS: Six Sigma- Level Delivery Process and Its Features
PBS Professional Job Scheduler at TCS: Six Sigma- Bhadraiah Karnam Analyst Tata Consultancy Services Whitefield Road Bangalore 560066 Level Delivery Process and Its Features Hari Krishna Thotakura Analyst
More informationWhite Paper from Global Process Innovation. Fourteen Metrics for a BPM Program
White Paper from Global Process Innovation by Jim Boots Fourteen Metrics for a BPM Program This white paper presents 14 metrics which may be useful for monitoring progress on a BPM program or initiative.
More informationBODY OF KNOWLEDGE CERTIFIED SIX SIGMA YELLOW BELT
BODY OF KNOWLEDGE CERTIFIED SIX SIGMA YELLOW BELT The topics in this Body of Knowledge include additional detail in the form of subtext explanations and the cognitive level at which test questions will
More informationBody of Knowledge for Six Sigma Green Belt
Body of Knowledge for Six Sigma Green Belt What to Prepare For: The following is the Six Sigma Green Belt Certification Body of Knowledge that the exam will cover. We strongly encourage you to study and
More informationRisk Workshop Overview. MOX Safety Fuels the Future
Risk Workshop Overview RISK MANAGEMENT PROGRAM SUMMARY CONTENTS: Control Account Element Definition ESUA Form Basis of Estimate Uncertainty Calculation Management Reserve 1. Overview 2. ESUA Qualification
More informationCA Clarity PPM. Portfolio Management User Guide. v13.0.00
CA Clarity PPM Portfolio Management User Guide v13.0.00 This documentation, which includes embedded help systems and electronically distributed materials, (hereinafter referred to as the Documentation
More informationPerformance Workload Design
Performance Workload Design The goal of this paper is to show the basic principles involved in designing a workload for performance and scalability testing. We will understand how to achieve these principles
More informationChapter 3 RANDOM VARIATE GENERATION
Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.
More informationLean Six Sigma Black Belt Body of Knowledge
General Lean Six Sigma Defined UN Describe Nature and purpose of Lean Six Sigma Integration of Lean and Six Sigma UN Compare and contrast focus and approaches (Process Velocity and Quality) Y=f(X) Input
More informationCA Clarity PPM. Project Management User Guide. v13.0.00
CA Clarity PPM Project Management User Guide v13.0.00 This documentation, which includes embedded help systems and electronically distributed materials, (hereinafter referred to as the Documentation )
More informationSix Sigma in Project Management for Software Companies
Six Sigma in Project Management for Software Companies Yogesh Chauhan Total Quality Engineering & Management PEC University of Technology, Chandigarh, India Dr. R M Belokar PEC University of Technology,
More information1 Define-Measure-Analyze- Improve-Control (DMAIC)
1 Define-Measure-Analyze- Improve-Control (DMAIC) Six Sigma s most common and well-known methodology is its problem-solving DMAIC approach. This section overviews the methodology and its high-level requirements,
More informationThe Total Economic Impact Of SAS Customer Intelligence Solutions Intelligent Advertising For Publishers
A Forrester Total Economic Impact Study Commissioned By SAS Project Director: Dean Davison February 2014 The Total Economic Impact Of SAS Customer Intelligence Solutions Intelligent Advertising For Publishers
More informationQuantitative Methods for Finance
Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain
More informationTAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW. Resit Unal. Edwin B. Dean
TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW Resit Unal Edwin B. Dean INTRODUCTION Calibrations to existing cost of doing business in space indicate that to establish human
More informationSix Sigma. Breakthrough Strategy or Your Worse Nightmare? Jeffrey T. Gotro, Ph.D. Director of Research & Development Ablestik Laboratories
Six Sigma Breakthrough Strategy or Your Worse Nightmare? Jeffrey T. Gotro, Ph.D. Director of Research & Development Ablestik Laboratories Agenda What is Six Sigma? What are the challenges? What are the
More informationLAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.
More informationDELL. Virtual Desktop Infrastructure Study END-TO-END COMPUTING. Dell Enterprise Solutions Engineering
DELL Virtual Desktop Infrastructure Study END-TO-END COMPUTING Dell Enterprise Solutions Engineering 1 THIS WHITE PAPER IS FOR INFORMATIONAL PURPOSES ONLY, AND MAY CONTAIN TYPOGRAPHICAL ERRORS AND TECHNICAL
More informationSCHEDULE 10.1A PRICING FORMAT REQUIREMENTS SCHEDULE 10.1.A
SCHEDULE 10.1.A APRIL 25, 2005 Table of Contents 1.0 General Financial Requirements...1 1.1 Pricing Structure and Fees...1 1.2 Transition Services and Fees...2 1.3 Annual Fees...3 1.4 Resource Usage Fees...3
More information3-Step Competency Prioritization Sequence
3-Step Competency Prioritization Sequence The Core Competencies for Public Health Professionals (Core Competencies), a consensus set of competencies developed by the Council on Linkages Between Academia
More informationData Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
More informationComparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3)
Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3) What is EngineRoom? A Microsoft Excel add in A suite of powerful, simple to use Lean and Six Sigma data analysis tools Built for
More informationI. Enterprise-wide Planning and Deployment (25 questions)
ASQ Certified Master Black Belt (MBB) Body of Knowledge Multiple-Choice Section 100 Questions 2 ½ hours The topics in this Body of Knowledge (BOK) include descriptive details (subtext) that will be used
More informationPOLAR IT SERVICES. Business Intelligence Project Methodology
POLAR IT SERVICES Business Intelligence Project Methodology Table of Contents 1. Overview... 2 2. Visualize... 3 3. Planning and Architecture... 4 3.1 Define Requirements... 4 3.1.1 Define Attributes...
More informationProcess simulation. Enn Õunapuu enn.ounapuu@ttu.ee
Process simulation Enn Õunapuu enn.ounapuu@ttu.ee Content Problem How? Example Simulation Definition Modeling and simulation functionality allows for preexecution what-if modeling and simulation. Postexecution
More informationCourse Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics
Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This
More informationPROJECT MANAGEMENT PLAN CHECKLIST
PROJECT MANAGEMENT PLAN CHECKLIST The project management plan is a comprehensive document that defines each area of your project. The final document will contain all the required plans you need to manage,
More informationT i. An Integrated Workbench For Optimizing Business Processes MODELING SIMULATION ANALYSIS OPTIMIZATION
O P T i M An Integrated Workbench For Optimizing Business Processes MODELING SIMULATION ANALYSIS OPTIMIZATION O P T i M MODEL SIMULATE ANALYZE OPTIMIZE Integrated process modeler with import/export functionality
More informationThe Storage Capacity Design Dilemma
The Storage Capacity Design Dilemma an ITIL approach LeRoy Budnik Knowledge Transfer SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA and portions are subject to other
More informationSystem Development and Life-Cycle Management (SDLCM) Methodology. Approval CISSCO Program Director
System Development and Life-Cycle Management (SDLCM) Methodology Subject Type Standard Approval CISSCO Program Director A. PURPOSE This standard specifies content and format requirements for a Physical
More informationEducation & Training Plan Accounting Math Professional Certificate Program with Externship
University of Texas at El Paso Professional and Public Programs 500 W. University Kelly Hall Ste. 212 & 214 El Paso, TX 79968 http://www.ppp.utep.edu/ Contact: Sylvia Monsisvais 915-747-7578 samonsisvais@utep.edu
More informationBrillig Systems Making Projects Successful
Metrics for Successful Automation Project Management Most automation engineers spend their days controlling manufacturing processes, but spend little or no time controlling their project schedule and budget.
More informationRecommendations for Performance Benchmarking
Recommendations for Performance Benchmarking Shikhar Puri Abstract Performance benchmarking of applications is increasingly becoming essential before deployment. This paper covers recommendations and best
More informationLean Certification Program Blended Learning Program Cost: $5500. Course Description
Lean Certification Program Blended Learning Program Cost: $5500 Course Description Lean Certification Program is a disciplined process improvement approach focused on reducing waste, increasing customer
More information44-76 mix 2. Exam Code:MB5-705. Exam Name: Managing Microsoft Dynamics Implementations Exam
44-76 mix 2 Number: MB5-705 Passing Score: 800 Time Limit: 120 min File Version: 22.5 http://www.gratisexam.com/ Exam Code:MB5-705 Exam Name: Managing Microsoft Dynamics Implementations Exam Exam A QUESTION
More informationMicrosoft Project Professional
Microsoft Project Professional A 100% practical workshop to master Microsoft Project, training the main features of the application for project management. Objective Insight into the functions required
More informationPosition Paper for Cognition and Collaboration Workshop: Analyzing Distributed Community Practices for Design
Position Paper for Cognition and Collaboration Workshop: Analyzing Distributed Community Practices for Design Jean Scholtz, Michelle Steves, and Emile Morse National Institute of Standards and Technology
More informationIMPROVEMENT MATERIAL INVENTORY TRACKING FOR MAINTENANCE AND PROJECT THROUGH LEAN SIGMA METHODOLOGY
IMPROVEMENT MATERIAL INVENTORY TRACKING FOR MAINTENANCE AND PROJECT THROUGH LEAN SIGMA METHODOLOGY Sotarduga Manurung, School of Business & Management, Bandung Institute of Technology (ITB), J1. Ganesha
More informationThe Power of Two: Combining Lean Six Sigma and BPM
: Combining Lean Six Sigma and BPM Lance Gibbs and Tom Shea Lean Six Sigma (LSS) and Business Process Management (BPM) have much to contribute to each other. Unfortunately, most companies have not integrated
More informationHow To Test For Elulla
EQUELLA Whitepaper Performance Testing Carl Hoffmann Senior Technical Consultant Contents 1 EQUELLA Performance Testing 3 1.1 Introduction 3 1.2 Overview of performance testing 3 2 Why do performance testing?
More informationSTATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE
STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE Perhaps Microsoft has taken pains to hide some of the most powerful tools in Excel. These add-ins tools work on top of Excel, extending its power and abilities
More informationEducation & Training Plan. Accounting Math Professional Certificate Program with Externship
Office of Professional & Continuing Education 301 OD Smith Hall Auburn, AL 36849 http://www.auburn.edu/mycaa Contact: Shavon Williams 334-844-3108; szw0063@auburn.edu Auburn University is an equal opportunity
More informationThe Business Case for Visual Studio Quality Assurance and Testing Tools
The Business Case for Visual Studio Quality Assurance and Testing Tools May 2011 This document is property of Pique Solutions. Reproduction is forbidden unless authorized. Visit www.piquesolutions.com
More informationRandomized Block Analysis of Variance
Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of
More informationITRM Guideline CPM 110-01 Date: January 23, 2006 SECTION 4 - PROJECT EXECUTION AND CONTROL PHASE
PROJECT MANAGEMENT GUIDELINE SECTION 4 - PROJECT EXECUTION AND CONTROL PHASE Table of Contents Introduction... 3 Project Execution and Control Phase Overview... 3 Activities and Documents in the Execution
More informationSoftware Quality Management
Software Lecture 9 Software Engineering CUGS Spring 2011 Kristian Sandahl Department of Computer and Information Science Linköping University, Sweden A Software Life-cycle Model Which part will we talk
More informationConfiguring budget planning for Microsoft Dynamics AX 2012 R2
Microsoft Dynamics AX 2012 R2 Configuring budget planning for Microsoft Dynamics AX 2012 R2 White Paper This document describes configuration considerations for implementing budget planning. October 2012
More informationReliability Block Diagram RBD
Information Technology Solutions Reliability Block Diagram RBD Assess the level of failure tolerance achieved RELIABIL ITY OPTIMIZATION System reliability analysis for sophisticated and large scale systems.
More informationBus u i s n i e n s e s s s Cas a e s, e, S o S l o u l t u io i n o n & A pp p r p oa o c a h
Work Load Modeling and Work Load Modeler in Performance Testing Business Case, Solution & Approach Case An application is made ready to go-live in the next 2 months, but the application performance behavior
More informationMANUFACTURING EXECUTION SYSTEMS INTEGRATED WITH ERP & SIX SIGMA FOR PROCESS IMPROVEMENTS
MANUFACTURING EXECUTION SYSTEMS INTEGRATED WITH ERP & SIX SIGMA FOR PROCESS IMPROVEMENTS Name: Sumanth Pandith Surendra Institution: Wichita State University Status: Current Full time graduate in Industrial
More informationIntroduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman
Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman Statistics lab will be mainly focused on applying what you have learned in class with
More informationProcess Solutions. Uniformance Process History Database (PHD) Product Information Note
Process Solutions Product Information Note Uniformance Process History Database (PHD) Uniformance PHD enables you to make sense of all the data in your plant to help you make the right decision and optimize
More informationRapidResponse Training Catalog
RapidResponse Training Catalog Contents About RapidResponse Training... 4 RapidResponse Roles... 4 Consumers... 5 Contributors... 6 Contributors + RapidResponse Applications... 6 Authors... 8 Basic Authors...
More informationSurvey, Statistics and Psychometrics Core Research Facility University of Nebraska-Lincoln. Log-Rank Test for More Than Two Groups
Survey, Statistics and Psychometrics Core Research Facility University of Nebraska-Lincoln Log-Rank Test for More Than Two Groups Prepared by Harlan Sayles (SRAM) Revised by Julia Soulakova (Statistics)
More informationUsing Lean Six Sigma to Accelerate
Using Lean Six Sigma to Accelerate CMMI Implementation Briefers: Diane A. Glaser Michael D. Barnett US Army LCMC SEC CMMI Coordinator Communication Software ASQ SSGB Engineering Support Division MTC Technologies,
More informationEvaluating Trading Systems By John Ehlers and Ric Way
Evaluating Trading Systems By John Ehlers and Ric Way INTRODUCTION What is the best way to evaluate the performance of a trading system? Conventional wisdom holds that the best way is to examine the system
More informationExcel Companion. (Profit Embedded PHD) User's Guide
Excel Companion (Profit Embedded PHD) User's Guide Excel Companion (Profit Embedded PHD) User's Guide Copyright, Notices, and Trademarks Copyright, Notices, and Trademarks Honeywell Inc. 1998 2001. All
More informationCertified Six Sigma Yellow Belt
Certified Six Sigma Yellow Belt Quality excellence to enhance your career and boost your organization s bottom line asq.org/cert The Global Voice of Quality TM Certification from ASQ is considered a mark
More informationMeasurement Information Model
mcgarry02.qxd 9/7/01 1:27 PM Page 13 2 Information Model This chapter describes one of the fundamental measurement concepts of Practical Software, the Information Model. The Information Model provides
More informationNormality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com
More informationSECTION 4 TESTING & QUALITY CONTROL
Page 1 SECTION 4 TESTING & QUALITY CONTROL TESTING METHODOLOGY & THE TESTING LIFECYCLE The stages of the Testing Life Cycle are: Requirements Analysis, Planning, Test Case Development, Test Environment
More informationDirections for VMware Ready Testing for Application Software
Directions for VMware Ready Testing for Application Software Introduction To be awarded the VMware ready logo for your product requires a modest amount of engineering work, assuming that the pre-requisites
More informationFairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
More informationThe Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
More informationArena 9.0 Basic Modules based on Arena Online Help
Arena 9.0 Basic Modules based on Arena Online Help Create This module is intended as the starting point for entities in a simulation model. Entities are created using a schedule or based on a time between
More information4 Testing General and Automated Controls
4 Testing General and Automated Controls Learning Objectives To understand the reasons for testing; To have an idea about Audit Planning and Testing; To discuss testing critical control points; To learn
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationLive Event Count Issue
Appendix 3 Live Event Document Version 1.0 Table of Contents 1 Introduction and High Level Summary... 3 2 Details of the Issue... 4 3 Timeline of Technical Activities... 6 4 Investigation on Count Day
More informationWhy Is EngineRoom the Right Choice? 1. Cuts the Cost of Calculation
What is EngineRoom? - A Web based data analysis application with an intuitive, drag-and-drop graphical interface. - A suite of powerful, simple-to-use Lean and Six Sigma data analysis tools that you can
More informationA Six Sigma Approach for Software Process Improvements and its Implementation
A Six Sigma Approach for Software Process Improvements and its Implementation Punitha Jayaraman, Kamalanathan Kannabiran, and S.A.Vasantha Kumar. Abstract Six Sigma is a data-driven leadership approach
More information15.062 Data Mining: Algorithms and Applications Matrix Math Review
.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop
More informationFour Key Elements of an Effective Continuous Process Advantage Series White Paper By Jeff Gotro, Ph.D., CMC
Four Key Elements of an Effective Continuous Process Advantage Series White Paper By Jeff Gotro, Ph.D., CMC Introduction Tough times call for bold actions. The manufacturing sector is going through a challenging
More informationWhite Paper: Application and network performance alignment to IT best practices
Unpublished White Paper: Application and network performance alignment to IT best practices This white paper briefly describes best practices; highlights IT best practices; and discusses in detail IT business
More informationSchedule Risk Analysis Simulator using Beta Distribution
Schedule Risk Analysis Simulator using Beta Distribution Isha Sharma Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, Haryana (INDIA) ishasharma211@yahoo.com Dr. P.K.
More informationProject Duration Forecasting a comparison of Earned Value Management methods to Earned Schedule
Project Duration Forecasting a comparison of Earned Value Management methods to Earned Schedule Walt Lipke Member of Oklahoma City Chapter Project Management Institute (USA) Abstract: Earned Value Management
More informationAn Integrated Methodology for Implementing ERP Systems
APDSI 2000 Full Paper (July, 2000) An Integrated Methodology for Implementing ERP Systems Su-Yeon Kim 1), Eui-Ho Suh 2), Hyun-Seok Hwang 3) 1) Department of Industrial Engineering, POSTECH, Korea (tomi@postech.edu)
More informationBig Data Services From Hitachi Data Systems
SOLUTION PROFILE Big Data Services From Hitachi Data Systems Create Strategy, Implement and Manage a Solution for Big Data for Your Organization Big Data Consulting Services and Big Data Transition Services
More informationAnalysis of a production-inventory system with unreliable production facility
Analysis of a production-inventory system with unreliable production facility Katrien Ramaekers Gerrit K Janssens Transportation Research Institute Hasselt University - Campus Diepenbeek Wetenschapspark
More informationCOMMUNICATING WITH MANAGEMENT ABOUT THE BENEFITS OF BUSINESS PROCESS SIMULATION
Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, eds. COMMUNICATING WITH MANAGEMENT ABOUT THE BENEFITS OF BUSINESS PROCESS SIMULATION
More informationTCO for Application Servers: Comparing Linux with Windows and Solaris
TCO for Application Servers: Comparing Linux with Windows and Solaris Robert Frances Group August 2005 IBM sponsored this study and analysis. This document exclusively reflects the analysis and opinions
More informationQUALITY FUNCTION DEPLOYMENT (QFD) FOR SERVICES HANDBOOK MBA Luis Bernal Dr. Utz Dornberger MBA Alfredo Suvelza MBA Trevor Byrnes
International SEPT Program QUALITY FUNCTION DEPLOYMENT (QFD) FOR SERVICES HANDBOOK MBA Luis Bernal Dr. Utz Dornberger MBA Alfredo Suvelza MBA Trevor Byrnes SEPT Program March 09 Contents DEFINITION...
More informationData Analysis for Yield Improvement using TIBCO s Spotfire Data Analysis Software
Page 327 Data Analysis for Yield Improvement using TIBCO s Spotfire Data Analysis Software Andrew Choo, Thorsten Saeger TriQuint Semiconductor Corporation 2300 NE Brookwood Parkway, Hillsboro, OR 97124
More informationActivity 3.7 Statistical Analysis with Excel
Activity 3.7 Statistical Analysis with Excel Introduction Engineers use various tools to make their jobs easier. Spreadsheets can greatly improve the accuracy and efficiency of repetitive and common calculations;
More informationHow to do AHP analysis in Excel
How to do AHP analysis in Excel Khwanruthai BUNRUAMKAEW (D) Division of Spatial Information Science Graduate School of Life and Environmental Sciences University of Tsukuba ( March 1 st, 01) The Analytical
More information