problem solving red x

Published: November 7, 2018 by Ken Feldman

The fundamental premise of the Shainin Red X® process is that for any problem, there is a dominant root cause that must be eliminated or mitigated for the process to be improved. In this article, we will define the Red X process, best practices, and how it can be applied to your organization.

Overview: What is Red X? 

The Red X method is based on the key assumption there is always a dominant cause of variation. This statement is based on the application of the Pareto principle to the causes of the variation. Generally, the variation of the output is caused by the variation of several inputs. These inputs (Xs) are categorized by color, with the Red X being the dominant root cause. Shainin defines the desired state of the output as the GreenY®.

Red X Pareto Chart

Instead of the DMAIC methodology of Define-Measure-Analyze-Improve-Control, the Red X approach uses the following structure, called FACTUAL:

Shainin Red X FACTUAL approach to problem-solving

An industry example of Red X 

The problem was Post Burning Blow Holes on automotive batteries. The post burner is an automatic burning machine designed to weld the cylindrical bushing of an automotive battery to a specified depth of burn. If blow holes are seen on the battery post, then the battery is rejected.

Shainin Red X techniques were used to reduce the percentage of reworks from 0.15% to 0.03%. DOE was used as the primary tool along with:

• Multivari analysis
• Variable search
• Paired comparison 
• Component search 
• Product / process search
• Scatter plots

3 best practices when thinking about Red X 

Here are a few tips on using Red X in your organization. 

1. Deep understanding of the process

You must have a deep understanding of the Y and the problem. 

2. Problem solving strategy 

The Red X approach is very diagnostic in nature. It is the identification, analysis and quick zooming in on the root cause. 

3. Measurement System Analysis (MSA) 

You must have confidence in the quality of the data captured by your measurement system. This requires you to do a MSA study to validate your data. 

Frequently Asked Questions (FAQ) about Red X

1. what are some of the common tools used in red x .

Two of the most common tools used in Red X are the Solution Tree™ developed by Shainin, Pareto Chart and full factorial design of experiments . Other tools include the Shainin ISO plot, component search, paired comparison, multi-vari chart and BOB/WOW.

2. What do BOB and WOW mean in Red X?

One of the techniques used in Red X is to examine the extremes to try and identify a possible root cause. You will look at the Best of the Best (BOB) versus the Worst of the Worst (WOW).

3. Who developed the Red X approach? 

Dorian Shainin (1914-2000) was an American quality consultant, aeronautical engineer, and author primarily noted for his contribution in the field of problem-solving, specifically the creation and development of the Red X concept.

Red X in a nutshell

Red X is a problem-solving technique based on the premise there is one dominant root cause for process variation and problems. Using a convergent process of analysis focusing on potential process input variables (X), the end result will be an identification of the primary Red X root cause along with lesser colors of Pink Xs.

About the Author

Ken Feldman

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Red X Methodology

• red x methodology
• lean six sigma
• shainin red x

Asked by Vishwadeep Khatri , January 31, 2023

Red X Methodology (or the Shainin System) is a problem solving methodology which states that for every problem there is a prominent root cause (or a Red X). In order to solve the problem and get the desired output (Green Y), the Red X must be eliminated.

An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Balaji Loganathan on 1st Feb 2023.

Applause for all the respondents - Balaji Loganathan, Vikas Choudhary, Anupam Goswami, Kirpa Shanker Tiwari, Nunhuck Oosman.

Vishwadeep Khatri

Q   537. What is Shainin Red X Methodology? Compare it with Six Sigma and highlight its pros and cons.

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Balaji loganathan.

Shainin RED X projects are evidence-based; converging on the main source of variation, the emphasizing principle is DY = f(Dx)The largest value will result from a combination of a significant coefficient and a large change in X.  

What is the difference between Shainin and Six Sigma?

The main difference between the Shainin Red X® approach ( FACTUAL ) and the Six Sigma methodology ( DMAIC ) is  the phase Approach . The Red X develops a strategy based upon the physics of the problem and the comparison of the BOB (Best of Best) and WOW (Worst of Worst) parts

Any problem-solving methodology involves two phases’ diagnostic and remedial phases. The diagnostic phase is concerned with measuring and analyzing the current process performance while the remedial phase involves of various corrective actions taken to improve the process and monitoring the new process to make it a culture.

Tables show the comparison between the six sigma and Shainin methodological approaches.

Vikas Choudhary

Shainin Red X Methodology is a problem-solving technique used in the manufacturing and engineering industries to identify the root cause of a particular issue quickly and effectively. It's a data-driven approach that utilizes statistical analysis, hypothesis testing, and experimentation to isolate the key factor causing the problem.

Compared to Six Sigma, Shainin Red X Methodology is a more streamlined and quicker approach to problem-solving. While Six Sigma is a comprehensive methodology that can take several weeks or months to complete, Shainin Red X can often find the root cause in a matter of days or even hours.

Pros of Shainin Red X Methodology include:

• Faster problem-solving times
• Reduced number of trial and error tests
• Higher accuracy in identifying root cause
• Emphasis on simplicity, making it easy for non-experts to understand and participate in the problem-solving process.

Cons of Shainin Red X Methodology include:

• Limited scope, as it's mainly focused on identifying root cause and not on process improvement or optimization.
• May not be suitable for complex problems or those requiring a deeper understanding of the underlying systems and processes.

Overall, Shainin Red X Methodology can be an effective tool for solving problems quickly in specific cases, but it may not always be the best choice for all situations.

Anupam Goswami

Shainin Red X Methodology is a statistical problem-solving approach used in industrial settings to quickly identify the root cause of complex and multifaceted issues. It is based on the idea that a small number of critical inputs (often referred to as the dominant "X's" based on pareto principle) are responsible for most of the variation in a system. The methodology involves a systematic process of testing, eliminating, and validating these inputs until the root cause of the issue is found.

Compared to Six Sigma, Shainin Red X Methodology is considered to be a more efficient and quicker approach to problem-solving, particularly when dealing with complex, multivariate issues. Six Sigma, on the other hand, is a more comprehensive process improvement methodology that involves extensive data analysis, statistical process control, and a structured DMAIC (Define, Measure, Analyze, Improve, Control) process.

Pros of Shainin Red X Methodology:

·          Quicker problem resolution time

·          Focuses on critical inputs for efficient problem-solving

·          Can be applied to a wide range of industrial settings

·          Can be used by individuals with limited statistical knowledge

Cons of Shainin Red X Methodology:

·          May not be as comprehensive as other problem-solving approaches such as Six Sigma

·          May not be suitable for all types of problems, particularly those that are not complex or multivariate in nature

·          Can be less data-driven compared to other methodologies, relying more on intuition and experience of the problem-solver.

Pros of Six Sigma:

·          Comprehensive approach to problem-solving and process improvement

·          Utilizes statistical tools and methodology to identify and eliminate causes of defects

·          Can be applied to a wide range of industries and processes

Cons of Six Sigma:

·          Can be time-consuming and resource-intensive to implement

·          May not be as quick as Shainin Red X Methodology in solving specific problems.

In summary, Shainin Red X Methodology is a fast and effective approach to solving complex problems, but it may not be as comprehensive as other methodologies like Six Sigma. The choice of methodology depends on the type and complexity of the problem, as well as the resources available.

kirpa Shanker Tiwari

The Shainin System is develop by Dorian Shainin . It is a tool for statistical engineering and generally used in Automobile sector. Shainin also called Red –X strategy. This is typically used to high volume processes where huge database exist and ease of data availability. This system is used in parts and assembly manufacturing processes.

This work on below underlying principles

1.     Assumption that there are large cause of variations

2.     Assumptions there is diagnostic processes and remedial actions.

Steps of Shainin system

1.     define the project

2.     Establish Measurement system

3.     generate hints

4.     list probable factors

6.     found Red –X

7.     Check interactions

8.     Irreversible corrective actions

10.   monitor outcome

11.   Consumer satisfaction

How it is different than Six sigma

Six sigma is more statistical however this is based on Statistics and more mechanistic.

Shainin is systems that are developed to achieve six sigma targets

Shainin systems are evidence based and covers maximum source of variations.

Shainin systems generally used FACTUAL path while Six sigma used DMAIC kind of methodology.

FACTUAL: Focus>>Approach>> converge>>Test >> understand>> Apply >> leverage

DMAIC: Define>> Measure>> Analyse>> Improve>> Control.

Nunhuck Oosman

The Shainin X methodology is described as a system for resolving issues created for medium- to high-volume processes where data are easily accessible, statistical techniques are frequently employed, and process intervention is challenging. It has mostly been used in facilities for part and assembly processes.

The basic principality of the Red X technique is that there is always a dominant cause of variation. This claim is supported by the Pareto principle's application to the causes of the variance. Usually, changes in a number of inputs lead to changes in the output. These inputs (Xs) are divided into groups based on color, with the Red X serving as the primary cause. The GreenY state is how Shainin describes the desirable state of the output.

Using Shainin tools has the benefit of requiring very low sample sets for problem analysis. Frequently, samples of just two or three are sufficient to draw statistically significant results. The data can be analyzed without the use of computerized statistical methods.

Moreover underlying causes are identified through "interacting to the parts" as opposed to assumptions or preconceived notions.

Due to the statistically robustness of the procedures, main effects and interaction effects were distinguished and quantified.

A great variety of versatility is offered by the 12 various approaches.

It is simpler to incorporate the entire workforce because the strategies are simple to implement and inexpensive to learn.

The below four groups can be used to group the 12 techniques:

• Generation Clue: Until the fundamental cause can be isolated, quasi causes of variation are removed using the multi-vari analysis filtering technique.
• Pictograph: Used to indicate where a flaw is located on a component, in a design, or on a grid. Either a random pattern or a concentration in a specific location will result (s).
• Components Search: To identify the source of the issue, parts and sub-assemblies are switched between good and problematic products.

-Comparing the greatest and worst product examples side by side will help you identify the traits or factors that set the best and worst goods apart.

List and quantify the process variables in the product/process search

-Search for Products/Processes: The process variables that affect a product's quality should be listed and measured. By contrasting data from a process that yields good parts with measurements from a process that yields faulty parts, you can identify which of these process factors is to blame for the problem.

DOE optimization

• By displaying one variable against the other, a scatterplot (also known as a scatter diagram) can be used to visually depict the relationship between two variables.
• RSM, or Response Surface Methodology When we want to improve the settings of the essential elements in a process once they have been isolated, we apply a DOE technique. When we are aware of or believe that the response variable has curvature (i.e., non-linearity), we use RSM designs.

DOE approach

• Variables Search: A grid search approach that distinguishes between significant and minor process variables through testing the optimal and limiting values for each variable
• Complete factor analyses: These tests cover all possible combinations of variables, and all of their interconnections, and work best when there are just a few variables that have a big effect on the answer variable. They take longer and cost more to execute than screening methods.
• B vs. C: B stands for the superior or improved method, whilst C stands for the existing process. Six samples—three B samples and three C samples—are used in the test. According to the Law of Combinations, there is simply one possibility in 20 that all three Bs will outrank all three Cs, providing us 95% certainty that this is not a coincidence.
• Positrol (or precontrol): Items are rated as red, yellow, or green depending on how closely they adhere to the standard or tolerance. Green represents the tolerance's middle portion; yellow represents its second half; and red represents the tolerance's upper limit. How frequently the process requires change determines the sampling frequency. Continue running if a specimen is green. Choose another sample if the first one is yellow. Stop the process and tweak or modify it if the second piece is yellow. If any of the samples are red, halt the procedure and make any necessary adjustments.
• The Process Certification (Process Control, and Management Plan) specifies the who, how, where, and when of controls that will guarantee that the significant variables or factors are kept under control.

Mayank Gupta

Balaji Loganathan has provided the best answer to this question. 

Response from Oosman is also a must read.

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GM's RED X - Supposed to be a simple tool to find Root Cause

• Thread starter erica 2005
• Start date May 2, 2005
• May 2, 2005

Hi, I've heard about a technique that called RED X that is supposed to be a very simple tool to find root cause of problems, it is used by GM (and maybe developed there???) Does anyone have any information on this? Thanks for your help. -Erica  

erica said: Hi, I've heard about a technique that called RED X that is supposed to be a very simple tool to find root cause of problems, it is used by GM (and maybe developed there???) Does anyone have any information on this? Thanks for your help. -Erica Click to expand...

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GM Canada's 16th Annual Continuous Improvement Symposium was held on April 29th, 2004, with over 135 GM employees and 145 suppliers such as Lear, Decoma and A.G. Simpson in attendance. Guest speakers had the opportunity to address the audience and share real incident case studies that resulted in significant quality improvements, and share the methodology used to gain that improvement. Since the events beginnings as a group of eighty at the Oshawa Car Assembly Plant training room, the symposium continues to grow each year. Representatives from General Motors' plants and administrative areas, as well as a diverse group of suppliers have participated in this event. Although the symposium has become an annual event, it really is a celebration of the continuous improvement process that is carried on every day, resulting in significant quality gains. Objectives The Continuous Improvement Symposium is a forum that brings together both GM and GM suppliers with the following objectives: * Promote the use of common improvement methods and tools * Increase and share knowledge * Recognize those using continuous improvement methods * Forum to network This year the theme was Design for Six-Sigma and various speakers addressed the audience on this topic. Maryann Combs, GM Canada Director of Engineering and Steve Rose, GM Canada Director of Purchasing & Logistics, gave opening remarks and welcomed the guest speakers and attendees. Throughout the day both GM employees and suppliers shared their case studies on real life issues that were resolved through continuous improvement methodologies, more specifically, Design for Six Sigma and Red X. Design for Six Sigma (DSFF) What is it: A methodology for driving process capable designs that satisfy the customer by identifying and optimizing critical design parameters. Heart of Methodology: I.D.D.O.V. I - Identify - identify the project through set criteria D - Define - define the opportunity through the customer voice and JD Power Results D - Develop - develop concepts and transfer functions through alternate designs O - Optimize - optimize the design and balance with conflicting designs V - Verify - validate results Objective: The objective of the process is to improve quality, reliability and durability (QRD) on current and future vehicles. GM works with major suppliers within the QRD focus areas. Red X What is it - Red X is the GM accepted problem solving methodology that uses engineering skills, common sense and simple statistics to solve technical problems with statistical confidence. Heart of Methodology: Red X, Green Y, Best of the best (BOB) and worst of the worst (WOW). Red X - root cause or variable that is causing the greatest amount of variation in the product or process of interest. Green Y - the failure mode or concern of the customer Best of the Best (BOB) - best part, assembly or component made in a process as determined by the Green Y Worst of the Worst (WOW) - worst part, assembly or component made in a process as determined by the Green Y Objective: Red X statistical engineering identifies a set of tools first used to identify the Red X, and then to monitor the effectiveness of controlling the Red X. Click to expand...

Red X The "Red X" is taken from the Shainin method. It has levels of expertise, but doesn't use "belts" like Six Sigma. All in all, they are pretty tight-lipped about the method. Just try and find some really solid information. It used to be that candidates had to sign all kinds of non-disclosures and such before being trained. The concept (similiar to the theory of constraints in that there is only "one" true constraint at any one time) is that every process or product has a "Red X" or one critical parameter and if that is controlled, the process should produce acceptable results. It uses terms such as "Best of the Best" or BOBs, "Worst of the Worst" or WOWs. It is largely a Design of Experiments method. As with any method (Shainin, TQM, Six Sigma, etc.) the ability for it to work depends on the the ability of the person using the method and managements committment to make the necessary changes. See attached file for some more information. I hope it helps. Wayne  

Attachments

• publication11.pdf 221.8 KB · Views: 2,627

I remember the red "x" (pink "x" and pale pink "x") method from a class I took in DOE, which included Shainin methods, years ago. I didn't know it had grown so much until I just did a little research. It seems it has grown outside of DOE into general root cause analysis (via GM). The are even people called "Red X"s. Here's one article of interest I found. I also noticed that Marc referred to the technique, on March 10, 2000, when acknowledging Shainin's death.  

• Shainin approach experimental design using a catapult.pdf 219.1 KB · Views: 2,366
• May 4, 2005

Thanks Thanks for the information - it is quite helpfull. -Erica  

Odiorne Point - 2006

• Apr 30, 2006

check out the web site  

Odiorne Point said: check out the web site Click to expand...

Larry.Jiang

• Jul 28, 2008

frankly speaking ,it's a comprehensive tool for solving problem rather than a sample tool .In my opinion there is a little complex. I think the primary concept is that compare the WOW and BOB ,and find out the deverse then do some test . WOW:worst of worst BOB:best of best I got a training before but I did't understand entirely.  

• Sep 9, 2008

Hello, I have the RED X Master Certification, this technique was developed by Dorian Shainin not by GM, there are several companies that uses this methodology, unfortunately there is few bibliography for reference, one book is: "World Class Quality" using design of experiment to make it happen, Keki R. Bhote & Adi K. Bhote, second edition, AMACOM.  

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• CAREER FEATURE
• 18 September 2024

Science-policy advisers shape programmes that solve real-world problems

• Amy DePaul 0

Amy DePaul is a freelance journalist in Irvine, California.

You can also search for this author in PubMed   Google Scholar

Javier Garcia-Martinez, who advises global leaders on public policies backed by scientific evidence, says more scientists should work to build trust with policymakers.

Many researchers think that science policy is a total snoozefest. They imagine policy workers churning out boring white papers that sit idly at the bottom of a drawer, never to be read. But behind the scenes at United Nations assemblies and legislative agencies, science-policy specialists know their work will be put to the test. They brief global leaders at the World Economic Forum Annual Meeting in Davos, Switzerland, on breakthrough technologies and present the White House’s climate agenda to the press, the public and government agencies. They work out how funding agencies can award research grants to a more diverse pool of scientists, and help emerging economies to reduce carbon emissions without their compromising development.

Whatever the form, crafting science policies is important and rewarding work, say four science-policy specialists interviewed by Nature . But there are challenges: getting on the radar of policymakers, for one. Another is building trust with government officials at the early stages of policymaking rather than only at moments of crisis.

Still, scientists say that developing, advising on or advocating for policy contributes to social progress in tangible, measurable ways. In a world reeling from the COVID-19 pandemic and looking ahead to upheavals caused by climate change and the potential of artificial intelligence (AI), science-policy advisers are needed now more than ever.

JAVIER GARCIA-MARTINEZ: Go beyond the report, build trust with policymakers

Director of the Molecular Nanotechnology Lab at the University of Alicante in Spain.

Alongside running my research group — an international team working on nanotechnology for sustainable chemistry and clean energy solutions — I volunteer at organizations such as the World Economic Forum (WEF) and the International Union of Pure and Applied Chemistry (IUPAC), helping global leaders to create more effective public policies that are backed by sound evidence.

Since 2012, I’ve helped the WEF to put together a yearly report called the Top 10 Emerging Technologies. In this report, scientists around the world identify technologies that they think are going to transform industry and the economy. We explain these tools in simple terms and, every January, we communicate that information to policymakers and world leaders during the WEF’s annual summit in Davos, Switzerland.

Our reports have identified emerging technologies that will shape the future of science. Some of the predictions have come true. For example, our 2015 report identified the gene-editing tool CRISPR–Cas9 as a transformative technology. The scientists who discovered CRISPR won a Nobel prize five years later.

We highlighted mRNA vaccines in the 2017 report. At the time, we didn’t know the COVID-19 pandemic was coming. We just thought that people weren’t paying enough attention to this technology, and that governments should put more resources into developing it.

Another area in which science policy can help to steer research is making AI more useful for chemists. To do that, we need an innovative chemistry ‘language’ that can be ‘read’ by machines. At IUPAC, we are creating a textual identifier for chemical substances that will provide a standard way of encoding molecular information. This will accelerate the implementation of AI in scientific discovery.

IUPAC is also reimagining science education using an approach called systems thinking. This means teaching science in all contexts, connecting scattered pieces of information and helping students to build a deeper understanding of the subject.

How to boost your research: take a sabbatical in policy

Systems thinking also means connecting various disciplines. In the case of chemistry, it means connecting the molecular description of compounds and their reactivity with their role in health, the economy and the environment, for instance — always putting people at the centre.

We’re working with governments worldwide to provide guidelines, teaching tools and training workshops. In the past five years, we’ve run workshops in South Africa, the United States and Egypt, training secondary-school teachers to bring this approach to their classrooms.

In policymaking, deep knowledge of a subject doesn’t automatically make you an effective science adviser. It’s more about having a holistic understanding of an issue and the ability to build trust with policymakers.

This requires producing high-quality reports that stand up to the most rigorous scrutiny. But beyond the report, you need to build trust through active listening and empathy. It starts on the day you are asked to advise on public policy, and it continues until the day that legislation is implemented.

Scientific advice is mainly about providing the best current knowledge in context. Just as decision makers work with lawyers to ensure their decisions are constitutionally sound, they need to work closely with scientific advisers to incorporate the latest knowledge into policies.

The world will be in a better place if we have effective science advisers.

Laura Petes works in the US White House Office of Science and Technology Policy to support environmental and climate priorities. Credit: OSTP

LAURA PETES: Engage early and often with society

Chief of staff, climate and environment, and assistant director for climate resilience in the White House Office of Science and Technology Policy in Washington DC.

Originally, I assumed I would be a tenure-track faculty member at a research university. As a postdoc at Florida State University’s Coastal and Marine Laboratory in St Teresa, I was doing research that I thought would be useful to the community. I was investigating local drought conditions, which were leading to declines in oyster populations.

A few months in, my research brought me in close proximity to oyster boat crews doing the harvesting. I wanted to understand how drought was affecting the fishing community’s lives and livelihoods.

I felt powerless to help a community that was strongly affected by the decline in its fisheries.

So I started engaging with the fishing crews and their families directly. I attended their meetings to hear about the drought’s impacts. Listening to the community informed my research and led to me becoming a fierce advocate for scientists who do work that can be applied directly to society. I make sure that they engage with the intended beneficiaries of their research early on, and on a continuous basis.

In 2009, after my postdoc, I took a two-year policy fellowship through the American Association for the Advancement of Science that placed me with the US National Oceanic and Atmospheric Administration’s (NOAA) Climate Program Office, Silver Spring, Maryland. There, I worked to build partnerships across NOAA on climate and coastal and marine ecosystems, including an initiative to establish a regional drought early-warning system for the southeastern United States. I was then hired as an ecosystem-science adviser at the office, working on cross-agency climate-adaptation and -resilience activities and programmes. During my career at NOAA, I have twice taken assignments at the White House Office of Science and Technology Policy (OSTP). Starting in 2014, I worked at the OSTP for three years, helping officials to implement then-president Barack Obama’s Climate Action Plan. I returned to the OSTP in 2021, and currently serve as its chief of staff for the Climate and Environment Team and as the assistant director for climate resilience. I got hooked in the science-policy world and I never left.

Help to shape policy with your science

In my current role, I support the environmental and climate priorities of President Joe Biden’s administration. For example, one area I work in is infrastructure policy. Working with the US Congress, the administration is investing billions of dollars in infrastructure across the nation, a watershed moment for climate action. We want to ensure that these future investments in communities are themselves resilient to the impacts of climate change. So, if communities are going to build a new road or repair a bridge, we share the science of climate change with the builders and planners so they can take that information into consideration.

We’re also working with government agencies to weave in nature-based solutions. As an ecologist by training, I know the science behind that. For example, restoring a marsh would also buffer nearby communities, buildings or roads against sea-level rise or storm surges. Such initiatives both strengthen nature and provide protective benefits for people.

AARON MAXWELL: Wield data to tackle policy problems

Data analyst at the National Sciences and Engineering Research Council of Canada in Ottawa.

I became interested in policy when I was pursuing my master’s degree in astronomy and astrophysics at the University of Victoria, Canada. A good friend encouraged me to be vice-president of the graduate student association, through which I eventually helped to negotiate dental coverage for members. Meeting with the university’s provosts during negotiations, I realized so much needed to be done. So many problems needed solving through policy change. I thought, ‘I actually really enjoy this.’

I now work at the National Sciences and Engineering Research Council (NSERC), which distributes government funds to university researchers throughout Canada. My job is to collect data to see whether council policies are working.

I work for the chief data officer to oversee all data-related infrastructure for the NSERC — including data stewardship, analysis and coordination with other government agencies and ministries, so that data are some of the core drivers of public service.

For example, Canada’s government is ploughing money into electric vehicles, their batteries and recycling those batteries. If someone in parliament says, “Electric vehicles are the future. Have we been supporting this?”, then we have to go through reports to find associated NSERC-funded projects. It’s time consuming. But we’re trying to incorporate machine learning into our processes so that you don’t have to read every report with, for example, engineering as a keyword. With machine learning, you can get your hands on a larger data set. And using those data, we can help officials to craft policy more efficiently.

Want to make a difference? Try working at an environmental non-profit organization

One of the areas we’re working on is equality, diversity and inclusion (EDI). Making EDI policies more effective is important to me because I’m mixed race (my father is Black Jamaican and my mother is white Canadian) and neurodiverse — I have attention deficit hyperactivity disorder, am on the autism spectrum and have mental-health challenges. All of these things worked against me in my science education. But I know I’m lucky to have made it to where I am.

To better understand EDI, our office put forward the idea that we needed to collect data on the people who apply for NSERC grants, to better understand EDI. Data had been collected here and there, but in silos. I and an NSERC colleague pulled those data together and analysed them.

We showed not only aspects in which the council is doing really well, but also places where it had considerable gaps — NSERC needs to focus on these areas. For example, we found that the diversity of people applying for funding is not yet representative of the Canadian population as a whole. We used census data to show how many applications would be needed from specific demographic groups for award recipients to be represented equitably. So, for example, we would expect 50 Black people to apply for a student fellowship if the student pool was representative of the population, but instead we received only 30 applications.

The point is, we can now find these discrepancies and explore how to help institutions improve the situation.

For example, the NSERC can say to a university, “We see that you didn’t report having any Black applicants to your PhD programmes last year. Are you OK with that?” Or, “Why might that be?” We know about these gaps, because we can see the data.

Forest ecologist Indu Murthy now works for a non-profit science-policy think tank in Bengaluru, India, because she wanted a stronger connection with decision makers.

INDU MURTHY: Speak the language of policymakers

Sector head for climate at the Center for the Study of Science, Technology and Policy in Bengaluru, India.

I work at the Center for the Study of Science, Technology and Policy (CSTEP), a non-profit think tank.

We put together science-based evidence to inform policymakers, whether they are government stakeholders at the national or state level, or at a non-governmental organization that is implementing a project. I manage a team of about 30 people who work on various issues, including long-term decarbonization scenarios — to decrease companies’ carbon footprints without compromising on development. This is really important for India, a lower-middle-income country.

For instance, India’s Ministry of Environment, Forest and Climate Change reached out to academics and think tanks involved in modelling. Our group came up with scenarios discussing what it would take for the country to reach net-zero carbon dioxide emissions by 2060, 2070, 2080 and 2100. The models gave ministers potential paths that the country could take to reach net zero, and these informed India’s decision to commit to doing so in the Glasgow Climate Pact at COP26, the 2021 UN climate change conference.

The hardest thing is trying to get an audience with policymakers. We really need to follow up several times. It’s not like I say something and they lap it up. That never happens. It takes many conversations.

You have to speak their language — to talk about what has the biggest impact. If you’re talking to a legislative member who works directly with people, then it could be social impacts. By contrast, for policymakers at the highest levels of government, it could be economic impacts and investments. These are the big tickets that resonate well. At the end of the day, they want to make a difference.

Earlier in my career, I spent 25 years as a forest ecologist at the Indian Institute of Science in Bengaluru. I did long-term monitoring in a biodiversity hotspot, India’s Western Ghats mountain range.

So, I came to CSTEP because I needed a change, and I wanted a stronger connection to the decision-making. I’m also an adviser to the UN Environment Programme, helping to draft the Global Environment Outlook report. In that effort, I help to bring perspectives of low- and middle-income countries to discussions of climate action and solutions. It’s definitely satisfying, trying to move the needle in the right direction: to influence people and how they look at a problem.

doi: https://doi.org/10.1038/d41586-024-03040-x

These interviews have been edited for length and clarity.

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