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MSC-431 exam Dumps Source : Lean Six Sigma Black Belt

Test Code : MSC-431
Test name : Lean Six Sigma Black Belt
Vendor name : Motorola
: 100 actual Questions

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Motorola Motorola scrawny Six Sigma

Lean Six Sigma | killexams.com actual Questions and Pass4sure dumps

Lean Six Sigma is an information-driven approach to enhancing efficiency, client pride and earnings. 

Lean Six Sigma combines Toyota's lean manufacturing philosophy with the qualitative and quantitative recommendations for using process growth that Motorola's Six Sigma administration approach offers.

Lean Six Sigma acknowledges the role that stakeholders play in each project's success, as neatly because the significance of gathering and analyzing facts to champion these stakeholders acquire into account the effectiveness -- or ineffectiveness -- of the workflow. The approach is pragmatic; it allows a firm to settle upon which ingredients of each and every discipline will champion the corporation meet its goals. 

the passage it fused with scrawny concepts

in an effort to acquire note the flexibleness that scrawny Six Sigma gives, it be essential to acquire into account a miniature about the similarities and modifications between the two disciplines. 

both scrawny administration and 6 Sigma are looking for to liquidate fritter and enrich enterprise tactics. an significant difference, however, is that scrawny management depends on americans to establish what issues are causing waste.

The philosophy of scrawny management is to sequel more with less, and it is constructed round two faultfinding principles: continual development (kaizen) and recognize for individuals.

All personnel are expected to aid liquidate waste; they obtain just-in-time (JIT) practising on a daily foundation, and the tools they are given to determine and salvage rid of fritter are designed to be easily understood and implemented. scrawny management apparatus for process growth encompass the 5 whys, kanban stock wield cues, heijunka bins for scheduling, Ishikawa fishbone diagrams, takt time calculations and price stream mapping.

Lean methods in healthcare

A health facility CIO explains how scrawny methods champion fitness IT techniques

Six Sigma, nevertheless, relies on records to determine issues in a commerce process. The administration philosophy became named after the Greek epistle sigma, which is used in data to denote edition from a standard.

to ensure that an organization to achieve Six Sigma, it cannot produce more than 3.four defects per a million opportunities for nonconformance. Its apparatus are designed for spend via a opt for number of employees who maintain been chosen to obtain formal practicing in modern levels of analyze.

Six Sigma stint management tools include statistical analysis, stochastic optimization and engineering procedure control. management concepts consist of DMAIC (define, measure, analyze, improve, manage) and DMADV (define, measure, analyze, design, assess), both of that are in keeping with Deming's plan-do-assess-act cycle.

what's scrawny Six Sigma used for?

As is mentioned above, scrawny Six Sigma methodology is the combination of the scrawny manufacturing ideology popularized with the aid of Toyota and Motorola's six sigma method. This combination makes scrawny Six Sigma an excellent device to optimize enterprise strategies and consumer relations and, in turn, to better profits.

Toyota's manufacturing technique has, for the intuition that shortly after World warfare II, prioritized minimizing bloat and maximizing flexibility. Hand in hand with this Toyota-inspired ideology is the Six Sigma management method, the brainchild of a Motorola engineer in the 1980s.

the Place scrawny manufacturing emphasizes streamlining commerce approaches, Six Sigma focuses on removing defects in these procedures. Six Sigma teams are seeking for to dispose of defects in company approaches by passage of minimizing model within the procedures.

Lean Six Sigma is often linked to the Xerox enterprise. within the early piece of this century, the enterprise carried out scrawny Six Sigma to multiply consumer event (CX) and to become more can charge competitive. on the grounds that then, Xerox has promoted the passage it has correctly combined the infrastructure of Six Sigma with the philosophy of continuous growth from scrawny manufacturing to streamline the business's inner methods and provide consumers with satisfactory items and capabilities at speeds and prices that purchasers value. a kindhearted of capabilities occurs to be assisting Xerox clients spend scrawny Six Sigma to better their personal enterprise techniques.

Certifications and practicing

Lean Six Sigma certification may moreover be attained at various stages in accordance with the practicing a person has bought. scrawny Six Sigma certification ranges are divided into belts similar to the degrees used in martial arts training.

the first level of scrawny Six Sigma certification is the Six Sigma white belt. The white belt aims to provide trainees with a basic realizing of scrawny Six Sigma methodology, including however no longer restricted to process improvement, variability, casting off negative consequences on manner efficiency and finding out what roles specific team contributors should still play.

subsequent in the scrawny Six Sigma certification ladder is the yellow belt. The yellow belt builds off of the practising from the white belt certification path and aims to give trainees a extra specified and complete knowing of scrawny Six Sigma methodologies.

Following the yellow belt is the third step within the scrawny Six Sigma practising technique, the green belt. The green belt specializes in the application of the scrawny Six Sigma methodologies outlined in the white and yellow belt courses. In eco-friendly belt training classes, trainees be taught to chart and passage the roles of people within scrawny Six Sigma teams, apart from studying the passage to dash statistical checks that are used to enrich strategies.

The final belt it truly is given out within the scrawny Six Sigma certification procedure is the black belt. The scrawny Six Sigma black belt teaches trainees to clarify varied regression, operate factorial experiments, check measurement calculations for experiments and portray several types of procedure optimization. in short, the black belt is the top of scrawny Six Sigma working towards.


Six Sigma Parody on "30 Rock" Creates Controversy | killexams.com actual Questions and Pass4sure dumps

April 24, 2009 06:23 ET

A third Season Episode of "30 Rock's" Parody of Six Sigma Creates a Stir within the Six Sigma group, however Many View the publicity as an indication of Six Sigma's recognition

CLEVELAND, OH--(Marketwire - April 24, 2009) - Six Sigma, a data-pushed enterprise administration thought, turned into first established in the Nineteen Eighties by the Motorola business. due to the fact that then, its scope has widened to champion be positive excellent, and crop the groundwork line, for a lot of businesses international.

"30 Rock" is a celebrated American television exhibit, set behind the scenes of an NBC sitcom, in the GE structure in ny city -- 30 Rockefeller Plaza. It has gained many awards, and has been renewed for a 4th season in 2009/2010. In a season three episode entitled, "Retreat to circulation ahead," which first aired on January 22, 2009, 6 Sigma is parodied. Two of the main characters, Jack, performed by Alec 1st earl baldwin of bewdley, and Liz, performed via Tina Fey, who's additionally the display's creator, trudge to a Six Sigma conference. Jack is scheduled to bring the keynote handle, and Liz accompanies him for support. Many jokes and parodies ensue about the "Six Sigmas," a bunch of guys who stand for "the pillars of the Six Sigma commerce philosophy" -- teamwork, insight, brutality, masculine enhancement, hand shakefulness, and play hard. These are definitely not the pillars of the actual Six Sigma program, and a few within the Six Sigma community maintain taken offense to the spoof.

The episode continues to parody the Six Sigma technique, and ends with the entire convention listening to Jack's pre-keynote address pep talk in the bathroom. Liz rushes in to order him that his microphone is on, and ends with her attempting to save him through grabbing the microphone, jumping on stage, and when her impersonations sequel not work, she rips open her shirt, and dances round on stage in her bra.

Many in the Six Sigma community had been offended with the aid of this parody, viewing it as disrespectful and demeaning to the Six Sigma manner. but Craig Setter, President of Aveta answer's Six Sigma online, elements out that seeing Six Sigma on primetime tv is evidence that Six Sigma is more universal than ever.

"despite the fact this is nothing fondness Six Sigma conferences I actually maintain ever been to, and although the television expound jokes concerning the Six Sigma technique, it proves one aspect -- Six Sigma is now a family unit name," Setter states. once referred to as a really stoic and company manner, Six Sigma is now evolving into some thing extra present, whatever in a position to be parodied on a well-liked tv display. terms typically common best from somebody who's licensed in Six Sigma, such as DFSS and six Sigma Black Belt, don't seem to be as vague as they once were.

Setter continues, "bound they're poking enjoyable on the manner, however what sequel most sitcoms do? They acquire some thing latest in today's society and build it funny. The Six Sigma application is an significant and valuable procedure. most likely with its publicity on primetime tv, greater individuals will learn of its charge and value, to the improvement of the Six Sigma neighborhood."

Six Sigma is a company administration strategy in the genesis developed by Motorola to enhance manufacturing strategies and salvage rid of procedure defects. Motorola has pronounced $17 billion in reductions as of 2006. approximately two-thirds of Fortune 500 agencies maintain used Six Sigma to reduce fritter in fresh years. Six Sigma on-line is the main provider for online Six Sigma Certification. For additional info on scrawny Six Sigma certification, seek counsel from http://www.sixsigmaonline.org.


Six Sigma, scrawny compass collectively | killexams.com actual Questions and Pass4sure dumps

green Belt, Black Belt, grasp Black Belt. Plant floors in synchronous years were sounding extra fondness martial arts courses than manufacturing operations. These warriors are knowledgeable to specialize in Six Sigma, the statistical analysis technique developed via Motorola Inc. within the Nineteen Eighties to salvage rid of waste. in the past, Six Sigma potential were regarded more captivating than generic scrawny advantage, in accordance with a study by passage of government search enterprise Avery factor neighborhood. however extra organizations this present day are searching for candidates who display a amalgamate of scrawny and 6 Sigma expertise.

"When it involves desired skill units, groups wish to maintain their cake and devour it, too," says Tim Noble, managing principal of Avery. "although corporations can moreover no longer maintain a full-blown Six Sigma or scrawny deployment underway, their examine shows every 12 months they more and more exigency candidates that possess Six Sigma, scrawny or each skill sets of their DNA."

in accordance with a sampling of cyber web job postings, the analyze indicates that require for scrawny talent grew from 2006 to 2007 to very nearly equal that of Six Sigma. The analyze moreover concludes that 38% of businesses looking for Six Sigma aptitude are searching for personnel who moreover maintain scrawny skills. in spite of this, 42% of organizations promoting for scrawny practitioners require some Six Sigma exposure.

moreover, the analyze shows corporations are inserting much less accent on "belt" certifications, with under one-fifth of Six Sigma postings seeking to in particular fill master Black Belt and Black Belt roles. "groups are more and more opting as an alternative to inject candidates who posses Six Sigma and scrawny aptitude sets into usual line roles versus creating standalone structured continuous improvement roles, as often turned into the case with Six Sigma positions in the past," Noble says.

See also




Killexams.com MSC-431 Dumps and actual Questions

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MSC-431 exam Dumps Source : Lean Six Sigma Black Belt

Test Code : MSC-431
Test name : Lean Six Sigma Black Belt
Vendor name : Motorola
: 100 actual Questions

simply attempt actual MSC-431 check questions and achievement is yours.
i used to be a lot disappointed in the ones days due to the fact I didnt any time to prepare for MSC-431 examination prep because of my a few each day routine paintings I ought to spend maximum time at the way, a long distance from my domestic to my paintings region. i used to be so much concerned about MSC-431 examination, due to the fact time is so near, then in the future my pal informed approximately killexams.com, that turned into the flip to my life, the reply of my every troubles. I should sequel my MSC-431 exam prep at the passage easily through the spend of my computer and killexams.com is so dependable and extremely good.


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Lean Six Sigma Black Belt

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Accelerating Six Sigma Research with the Definitive Screening Design (DSD) Technique | killexams.com actual questions and Pass4sure dumps

DSD is a fresh design-of-experiments (DOE) technique that is expected to bring huge benefits when using a Six Sigma optimization strategy

Statistical tools are deeply ingrained in the Six Sigma methodology to optimize processes and products during chemical process industries (CPI) operations. In fact, Six Sigma has been an significant contributing factor for the widespread spend of statistics in many different industrial sectors over the past several decades [ 1]. In Six Sigma’s DMAIC (define-measure-analyze-improve-control) roadmap, many statistical methods are pivotal for the proper collection of data and the aptitude to translate gathered data into useful information and actionable knowledge. In particular, the design of experiments (DOE) methodology appeals to many chemical engineers (see, for example, Chem. Eng., Nov. 2014 and Sept. 2016 issues [ 2,3]) as a methodology for systematically quantifying cause-and-effect relationships between input and output variables during both manufacturing processes and laboratory research and progress (R&D) efforts. DOE is moreover widely used during the better side of Six Sigma projects.

This article discusses the significance of DOE in R&D, and the fresh definitive screening design (DSD) technique. It moreover presents a case study to illustrate the power of the DSD technique by comparing its results with those obtained from a classic DOE process, the latter with a much larger number of experimental runs. Overall, with this practical tutorial, they level to bring the DSD instrument to the attention of individuals throughout the CPI.

DOE for scientific investigation

When a chemical engineer is confronted with a manufacturing or product-related problem, he or she will initially pose a first tentative model or hypothesis (which is usually based on speculation) to expound or decipher the issue, as shown at the left side of device 1 [4]. From this first model, the engineer will then deduce positive inherent consequences, which, in a next rational step, should be compared with data to champion or refute the first model. Here DOE comes into play most of the time, as a technique to acquire fresh data using a scientific approach.

FIGURE 1. DOE is a useful scientific method to gain lore about a particular problem in a manufacturing process. A classic research strategy follows the sequence of a screening DOE, a plenary factorial DOE, and a response surface method to find optimal process settings. A DSD may find these settings with a significantly reduced number of experimental runs (adapted from [4])

In many cases, the newly gathered data will not harmonize (or will only partially match) with the consequences of the initial model. As a result, engineers will usually muster for a second, data-driven DOE model, which, likewise, will often lead again to some necessary consequences. Some variables from the DOE will turn out to maintain or not maintain an sequel on the studied problem; some unexpected insights may flaunt and the engineer’s passage of thinking about the studied problem will change accordingly. Hence, a second cycle in the iteration of the deduction-induction process is initiated, and so on [ 4]. Throughout this process, the discrepancy between model consequences and data diminishes (this is visualized by the converging lines, from left to right, in device 1), and fresh lore is generated — the key to process and product improvement.

Through the different stages of experimentation, different types of DOE are typically used, as shown in device 1. In the early stages of research, a screening DOE (called fractional factorial) is used to identify the significant input variables (called the vital few) and to liquidate the irrelevant ones (called the petty many) that strike the process performance or product quality. Starting with a great number of potentially significant input variables (also called factors), screening DOEs level to identify the vital few variables that require further investigation; that is, the active factors that maintain the largest sequel on the response of interest [5].

In the next stage of research, a plenary factorial DOE consists of every workable combinations of levels for the active factors. The purpose here is to quantify the effects of the latter on the response in a more precise and amenable passage using linear models (main effects and interactions). Because the input variables are changed simultaneously in a DOE, workable synergistic and antagonistic interactions between the input variables can be detected; this is in contrast to a so-called OVAT approach, which involves changing only “one variable at a time.” Equally significant to the detection of workable interactions is the recognition of eventual departure from linear relationships between response and input variables by including a focus point in the design. This is shown on the left side of device 2.

FIGURE 2. Shown here is a central composite design (CCD; left side) and a definitive screening design (DSD; privilege side) for three factors. For the CCD, a is the axial distance of the start points, which are included in the experimental design to quantify quadratic (curvature) effects between the response and the input factors

The first-order design can detect global curvature, but it cannot separately rate the quadratic effects of each factor. At this stage of experimentation, interpretation of the results combined with process and product expertise may allow the identification of the direction of steepest improvement toward an optimum in the response, possibly leading to another chain of designed experiments — this time closer to the optimum — which can bow another first-order model before proceeding to the final stage. Finally, when it comes to optimization of the manufacturing process or product, a more intricate model will be needed to portray the region around the optimal response and to locate the latter. A linear model will no longer be sufficient. Instead, a quadratic model will be used in a so-called response surface method (RSM) to apt the optimum.

Central composite design

Since they initially made spend of a central composite design (CCD) in their case study discussed below, they will shortly contend this sort of RSM first. At the left side of device 2, a CCD is depicted for three factors. The CCD is very supple as it can be set up in a modular way: initially, one starts with the execution of a two-level factorial DOE, where each factor is set at its low (–1) and lofty (+1) level to verify the effects on the response of interest. A focus point, depicted as (0,0,0) in the focus of the 3–D representation in device 2, may help the engineer to detect curvature in the relation between response and the factors. At that moment, star points (at levels –a -and +a) can be added to the design afterward to allow the engineer to properly quantify quadratic effects. This flexibility makes a CCD very celebrated in industrial process progress [ 6]. From the CCD representation shown in device 2, it is limpid that the design points are uniformly distributed in the experimental space.

Definitive screening design

In contrast to the most close screening designs where input variables are set at only two levels (–1 and +1, or low and lofty level), the definitive screening design (DSD) introduced in 2011 by Jones and Nachtsheim, employs three levels for the variables: –1, 0 and +1; or low, focus and lofty level [7]. For N variables the DSD requires only 2 N+1 experimental runs. In Table 1, the designs for the case of 4 to 6 factors are shown, as presented in the paper from the DSD-inventors Jones and Nachtsheim. The DSDs are comprised of N fold-over pairs plus one overall focus dash consisting of the focus values of every variables (This is indicated in red color on the final rows of Table 1).

The following design pattern further characterizes a DSD [7]:

  • Regarding the location of the zeros (highlighted in grey color in Table 1) — The first two runs maintain zeros in the column of the first variable X1; the next two runs maintain zeros in the column of the second variable X2 and so on
  • Regarding the pair of runs— These are mirrored (folded over), which means that the second dash of a pair is found by multiplying the first dash of this pair by –1. Hence, the first 2 N runs maintain exactly one variable at its focus value (0), while every other variables are at their extremes (–1 or +1) and are referred to as “edge runs,” because the 3-D projections involving these variables, they are on the edges of the cube [8]. In case the number of factors N is uneven, it is recommended to select the DSD design for N+1 factors and then to drop the extraneous column, which results then again in an N-factor design with 2 N+3 runs [8]. The two extra runs are, inherently, without focus values for any factor and are referred to as “vertex runs” by the DSD-inventors [8]. Likewise, to multiply the power of any DSD, one may select initially a design with a larger number of factors than necessary (for example, in total N+k factors), and then drop the extra k columns [7–9].
  • For the sake of visualization, a 3-D representation for a DSD with three factors is shown at the privilege side of device 2, although a DSD with only three factors is not recommended [ 9]. For this visualization, they took the DSD design for N= 4 from Table 1 and then dropped the final column. One can clearly espy the so-called DSD edge runs and vertex runs (which comes down to cube points), together with the single DSD focus dash at the privilege side of device 2.

    The advantages of a DSD are as follows (For more, espy [7–9]):

  • Each factor is analyzed at three levels, which makes it workable to analyze quadratic effects, and, thus, to model curvature with a very limited amount of runs, rendering DSDs a relatively inexpensive technique for users in the field
  • Advantages are specifically found with respect to confounding variables (note: The term confounding, used in statistics, indicates that the effects of model terms cannot be calculated separately):-The main effects are completely independent of each other, completely independent of two-factor interactions, and completely independent of quadratic effects (thus, there is no confounding at all)-The two-factor interactions are not completely confounded with other two-factor interactions, although they may be correlated a little-The quadratic effects are estimable and are independent of the main effects (no confounding) and not completely confounded, though correlated, with the two-factor interaction effects-Last but not least, for DSDs with at least six factors, the DSD can apt — with very lofty level of statistical efficiency — the plenary quadratic models in case only three or fewer factors turn out to be active. As such, the DSDs become efficient response surface designs with three or fewer factors, rendering follow-up experiments (for the purpose of model optimizing) not necessary in many circumstances [7]. “The capability to project these designs to efficient response surface designs makes workable the screening and optimization of a system in a single step,” according to the co-inventors Jones and Nachtsheim [8]. This potentially huge profit (making a shortcut from screening straight to optimization) is shown in device 1 with the dashed purple arrow. For comparison, a response surface design with three factors would require 20 runs.
  • In the original DSDs, the factors had to be quantitative to allow for 3 levels. fresh DSDs maintain very recently been developed that allow for combinations of continuous and categorical input variables [ 10].

    Case study

    Part of a particular Six Sigma project consisted of making a model using a CCD for the pH of formulations made of six ingredients ( A–F), whereby the ingredients were varied independently of each other. The number of laboratory test formulations prepared and evaluated was 90. In a laboratory-scale environment, this is doable. However, in a manufacturing plant, this would be difficult, perhaps impossible. The different test runs are shown in Table 2, where the cube points, star points and focus points are clustered together for the sake of overview. The dash order as executed in the laboratory is shown in the first column. The variables are shown with their coded levels, with the value of a (star points) being 2.83. The corresponding concentrations (wt.%) of the variables A– F are summarized in Table 3. The measured pH of the formulations can be found in the final column.

    After these CCD experiments were executed, Six Sigma training was given at the Bayer Antwerp site (where the labor was carried out), including the fresh statistical technique of DSD. In the context of the training, the Six Sigma project leader sought to verify which model would be found using a DSD with only 13 runs for the six factors, compared to the CCD model obtained earlier. For this, 13 extra formulations were prepared, as shown in Table 4.

    The data sets were analyzed using Design-Expert (from Stat-Ease Inc.), including only model terms at a significance level of 0.05. A summary of both models is depicted in Table 5. The CCD model includes the main effects A, B, Cand F, the interaction effects AB, AC and BC, and the quadratic effects A², B² and C². The DSD model includes fewer terms, namely the main effects A, B and C, and the interaction effects AB and BC. It is remarkable that the coefficients in the DSD model are very similar to the one of the CCD model.

    Regarding the CCD model, the R² adj (that is, the raw coefficient of determination, R², being adjusted for the number of model predictors) indicates that 99.5% of the variation in the pH is explained by the variation in the input variables; based on the complete CCD statistical analysis (not shown) the terms F, AC, A², B² and C² — which are not included in the DSD model — account only for 2% of the explained pH variation in the data set of the CCD, which is, after all, a rather minor contribution of these extra CCD model terms. Based on their lofty R²adj (99.5% for the CCD, and 98.5% for the DSD) they can condition that they each apt well with their respective existing data set.

    But how sequel both models compare when it would compass down to prognosticate the pH of fresh mixtures? They will first maintain a leer to their predicted R² ( R²pred). This model summary is calculated by setting aside a single observation from the gross model data set, and then re-estimating the model based on every observations minus the one that is excluded (for example, in the case of the CCD, this would be 90 – 1 = 89 observations). Then the pH is calculated for the compund that was intentionally ignored to build the model. Next, the predicted residual error is estimated for this single observation; that is the dissimilarity between the calculated pH ( pHcalc) and the experimentally determined pH ( pHobs) for the observation. This procedure is repeated for every observations. Finally, the so-called predicted residual error sum of squares ( PRESS) is calculated according to Equation (1):

      (1)

    This PRESS value is compared to the Sum of Squares ( SS) around the signify value of the gross data set (SS tot); in other words, in the latter case, a “mean model” is used that simply takes the signify response as a prediction for every compund in the data set. The R ²pred is finally found via Equation (2):

      (2)

    The partially larger R²pred of 99.2% for the CCD compared to 97.7% for the DSD model suggests that the CCD has some greater predictive ability. However, the DSD model as such can certainly be classified as a model having a mighty predictive power.

    Another passage (and by far a better way) to validate a model is to spend a completely fresh data set that was not used during the progress of the model. This fresh data set — used in the stage of model validation — is frequently called a test set, in contrast to a training set that is used to calibrate (build) the model [11]. The test set should ideally span the very “space” of the input variables as was the case for the training set. They then let the calibrated model prognosticate the responses of the test set and compare them to the known, actual responses.

    FIGURE 3. Predicted pH values of 78 mixtures (from the CCD experiments) using the DSD model, and the predicted pH values of 13 mixtures (from the DSD experiments) using the CCD model, to validate both models.

    In this case study, they will spend the data set of the CCD as a test set for the DSD model; likewise, they will spend the DSD data as a test set to validate the CCD model. Because the 12 axial points in the CCD data set are beyond the design space, wherein the DSD model was built (remember, the DSD model factor settings were between –1 and +1, whereas the CCD star points were going from –2.83 to +2.83), they will exclude the CCD mixtures with the star points to validate the DSD model; hence, the pH of the remaining 78 CCD mixtures were predicted with the DSD model and compared with the measured pH values. device 3 presents the predicted versus measured pH values for the validation of both models. Visually, one can espy that both models adequately prognosticate the pH, with some more variation in the case of the DSD model. In the context of model validation, the root signify square error of prediction (RMSEP) can be calculated as an rate of the prediction error [11], using Equation (3):

      (3)

    The RMSEP can be interpreted as the tolerable error to be associated with future predictions. In practice,t 0.025;n-1 times the RMSEP (with t obtained from a Student’s t table [12], and n the number of data points of the test set; for example, based on the test set to validate the CCD model, t 0.025; 13-1 is 2.16. With larger test sets, the t value will approach 1.96. This may be used as estimated precision for predicted pH values. For the CCD model the RMSEP is 0.07 pH units, and for the DSD model, it amounts to a partially higher value of 0.09 pH units. For example, the pH of the compund of DSD dash 13 with an observed pH of 7.40 (first row in Table 4) is predicted by the CCD model to be 7.30 +/– 0.15 (the latter being 2.16 times 0.07).

    Final thoughts

    Chemical engineers and scientists in the CPI embrace the DOE methodology developed by statisticians to efficiently investigate and optimize products and manufacturing processes. They therefore gratefully own the labor done by statisticians to constantly search for even more efficient design-of-experiments approaches.

    The DSD methodology lately developed by Jones and Nachtsheim is highly efficient with the number of runs required far below the number needed in classical screening designs. As a low number of experimental runs is in most cases a desirable requirement for many experimenters in the CPI, and certainly in cases where experiments exigency to be done in manufacturing plants, the DSD can be called a revolution in performing designed experiments. In case the statistical analysis of the experiments, a fortioriindicates that the number of active factors is limited, so for these cases, the spend of definitive screening allows the engineer to build a shortcut from screening straight to optimization. It can be expected that the CPI will profit from this latest statistical research, and that definitive screening designs will be applied with growing frequency over time, especially in CPI Six Sigma projects where a lot of factors exigency to be tested.

    Edited by Suzanne Shelley

    Authors

    Bart Peeters is a manufacturing technologist at Bayer Crop Science (Haven 627, Scheldelaan 460, 2040 Antwerp, Belgium; Phone: +32 3 568 5762; Email: bart.peeters@bayer.com), where he has been working since 1998. He first served as a process improvement engineer at Eastman’s PVB polymer manufacturing plant onsite (until 2004). Since then, he has been working at the environmental department of the company. Peeters is a certified Six Sigma Black Belt and coordinates the Six Sigma program at the Bayer Antwerp site. While working at Bayer’s WWTP, he obtained his Ph.D. in engineering from the KU Leuven (Belgium) on the research topic “Effect of activated sludge composition on its dewaterability and sticky phase.”Prior to that, he earned an M.S.Ch.E. degree from the KU Leuven, and an M.Bio.Ch.E. degree in 1996 from the university college De Nayer. Peeters is the author of 20 articles in scientific journals, technical magazines and international conferences.

    Guido Desmarets is a senior management consultant at Stanwick NV (Axess commerce park- structure B-Guldensporenpark 20, 9820 Merelbeke, Belgium; Phone +32 9 210 59 50; Email: guido.desmarets@stanwick.be), where he has been working since 1988. As a Master Black Belt he coaches worldwide companies in the field of Continuous Improvement, scrawny Six Sigma, Operational Excellence, with major results in efficiency and trait improvements, cost reductions and delivery performance. Prior to joining Stanwick NV he held positions as R&D manager, process engineer, trait assurance and production management at international companies, where his interest to spend DOE as a major process improvement instrument was triggered and developed. He holds a master degree in chemistry (University of Ghent 1976). Subsequent research labor resulted in several publications and patents on slow-release formulations for pesticides.

    Marc Roels is a crop protection products laboratory specialist at Bayer Crop Science Europe N.V. (Phone +32 3 568 5185; Email: marc.roels@bayer.com), where he has been working since 1984. Roels provides analytical champion for research, development, registration and manufacturing of herbicides at the Bayer Antwerp site and at different toller operations. For more than 15 years he is an enthusiastic user of DOE to study formulation robustness and formulation optimization. He received his B.S. in pharmaceutical and biological techniques in 1983. He is a certified Six Sigma Green Belt.

    Sam Van Aeken was the team lead of the analytical services laboratory at Bayer Crop Science (Haven 627, Scheldelaan 460, 2040 Antwerp, Belgium), where he has been working since 2014. Prior to joining the firm, he worked from 2010 until 2012, as a process chemist in Eastman Ghent, managing fresh product trials. He was later employed at W.R. Grace / DeNeef as a R&D engineer, where he researched fresh urethane based grouting systems. Van Aeken graduated as a bio-engineer in 2004 at the Vrije Universiteit Brussel and obtained a Ph.D. in organic chemistry in 2010 with a dissertation on fresh synthetic routes towards aza-heterocyclic quinone compounds.

    References

    1. Steinberg, D.M., Industrial statistics: the challenges and the research, trait Engineering, 28 (1), pp. 45–59, 2016.

    2. Kleppmann, W., Design of Experiments (DOE): Optimizing products and processes efficiently, Chem. Eng., November, pp. 50–57, 2014.

    3. Anderson, M.J., Design of Experiments (DOE): How to wield hard-to-change factors using a split plot, Chem. Eng, September, pp. 83–86, 2016.

    4. Box, G.E.P., Hunter, J.S. and Hunter, W.G. “Statistics for experimenters: design, innovation, and discovery”, 2nd Ed., John Wiley & Sons, Inc., 2005.

    5. Jones, B., 21st century screening experiments: what, why and how. trait Engineering, 28 (1), pp. 98–106, 2016.

    6. Anderson, M.J. “RSM simplified: optimizing processes using surface methods for design of experiments”, 2nd Ed., Productivity Press, 2016.

    7. Jones, B. and Nachtsheim, C.J. A class of three-level desings for definitive screening in the presence of second-order effects, Journal of trait Technology, 43, pp. 1-15, 2011.

    8. Jones, B. and Nachtsheim, C.J. Blocking schemes for definitive screening designs. Technometrics 58 (1), pp. 74-83, 2016.

    9. Jones, B., JMP Blog on proper and indecorous spend of Definitive Screening Designs (DSDs) (https://community.jmp.com/t5/JMP-Blog/Proper-and-improper-use-of-Definitive-Screening-Designs-DSDs/ba-p/30703), 2016.

    10. Jones, B. and Nachtsheim, C.J., Definitive screening designs with added two-level categorical factors. Journal of trait Technology, 45, pp. 121–129, 2013

    11. Esbensen, K.H. “Multivariate Data Analysis – In Practice. An Introduction to Multivariate Data Analysis and Experimental Design”, 5th Ed., CAMO software, 2010.

    12. Bass, I. “Six Sigma Statistics with transcend and Minitab,” 1st Ed., Mc. Graw Hill, 2007.


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    Operations & Process Management: Principles & Practice for Strategic ImpactOperations & Process Management: Principles & Practice for Strategic Impact
    By Nigel Slack, Alistair Jones
    Publisher : Pearson (Feb 2018)
    ISBN10 : 129217613X
    ISBN13 : 9781292176130
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    Subject : Business & Economics
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    Computer Security: Principles and PracticeComputer Security: Principles and Practice
    By William Stallings, Lawrie Brown
    Publisher : Pearson (Aug 2017)
    ISBN10 : 0134794109
    ISBN13 : 9780134794105
    Our ISBN10 : 1292220619
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    Subject : Computer Science & Technology
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    Urban EconomicsUrban Economics
    By Arthur O’Sullivan
    Publisher : McGraw-Hill (Jan 2018)
    ISBN10 : 126046542X
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    Subject : Business & Economics
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    Urban EconomicsUrban Economics
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    Publisher : McGraw-Hill (Jan 2018)
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    Understanding BusinessUnderstanding Business
    By William G Nickels, James McHugh, Susan McHugh
    Publisher : McGraw-Hill (Feb 2018)
    ISBN10 : 126021110X
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    Our ISBN10 : 126009233X
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    Understanding BusinessUnderstanding Business
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    Publisher : McGraw-Hill (May 2018)
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    Understanding BusinessUnderstanding Business
    By William Nickels, James McHugh, Susan McHugh
    Publisher : McGraw-Hill (Jan 2018)
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    Our ISBN10 : 126009233X
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    Understanding BusinessUnderstanding Business
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    Publisher : McGraw-Hill (Jan 2018)
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    MSC-431MSC-431
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    Publisher : McGraw-Hill (Jan 2017)
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    Publisher : McGraw-Hill (Feb 2017)
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    ISBN13 : 9781260147155
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    Subject : Business & Economics, Communication & Media
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