Choose Language Hide Translation Bar

cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
Brian Corcoran, JMP Director of Research and Development, SAS Dieter Pisot, JMP Principal Application Developer, SAS Eric Hill, JMP Distinguished Software Developer, SAS   You know the value of sharing insights as they emerge. JMP Live — the newest member of the JMP product family — reconceptualizes sharing by taking the robust statistics and visualizations in JMP and extending them to the web, privately and securely. If you'd like a more iterative, dynamic and inclusive path to showing your data and making discoveries, join us. We'll answer the following questions: What is JMP Live? How do I use it? How do I manage it? For background information on the product, see this video from Discovery Summit Tucson 2019 and the JMP Live product page.   JMP Live Overview (for Users and Managers) – Eric Hill What is JMP Live? Why use JMP Live? Interactive publish and replace What happens behind the scenes when you publish Groups - From a user perspective Scripted publishing Stored credentials API Key Replacing reports Setup and Maintenance (for JMP Live Administrators) – Dieter Pisot Administering users and groups Limiting publishing Setting up JMP Live Windows services .env files Upgrading Applying a new license Using Keycloak single sign-on Installing and Setting up the Server (for IT Administrators) – Brian Corcoran Choosing architectural configurations based on expected usage Understanding SSL Certificates and their importance Installing the JMP Live Database component Installing the JMP Pro and JMP Live components on a separate server Connecting JMP Live to the database Testing installed configuration to make sure it is working properly (view in My Videos)     (view in My Videos)   (view in My Videos)
Dieter Pisot, JMP Principal Systems Engineer, SAS Stan Koprowski, JMP Senior Systems Engineer, SAS   Data changes, and so do your JMP Live reports. Typical data changes that involve additional observations or modifications to columns of data necessitate updates to published reports. With the first scenario, an existing report might need to be recalculated to reflect the new observations or rows of data that are used in the report. A second option is when you want to restructure the underlying data by adding or removing columns of information that are used in the report. With both situations you must update your report on a regular basis. In this paper we will provide practical examples of how to organize JSL scripts that facilitate the replacement of an existing JMP Live report with a current version. Prior to the live demonstration, we will discuss all key security protocols, including protecting credentials needed to connect to JMP Live. The code presented are designed to be reused and shared with anyone who has a need to publish or replace a JMP Live report on a predefined time interval, such as hourly, daily, weekly or monthly. With some basic JSL knowledge you can easily adopt them for your automated updates to any of your other existing JMP Live reports. Not a coder? No worries, we've got your back. Additionally, we will provide a JMP add-in that schedules the publishing of a new report or the publishing of a replacement report for those with little JSL knowledge using a wizard-based approached.
Zhiwu Liang, Principal Scientist, Procter & Gamble Pablo Moreno Pelaez, Group Scientist, Procter & Gamble   Car detailing is a tough job. Transforming a car from a muddy, rusty, full of pet fur box-on-wheels into a like-new clean and shiny ride takes a lot of time, specialized products and a skilled detailer. But…what does the customer really appreciate on such a detailed car cleaning and restoring job? Are shiny rims most important for satisfaction? Interior smell? A shiny waxed hood? It is critical for a car detailer business to know the answers to these questions to optimize the time spent per car, the products used, and the level of detailing needed at each point of the process. With the objective of maximizing customer satisfaction and optimizing the resources used, we designed a multi-stage customer design of experiments. We identified the key vectors of satisfaction (or failure), defined the levels for those and approached the actual customer testing in adaptive phases, augmenting the design in each of them. This poster will take you through the thinking, designs, iterations and results of this project. What makes customers come back to their car detailer? Come see the poster and find out! (view in My Videos) View more... (Highlight to read)   Speaker Transcript Zhiwu Liang Hello, everyone. I'm Zhiwu Liang statistician for Brussels Innovation Center for Procter and Gamble company I'm I'm working   For the r&d department. Hello. Pablo Moreno Pelaez Yep. So I'm Pablo Moreno Pelaez I'm working right now in Singapore in the r&d   Department for Procter and Gamble's   So we wanted to introduce to you this poster where we want to share a case study in which we wanted to figure out what makes a car detailing your grade.   So as you know, Procter and Gamble, the very famous company about job detailing for cars. No, just a joke. So we had to anonymize or what have they done. So this is the way   We wanted to share this case study, putting it in the context of a car detailing job and what we wanted to figure out here is what were the key customer satisfaction factors for which we then   Build interactive design that we then tested with some of those customers to figure out how to build the model and how to optimize   Job detailing for the car. So how do we minimize the use of some of our ingredients. How do we minimize the time we take for some of the tasks that it takes to do the job details.   So if you go to the next slide. And the first thing that we went to, to take a look. Yes.   Okay, what are the different vectors that a customer we look at when they they take the car to get detail and to get clean and shiny and go back home with a buddy.   A brand new car. What are they looking at clean attributes, they're looking at Shane attributes and they are looking at the freshness of the guy.   From a culinary view that we looked at the exterior cleaning the cleaning of the rooms are the king of the interior   The shine of the overall body, the rooms that windows and of course the overall freshness of the interior   And then we'll wanted to build this by modifying these attributes in different ways and combining the different finishes that it a potential   Car detailing job would give you wanted to estimate and be able to build the model to calculate what the overall satisfaction.   And also what the satisfaction with a cleaning and what their satisfaction with the shine.   Would be modifying those different vectors. These will allow us in the future to use the model.   To estimate. Okay, can we reduce the time that we spend on the rooms, because it's not important, or can we reduce the time that we spend on the interior or reduce the amount of products that we use for freshness. If those are not important.   So really, to then optimize how do we spend the resources on delivering the the car detailing jobs.   So in the next slide.   You can see a little bit with the faces of the study where Zhiwu Liang Yeah, so as popular as sad as the cart. The planning job company. We are very focused on the consumer satisfaction. So for this particular job.   What we have to do is identify what is the key factors which drive the consumer overall satisfaction and clean and shine satisfaction. So in order to do that we separate or our study design and   Data collection experiments industry step. First, we do the Pilar, which is designed to five different scenario. Okay, using the fire cars.   To set up the different level of each offer factors as a moment. We said, have to all of these five factor previous public described in the to level one is low and not as high.   Then we recruit the 20 consumers to evaluate all of the five cards in a different order. The main objective for this Pilar is check the methodology and track the   If the question we asked consumers consumers understand and provide the correct answer, and also define the proper range of each factor.   So after that, we go to the phase one, which is the extend our design space by seven factors. Okay. Some factors to keep, low, high level as we do the Pilar. Some extent to the low, medium, high because we think is more relevant to the consumer by including the more level in our factor.   And since we got more factor and from the customer design point of view, you will generate more   Experiments runs in our study so totally we have an it runs of the cost setting and each of the panelists. We ask them to   Evaluate still five but using the different order or different combination therefore accepted the custom design. When the consumer need to evaluate   Five out of the 90 I said him. We have to using the balance in company blog design technique and to use 120 customers, each of them evaluate five cars.   So totally this   120 customer data we collect we run the model identify what is the main effect. Okay, and what is the interaction in our model.   Then through that we three hours. Not important factor and go to the face to face to using the funnel identified the six factors and for course adding more level for some factor because we saying that low is not low enough in the faith from phase one study and middle it's not   Really matched to our consumer satisfaction. So we had some level of quality lol some factor level for the middle, high   Inserting currently design space, then   The face to design experiments his argument from the phase one.   Was that we get a different okay setting for the 90 different cars, then asked 120 consumer evaluate five in a different camp one   Through that we can remove non we can identify okay what is, what is the past.   Factor setting which have the optimal solution for the consumer satisfaction and clean and shine satisfaction. So as you can see here   We run the 3D model using our   Six factors setting.   Which each of them has played some role for the consumer satisfaction intense or cleaning as shine satisfaction.   For the overall. Clearly, we can see the cleaning ring and shine window cleaning in interior is the key driver for the overall satisfaction. So if consumers in the ring clean and window shine. Normally, either we all agree, he was satisfied for the   For our car detailing job and also we identified significant interaction.   Exterior clean and intuitive clean these two things combined together has different rate to the overall satisfaction with a clean satisfaction and the shine satisfaction model.   We identified very close, very significantly impact factors for clean   Clearly, all of the clean factor relate to the clean satisfaction and for shine also all of the shine one relate to the shine satisfaction.   But still the different perspective lighter clean his focus on the ring and shine is focused on the window. So from validating, we can have the better setting for all the car.   Relief factor which helpers to divide the new projects which achieved the best consumer satisfaction based on all of the   Factors setting. I think   Speaker Transcript Zhiwu Liang Hello, everyone. I'm Zhiwu Liang statistician for Brussels Innovation Center for Procter and Gamble company I'm I'm working   For the r&d department. Hello. Pablo Moreno Pelaez Yep. So I'm Pablo Moreno Pelaez I'm working right now in Singapore in the r&d   Department for Procter and Gamble's   So we wanted to introduce to you this poster where we want to share a case study in which we wanted to figure out what makes a car detailing your grade.   So as you know, Procter and Gamble, the very famous company about job detailing for cars. No, just a joke. So we had to anonymize or what have they done. So this is the way   We wanted to share this case study, putting it in the context of a car detailing job and what we wanted to figure out here is what were the key customer satisfaction factors for which we then   Build interactive design that we then tested with some of those customers to figure out how to build the model and how to optimize   Job detailing for the car. So how do we minimize the use of some of our ingredients. How do we minimize the time we take for some of the tasks that it takes to do the job details.   So if you go to the next slide. And the first thing that we went to, to take a look. Yes.   Okay, what are the different vectors that a customer we look at when they they take the car to get detail and to get clean and shiny and go back home with a buddy.   A brand new car. What are they looking at clean attributes, they're looking at Shane attributes and they are looking at the freshness of the guy.   From a culinary view that we looked at the exterior cleaning the cleaning of the rooms are the king of the interior   The shine of the overall body, the rooms that windows and of course the overall freshness of the interior   And then we'll wanted to build this by modifying these attributes in different ways and combining the different finishes that it a potential   Car detailing job would give you wanted to estimate and be able to build the model to calculate what the overall satisfaction.   And also what the satisfaction with a cleaning and what their satisfaction with the shine.   Would be modifying those different vectors. These will allow us in the future to use the model.   To estimate. Okay, can we reduce the time that we spend on the rooms, because it's not important, or can we reduce the time that we spend on the interior or reduce the amount of products that we use for freshness. If those are not important.   So really, to then optimize how do we spend the resources on delivering the the car detailing jobs.   So in the next slide.   You can see a little bit with the faces of the study where Zhiwu Liang Yeah, so as popular as sad as the cart. The planning job company. We are very focused on the consumer satisfaction. So for this particular job.   What we have to do is identify what is the key factors which drive the consumer overall satisfaction and clean and shine satisfaction. So in order to do that we separate or our study design and   Data collection experiments industry step. First, we do the Pilar, which is designed to five different scenario. Okay, using the fire cars.   To set up the different level of each offer factors as a moment. We said, have to all of these five factor previous public described in the to level one is low and not as high.   Then we recruit the 20 consumers to evaluate all of the five cards in a different order. The main objective for this Pilar is check the methodology and track the   If the question we asked consumers consumers understand and provide the correct answer, and also define the proper range of each factor.   So after that, we go to the phase one, which is the extend our design space by seven factors. Okay. Some factors to keep, low, high level as we do the Pilar. Some extent to the low, medium, high because we think is more relevant to the consumer by including the more level in our factor.   And since we got more factor and from the customer design point of view, you will generate more   Experiments runs in our study so totally we have an it runs of the cost setting and each of the panelists. We ask them to   Evaluate still five but using the different order or different combination therefore accepted the custom design. When the consumer need to evaluate   Five out of the 90 I said him. We have to using the balance in company blog design technique and to use 120 customers, each of them evaluate five cars.   So totally this   120 customer data we collect we run the model identify what is the main effect. Okay, and what is the interaction in our model.   Then through that we three hours. Not important factor and go to the face to face to using the funnel identified the six factors and for course adding more level for some factor because we saying that low is not low enough in the faith from phase one study and middle it's not   Really matched to our consumer satisfaction. So we had some level of quality lol some factor level for the middle, high   Inserting currently design space, then   The face to design experiments his argument from the phase one.   Was that we get a different okay setting for the 90 different cars, then asked 120 consumer evaluate five in a different camp one   Through that we can remove non we can identify okay what is, what is the past.   Factor setting which have the optimal solution for the consumer satisfaction and clean and shine satisfaction. So as you can see here   We run the 3D model using our   Six factors setting.   Which each of them has played some role for the consumer satisfaction intense or cleaning as shine satisfaction.   For the overall. Clearly, we can see the cleaning ring and shine window cleaning in interior is the key driver for the overall satisfaction. So if consumers in the ring clean and window shine. Normally, either we all agree, he was satisfied for the   For our car detailing job and also we identified significant interaction.   Exterior clean and intuitive clean these two things combined together has different rate to the overall satisfaction with a clean satisfaction and the shine satisfaction model.   We identified very close, very significantly impact factors for clean   Clearly, all of the clean factor relate to the clean satisfaction and for shine also all of the shine one relate to the shine satisfaction.   But still the different perspective lighter clean his focus on the ring and shine is focused on the window. So from validating, we can have the better setting for all the car.   Relief factor which helpers to divide the new projects which achieved the best consumer satisfaction based on all of the   Factors setting. I think
Phil Kay, JMP Senior Systems Engineer, SAS   People and organizations make expensive mistakes when they fail to explore their data. Decision makers cause untold damage through ignorance of statistical effects when they limit their analysis to simple summary tables. In this presentation you will hear how one charity wasted billions of dollars in this way. You will learn how you can easily avoid these traps by looking at your data from many angles. An example from media reports on "best places to live" will show why you need to look beyond headline results. And how simple visual exploration - interactive maps, trends and bubble plots - gives a richer understanding. All of this will be presented entirely through JMP Public, showcasing the latest capabilities of JMP Live.   In September 2017 the New York Times reported that Craven was the happiest area of the UK. Because this is an area that I know very well, I decided to take a look at the data. What I found was much more interesting than the media reports and was a great illustration of the small sample fallacy.   This story is all about the value of being able to explore data in many different ways. And how you can explore these interactive analyses and source the data through JMP Public. Hence, "see fer yer sen", which translates from the local Yorkshire dialect as "see for yourself".   If you want to find out more about this data exploration, read these two blogs posts: The happy place?  Crisis in Craven? An update on the UK happiness survey    (view in My Videos)     This and more interactive reports used in this presentations can be found here in JMP Public.
Roselinde Kessels, Assistant Professor, University of Antwerp and Maastricht University Robert Mee, William and Sara Clark Professor of Business Analytics, University of Tennessee   Past discrete choice experiments provide clear evidence of primacy and recency effects in the presentation order of the profiles within a choice set, with the first or last profiles in a choice set being selected more often than the other profiles. Existing Bayesian choice design algorithms do not accommodate profile order effects within choice sets. This can produce severely biased part-worth estimates, as we illustrate using a product packaging choice experiment performed for P&G in Mexico. A common practice is to randomize the order of profiles within choice sets for each respondent. While randomizing profile orders for each subject ensures near balance on average across all subjects, the randomizations for many individual subjects can be quite unbalanced with respect to profile order; hence, any tendency to prefer the first or last profiles may result in bias for those subjects. As a consequence, this bias may produce heterogeneity in hierarchical Bayesian estimates for subjects, even when the subjects have identical true preferences. As a design solution, we propose position balanced Bayesian optimal designs that are constrained to achieve sufficient order balance. For the analysis, we recommend including a profile order covariate to account for any order preference in the responses. (view in My Videos)  
Corinne Bergès, Six Sigma Black Belt, NXP Semiconductors Kurt Neugebauer, Analog Design Engineer, NXP Semiconductors Da Dai, Design Automation Engineer, NXP Semiconductors Martin Kunstmann, R&D-SUP-Working student, NXP Semiconductors Alain Beaudet, Product and Test Engineer, NXP Semiconductors   Structured Problem Solving (SPS) is one of the three pillars of NXP Six Sigma system, with Quality Culture and Continuous Improvement, and demonstrates still more NXP Quality system maturity. Some key approaches in NXP SPS are fitting with the DMAIC/DMADV, 8D or 5-Why frameworks. They widely use statistics to change assumptions into evidences, necessary for a real defect root cause elimination: modeling, DOE, multivariate analysis, …Two specific statistical analysis are described. In design for automotive, about simulation of parametric, hard or soft defects, purpose is to implement the best algorithm to reduce number of simulations, without impacting test coverage or failure rate estimation precision: for this, JMP provides interesting options in clustering. NXP experiments will result in an algorithm and in some recommendations for the new IEEE standard on study about defect coverage accounting method. Now, downstream in manufacturing, when it deals with capability index computation, and with normality test, to bypass high sensitivity of these tests for a slight abnormality, a methodology was designed in JMP to quantify shift from normality, by using the Shash distribution and its Kurtosis and Skewness parameters. A script was implemented to automate it on the more than 3000 tests for an automotive product.  (view in My Videos)