Discoversim™ Version 2.1 Workbook

Total Page:16

File Type:pdf, Size:1020Kb

Discoversim™ Version 2.1 Workbook DiscoverSim™ Version 2.1 Workbook Contact Information: Technical Support: 1-866-475-2124 (Toll Free in North America) or 1-416-236-5877 Sales: 1-888-SigmaXL (888-744-6295) E-mail: [email protected] Web: www.SigmaXL.com Published: July 2018 Copyright © 2011 - 2018, SigmaXL, Inc. Table of Contents DiscoverSim™ Feature List Summary, Installation Notes, System Requirements and Getting Help ................................................................................................................................ 1 DiscoverSim™ Version 2.1 Feature List Summary ................................................................... 3 Key Features (bold denotes new in Versions 2.0 and 2.1): ............................................... 3 Common Continuous Distributions: .................................................................................. 4 Advanced Continuous Distributions: ................................................................................. 4 Discrete Distributions: ....................................................................................................... 5 Stochastic Information Packet (SIP): .................................................................................. 5 What’s New in Version 2.1 ................................................................................................. 6 What’s New in Version 2.0 ................................................................................................. 7 Installation Notes ............................................................................................................... 9 DiscoverSim™ System Requirements ............................................................................... 13 Getting Help and Product Registration ............................................................................ 14 Initializing DiscoverSim Workbook .................................................................................. 15 Introduction to DiscoverSim™, Getting Started Tutorial and DiscoverSim™ Menu & Dialogs 17 Introduction to Monte Carlo Simulation and Optimization with DiscoverSim™ ................... 19 The Y=f(X) Model ............................................................................................................. 19 Monte Carlo Simulation ................................................................................................... 20 Components of Uncertainty ............................................................................................ 22 Selecting a Distribution .................................................................................................... 23 Specifying Truncation Values ........................................................................................... 25 Specifying Correlations .................................................................................................... 26 Optimization: Stochastic Versus Deterministic ............................................................... 27 Optimization: Local Versus Global ................................................................................... 28 Components of Optimization........................................................................................... 29 Summary of Optimization Features ................................................................................. 30 Getting Started Tutorial ......................................................................................................... 31 Overview of DiscoverSim™ Menu and Dialogs ...................................................................... 38 DiscoverSim Menu ........................................................................................................... 38 iii DiscoverSim: Table of Contents DiscoverSim Options ........................................................................................................ 39 Create/Edit Input Distribution ......................................................................................... 40 Create/Edit Output Response .......................................................................................... 42 Copy Cell, Paste Cell, Clear Cells ...................................................................................... 43 Model Summary ............................................................................................................... 44 Run Simulation ................................................................................................................. 45 Distribution Fitting: Batch Distribution Fit ....................................................................... 49 Distribution Fitting: Specified Distribution Fit ................................................................. 51 Distribution Fitting: Nonnormal Process Capability ........................................................ 53 Distribution Fitting: Percentiles to Parameters Calculator .............................................. 55 Correlations ...................................................................................................................... 56 Optimization: Control ...................................................................................................... 57 Optimization: Constraint.................................................................................................. 58 Optimization: DSIM Function ........................................................................................... 59 Run Optimization ............................................................................................................. 60 DiscoverSim™: Case Studies .......................................................................................... 67 Case Study 1 – Basic Profit Simulation ................................................................................... 68 Introduction: Profit Simulation ........................................................................................ 68 Profit Simulation with DiscoverSim ................................................................................. 70 Case Study 2 –Magazine Production Optimization ............................................................... 89 Introduction: Simulation and Optimization to Determine Optimal Magazine Production Quantity to Maximize Profit ............................................................................................ 89 Optimization of Magazine Production Quantity with DiscoverSim ................................. 91 Case Study 3 – Six Sigma DMAIC Project Portfolio Selection .............................................. 101 Introduction: Optimizing DMAIC Project Portfolio to Maximize Cost Savings .............. 101 Optimization of Project Portfolio with DiscoverSim ...................................................... 102 Case Study 4 – Catapult Variation Reduction ...................................................................... 124 Introduction: Optimizing Catapult Distance Firing Process ........................................... 124 Catapult Simulation and Optimization with DiscoverSim .............................................. 126 Case Study 5 – Robust New Product Design ........................................................................ 141 Introduction: Optimizing Shutoff Valve Spring Force .................................................... 141 Spring Force Simulation and Optimization with DiscoverSim ....................................... 144 iv DiscoverSim: Table of Contents Case Study 6 – Multiple Response Optimization and Practical Tolerance Design .............. 162 Introduction: Optimizing Low Pass RC Filter.................................................................. 162 Multiple Response Optimization and Practical Tolerance Design of Low Pass RC Filter with DiscoverSim .................................................................................................................... 166 Case Study 6 References ................................................................................................ 191 Case Study 7 – The Travelling Salesperson Problem (TSP) .................................................. 192 Introduction: Optimizing Travel to 30 International Cities ............................................ 192 Optimizing Travel to 30 International Cities with DiscoverSim ..................................... 194 Case Study 7 References ................................................................................................ 202 DiscoverSim™ Appendix: Statistical Details for Distributions and Optimization Methods203 Statistical Details for Distributions and Optimization Methods .......................................... 205 DiscoverSim™ Engine and Excel Formula Interpreter ................................................... 205 Input Distribution Random Number Generation ........................................................... 206 Input Distribution Correlations ...................................................................................... 207 Input Distribution Sampling ........................................................................................... 209 Input Distribution Truncation ........................................................................................ 211 Table of DSIM Functions ................................................................................................ 212 Specifying the Optimization Objective Function ..........................................................
Recommended publications
  • Package 'Prevalence'
    Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2013 Package ‘prevalence’ Devleesschauwer, Brecht ; Torgerson, Paul R ; Charlier, Johannes ; Levecke, Bruno ; Praet, Nicolas ; Dorny, Pierre ; Berkvens, Dirk ; Speybroeck, Niko Abstract: Tools for prevalence assessment studies. IMPORTANT: the truePrev functions in the preva- lence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user’s system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/ Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-89061 Scientific Publication in Electronic Form Published Version The following work is licensed under a Software: GNU General Public License, version 2.0 (GPL-2.0). Originally published at: Devleesschauwer, Brecht; Torgerson, Paul R; Charlier, Johannes; Levecke, Bruno; Praet, Nicolas; Dorny, Pierre; Berkvens, Dirk; Speybroeck, Niko (2013). Package ‘prevalence’. On Line: The Comprehensive R Archive Network. Package ‘prevalence’ September 22, 2013 Type Package Title The prevalence package Version 0.2.0 Date 2013-09-22 Author Brecht Devleesschauwer [aut, cre], Paul Torgerson [aut],Johannes Charlier [aut], Bruno Lev- ecke [aut], Nicolas Praet [aut],Pierre Dorny [aut], Dirk Berkvens [aut], Niko Speybroeck [aut] Maintainer Brecht Devleesschauwer <[email protected]> BugReports https://github.com/brechtdv/prevalence/issues Description Tools for prevalence
    [Show full text]
  • MIDACO on MINLP Space Applications
    MIDACO on MINLP Space Applications Martin Schlueter Division of Large Scale Computing Systems, Information Initiative Center, Hokkaido University, Sapporo 060-0811, Japan [email protected] Sven O. Erb European Space Agency (ESA), ESTEC (TEC-ECM), Keplerlaan 1, 2201 AZ, Noordwijk, The Netherlands [email protected] Matthias Gerdts Institut fuer Mathematik und Rechneranwendung, Universit¨atder Bundeswehr, M¨unchen,D-85577 Neubiberg/M¨unchen,Germany [email protected] Stephen Kemble Astrium Limited, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2AS, United Kingdom [email protected] Jan-J. R¨uckmann School of Mathematics, University of Birmingham, Birmingham B15 2TT, United Kingdom [email protected] November 15, 2012 Abstract A numerical study on two challenging MINLP space applications and their optimization with MIDACO, which is a recently developed general purpose optimization software, is pre- sented. The applications are in particular the optimal control of the ascent of a multiple-stage space launch vehicle and the space mission trajectory design from Earth to Jupiter using mul- tiple gravity assists. Additionally an NLP aerospace application, the optimal control of an F8 aircraft manoeuvre, is furthermore discussed and solved. In order to enhance the opti- mization performance of MIDACO a hybridization technique, coupling MIDACO with a SQP algorithm, is presented for two of the three applications. The numerical results show, that the applications can be solved to their best known solution (or even new best solutions) in a reasonable time by the here considered approach. As the concept of MINLP is still a novelty in the field of (aero)space engineering, the here demonstrated capabilities are seen as promising.
    [Show full text]
  • Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
    UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi.
    [Show full text]
  • QUICK OVERVIEW of PROBABILITY DISTRIBUTIONS the Following Is A
    distribution is its memoryless property, which means that the future lifetime of a given object has the same distribution regardless of the time it existed. In other words, time has no effect on future outcomes. Success Rate () > 0. QUICK OVERVIEW OF PROBABILITY DISTRIBUTIONS PROBABILITY DISTRIBUTIONS: ALL OTHERS The following is a quick synopsis of the probability distributions available in Real Options Valuation, Inc.’s various software applications such as Risk Simulator, Real Options SLS, ROV Quantitative Data Miner, ROV Modeler, and others. Arcsine. U-shaped, it is a special case of the Beta distribution when both shape PROBABILITY DISTRIBUTIONS: MOST COMMONLY USED and scale are equal to 0.5. Values close to the minimum and maximum have high There are anywhere from 42 to 50 probability distributions available in the ROV probabilities of occurrence whereas values between these two extremes have very software suite, and the most commonly used probability distributions are listed here in small probabilities or occurrence. Minimum < Maximum. order of popularity of use. See the user manual for more technical details. Bernoulli. Discrete distribution with two outcomes (e.g., head or tails, success or failure), which is why it is also known simply as the Yes/No distribution. The Bernoulli distribution is the Binomial distribution with one trial. This distribution is the fundamental building block of other more complex distributions. Probability of Success 0 < (P) < 1. Beta. This distribution is very flexible and is commonly used to represent variability over a fixed range. It is used to describe empirical data and predict the random behavior of percentages and fractions, as the range of outcomes is typically between 0 and 1.
    [Show full text]
  • Treball (1.484Mb)
    Treball Final de Màster MÀSTER EN ENGINYERIA INFORMÀTICA Escola Politècnica Superior Universitat de Lleida Mòdul d’Optimització per a Recursos del Transport Adrià Vall-llaura Salas Tutors: Antonio Llubes, Josep Lluís Lérida Data: Juny 2017 Pròleg Aquest projecte s’ha desenvolupat per donar solució a un problema de l’ordre del dia d’una empresa de transports. Es basa en el disseny i implementació d’un model matemàtic que ha de permetre optimitzar i automatitzar el sistema de planificació de viatges de l’empresa. Per tal de poder implementar l’algoritme s’han hagut de crear diversos mòduls que extreuen les dades del sistema ERP, les tracten, les envien a un servei web (REST) i aquest retorna un emparellament òptim entre els vehicles de l’empresa i les ordres dels clients. La primera fase del projecte, la teòrica, ha estat llarga en comparació amb les altres. En aquesta fase s’ha estudiat l’estat de l’art en la matèria i s’han repassat molts dels models més importants relacionats amb el transport per comprendre’n les seves particularitats. Amb els conceptes ben estudiats, s’ha procedit a desenvolupar un nou model matemàtic adaptat a les necessitats de la lògica de negoci de l’empresa de transports objecte d’aquest treball. Posteriorment s’ha passat a la fase d’implementació dels mòduls. En aquesta fase m’he trobat amb diferents limitacions tecnològiques degudes a l’antiguitat de l’ERP i a l’ús del sistema operatiu Windows. També han sorgit diferents problemes de rendiment que m’han fet redissenyar l’extracció de dades de l’ERP, el càlcul de distàncies i el mòdul d’optimització.
    [Show full text]
  • Dell VITA RFP- Revised COTS Pricing 12-17-08
    COTS Software COTS Software is considered to be commercially available software read to run without customization, Gov't Pricing Academic Pricing Gov't Pricing Enter discount for publisher (This Enter discount for publisher (This will be the will be the lowest discount that you lowest discount that you will offer during the will offer during the term of the term of the contract) contract) COTS Discount % COTS Discount % Title Price Adobe 0.00% Adobe 0.00% Adobe Acrobat Professional Version 9 Windows 218.33 Autodesk 0.00% Autodesk 0.00% Adobe Photoshop CS4 Windows 635.24 Citrix 0.00% Citrix 0.00% Autodesk Sketchbook 2009 Pro 120 Corel 0.00% Corel 0.00% McAfee Active Virus Defense 14.87 DoubleTake 0.00% DoubleTake 0.00% Symantec Norton Antivirus 2009 25 Intuit 0.00% Intuit 0.00% Symantec Backup Exec 12.5 for Windows Servers 454.25 McAfee 0.00% McAfee 0.00% PRICE FOR ABOVE TITLES SHOULD BE Novell 0.00% Novell 0.00% QUOTED FOR PURCHASE OF ONE (1) COPY Nuance 0.00% Nuance 0.00% Quark Software 0.00% Quark Software 0.00% Quest Software 0.00% Quest Software 0.00% Riverdeep 0.00% Riverdeep 0.00% Academic Pricing Symantec 0.00% Symantec 0.00% Title Price Trend Micro 0.00% Trend Micro 0.00% Adobe Acrobat Professional Version 9 Windows 131.03 VMWare 0.00% VMWare 0.00% Adobe Photoshop CS4 Windows 276.43 WebSense 0.00% WebSense 0.00% Autodesk Sketchbook 2009 Pro 120 McAfee Active Virus Defense 14.87 Symantec Norton Antivirus 2009 25 Symantec Backup Exec 12.5 for Windows Servers 454.25 PRICE FOR ABOVE TITLES SHOULD BE QUOTED FOR PURCHASE OF ONE (1) COPY LICENSE AND MEDIA For purposes of evaluation VITA will create a market basket.
    [Show full text]
  • Nonlinear Mixed Integer Based Optimization Technique for Space Applications
    Nonlinear mixed integer based Optimization Technique for Space Applications by Martin Schlueter A thesis submitted to The University of Birmingham for the degree of Doctor of Philosophy School of Mathematics The University of Birmingham May 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract In this thesis a new algorithm for mixed integer nonlinear programming (MINLP) is developed and applied to several real world applications with special focus on space ap- plications. The algorithm is based on two main components, which are an extension of the Ant Colony Optimization metaheuristic and the Oracle Penalty Method for con- straint handling. A sophisticated implementation (named MIDACO) of the algorithm is used to numerically demonstrate the usefulness and performance capabilities of the here developed novel approach on MINLP. An extensive amount of numerical results on both, comprehensive sets of benchmark problems (with up to 100 test instances) and several real world applications, are presented and compared to results obtained by concurrent methods. It can be shown, that the here developed approach is not only fully competi- tive with established MINLP algorithms, but is even able to outperform those regarding global optimization capabilities and cpu runtime performance.
    [Show full text]
  • Optimization of Circuitry Arrangements for Heat Exchangers Using
    Optimization of circuitry arrangements for heat exchangers using derivative-free optimization Nikolaos Ploskas1, Christopher Laughman2, Arvind U. Raghunathan2, and Nikolaos V. Sahinidis1 1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA 2Mitsubishi Electric Research Laboratories, Cambridge, MA, USA Abstract Optimization of the refrigerant circuitry can improve a heat exchanger’s performance. Design engineers currently choose the refrigerant circuitry according to their experience and heat exchanger simulations. However, the design of an optimized refrigerant circuitry is difficult. The number of refrigerant circuitry candidates is enormous. Therefore, exhaustive search algorithms cannot be used and intelligent techniques must be developed to explore the solution space efficiently. In this paper, we formulate refrigerant circuitry design as a binary constrained optimization problem. We use CoilDesigner, a simulation and design tool of air to refrigerant heat exchangers, in order to simulate the performance of different refrigerant circuitry designs. We treat CoilDesigner as a black-box system since the exact relationship of the objective function with the decision variables is not explicit. Derivative-free optimization (DFO) algorithms are suitable for solving this black-box model since they do not require explicit functional representations of the objective function and the constraints. The aim of this paper is twofold. First, we compare four mixed-integer constrained DFO solvers and one box-bounded DFO solver and evaluate their ability to solve a difficult industrially relevant problem. Second, we demonstrate that the proposed formulation is suitable for optimizing the circuitry configuration of heat exchangers. We apply the DFO solvers to 17 heat exchanger design problems. Results show that TOMLAB/glcDirect and TOMLAB/glcSolve can find optimal or near-optimal refrigerant circuitry designs arXiv:1705.10437v1 [cs.CE] 30 May 2017 after a relatively small number of circuit simulations.
    [Show full text]
  • Field Guide to Continuous Probability Distributions
    Field Guide to Continuous Probability Distributions Gavin E. Crooks v 1.0.0 2019 G. E. Crooks – Field Guide to Probability Distributions v 1.0.0 Copyright © 2010-2019 Gavin E. Crooks ISBN: 978-1-7339381-0-5 http://threeplusone.com/fieldguide Berkeley Institute for Theoretical Sciences (BITS) typeset on 2019-04-10 with XeTeX version 0.99999 fonts: Trump Mediaeval (text), Euler (math) 271828182845904 2 G. E. Crooks – Field Guide to Probability Distributions Preface: The search for GUD A common problem is that of describing the probability distribution of a single, continuous variable. A few distributions, such as the normal and exponential, were discovered in the 1800’s or earlier. But about a century ago the great statistician, Karl Pearson, realized that the known probabil- ity distributions were not sufficient to handle all of the phenomena then under investigation, and set out to create new distributions with useful properties. During the 20th century this process continued with abandon and a vast menagerie of distinct mathematical forms were discovered and invented, investigated, analyzed, rediscovered and renamed, all for the purpose of de- scribing the probability of some interesting variable. There are hundreds of named distributions and synonyms in current usage. The apparent diver- sity is unending and disorienting. Fortunately, the situation is less confused than it might at first appear. Most common, continuous, univariate, unimodal distributions can be orga- nized into a small number of distinct families, which are all special cases of a single Grand Unified Distribution. This compendium details these hun- dred or so simple distributions, their properties and their interrelations.
    [Show full text]
  • Discoversim™ Version 2 Workbook
    DiscoverSim™ Version 2 Workbook Contact Information: Technical Support: 1-866-475-2124 (Toll Free in North America) or 1-416-236-5877 Sales: 1-888-SigmaXL (888-744-6295) E-mail: [email protected] Web: www.SigmaXL.com Published: December 2015 Copyright © 2011 - 2015, SigmaXL, Inc. Table of Contents DiscoverSim™ Feature List Summary, Installation Notes, System Requirements and Getting Help ..................................................................................................................................1 DiscoverSim™ Version 2 Feature List Summary ...................................................................... 3 Key Features (bold denotes new in Version 2): ................................................................. 3 Common Continuous Distributions: .................................................................................. 4 Advanced Continuous Distributions: ................................................................................. 4 Discrete Distributions: ....................................................................................................... 5 Stochastic Information Packet (SIP): .................................................................................. 5 What’s New in Version 2 .................................................................................................... 6 Installation Notes ............................................................................................................... 8 DiscoverSim™ System Requirements ..............................................................................
    [Show full text]
  • Essential Statistics for Applied Linguistics : Using R Or Jasp
    ESSENTIAL STATISTICS FOR APPLIED LINGUISTICS : USING R OR JASP Author: Hanneke Loerts Number of Pages: 250 pages Published Date: 06 Feb 2020 Publisher: MacMillan Education UK Publication Country: London, United Kingdom Language: English ISBN: 9781352007817 DOWNLOAD: ESSENTIAL STATISTICS FOR APPLIED LINGUISTICS : USING R OR JASP Essential Statistics for Applied Linguistics : Using R or JASP PDF Book The newspaper headlines tell us that Britain's wildlife is in trouble. Every chapter opens up with a vignette called a "Decision Dilemma" about real companies, data, and business issues.Ch. This book will be an invaluable source of information for a variety of professionals working with patients with advanced disease, including palliative care doctors and specialist nurses, as there is a scarcity of consultants in pain management in the field of palliative care. Although the units are diverse and have a range of poetry and prose for teachers to use, the book presents cohesive methods for engaging children with a variety of different literary texts and improving standards of literacy. StateResponsibility. With these questions providing the building blocks for your essay, Sawyer guides you through the rest of the process, from choosing a structure to revising your essay, and answers the big questions that have probably been keeping you up at night: How do I brag in a way that doesn't sound like bragging. Important topics such as vascular lesions, warts, acne, scars, and pigmented lesions are presented and discussed in all aspects. His discovery, which he described to his king in the presence of Christopher Columbus, opened up the sea route around Africa to India and the rest of Asia.
    [Show full text]
  • Certification Training Materials PRIMERS
    QUALITY PROGRESS QUALITY Putting Best Practices to Work www.qualityprogress.com | June 2014 | JUNE 2014 Supply Chains Speed Up p. 22 QUALITYP PROGRESS Chain of SUPPLY CHAIN SUPPLY Command Quality tools linked to supply chain efficiency, reduced waste p. 14 Plus: VOLUME 47/NUMBER 6 Process factors: A cornerstone of supplier audits p. 28 A fail-safe FMEA p. 42 The Global Voice of QualityTM Certification Training Materials PRIMERS Our Primers are the most widely used texts for certification training. They can be taken into the exam. QCI offers 16 different Primers. SOLUTION TEXTS Detailed solutions to all questions in the corresponding Primer. CD-ROMS Interactive software to assist students preparing for ASQ exams. QUALITY COUNCIL OF INDIANA Online Orders: www.qualitycouncil.com Phone Orders: 800-660-4215 Fax Orders: 812-533-4216 Mail Orders: QCI Order Department, 602 W. Paris Ave., W. Terre Haute, IN 47885-1124 Check out a few of the NEW books from ASQ Quality Press! The FDA and Worldwide Current Good Manufacturing Practices and Quality System Requirements for Finished Pharmaceuticals This guidance book is meant as a resource to Continuous Permanent Improvement manufacturers of pharmaceuticals, providing The purpose of this book is not to expound any up-to-date information concerning required and new theory or tools, but to share experiences in recommended quality system practices. implementing existing methods with a bias toward Item: H1458 business results. In fact, one of the important lessons we have learned is that most existing models or methods, if adhered to in the right spirit, will give results. The Certified Pharmaceutical GMP Item: H1466 Professional Handbook The purpose of this handbook is to highlight and partially annotate what the founders of the Certified Pharmaceutical Good Manufacturing Practices Professional (CPGP) examination believed to be the main topics comprising worldwide pharmaceutical good manufacturing practices (GMPs).
    [Show full text]