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Virtual Tools for Innovative Product Design

Design influences a product’s lifecycle performance and cost, starting from its development. Product development costs rise significantly if a defect is identified at a later stage. Using virtual tools for new product introduction simulates possible scenarios upfront for comprehensive testing. It gets products to the market quickly and saves money for a successful launch.

Insights

Design influences a product’s lifecycle performance and cost, starting from its development.
Conceptualization and design stages determine more than 70% of a product’s lifecycle decisions and cost.
Virtual tools are an effective way to design new products that serve specific customer needs.
Virtual models of new products accelerate their evaluations to shrink the development cycle time.
Organizations should create virtual replicas of workplaces for human-machine interactions studies from multiple perspectives.

Lifecycle cost is the total cost (direct and indirect) a product incurs in its life span. Conceptualization and design stages determine more than 70% of a product’s lifecycle decisions and cost.¹ The earlier an issue is identified, specifically in the design stage, the easier it is to fix and avoid costly rework. Virtual replicas (or digital twins) of products, processes, and environments streamline design and new product development to reduce costs and time to market.

A common assertion is between 80% and 90% of new products fail. However, realistic failure rates vary by industry, from 36% in healthcare to 45% in consumer goods.² Professor Clayton Christensen, best known for his theory of disruptive innovation, believes the success mantra is to design products that serve its intended customers. Manufacturers should focus on the function that a customer who buys a product would want it to do.³

To enable that, virtual representations of the product under development, in orchestration with humans and other entities in the ecosystem, is an effective approach. The approach encourages innovation. Designers visualize the product’s operating condition, create digital prototypes for trial runs, and carry out tests on a global scale. Virtual tools like 3D computer models and digital twins support informed decisions in early product design stages. This mitigates the risk of a wrong product release or a poor customer experience.

→ Virtual products are an effective way to design new products that serve specific customer needs

When end users receive virtual training of a complicated product’s operation (like an aircraft engine), memory retention happens in the background. Any number of such instances can be created at a negligible marginal cost for repetitive usage. A central digital setup saves the cost of setting up multiple physical arrangements at different locations.

PARAMETERS OF SUCCESSFUL NEW PRODUCTS

Product failures are more from a commercial perspective than technical. More than 25% of revenue and profits across industries come from new products, according to a study by McKinsey. Successful products relate to a set of core capabilities, with the top-most as follows:⁴

Collaboration to execute tasks as a team.
Investment to mine market insights and their inclusion in the product.
Plans for new product launches, comprising target customer segments, key messages to communicate, and objectives to achieve.
Talent development for new product launches with defined career paths and incentives.

At the same time, the primary reasons for product failures and mitigants are the following:⁵

Gap in meeting product expectations; delay launch until product completion.
Inability to support rapid growth if a product is successful; set ramp-up plans to avoid this.
Low demand for a new product; perform due diligence for customer requirement before planning a product. Launch products in suitable markets.
Difficulty in new product usage; provide proper customer orientation and training.

Virtual tools for product design address the above reasons for failure and increase the chances of successful product launches.

DESIGN THINKING WITH VIRTUAL TOOLS

Design thinking is a popular, technology-agnostic approach for new systems design and problem solving. It balances the technical feasibility of products, financial viability, and desirability from a customer’s perspective (see Figure 1). It is even more impactful when implemented along with virtual product design tools.

Figure 1. Design thinking at the sweet spot of desirability, viability, and feasibility

Source: Infosys

The design thinking cycle starts from empathy to understand a customer’s needs from their perspective, followed by defining, ideating, prototyping, and validating, in iterative loops. New product development and customer participation encourage collaboration in a virtual environment to practice design thinking. Immersive environments using mixed reality (combinations of augmented reality or AR and virtual reality or VR) create a working environment close to the real world, to identify and correct issues much ahead (see Figure 2).

Figure 2. Virtual tools used across design thinking stages

Source: Infosys

→ Virtual models of new products accelerate their evaluations to shrink the development cycle time

Design firm IDEO, for example, wanted to perform ethnographic research to capture customer requirements for new products. However, it was difficult to identify key observations from many data points and recreate them later, even with expensive videos or photos. It addressed the challenge through a VR camera.⁶

Kaleidoscope Innovation, a design and development unit within Infosys, designed a large freezer project using virtual tools. Such projects usually undergo several time-consuming team reviews. The team created a 3D model in a VR environment that helped designers walk around the product early in the design phase, evaluate its usability from multiple perspectives, and tackle proposed changes to design.

This virtual model did not change the overall project plan, but accelerated evaluation and decisions around it, shrinking the product development cycle time. The team selected the best design without spending time and money on physical prototypes.

CUSTOMER PRODUCT INTERACTION

Mathematical models of an individual product’s performance are important and popular. However, the product should also be ergonomic for safe and productive human-machine interaction. The operations and other associated systems (such as material handling) should be easy to perform. Its ease of making is important for the manufacturer to ensure quality and cost control. The carbon footprint across the lifecycle and its end-of-life disposal should be sustainable.

For example, a hospital has large equipment such as scanners, working in orchestration with other smaller tools, and humans. Room layout is an important aspect of such an infrastructure. Any change post implementation is expensive.

Infosys has created a three-dimensional room planning VR tool. Non-technical sales team members create, change, import layouts, view them from multiple perspectives along with the human and machine elements, and finalize the layout. That final layout is exported as two-dimensional drawings for implementation. This streamlined the sales process and significantly reduced iterations and sales cycle time.

AUTOMATION IN WAREHOUSES

Humans work with machines in warehouses. Material handlers carry out order fulfillment along with pick-and-place robots. Workers’ safety in all situations is important.

A leading e-commerce player wanted to validate design decisions for robots working in its order fulfillment warehouses to gain insights into their safe working alongside humans. Kaleidoscope Innovation created a virtual environment where employees interacted with robots in different situations. The team created a digital twin to simulate several configurations of robots and their working environment. The company recorded the results and interviewed employees about pros and cons of each situation.

The VR-based solution provided a cost-effective and safe way for the e-commerce firm to test new concepts in human-robot interaction and capture data and feedback before implementation. It helped the managers zoom out and look at the big picture, in contrast to one robot or equipment at a time.

TRAINING FOR PRODUCT USAGE

Operators need training to work on machines with complex functionality and procedures, to stay safe and productive. VR-based training prepares humans before hands-on operation on a machine. For instance, Rolls-Royce has rolled out a VR-based training kit for its airline customers to manage aircraft engine maintenance and repair.

Infosys’s VR-based program provides step-by-step instructions to train employees in a hospital environment. The program uses physical gestures to simulate actual tasks involved in a job. Gamification with scores and points keeps employees engaged and motivated. Scores reflect an individual’s strengths and weaknesses. Training data is integrated with the central learning management system for records.

A multinational industrial and consumer goods manufacturer wanted to create an e-training platform for its new operators. It had a few integrated assembly lines for its finished items. The Kaleidoscope Innovation team created a virtual training module along the assembly line, one workstation at a time. The team used front-end user interface elements to guide users for equipment operations. It tracked performance metrics in the backend to provide feedback for correction. Best practices of creating a virtual replica of one workstation are used at later stations.

FUTURISTIC WORKPLACES

While collaborative, remote and hybrid working has surged since the pandemic, the future is in three-dimensional virtual and mixed reality workspaces. Organizations benefit from a virtual 3D replica of its workspaces, equipment, products, avatars, or personas. Employee collaborations lead to faster new product development with effective interactions. Teams share ideas, explore, and invent new concepts. Early collaboration of team members in multiple locations enables them to make more informed decisions in the product development process.

→ Organizations should create virtual replicas of workplaces for human-machine interactions studies from multiple perspectives

The future of work in healthcare, retail, engineering, and manufacturing is where humans and human-like machines work together. Organizations should proactively create such workspaces virtually and study human-machine interaction from safety, productivity, and employee morale perspectives before any physical implementation.

Full article can be found on Infosys.com

References

Product life cycle cost analysis: State of the art review, Y. Asiedu &P. Gu, 2010, International Journal of Production Research.
Myths About New Product Failure Rates, George Castellion, Stephen K. Markham, 2013, published in the Journal of Product Innovation & Management 30 pp. 976-979.
What Customers Want from Your Products, Clayton M. Christensen, Scott Cook and Taddy Hall, January 16, 2006, Harvard Business School.
How to make sure your next product or service launch drives growth, Alessandro Buffoni, Alice de Angelis, Volker Grüntges, and Alex Krieg, October 13, 2017, McKinsey.
Why Most Product Launches Fail, Joan Schneider and Julie Hall, April 2011, Harvard Business Review.
IDEO: Getting closer to the customer through virtual reality, Lauren, April 27, 2017, Harvard Business School.

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Authors

Karl Vanderbeek

VP of Design & Human Factors
Karl is passionate about creating worthwhile and valuable product design with an amazing team, focused on building beautiful and intuitive experiences prioritizing the user’s needs and expectations. His team brings these visions to reality by collaborating closely with Kaleidoscope’s Insights, Human Factors and Engineering teams. Karl’s Industrial Design background includes working with brands across a wide variety of industries, ranging from healthcare and medical, to consumer and housewares, to industrial products and toys. He has earned more than 40 design and utility patents over the past 20+ years.
Ramachandran S

Principal Consultant at Infosys

Qualitative Research: AI’s Role in Analysis Advancement

BACKGROUND

Qualitative research plays a pivotal role in enriching our comprehension of individual narratives and experiences. It is a cornerstone methodology for design researchers seeking to forge a deep connection with user perspectives, particularly during the initial phases of the design process. This approach is instrumental in guiding iterative design developments, ensuring that end-user needs are comprehensively addressed. Qualitative data encompasses a diverse array of formats, including textual content, photographs, and videos. Typically, these studies involve a more focused sample size, often with 10 or fewer participants, to facilitate an intensive, detail-oriented analysis that quantitative methods may not capture.

Although qualitative research is the methodology of choice for design researchers, the approach requires a considerable time commitment. Qualitative data is known for being unwieldy at times, and words and images require more hours of analysis than numeric data. Often, our clients are eager to obtain research findings as quickly as possible to move a product or system into production. Therefore, large scale qualitative studies are not feasible for most design research projects. With the recent surge in the availability of AI language model tools, we speculated that ChatGPT could be used to analyze extremely large sets of qualitative data more efficiently. To that end, we conducted a 6-month project testing ChatGPT as a potential tool for qualitative data analysis.

THE CURRENT PROJECT

Our aim in conducting this project was to determine if AI could produce insights from a large dataset that would otherwise be unmanageable and time prohibitive for a human researcher. We used data from 25,000 open response questions to explore the capacity and capability of ChatGPT as computer-assisted qualitative data analysis software (CAQDAS). The dataset we used was provided by the VIA Institute on Character, a local non-profit organization with which we are affiliated. We decided to experiment with ChatGPT to determine if it could reliably and accurately analyze text data. Our expectation was that if ChatGPT could analyze qualitative datasets with tens or hundredsof thousands of respondents, new pathways for qualitative researchers may develop.Using AI for data analysis could change the trajectory of a research design and lead to large scale qualitative studies that were not possible before now.

PROCEDURE

To test the limits of ChatGPT 4.0 (the only version with the means to upload files), we tried two different approaches to determine the capability of the tool.

METHOD 1: QUICK AND EASY

We started with a vague set of user queries to place the data preparation load on the CAQDAS and to determine if it would complete the same tasks a human researcher would.

User Query: Analyze the data in column AQ, identify themes, and provide 3-5 insights based on participant responses.

Result: Not useful.

ChatGPT did not automatically clean the data without instruction which caused an error. The output from ChatGPT indicated the data file was either too long or too complex and it was unable to proceed with analysis. The raw data included responses such as “N/A” or random strings of letters, which a researcher would have deleted or ignored before analysis.
Lacking more specific instruction, ChatGPT defaulted to a quantitative approach to data analysis, even though the data were text responses. One of the first outputs ChatGPT produced was a count of the most common phrases in the dataset.

We concluded that this approach to creating user queries was not useful. ChatGPT attempted to analyze the data but quickly became overwhelmed and either produced an error message or continued to attempt analysis, getting caught in the AI version of theMac’s “spinning wheel of death.”

METHOD 2: THE GUIDED ANALYST

We then provided ChatGPT with more specific instructions. We instructed it toclean, review and code the data, then create insights using a theoretical framework as a guide for analysis.

User Query: I'd like to analyze some text data using Peace Psychology and Positive Psychology as theoretical frameworks. Include content from the VIA Institute on Character as an additional framework. Focus on data in the 'Open Responses_Political Differences' column.

First, ignore text that indicates a respondent did not want to answer such as 'N/A' or random strings of letters. Leave those cells blank. Next, use descriptive codessuch as a phrase that describes the content of the targeted data.

Create a new document and filter the data from columnAD. Group the data according to the codes created in columnAD and list each data point that corresponds to the code.

Create 3-5 insights using the coded data in ColumnAD using positive psychology and peace psychology as theoretical frameworks.

Create a Word document and place the insights you created in it. Make the file available for download.

Result: Success

ChatGPT produced a list of five insights using the VIA classification of character strengths as a framework, supported by references to positive psychology and peace psychology.

Our last step was to think about how to test the accuracy and reliability of the findings. Rachael has a strong background in qualitative methodology, so she extracted a sample of 20 data points and used a traditional qualitative approach to code the data. We also queried ChatGPT using the same data set with similar user queries to determine the degree of similarity between both sets of outputs.

Accuracy

Within the first 20 rows of participant responses, we identified at least three codes related to the five insights ChatGPT produced. We were pleased with the accuracy of the codes as they related to the insights from our sample data.

Reliability

Both researchers requested insight generation from ChatGPT using similar but not identical queries. Chat GPT’s output for both sets of queries was closely aligned. Three of the five generated insights were nearly identical between the two researchers and two were unique per researcher. It is important to note that even within research teams, differences arise between researchers’ interpretation of qualitative data. Disagreements are resolved through discussion and are typical of a qualitative research project.

Finally, we tested some of the limits of ChatGPT with several ad hoc user queries. Ourqueries produced a great deal of information in a very short period.

AD HOC REQUESTS:

Code Participant Country of Origin: We queried the data set to determine if each participants’ country of origin represented an individualistic or collectivist nation(coded as “country designation”). We asked ChatGPT to code each participant’s country of origin using this designation. The codes were placed in a new column created by ChatGPT.
Group Data and Analyze Frequency: We asked ChatGPT to examine the most common keywords and themes by country of origin. It provided a summary for all countries represented in this data set with the most frequently used codes according to participant’s country of origin.
Character Strength Frequency by Country Designation: We prompted ChatGPT to organize the data according to country designation and create a bar chart to illustrate the frequency of character strengths chosen as most beneficial by participants.

All the ad hoc requests were completed successfully. ChatGPT confirmed its approach with users before completing each step. For example, after requesting the data be coded according to country designation, ChatGPT responded:

Sure! In order to label countries as "collectivist" or "individualist," I would need a reference or criteria to determine which category each country falls into. There are various sources and studies that have categorized countries based on their cultural dimensions, including the work by Geert Hofstede.

Do you have a specific reference or criteria in mind that you'd like me to use? Alternatively, I can use Hofstede's cultural dimensions as a general guideline.

ChatGPT also encouraged us to use caution when interpreting the findings from the between-country analysis, because significantly more participants were from individualistic nations.

LESSONS LEARNED

We learned through this project that ChatGPT exhibits several esoteric preferences for working with Excel files. We only used Excel to upload data sets, so our suggestions are restricted to this software.

1. ChatGPT cannot analyze data if it has been tagged with a data type. The output will state that it completed the user query, but new files will not show any changes.

SOLUTION: Remove any Data Types tags before uploading Excel files toChatGPT.

2. ChatGPT prefers references to column names instead of the letters Excel uses to identify columns.

SOLUTION: If a user query contains a letter identifier instead of a column name, remove the space between the word “Column” and the letter.

CORRECT: “Provide a mean for the data in columnAI.”

INCORRECT: “Provide a mean for the data in Column AI.”

3. Unless instructed, ChatGPT will not automatically clean uploaded data. If a user attempts to request analysis before cleaning, it will respond with an error message.

SOLUTION: Provide explicit instructions for data cleaning before analysis.

HUMAN RESEARCHER VALUE

We shared just a fraction of the user queries we submitted over a 6-month period to test ChatGPT as a qualitative analysis tool. We presented the successes and failuresas linear, concise processes for readability. However, early in the project, ChatGPT was often overwhelmed with requests and our queries resulted in error messages. Queries usually required several back-and-forth inputs between researchers and the AI to clarify instructions. With little or no guidance, ChatGPT was unable to produce results. We found that the AI required specific instructions to function as computer-assisted qualitative data analysis software. Our bottom-line recommendation is that well trained researchers test the tool using a data set for which they already possess human produced findings. Compare those findings with ChatGPT's output and evaluate its reliability and accuracy.

Based on our brief examination of ChatGPT’s capability, we advise only well-trained researchers with extensive qualitative research experience to use AI as a computer-assisted data analysis tool. As in any other profession, expertise and training are the best predictors of quality work. As the saying goes, garbage in garbage out.Users with no idea how to design a rigorous research study will not provide the needed input for AI to perform adequately.

Our early work indicates the potential for AI to assist in qualitative data analysis. Like other CAQDAS products such as MAXQDA and NVivo, the software serves as a management and organizational tool. We envision ChatGPT as a marginally higher-leveltool with the capacity for categorizing and summarizing qualitative data, with the proper guidance and instruction.

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Authors

Rachael Clark

Senior Design Researcher | [email protected]
Rachael brings over 10 years of research experience to her role at Kaleidoscope Innovation. She has advanced training in clinical psychology and mixed methods research methodology. Guided by the principles of positive psychology, Rachael uses a human-centered lens for deeply understanding the user experience. Her work at Kaleidoscope focuses on human-machine interaction and identifying design changes capable of positively impacting well-being at the individual and institutional levels.
Grant Cothrel

Senior Design Engineer | [email protected]
Grant is a Senior Design Engineer who enjoys being faced with new challenges, and recognizes that well-designed products lead to better experiences and outcomes for users. He loves the design process, and has operated primarily in medical device and industrial applications. His passion to understand, innovate, and simplify has been supported and strengthened by the Kaleidoscope team and their talented partners. At home, he always has a fun project in the works (think: wooden bicycle, handheld Theremin, one-string electric guitar)!

Kaleidoscope Earns Gold

We are thrilled to share the exciting news that Kaleidoscope Innovation has achieved a Gold EcoVadis rating for 2023. This marks a major milestone in our journey to being a leader in environmental, social, and governance (ESG) excellence.

EcoVadis, a globally recognized platform, evaluates companies based on their policies, training initiatives, and reporting mechanisms related to ESG practices. This comprehensive assessment provides a holistic view of our organization's sustainability performance, positioning us as a standout in our commitment to environmental stewardship, social responsibility, and corporate governance.

Our latest EcoVadis assessment resulted in a remarkable increase of 15 points, propelling us to the 97th percentile among companies of similar size. This accomplishment not only highlights our dedication to sustainability but also underscores our team’s ability to outperform industry benchmarks.

This achievement is a testament to the collective efforts of every member of the Kaleidoscope Innovation family, and it reflects our shared values and determination to make a positive impact on the world. Our success in this area wouldn't be possible without the dedication and hard work of each team member who has contributed to our sustainability initiatives.

"We are grateful to every one of our dedicated team members for their contributions to this achievement," said Pam Ridgley, Associate Director of Business Planning. "Together, we are shaping a brighter and more sustainable future for Kaleidoscope Innovation and the communities we serve."

Here is just a sample of how we achieved our goals in each of the three ESG categories.

ENVIONMENTAL

We created a Design for Environment (DfE) Guide to help our team members ensure we use sustainable practices not just in our everyday work environment but also in the products we design and develop.
We reduced facility energy consumption by nearly 18% by resealing windows and creating an energy conservation culture
Our action team tested and implemented recommendations for sustainable 3D printing as they related to energy consumption, filament usage, and more eco-friendly materials
Our K-Cup recycling program diverted over 13,000 pods from the landfill and converted 479 lbsof material to compost and 160 lbs of material to recycled products.

SOCIAL

Community outreach including May We Help, Adopt-a-Class, IDSA Cincinnati, Ronald McDonald House, Habitat for Humanity, and more.
Expanded employee engagement & wellness teams and programs including the Employee Engagement Group, DEI Committee, Sustainability Committee, Employee Lunch & Learns, Women in Product Development Monthly Lunch, and more.

GOVERNANCE

Added numerous policies regarding diversity, equity, and inclusion, particularly with a focus on labor and human rights to our employee handbook
Created our own Institutional Review Board to ensure the ethical and safe development of healthcare products.

Our journey doesn't end here. This accomplishment serves as a stepping-stone toward even higher standards that continuously improve our ESG practices. The world is evolving, and so are the expectations placed upon businesses to operate sustainably. Kaleidoscope will remain agile and proactive in our approach to ensure we remain at the forefront of responsible corporate citizenship.

“We are incredibly proud of our EcoVadis achievement,” said Kaleidoscope CEO Ben Ko. “And we’re not stopping there. Our DfE program sets a new standard for ensuring that all products Kaleidoscope creates are more sustainable than they have been in the past, thereby amplifying the impact we can have on the world through good design principles.”

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Precision in Practice: Navigating Usability Studies

Conducting a successful usability study, particularly for summative validation of medical products, can require a great deal of preparation and coordination of many moving parts. Whether it’s successfully simulating your target use environment or nailing down the exact scenarios and tasks to be presented, everything must come together. After all, nobody wants to waste the time and money it requires to complete a study if the result doesn’t align with what was intended.

One of the best ways to ensure success is to perform a pilot study with your protocol before starting the actual study. A pilot study is like a miniature version of the actual study conducted with far fewer participants. This approach helps confirm the study design will work as expected, the desired data can be obtained, the participants understand the task prompts, and more. For best results, the pilot study participants should be as close to actual participants as feasible; the same applies to the use environment. You’ll also want to conduct the pilot study early enough before the actual study to ensure there is sufficient time to update the protocol according to the findings.

Many people use the terms “dry run” and “pilot study” interchangeably, but there is technically a difference. A dry run is done to practice the protocol both before the pilot study and after the finalization of the protocol, ensuring the moderator and notetaker/analyst are comfortable with the product and script. This activity can be done with proxy participants or with no participants at all. What’s notable about a dry run is that it can be done in place of a pilot study if the participant population is expensive or difficult to recruit, if the protocol is very simple or essentially a repeat of a previous study, or if the timing does not permit a full pilot study.

While there can never be guarantees, these are proven ways to exponentially increase the odds of a study’s success. Taking the time upfront will ultimately save time and effort when it’s time to conduct the actual study. At Kaleidoscope, we've conducted hundreds of studies, so our process is as rigorous and dialed in as possible. If you're looking for a rock-solid research partner, we're ready to roll up our sleeves.

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Author

Andrew Bryan

Director of Human Factors | [email protected]

Designing for the Unknown

ENCOUNTERING DATA GAPS

For a product to be successful, it’s critical for designers to understand the environment in which the product will be used. For a medical device, this environment is often inside the human body. While some anthropometric data such as height, weight, and arm reach, are well documented, there are many critical anatomical measures that remain unknown, particularly in the realm of women’s health.

While collaborating with a medical partner to address postpartum hemorrhage (PPH,) the leading cause of maternal death worldwide, Kaleidoscope encountered this common product design challenge. During preliminary research, the team found that there was little to no readily available data on vaginal dimensions immediately following childbirth. The scarcity of this particular data is not surprising, as the anatomy changes rapidly postpartum. Understandably, collecting this data isn’t a priority for mothers or caregivers, who are focused on the wellbeing of the newborn. Nevertheless, this lack of data created a significant challenge for the Kaleidoscope PPH design team.

TOOLS FOR BRIDGING THE GAP

Whether we are creating a medical device, a smart pet collar, or an industrial freezer, the team at Kaleidoscope utilizes a number of different methods when designing for the unknown. One way we obtain the data we need is simply to collect it ourselves! Armed with calipers and tape measures, we might venture into the field or bring samples into our studio to take direct measurements. Direct observation, whether in person or through videos and photos, is another way we round out our understanding of a unique user experience.

Sometimes—like trying to determine dimensions of internal anatomy—this just isn’t feasible. In those cases, we turn to subject matter experts. Surgeons, with their deep experiential knowledge of anatomy, are able to describe what they have encountered in situ, providing additional insights into the nuanced aspects of human anatomy, such as texture, firmness and what it feels like to manipulate different anatomical structures. These insights proved to be a vital element in overcoming the data gaps encountered by the PPH design team.

OUT-OF-THE-BOX INSPIRATION

When the Kaleidoscope team explores new product categories, we find that drawing inspiration from successful analogous products is another valuable strategy. If we’re creating a handheld device, referencing power tools, hair dryers, or hot glue guns as adjacent products can help guide the design in the correct direction. The key here is relevance—referencing products familiar to end-users ensures that the design resonates with their expectations. If we are developing a surgical device for ophthalmologists, (who are used to small, delicate instruments that they control with their fingertips,) it would be more appropriate to reference delicate tools such as those used by sculptors than it would be to reference tools used by auto mechanics.

While designing for a post-partum hemorrhage solution, analogous products included menstrual cups and discs, which share similar placement within the vaginal canal. These adjacent products provided the Kaleidoscope team with a good starting point for shape and dimensions of the device, as well as inspiration for materials and durometers to explore. These analogous references were part of the constellation of information used by the PPH team while exploring potential solutions to our data gap.

EMBRACING FLEXIBLE SOLUTIONS

At the end of the day, secondary research can only get us so far. In the absence of precise anatomical dimensions, adaptability can be a powerful tool in the designer’s toolbox. Whether the solution is fully adjustable (like an office chair) or offers different size options (like audio earbuds with multiple size tips,) a thoughtfully designed adjustable or flexible product ensures that one size does NOT need to fit all—rather, we can design a solution that easily adapts to meet the needs of all users.

Being on the cutting edge of new product development often means navigating uncharted territory. At Kaleidoscope, we've mastered the art of designing for the unknown with a combination of creative data collection, analogous product inspiration, and thoughtful adaptability. By transforming uncertainty into opportunity for our partners, we create products and experiences that improve outcomes for everyone.

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Author

Tom Gernetzke

Senior Industrial Designer | [email protected]
Tom Gernetzke is a senior lead industrial designer at Kaleidoscope Innovation and has spent the last 12 years creatively bringing new product ideas to life.

CES Optimist or Cynic?

Connectivity of people and devices is a significant macro trend across various products and technologies. Throughout history, most tech innovations have stemmed from the essential need for human interconnectivity including language, printing, roads, telecommunication, and the internet.

Optimistically, products like Withings' new "BeamO" represent a noteworthy evolution—a handheld device sensing temperature, heart rate, ECG, O2 levels, and features a stethoscope. This advancement enables a mobility-limited population to interact more effectively with healthcare providers, providing higher fidelity and real-time patient information. These products accumulate valuable data points over time, facilitating meaningful trend analysis.

However, my inner cynic sees that these types of products are usually marketed toward able-bodied and mobile people. Products like this can be used as excuses to avoid human-to-human interaction… or be reasons to replace human interaction. (You may have seen the AI powered robotic barista!) Every day, we see the impact of people losing the skills of basic public human interaction and common courtesy.

Overall, my optimistic perspective embraces the potential these new technologies bring to the human experience. While acknowledging potential pitfalls, I advocate for a larger role of Design within companies exploring these technologies. Engaging in discourse is crucial to finding a balance that enhances rather than degrades our collective experiences.

Now, are you more of an optimist or a cynic?

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Author

Karl Vanderbeek

VP of Design & Human Factors
Karl is passionate about creating worthwhile and valuable product design with an amazing team, focused on building beautiful and intuitive experiences prioritizing the user’s needs and expectations. His team brings these visions to reality by collaborating closely with Kaleidoscope’s Insights, Human Factors and Engineering teams. Karl’s Industrial Design background includes working with brands across a wide variety of industries, ranging from healthcare and medical, to consumer and housewares, to industrial products and toys. He has earned more than 40 design and utility patents over the past 20+ years.

The Future of Industrial Design

It’s time to evolve the way we train young industrial designers.

I’ve often heard designers express frustration about the decline in the quality of education for industrial designers and the curriculum’s failure to teach the foundational skills needed to enter the profession.

Having managed Kaleidoscope’s industrial design co-op program for five years and mentored co-ops throughout my career, I can’t say I disagree. Though young designers still have so much passion for their craft, their portfolios often reveal that the required skills are lacking.

What’s changed over the last decade? Our world.

Born after the dawn of the internet, today’s young designers have grown up surrounded by technology and unlimited access to information. While their innate curiosity has not changed, the way they learn and the tools they use certainly have.

For me, growing up pre-internet came with advantages and disadvantages. Much of my understanding of the world and the products around me came from my curiosity about and interaction with tangible objects. Whether it was building things with my father in the garage or taking objects apart and putting them together to see how they worked, my learning came from the physical world around me.

So how can we adapt the way we train young designers? I don’t have an encompassing solution to this question. But I do believe the first step is to recognize the impact of these generational and cultural changes so we can tweak the curriculum by incorporating literal hands-on experiences into students’ education. Let’s bring back model-making and shop courses, all the while emphasizing why these efforts are essential to one’s foundation and growth as a designer.

I’m sure all of you new and veteran designers have your own perspectives on the subject. What do you think of the state of today’s educational design programs? How can we ensure the next generation of designers are as prepared and skilled as possible?

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Author

Jake Schubert

Senior Lead Industrial Designer | [email protected]
Jake Schubert is a Senior Lead Industrial Designer at Kaleidoscope Innovation. His technical expertise and collaboration with our engineering team are instrumental in ensuring that user needs, design, and engineering intent converge seamlessly in our market-ready products.

Orthopedic Best Practices You May Have Overlooked

Device development and commercialization requires a comprehensive approach that encompasses scientific rigor, innovation, regulatory compliance, and market readiness. With the increasing demand for advanced orthopedic solutions, it is crucial for organizations to adopt best practices to ensure successful device development from concept to commercialization. In this article, we explore the practices used in product development that can drive excellence in orthopedic development by accelerating time to market and delivering optimal patient outcomes.

1. MULTIDISCIPLINARY COLLABORATION:

By fostering a multidisciplinary approach, organizations can leverage diverse expertise, perspectives, and insights throughout the development process. Often, the best solution is provided by a new perspective. Successful orthopedic product development teams should include the following stakeholders: orthopedic surgeons (outside the ‘friends and family’ network), engineers, material scientists, industrial designers, regulatory experts, and market strategists. This collaboration enables the integration of clinical insights, engineering advancements, and market trends to create user-friendly devices that meet the needs of both patients and healthcare professionals.

2. HUMAN FACTORS ENGINEERING:

Integrating human factors engineering (HFE) into orthopedic device development is critical for ensuring usability, safety, and patient satisfaction. HFE focuses on optimizing the interaction between users and devices, considering factors such as ergonomics, user interfaces, and intuitive design. By conducting thorough user research, usability testing, and risk assessments, organizations can identify and address potential usability issues early in the development process, resulting in orthopedic instrumentation and implants that are intuitive, effective, and aligned with user needs.

3. REGULATORY COMPLIANCE:

Orthopedic products are subject to stringent regulatory requirements to ensure patient safety and device effectiveness. Adhering to regulatory standards and engaging with regulatory authorities early in the development process is essential. By establishing a robust regulatory strategy, organizations can navigate the complex regulatory landscape, streamline the approval process, and accelerate time to market. Companies need to explore all regulatory avenues and not limit themselves to the 510(K) and take advantage of other regulations such as the FDA’s new De Novo process for unique claims and gain a competitive advantage. Compliance with regulations is crucial for successful commercialization and market access.

4. DESIGN FOR MANUFACTURING AND ASSEMBLY (DFMA):

Designing orthopedic devices with manufacturability and assembly in mind is a best practice that can enhance efficiency, reduce costs, and improve scalability. DFMA principles involve optimizing device design to minimize complexity, facilitate efficient manufacturing processes, and ensure consistency in quality. By collaborating closely with manufacturing partners early in the development process and incorporating DFMA considerations in prototypes, organizations can streamline production, minimize design iterations, and accelerate time to market.

5. POST-MARKET SURVEILANCE AND FEEDBACK:

Monitoring the performance and safety of orthopedic devices in real-world settings is crucial for continuous improvement and regulatory compliance. Establishing post-market surveillance programs to actively collect feedback from healthcare professionals and patients can provide valuable insights into device performance. Potential issues can be identified, and iterative enhancements will drive long term product life. This ongoing feedback loop helps organizations address any concerns, optimize device performance, and maintain regulatory compliance throughout the device lifecycle.

By embracing these best practices, organizations can enhance the development and commercialization of orthopedic devices, delivering solutions that improve patient outcomes and meet market demands. Multidisciplinary collaboration, human factors engineering, regulatory compliance, design for manufacturing and assembly, and post-market surveillance form a comprehensive framework for success in this evolving field. As the demand for innovative orthopedic solutions continues to grow, adopting these best practices is essential for organizations aiming to make a significant impact in the orthopedic device market and contribute to the advancement of patient care.

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Author

Nick Bailey

Staff Design Engineer | [email protected]
Nick Bailey, PE, is a mechanical engineer at Kaleidoscope Innovation based in Cincinnati, Ohio, and has over 9 years of experience designing and developing products from concept to market. Nick has spent the majority of his time bringing medical devices through the FDA from R&D and has designed over 100 patient matched implants and custom instruments.

Orthopedics Unleashed: The AI-Implant Revolution

The orthopedic segment is currently undergoing a remarkable transformation that is fueled by the convergence of smart implants and artificial intelligence (AI), ushering in a new era of innovation. This powerful combination is revolutionizing the field, enhancing diagnosis, improving surgical precision, optimizing post-operative care, and delivering better outcomes for patients. In this article, we delve into the top 10 ways in which smart implants and AI are driving this transformation, paving the way for a new standard of personalized and data-driven patient care.

REAL-TIME MONITORING AND FEEDBACK: Smart implants embedded with sensors and wireless connectivity enable real-time monitoring of vital parameters such as joint movement, implant performance, and tissue response. This data provides valuable insights to healthcare professionals, facilitating early detection of complications and enabling timely interventions.
PREDICTIVE ANALYTICS FOR PROACTIVE INTERVENTION: By harnessing the power of AI and machine learning algorithms, smart implants can analyze large volumes of patient data to predict and prevent adverse events. These predictive models help identify patients at high risk of implant failure or post-operative complications, allowing for proactive interventions and personalized care plans.
PRECISION SURGERY AND NAVIGATION: AI-powered surgical planning and navigation systems provide surgeons with detailed anatomical information, assisting in precise implant placement and alignment. This technology improves surgical outcomes, reduces complications, and enhances patient satisfaction.
INTELLIGENT REHABILITATION AND PHYSICAL THERAPY: Smart implants, in conjunction with AI-driven rehabilitation programs, enable personalized and adaptive physical therapy. By monitoring patient progress and adjusting therapy regimens in real-time, these systems optimize recovery and rehabilitation, leading to faster and more successful outcomes.
ENHANCED PATIENT ENGAGEMENT AND EDUCATION: Smart implants equipped with patient-centric interfaces and mobile applications empower patients to actively participate in their own care. These technologies provide educational resources, track progress, offer reminders, and enable direct communication with healthcare providers, fostering a collaborative and engaged patient experience.
REMOTE MONITORING AND TELEHEALTH: AI-powered remote monitoring solutions enable healthcare providers to remotely assess patient progress, detect potential complications, and provide virtual consultations. This approach improves access to care, reduces healthcare costs, and enhances patient convenience, particularly for those in remote or underserved areas.
DATA-DRIVEN TREATMENT DECISION MAKING: AI algorithms can analyze vast amounts of patient data, clinical trials, and research studies to provide evidence-based treatment recommendations. This data-driven approach enhances treatment decision-making, optimizing outcomes and reducing variability in care.
PERSONALIZED IMPLANT DESIGN AND MANUFACTURING: AI algorithms can analyze patient-specific data, such as anatomical scans and biomechanical parameters, to design and manufacture personalized orthopedic implants. This customization improves implant fit, functionality, and patient satisfaction.
PREDICTIVE MAINTENANCE AND LONGEVITY ASSESSMENT: Smart implants equipped with AI algorithms can continuously monitor implant performance and assess the risk of wear, fatigue, or failure. This predictive maintenance approach allows for proactive interventions, reducing the likelihood of unplanned revisions and improving implant longevity.
BIG DATA ANALYTICS FOR RESEARCH AND INNOVATION: The integration of smart implants and AI generates vast amounts of patient data, contributing to large-scale data repositories for research and innovation. AI algorithms can analyze this data to identify trends, patterns, and insights, leading to breakthroughs in orthopedic treatments, implant designs, and surgical techniques.

The integration of smart implants and AI is reshaping the orthopedic segment, unlocking new possibilities for personalized, data-driven, and patient-centric care. From real-time monitoring to precision surgery, predictive analytics to remote monitoring, these innovations are transforming the way orthopedic conditions are diagnosed, treated, and managed. As we continue to explore the potential of smart implants and AI in orthopedics, it is evident that this convergence will play a pivotal role in improving patient outcomes, reducing healthcare costs, and advancing the field of orthopedic medicine into a new era of innovation and excellence.

Moreover, the article also acknowledges the importance of orthopedic product development, design, and consulting in driving these advancements. Medical device engineering consultants are key players in translating concepts to commercialization, ensuring the successful development of products such as Total Hip Arthroplasty Systems, Total Knee Arthroplasty Systems, Trauma Plates and Screw Systems, Surgical Navigation Systems, Cervical Plates and Screws, Facet Screw Systems, and Interbody Fusion Devices. This collaborative effort between orthopedic design experts and engineering consultants contributes to the overall quality improvement projects within the orthopedic field. Additionally, the incorporation of concepts like augmented reality, fixation, and retractor systems further underscores the comprehensive scope of innovation within orthopedics.

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Author

Matt Suits

Head of Sales | [email protected]
Matt has always loved interacting with clients to find solutions for their challenges. He was drawn to business development at Kaleidoscope Innovation because of the great potential he saw. After graduating from the Lindner College of Business at the University of Cincinnati, he worked with two startups, a marketing consultancy, a financial services company and the non-profit 3CDC. He believes that listening is the most important part of sales. In his free time, Matt enjoys movies, trying new foods, traveling and the great outdoors.

A Research Outline for Industrial Designers: Unleashing the Power of Data-driven Creativity

In today's fast-paced and competitive world, industrial designers face the exciting challenge of creating innovative and user-centric products that capture the market's attention. While their expertise lies in design aesthetics and functionality, the role of research in the design process cannot be underestimated. Research is the key that unlocks valuable insights, fuels inspiration, and ensures that designs are grounded in real-world needs and preferences. However, for industrial designers and other professionals who are not trained in research methods, navigating the realm of research can feel daunting. In this article, we will define research methodology and provide suggestions for selecting the right one for your project.

RESEARCH METHODOLOGY

Once a client settles on a research question, it is up to the design researcher to select the methodology that facilitates a rigorous approach. Think of methodology as a framework for conducting a research study. The chosen methodology will guide a researcher in methods and procedures that ensure the results or findings are valid and reliable.

QUANTITATIVE: Quantitative methodology is used to determine if relationships between variables exist, to test a hypothesis, or to measure a phenomenon. Quantitative data is used to make group comparisons or identify patterns. Data are numbers and reported in a standard reporting structure. Descriptive and inferential statistics require quantitative data. The output of quantitative analysis is referred to as results.

QUALITATIVE: Qualitative methodologies are used to understand a phenomenon more deeply, to obtain a detailed description of an experience, or to understand how or why an event occurs. Qualitative data may be text or images and uses a flexible reporting structure. Interview transcripts and video recordings represent qualitative data types. The output of qualitative analyses is called findings.

MIXED METHODS: Mixed methods research includes aspects of quantitative and qualitative methodologies in the same study or series of studies. Mixed methods approaches can be used sequentially or concurrently. Often, results or findings from one phase will be used to design a subsequent phase of a project. A time and motion study consisting of quantitative measurement of a motion in a workflow followed by a one-on-one interview is an example of a mixed methods study. The qualitative findings could be used to understand the results of the quantitative phase more deeply, to provide context for interpreting the results, or to triangulate the results and findings.

WHICH ONE SHOULD I USE?

Choice of research methodology should be determined using several factors:

Research Purpose: If the purpose is to understand or explore, a qualitative methodology is likely the best approach. If a client wants to know how much or to determine if a new workflow is more productive than the old one, a quantitative approach will likely be appropriate. If a client wants both, a mixed methods approach will be best.
Budget: Generally, qualitative studies are more time-intensive than quantitative studies. If a client’s budget is limited, a quantitative approach may be best.
Decisions: If a client wants to use the findings of a study to generate ideas or inform iterative design requirements, a qualitative approach may be best. If a client wants to evaluate changes to a process or product, a quantitative approach is required.

The next step in planning a research study is to decide what methods will be used to collect data. Methods specific to each methodology exist but are beyond the scope of this article. If you are interested in learning more, check out some of the popular methods from a reliable source: narrative inquiry, survey, and ethnography are a few examples of methods you may encounter in the field. By embracing research methodology as an integral part of the design process, industrial designers can confidently embark on their creative journey, armed with insights that empower them to craft extraordinary products that not only meet user needs but also set new standards of innovation in their industry.

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Author

Rachael Clark

Senior Design Researcher | [email protected]
Rachael brings over 10 years of research experience to her role at Kaleidoscope Innovation. She has advanced training in clinical psychology and mixed methods research methodology. Guided by the principles of positive psychology, Rachael uses a human-centered lens for deeply understanding the user experience. Her work at Kaleidoscope focuses on human-machine interaction and identifying design changes capable of positively impacting well-being at the individual and institutional levels.