Stat-Ease Blog

Next in our 40th anniversary “Ask an Expert” blog series is John "Jay" Davies, who's an absolute rock star when it comes to teaching and implementing DOE. He's lent us his expertise before - see this talk from our 2022 Online DOE Summit - and he shared an anecdote with our statistical experts about how he approaches switching to DOE methods when working with new groups in the Army. He kindly agreed to let us publish it as part of this series.

For the past 14 years, I’ve been a Research Physicist with the U.S. Army DEVCOM Chemical Biological Center at Aberdeen Proving Grounds, MD as a member of the Decontamination Sciences Branch, which specializes in developing techniques/chemistries to neutralize chemical warfare agents. I’m dedicated to applied statistical analysis ranging from multi-laboratory precision studies to design of experiments (DOE). The Decontamination Sciences Branch has been integrating DOE methods into many of their chemical agent decontamination research programs.

I’m happy to report that the DOE methods here at the Chemical Biological Center are really catching on. I collaborated on 24 DOEs from 2014 through 2021. Then, in 2021-22, we completed 26 DOEs across 10 different programs. I’ve been doing a lot of mixture-process DOEs with the Bio Sciences groups for synthetic biology and bio manufacturing applications, and once the other groups saw the information that we were getting from just a single day’s worth of data, they too wanted to try DOE.

Lately, I’ve changed the formula that I use for the initial consultations when visiting a group that has expressed an interest in DOE but has never used DOE before. Previously we’d go right into their project, and I’d tell them how we might construct a DOE for their application. However, I’ve found that it’s too much of a culture shock if we go right into talking about what a DOE for their application might look like. Instead, especially if I’m working with a group that has no DOE experience at all, I now devote about 1 hour to discuss DOE methods in general before we even mention their actual application. In this discussion, I reveal the major differences that they are going to see with a DOE, which are:

• We’re not going to replicate every sample, we may even have zero exact replication.
• We’re not going to test every possible combination of factors.
• Sample sizes are going to be 70% to 95% smaller than what they are used to doing.
• We are not going to change “one factor at a time”, in fact we’ll be changing all factors at once.
• The designs might look chaotic, but they are strategically created to contain a hidden orthogonal structure, along with hidden replication, that is not apparent.
• You will see that many of the DOE samples contain factor combinations that don’t seem to make sense. This is because each sample is not designed as a stand-alone shot at optimization. Rather, the samples cumulatively are working in concert to tease out the influences of each factor. This will let us fit a predictive model that we will then use to predict the optimal settings of factors.

Recently, I was following this format with a group that had never used DOE before. We had a great back-and-forth dialog as I went through the bullets above and explained a bit about each point. They asked many questions and were really following along. Then, after about an hour we got into their application and I just sketched out a prototype quadratic mixture-process DOE that I thought would give them a good idea of what the initial DOE might look like, with 30 samples in total. I then went over what some simulated outputs for the DOE generated prediction model might look like. At this point one of them stopped me and with a very perplexed look on his face, said “hold on, hold on…wait a minute here. Are you telling us that if we run just those 30 samples, we would be able to predict the optimal formulation and the optimal process setting for this system?“

This scientist had been following along, asking questions and really absorbing the information in the past hour as we walked through the DOE basics, but I could see that at that moment things were just sinking in. He realized the ramifications of what we had just discussed – typically, this group might have had to run several hundreds of samples to characterize similar systems, but with DOE they would only need about 30 samples. I responded to his question saying, “Yes that’s exactly what I’m telling you. We’ve run dozens of these mixture-process DOEs, many of them much more complex than this system, and they do work.” This individual, a mid-career researcher, then responded, “How is it possible that we have not heard of this stuff before?” I told him, “I can’t give you a good answer to that one.”

And there you have it! Let us know if you want to talk about saving time & money with DOE: our statistical experts and first-in-class software make it easier than ever.

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Here's the latest Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!

Mark's comment: Make sure to check out article #10, where the authors deploy response surface methods (with lots of impressive 3D plots!) to produce an eco-friendly material for civil engineering. 

1. Application of Novel Biochar Derived from Experimental Sewage Sludge Gasification as an Adsorbent for Heavy Metals Removal
   Sustainability 2025, 17(3), 997
   Authors: Domagoj Nakić, Hana Posavčić, Katarina Licht, and Dražen Vouk
   
2. Azadirachta indica Fruit Mucilage Aided Mucoadhesive Microspheres of Acyclovir for Drug Entrapment and Mucoadhesive Time Assets with Design-Expert Software
   Indian Journal of Pharmaceutical Education and Research, 2025; 59(1s): s243-s255.
   Authors: Gorantla Naresh Babu, M Menaka and Hindustan Abdul Ahad
   
3. Optimizing Water Hyacinth Compost and Chicken Manure for Enhancing Spinach Growth An Eco Friendly Approach to Sustainable Agriculture
   Indian Journal of Natural Sciences, 2024, 15(87), 976-977
   Authors: Mahmuda Parveen, Sujit Ghosh
   
4. Enhancing the tensile strength and morphology of Sansevieria trifasciata Laurentii fibers using liquid smoke and microwave treatments: an RSM approach
   Scientific Reports 15, Article number: 4420 (2025)
   Authors: Muhammad Arsyad Suyuti, Djarot B. Darmadi, Winarto, Putu Hadi Setyarini
   
5. Optimization of Biodiesel Production from Tannery Industry Fleshing Wastes Using Response Surface Methodology
   SAE Technical Paper 2025-28-0115
   Authors: Kanthasamy P, Arul Mozhi Selvan, Shanmugam P
   
6. Efficiency comparison of natural coagulants (Cactus pads and Moringa seeds) for treating textile wastewater (in the case of Kombolcha textile industry)
   Heliyon Volume 11, Issue 4, 28 February 2025
   Authors: Getahun Demeke Worku, Shimeles Nigussie Abate
   
7. Physico-chemical Characterisation of Ultrasound Processed Finger Millet Malt
   Asian Journal of Dairy and Food Research 14 February 2025
   Authors: Almas Begum Adoni, Madhava Mondru, Kaliramesh Siliveru, Vishnuvardhan Sidlagatta, Sandeep Raja Donepudi, Swapna Motamarri
   
8. Preparation of Gluten Free Cookie using Chestnut and Foxnut Flour Blend: Composition Optimization Through Response Surface Methodology
   Asian Journal of Dairy and Food Research Volume 44 Issue 1 (February 2025) : 84-91
   Authors: Divya Singh Chauhan, Apoorva Behari Lal, Anto Pradeep Raja Charles, Amit Pratap Singh, Ashish Khare, Pranav Vashisht
   
9. Optimization of Biochar Production from Cassava Peels: An Application of Response Surface Methodology
   Archives of Advanced Engineering Science 1-9
   Authors: Timothy Adekanye, Abiodun Okunola, Adeolu Adediran, Afolabi Tokunbo Yemisi, Aisha Aderibigbe
   
10. Investigations on mechanical and stress strain characteristics of geopolymer concrete reinforced with glass fibers
    Scientific Reports volume 15, Article number: 6335 (2025)
    Authors: Thunuguntla Chaitanya Srikrishna, Venkatesh Noolu, B. Sudheer Kumar Reddy, George Uwadiegwu Alaneme, Ravella Durga Prasad & M. VishnuPriyan
    
11. Study on hydration mechanism and ratio optimization of slag powder modified high-water material
    Scientific Reports volume 15, Article number: 6175 (2025)
    Authors: Xiang Ma, Chenyang Liu, Liwei Zhai, Shengrong Xie, Chaowen Wu & Jian Yang
    
12. Extraction and purification of total flavonoids from Zanthoxylum planispinum Var. Dintanensis leaves and effect of altitude on total flavonoids content
    Scientific Reports volume 15, Article number: 7080 (2025)
    Authors: Jiyue Wang, Xianqi Huang, Zhenyu Chen, Nian Chen, Mingli Yang, Chenggang Liang, Yanghua Yu & Denghong Shi
    
13. Preparation and characterization of inorganic foam modified with nano-magnesium hydroxide for inhibiting coal spontaneous combustion
    Physics of Fluids 37, 022152 (2025)
    Authors: Jingxia Tang, Jiawen Cai, Shengqiang Yang, Zhaoyang Yu, Kexin Chen, Xincheng Hu
    
14. Experimental approach and assessment of Zr conversion coatings on Al alloy using response surface methodology
    Corrosion Science, Volume 249, 2025, 112824
    Authors: Ana Kraš, Davorin Kramar, Ingrid Milošev

Next in our 40th anniversary “Ask an Expert” blog series is Leonard “Len” Rubinstein, a Distinguished Scientist at Merck. He has over 3 decades of experience in the pharmaceutical industry, with a background in immunology. Len has spent the last couple of decades working on bioanalytical development, supporting bioprocess and clinical assay endpoints. He’s also a decades-long proponent of design of experiments (DOE), so we reached out to learn what he has to say!

When did you first learn about DOE? What convinced you to try it?

I first learned about DOE in 1996. I enrolled in a six-day training course to better understand the benefits of this approach in my assay development.

What convinced you to stick with DOE, rather than going back to one-factor-at-a-time (OFAT) designs?

Once I started using the DOE approach, I was able to shorten development time but, more importantly, gained insights into understanding interactions and modeling the results to predict optimal parameters that provided the most robust and least variable bioanalytical methods. Afterward, I could never go back to OFAT!

How do you currently use & promote DOE at your company?

DOE has been used in many areas across the company for years, but it has not been explicitly used for the analytical methods supporting clinical studies. I raised awareness through presentations and some brief training sessions. Afterward, after my management adopted it, I started sponsoring the training. Since 2018, I have sponsored four in-person training sessions, each with 20 participants.

Some examples of where we used DOE can be found at the end of this interview.

What’s been your approach for spreading the word about how beneficial DOE is?

Convincing others to use DOE is about allowing them to experience the benefits and see how it’s more productive than using an OFAT approach. They get a better understanding of the boundaries of the levels of their factors to have little effect on the result and, more importantly, sometimes discard what they thought was an important factor(s) in favor of those that truly influenced their desired outcome.

Is there anything else you’d like to share to further the cause of DOE?

It would be beneficial if our scientists were exposed to DOE approaches in secondary education, be it a BA/BS, MA/MS, or PhD program. Having an introduction better prepares those who go on to develop the foundation and a desire to continue using the DOE approach and honing their skills with this type of statistical design in their method development.

And there you have it! We appreciate Len’s perspective and hope you’re able to follow in his footsteps for experimental success. If you’re a secondary education teacher and want to take Len’s advice about introducing DOE to your students, send us a note: we have “course-in-a-box” options for qualified instructors, and we offer discounts to all academics who want to use Stat-Ease software or learn DOE from us.

Len’s published research:

Whiteman, M.C., Bogardus, L., Giacone, D.G., Rubinstein, L.J., Antonello, J.M., Sun, D., Daijogo, S. and K.B. Gurney. 2018. Virus reduction neutralization test: A single-cell imaging high-throughput virus neutralization assay for Dengue. American Journal of Tropical Medicine and Hygiene. 99(6):1430-1439.

Sun, D., Hsu, A., Bogardus, L., Rubinstein, L.J., Antonello, J.M., Gurney, K.B., Whiteman, M.C. and S. Dellatore. 2021. Development and qualification of a fast, high-throughput and robust imaging-based neutralization assay for respiratory syncytial virus. Journal of Immunological Methods. 494:113054

Marchese, R.D., Puchalski, D., Miller, P., Antonello, J., Hammond, O., Green, T., Rubinstein, L.J., Caulfield, M.J. and D. Sikkema. 2009. Optimization and validation of a multiplex, electrochemiluminescence-based detection assay for the quantitation of immunoglobulin G serotype-specific anti-pneumococcal antibodies in human serum. Clinical and Vaccine Immunology. 16(3):387-396.

Welcome to our first Publication Roundup! In these monthly posts, we'll feature recent papers that cited Design-Expert® or Stat-Ease® 360 software. Please submit your paper to us if you haven't seen it featured yet!

Mark's comment: make sure to check out publication #4 by researchers from GITAM School of Science in Hyderabad, India. They provide all the raw data, the ANOVAs, model graphs and, most importantly, enhancing the quality of medicines via multifactor design of experiments (DOE).

1. Innovative study on chalcopyrite flotation efficiency with xanthate and ester collectors blend using response surface methodology (B.B.D): towards sustainability
   Scientific Reports volume 15, Article number: 65 (2025)
   Authors: Imkong Rathi & Shravan Kumar
   
2. Fabrication and In Vivo Evaluation of In Situ pH-Sensitive Hydrogel of Sonidegib–Invasomes via Intratumoral Delivery for Basal Cell Skin Cancer Management
   Pharmaceuticals 2025, 18(1), 31
   Authors: Maha M. Ghalwash, Amr Gamal Fouad, Nada H. Mohammed, Marwa M. Nagib, Sherif Faysal Abdelfattah Khalil, Amany Belal, Samar F. Miski, Nisreen Khalid Aref Albezrah, Amani Elsayed, Ahmed H. E. Hassan, Eun Joo Roh, & Shaimaa El-Housiny
   
3. Formulation development and evaluation, in silico PBPK modeling and in vivo pharmacodynamic studies of clozapine matrix type transdermal patches
   Scientific Reports volume 15, Article number: 1204 (2025)
   Authors: Abdul Qadir, Syed Umer Jan, Muhammad Harris Shoaib, Muhammad Sikandar, Rabia Ismail Yousuf, Fatima Ramzan Ali, Fahad Siddiqui, Abdul Jabbar Magsi, Ghulam Mustafa, Muhammad Talha Saleem, Shafi Mohammad, Mohammad Younis & Muhammad Arsalan
   
4. Unique Research for Developing a Full Factorial Design Evaluated Liquid Chromatography Technique for Estimating Budesonide and Formoterol Fumarate Dihydrate in the Presence of Specified and Degradation Impurities in Dry Powder Inhalation
   Biomedical Chromatography: Volume 39, Issue 2, February 2025
   Authors: Lova Gani Raju Bandaru, Naresh Konduru, Leela Prasad Kowtharapu, Rambabu Gundla, Phani Raja Kanuparthy, Naresh Kumar Katari
   
5. Synergistic effects of fly ash and graphene oxide composites at high temperatures and prediction using ANN and RSM approach
   Scientific Reports volume 15, Article number: 1604 (2025)
   Authors: I. Ramana & N. Parthasarathi
   
6. Enhancement Strategy for Protocatechuic Acid Production Using Corynebacterium glutamicum with Focus on Continuous Fermentation Scale-Up and Cytotoxicity Management
   International Journal of Molecular Sciences 2025, 26(1), 396
   Authors: Jiwoon Chung, Wooshik Shin, Chulhwan Park, and Jaehoon Cho
   
7. An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach
   Scientific Reports volume 15, Article number: 2979 (2025)
   Authors: Sheetal Kumari, Smriti Agarwal, Manish Kumar, Pinki Sharma, Ajay Kumar, Abeer Hashem, Nouf H. Alotaibi, Elsayed Fathi Abd-Allah & Manoj Chandra Garg
   
8. Manipulated Slow Release of Florfenicol Hydrogels for Effective Treatment of Anti-Intestinal Bacterial Infections
   International Journal of Nanomedicine, Volume 2025:20, Pages 541—555, 13 January 2025.
   Authors: Luo W, Zhang M, Jiang Y, Ma G, Liu J, Dawood AS, Xie S, Algharib SA
   
9. Preparation of slow-release fertilizer derived from rice husk silica, hydroxypropyl methylcellulose, polyvinyl alcohol and paper composite coated urea
   Heliyon, Volume 11, Issue 2, 30 January 2025
   Authors: Idayatu Dere, Daniel T. Gungula, Semiu A. Kareem, Fartisincha Peingurta Andrew, Abdullahi M. Saddiq, Vadlya T. Tame, Haruna M. Kefas, David O. Patrick, Japari I. Joseph
   
10. Elimination of Ni(II) from wastewater using metal-organic frameworks and activated algae encapsulated in chitosan/carboxymethyl cellulose hydrogel beads: Adsorption isotherm, kinetic, and optimizing via Box-Behnken design optimization
    International Journal of Biological Macromolecules, 21 January 2025, In Press, Journal Pre-proof
    Authors: Gamil A.A.M. Al-Hazmi, Nadia H. Elsayed, Jawza Sh. Alnawmasi, Khadra B. Alomari, Ali Hamzah Alessa, Shareefa Ahmed Alshareef, A.A. El-Bindary
    
11. QbD-Driven preparation, characterization, and pharmacokinetic investigation of daidzein-l oaded nano-cargos of hydroxyapatite
    Scientific Reports volume 15, Article number: 2967 (2025)
    Authors: Namrata Gautam, Debopriya Dutta, Saurabh Mittal, Perwez Alam, Nasr A. Emad, Mohamed H. Al-Sabri, Suraj Pal Verma & Sushama Talegaonkar
    
12. Lubricity potentials of Azadirachta indica (neem) oil and Cyperus esculentus (tiger nut) oil extracts and their blends in machining of mild steel material
    Heliyon, Volume 11, Issue 2, 30 January 2025
    Authors: Ignatius Echezona Ekengwu, Ikechukwu Geoffrey Okoli, Obiora Clement Okafor, Obiora Nnaemeka Ezenwa, Joseph Chikodili Ogu
    
13. Process Evaluation and Analysis of Variance of Rice Husk Gasification Using Aspen Plus and Design Expert Software
    Chemistry Africa (2025)
    Authors: Ernest Mbamalu Ezeh, Isah Yakub Mohammed, Epere Aworabhi, Yousif Abdalla

Welcome to the first entry in our 40th anniversary Ask An Expert series, where we talk to current and past power users of Design-Expert® and Stat-Ease® 360 software about their experience with design of experiments (DOE) and our software. For this post, we interviewed Shari Kraber, formerly the Client Success Manager, Workshop Manager, and Senior Instructor for Stat-Ease. Shari retired in 2022 after nearly 3 decades of helping clients across all industries learn DOE and implement it to save time & money making breakthrough improvements on their products & processes.

What’s the biggest benefit to educating your team about DOE?

You’ll break the habit of testing changes one at a time. Many systems will have unknown interactions, and only structured DOE test plans will reveal them. Your team will learn a new way of approaching problems, which helps the company in the long run.

You spent so many years helping folks change from one-factor-at-a-time testing to using DOE. What’s something about DOE that more people should know?

Remember that DOE is about trying to get a bunch of information from a small sample of a large process. The analysis does not need to be perfect in order to be useful. Don’t get paralysis by analysis – just find a simple and reasonable model and then CONFIRM the results. You should use software to design and plan the experiments – a good (robust) design will help offset the inevitable problems encountered while running the physical experiment, so that the analysis will be useful enough to make business decisions.

Your background is as a process engineer at 3M, but you always insisted that anyone working with formulations should use mixture designs. Why?

When the response is dependent on the proportions of the ingredients, then two things make this different from a process design:

1. The design space changes to accommodate the proportions of the ingredients.
2. The polynomial model is a different form (use a Scheffé polynomial) so that the nonlinear components of the system are modeled correctly. A traditional polynomial will NOT model this system correctly!!

So, what’s the best way to train your team on DOE?

I think distance learning is great. Stat-Ease started doing distance-learning training via Zoom during the COVID-19 pandemic, and it remains a popular choice for teams. The big advantage of distance learning is that the massive amount of information provided is more digestible in half-day segments. The in-person training is pretty intense and is not as ideal educationally. Yes, it is nice to have a live trainer, but honestly the retention of the materials is BETTER using distance learning.

Finally, what features of Stat-Ease software do you want more folks to know about?

I have quite a few recommendations!

First, there are several great editing features if you right-click on any graphs in the software:

• Edit axes
• Plant flags (then right-click on them to edit info)
• Graph Preferences to edit font sizes, colors, display options

And some more:

1. Confirmation node – add the settings for the confirmation runs that you want to do and it provides the 95% interval that should contain the mean of those runs.
2. You can change the layout options when you have multiple tabs or windows in your analysis. The green + allows you to add more graphs to the display, such as viewing 2 interaction graphs at once, or a contour and 3D plot at the same time.
3. In the Analysis Summary, the Coefficients Table can be Transposed by right-clicking on the top left corner square. (Transpose is now available on all tables, but particularly helpful on this one.)

If you’re ready to train your team on DOE, check out our public training options or email us with your questions. Shari still teaches classes on a part-time basis, and our whole team would love to get you rolling with best practices for DOE.