Latest Posts

  • Drag, Drop, and Deploy: Can JMP Really Play a Significant Role in the Future of AI/ML? We provide some friendly banter and debate on the role that JMP and JMP Pro are likely to play amidst modern artificial intelligence (AI) and machine learning (ML) trends. In the evolving landscape of AI/ML, scientists and engineers across industries need intuitive tools for data exploration, model building, and deployment. JMP and JMP Pro, as no-code platforms, simplify and speed up the process fr...
    Skill level: advanced
    photo of Florian Vogt Florian Vogt and others
  • Predictive Modelling in JMP: Agricultural Yields Within agricultural businesses, the ability to accurately predict the yield of a crop each year is critical for enhancing the efficiency, profitability, and sustainability of that business. The earlier the yield can be predicted, the more efficiently that resources can be allocated, supply chain managed, the harvest scheduled, and the storage logistics of the business be determined. ...
    Skill level: intermediate
    photo of Tom Dale Tom Dale
  • Leveraging JMP for QbD-Driven Lentiviral Vector Process Development The demand for robust and scalable lentiviral vector manufacturing processes for cell and gene therapy has driven the adoption of advanced methodologies. This presentation delves into an innovative approach to process development, focusing on a Quality by Design (QbD) strategy, with a particular emphasis on the powerful capabilities of JMP software. In adherence to stringent regulatory requirements...
    Skill level: intermediate
    photo of Johann-Christoph Dettmann Johann-Christoph Dettmann
  • The Use of JMP During the Lifecycle of a Relative Potency Assay for CMC Relative potency assays are critical in evaluating the biological activity of drug products throughout the lifecycle of biopharmaceuticals. Managing variability, optimizing assay conditions, and ensuring consistent performance are essential but challenging. This presentation explores how JMP supports developing, validating, and monitoring relative potency assays in CMC (chemistry, manufac...
    Skill level: intermediate
    photo of Zeban Kolen Zeban Kolen
  • Resolve Costly Out-of-Control Processes More Confidently with Control Chart Triage in JMP Live 19 It can be costly and stressful to learn about out-of-control processes late or to react to them with incomplete information.If you are one of the tens of thousands of JMP users working with control charts, you don't want to miss this talk. It gives you an early look at a much-requested JMP Live 19 feature that can help you find and resolve issues more quickly, more accurately, and more co...
    Skill level: intermediate
    photo of Annie Dudley Annie Dudley and others
  • Analytics Champions: Trials and Triumphs in Pursuit of Enterprise Analytics As practitioners of applied statistics – regardless of industry, background, or skill level – we all know the power that data holds as a strategic asset. Despite overwhelming consensus around the promise of statistics, however, many organizations are struggling to go beyond buzzwords like “AI” and “digital transformation” to actually build a culture of data literacy at an enterprise scale. In this ...
    Plenary
    Skill level: intermediate
    photo of Meg Hermes Meg Hermes and others
  • From Development to Testing: A Journey of Creating and Validating a JMP Add-In In this talk, we take you through the journey of developing and testing a JMP add-in. Along the way, we first introduce the statistical problem that we were facing, which was designing discrete choice experiments tailored to different demographic groups so that we could more efficiently analyze the usability of JMP's Easy DOE platform. Following this, we discuss the process of developing an add-in ...
    Skill level: intermediate
    photo of Bill Fisher Bill Fisher
  • A Complete Workflow for Analyzing and Reporting Experimental Data Developing and manufacturing biopharmaceuticals involve many standardized experiments and reports. Most reports contain information from the associated protocol, product quality data from a laboratory information management system (LIMS), information from lab documentation, and performance data from different devices. Currently, these data sources are processed with JMP, Excel, and other softw...
    Skill level: beginner
    photo of Ole Herud-Sikimic Ole Herud-Sikimic
  • Using Conditional Feature in Workflow Builder to Combine Six Smaller Workflows into One Workflow In the past, comparisons of chamber configuration setups between chambers on the same mainframe or across different mainframes involved a manual method that took a day. Recently, an automated process has been developed using Workflow Builder in JMP 17. Individual workflow files were created to speed up the efficiency of the process, using an individual workflow, depending on the number of cham...
    Skill level: intermediate
    photo of Gary O'Driscoll Gary O'Driscoll
  • Understanding Positional Temperature Trends to Increase Testing Reliability Using JMP Pro Fire testing is one of the most critical and expensive aspects when developing intumescent products to protect steel structures from fire. Understanding the nature and performance of a furnace during testing is imperative for reliably interpreting the results from formulation development. In this case, the temperature data from sensors (thermocouples) in bespoke furnaces were utilise...
    Skill level: intermediate
    photo of Jay Richardson Jay Richardson
  • A JSL Application for Accelerated Stability Modelling If a pharmaceutical product is likely to degrade after 12 months, how can we get an early insight into that risk? The answer is to perform an accelerated stability study that collects data over a period of a few weeks under high-stress conditions, such as elevated temperature, pH for liquids, and moisture for solids. Once a model has been selected, it can be extrapolated over time to assess the ove...
    Skill level: beginner
    photo of David Burnham David Burnham
  • Finding Optimal Operating Regions with the JMP Design Space Profiler and Simulator Quality by Design (QbD) is a systematic approach for building quality into a product. The Design Space Profiler in JMP helps solve the fundamental QbD problem of determining an optimal operating region that assures quality as defined by specifications associated with Critical Quality Attributes (CQAs) while still maintaining flexibility in production.  In this demonstration, learn how to ...
    Skill level: intermediate
    photo of Laura Lancaster Laura Lancaster and others
  • Optimizing Recipe Formulation Using Machine Learning, Sequential Learning, and Design of Experiments Machine learning (ML) methods have been widely applied to analyze design of experiments (DOE) data in such industries as chemical, mechanical, and pharmaceuticals, yet receive limited attention in the food industry, especially for recipe optimization. To address this, we explored ML and sequential learning for recipe formulation, aiming to optimize product quality. We combined DOE with ML...
    Skill level: beginner
    photo of Junaid Mehmood Junaid Mehmood
  • Smart Subsampling vs. Brute Force: A Strategic Approach to Predictive Modelling Handling large data sets continues to present unique challenges, even in an era where advanced machine learning algorithms can process vast amounts of information. Relying on brute-force techniques to analyze massive data sets can lead to inefficiencies, model overfitting, noise accumulation, and diminishing returns from adding more data. Intelligent subsampling, which involves selecting a rep...
    Skill level: intermediate
    photo of Carole Soual Carole Soual and others
  • Measurement System Analysis for Non-normal Data Measurement system analysis (MSA) is very important in the semiconductor industry to estimate the quality of the measurements. Most MSA indicators, especially the precision to tolerance (P/T) ratio, implicitly assume a normal distribution, with +/- kσ covering a given percentage of the distribution. In the reference documents (AIAG MSA Manual), there are no alternative calc...
    Skill level: intermediate
    photo of François Bergeret François Bergeret
  • A Year of Implementing Learnings: How to Design and Analyse Experiments with Pass/Fail Responses As a follow-up to a previous presentation by Don McCormack, this session offers insight into the learnings implemented and the value gained. This designed experiment with 10 input factors and a yes/no response was made possible by attending JMP Discovery Summit in Manchester last year. The story includes the following steps: First design phase, which consisted of 24 runs. First analysis ...
    Skill level: intermediate
    photo of Don McCormack Don McCormack
  • Improving Machine Learning Using Space Filling DOE to Tune Hyperparameters Tuning hyperparameters is crucial for optimizing machine learning models, but the process can be computationally expensive and complex. Traditional grid, random search, or even Bayesian optimization methods often miss critical areas of the hyperparameter space, leading to suboptimal models. In this talk, we show a JMP add-in we have developed that uses space-filling DOE to more efficiently approach...
    Skill level: advanced
    photo of Peter Hersh Peter Hersh and others
  • Synthetic Chromatograms: A New Approach to Chromatographic Modelling Chromatographic methods such as HPLC, GC, and CGE are essential for analytics across various industries. Optimizing these methods to ensure high accuracy and precision is crucial but challenging due to numerous parameters and complex chromatograms. Often, chromatographic targets (e.g., resolution, peak-to-valley) are extracted and modeled, but interpreting these results and their impact on the chro...
    Skill level: advanced
    photo of Phil Kay Phil Kay and others
  • Scaling Innovation: Integrating Data Science into Engineering Workflows Today’s engineering challenges demand more than localized solutions – they require scalable, flexible tools that integrate data science techniques into the fabric of data and analytic workflows. The JMP family of products enables engineers to move beyond routine problem solving by providing powerful platforms for statistical analysis, process optimization, and data visualization. This plenary sessi...
    Plenary
    Skill level: intermediate
    photo of John Sall John Sall and others
  • JMP R&D Lightning Talks 오전 첫 번째 발표에서 JMP 제품 관리자로부터 JMP 제품 로드맵에 대해 알아보았습니다. 이 세션에서 JMP R&D 관리자는 가장 최신의 혁신적인 내용을 발표합니다. 참석자는 최첨단 개발을 미리 보고 이러한 발전이 조직 전체에서 혁신과 효율성을 지원하고 향상시킬 수 있는 방법을 알아볼 기회를 갖게 될 것입니다.
    Plenary
    photo of Brian Corcoran Brian Corcoran and others
  • Leveraging JMP Clinical for Clinical Data Review and Integrative Analysis of Genomic Data with Biomarkers 임상 데이터와 유전체 데이터의 통합은 점점 더 중요한 트렌드가 되고 있습니다. 많은 경우, 병원이나 연구기관에서 임상 프로토콜 외에도 추가적인 바이오마커 데이터를 연구 목적으로 수집하는 경우가 많습니다. 이러한 바이오마커 데이터는 임상 데이터베이스 내에 저장되지 않고 별도의 데이터셋으로 관리되는 경우가 흔합니다. 이처럼 분리된 데이터를 JMP Clinical에서 효과적으로 분석하려면 고급 기능이 필수적입니다. JMP Clinical은 JMP Genomics 및 JMP/JMP Pro의 내장 기능과 결합하여 기본적인 보고 기능을 넘어서는 종합적이고 통합된 분석을 가능하게 합니다. 먼저, JMP Clinical을 활용한 임상 안전성 검토 프로세스를 소개합니다. 그런 다음, JMP Pro와 JMP의 기능을 활용하여 ...
    Skill level: intermediate
    photo of 정혜원 정혜원 JMP
  • Artificial intelligence (AI) meets the aviation industry: How can AI help the aviation industry? 머신 러닝과 최적화 방법을 JMP와 함께 사용하여 항공 산업의 실제 문제를 해결하는 방법에 대해 이야기를 나누어 보고자 합니다. 특히 American Airlines, Boeing, Federal Aviation Administration, Honeywell Aerospac 등의 항공 관련 회사에서 일하면서 얻은 연구 경험을 공유하고자 합니다
    Skill level: intermediate
    photo of 김정현 김정현 세종대학교
  • Enhancing Bioprocess Understanding with Functional DOE 바이오의약품 생산에서 세포 배양의 시간 기반 동적 변화는 공정 성능과 제품 품질에 큰 영향을 미칩니다. 기존의 실험계획법(DOE) 분석은 주로 최종 결과에 초점을 맞추어, 배양 과정에서 시간에 따른 복잡한 상호작용을 충분히 반영하지 못하는 한계가 있습니다. 반면, Functional DOE는 시간 기반 데이터를 활용하여 세포 생존밀도(VCD : Viable Cell Density)와 대사 물질들의 변화를 예측할 수 있습니다. 이를 통해 개발 단계에서는 공정 최적화를 지원하고, 생산 단계에서는 설정값 변경으로 인한 문제를 사전에 예측하여 안정적인 생산을 위한대응이 가능합니다.본 발표에서는 일반적인 Fed-batch 동물세포 배양공정을 사례로, Functional DOE가 세포 배양의 변동성을 어떻게 예측하고 이...
    photo of 김성진 김성진 삼성바이오로직스
  • 바이오의약품 개발을 위한 DOE에서 curve data의 해석 사례 바이오의약품을 개발하는 동안 다양한 연구를 수행하게 되며, 가장 많이 사용하는 연구기법 중 하나가 DOE입니다. DOE를 활용하여 여러 factor로부터 특정 response를 modeling 하여, 중요한 factor를 확인하고 최적의 공정 운용방안을 도출/검증하고 있습니다. 바이오의약품 공정을 개발을 위해 DOE를 수행하다 보면 다양한 curve data를 확인할 수 있습니다. 결과가 항상 특정 수치로 표현되는 것은 아니며, 오히려 다양한 시점의 curve data로 결과가 나오는 경우가 빈번합니다. 배양 공정에서의 세포생장곡선, 생산성부터 정제에서의 chromatograph, 안정성 패턴, 분석에서의 다양한 곡선 등이 결과로 도출됩니다. 이럴 경우 특정 시점에서의 값을 response로 취해서 DOE 분...
    Skill level: intermediate
    photo of 권상오 권상오 종근당
  • Design Space Profiler 가능한 한 최고의 품질을 보장하기 위해 제품과 프로세스를 설계하려면 어떻게 해야 할까요? 중요 품질 속성(CQA: Critical Quality Attributes)에 대해 스펙에 맞는 제품으로 이어지는 프로세스 작동 영역을 어떻게 찾을 수 있을까요? JMP의 Design Space Profiler는 스펙을 벗어난 제품의 비율을 직접 제어하여 고품질 제품을 보장하는 제품 및 프로세스 작동 영역에 대한 "해결사"입니다. 이번 발표에서는 예시를 통해 Design Space Profiler를 설계된 실험에 맞는 모델에 적용하여 스펙을 벗어난 제품이 생성되는 비율을 최소화하고 제어하는 ​​요인에 대한 작동 영역을 찾는 방법을 보여드리겠습니다.
    Skill level: intermediate
    photo of Christopher Gotwalt Christopher Gotwalt
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