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Iteration Leads To Powerful Results in Design.

“You can only make it once but you can make it better as many times as you need”

Clients rarely arrive at a design firm with a detailed project roadmap in hand. Instead, they have a hazy idea of what they require – make it pop, bring a wow factor, make it look good, and so on. In such cases, the designer’s main challenge is to get into the clients’ heads and create things exactly how they want the product to look, even if the clients themselves lack understanding.

The best way to ensure your design is a perfect fit is to work in iterations. This allows us to create a solution that satisfies the client and meets the needs of the customer.

Iteration Leads To Powerful Results in Design

Iteration, the most fundamental concept in design

In its most basic form, iteration is simply a series of steps that you repeat, tweaking and improving your product each time. With every repetition, iteration aims to move a little bit closer to the optimal situation. As designers, we are always looking to improve on the current design approach and this is where an iterative design process comes in handy.

​​You can think of the iterative design process as a continuous cycle of prototyping, testing, and making adjustments and refinements – it is an ongoing, incremental process leading to the best possible outcome.

The 1997 version of Apple.com
The 1997 version of Apple.com
The 2022 version of Apple.com
The 2022 version of Apple.com

It’s fascinating to observe how the product gradually changed the appearance of its own homepage, going from its ugly beginnings to its current minimalism to align with the current design trends and in response to user feedback.

The do’s and don’ts of design Iteration

  1. Do: Fail Faster
    Embrace trial and error to learn what not to do even when you miss the mark by adopting a “fail faster” mentality. Since failure is unavoidable, it is best to deal with it as soon as possible while still taking note of what can be learned.
  1. Do: Be Flexible
    Design methodologies still allow for some flexibility even though they have strict guidelines to help us express our creative freedom without devoting too much time to each iteration. In the end, we must choose which opportunities to prioritize first, when to iterate or test more, and how many concurrent design iteration processes should be running at once.

    These choices are largely based on intuition and experience, utilizing any data and research that may be available.
  2. Do: Work Asynchronously
    Utilizing all resources (tools, teammates, etc.), complete tasks as quickly as possible by allowing other designers to work on unrelated aspects of the product in parallel and developers to start putting validated solutions into practice. By doing both of these, product turnaround times will be drastically reduced.
  1. Do: Collaborate and Listen
    Which issue ought to be resolved? What version is the best? Is the testable prototype ready? What do all of these comments mean? We are confident in our ability to respond to these questions because of the unique expertise and new perspective that our teamwork partners have to offer.
Iteration in Design
  1. Don’t: Try to Solve Everything
    Avoid attempting to solve new problems once the issue we’re solving during the design iteration process has been selected. Even though it’s common to find areas that can be improved (during testing or through observation), make a note of them since they might make excellent starting points for subsequent iterations.

    We cannot measure the effect that design iterations are having on key metrics if we allow scope creep to occur.

Benefits of Iteration in Design

  1. It Saves Resources
    Because iterative design processes frequently give us user feedback (or stakeholder feedback, at the very least), which drives us forward at a steady pace, they almost always save the most time.

    Positive feedback can help us know when we’re heading in the right direction, and negative feedback can help us know when we’re heading in the wrong direction, so we’re always moving forward and never really wasting any precious time.

    Without any feedback, we run the risk of racing to the finish line only to fall short, wasting a lot of time and bandwidth. Design iteration is also the most economical choice because time is money.
  1. It Facilitates Collaboration
    Healthy collaboration is facilitated by an iterative design process because it gives stakeholders the chance to provide feedback and even share their own ideas. This gives us information that we wouldn’t have learned on our own because we can only see things from our own point of view.
  1. It Addresses Real User Needs
    Designers have a tendency to work alone if they don’t follow a methodical iteration process (especially one that includes collaboration). Being siloed makes us overly introspective, which causes us to jump to conclusions and engage in counterproductive perfectionist tendencies.

    But using an iterative design process makes sure we remain focused on user needs and make choices based on their input. Additionally, prioritizing the next best design improvement method rather than concentrating on haphazard ones helps us.
  1. Facilitates Regular Updates
    Instead of just dumping the end result on stakeholders and keeping them in the dark until then, an iterative design process allows us to regularly update them on the status of the project.

    It means that developers can start even while the design is still in progress, which is especially advantageous for developers.

In Conclusion

Designers can quickly create and test ideas thanks to the iterative design. Those that show promise can be quickly iterated until they take enough shape to be developed, while those that don’t show promise can be abandoned right away.

The 90’s version of arngren.com
The 90’s version of arngren.com

Here’s an example of what happens when we don’t iterate – this 90s website is still around.

So do it, then do it again!

About the Author: Unnathi is a UI/UX designer, currently working at Mantra Labs. She is passionate about research and has expertise in building digital systems that provide engaging experiences. 

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Will AI Be the Future’s Definition of Sustainable Manufacturing?

Governments worldwide are implementing strict energy and emission policies to drive sustainability and efficiency in industries:

  • China’s Dual Control Policy (since 2016) enforces strict limits on energy intensity and usage to regulate industrial consumption.
  • The EU’s Fit for 55 Package mandates industries to adopt circular economy practices and cut emissions by at least 55% by 2030.
  • Japan’s Green Growth Strategy incentivizes manufacturers to implement energy-efficient technologies through targeted tax benefits.
  • India’s Perform, Achieve, and Trade (PAT) Scheme encourages energy-intensive industries to improve efficiency, rewarding those who exceed targets with tradable energy-saving certificates.

These policies reflect a global push toward sustainability, urging industries to innovate, reduce carbon footprints, and embrace energy efficiency.

What’s driving the world to impose these mandates in manufacturing?

This is because the manufacturing industry is at a crossroads. With environmental concerns mounting, the sector faces some stark realities. Annually, it generates 9.2 billion tonnes of industrial waste—enough to fill 3.7 million Olympic-sized swimming pools or cover the entire city of Manhattan in a 340-foot layer of waste. Manufacturing also consumes 54% of the world’s energy resources, roughly equal to the total energy usage of India, Japan, and Germany combined. And with the sector contributing around 25% of global greenhouse gas emissions, it outpaces emissions from all passenger vehicles worldwide.

These regulations are ambitious and necessary. But here’s the question: Can industries meet these demands without sacrificing profitability?

Yes, sustainability initiatives are not a recent phenomenon. They have traditionally been driven by the emergence of smart technologies like the Internet of Things (IoT), which laid the groundwork for more efficient and responsible manufacturing practices.

Today, most enterprises are turning to AI in manufacturing to further drive efficiencies, lower costs while staying compliant with regulations. Here’s how AI-driven manufacturing is enhancing energy efficiency, waste reduction, and sustainable supply chain practices across the manufacturing landscape.

How Does AI Help in Building a Sustainable Future for Manufacturing?

1. Energy Efficiency

Energy consumption is a major contributor to manufacturing emissions. AI-powered systems help optimize energy usage by analyzing production data, monitoring equipment performance, and identifying inefficiencies.

  • Siemens has implemented AI in its manufacturing facilities to optimize energy usage in real-time. By analyzing historical data and predicting energy demand, Siemens reduced energy consumption by 10% across its plants. 
  • In China, manufacturers are leveraging AI-driven energy management platforms to comply with the Dual Control Policy. These systems forecast energy consumption patterns and recommend adjustments to stay within mandated limits.

Impact: AI-driven energy management systems not only reduce costs but also ensure compliance with stringent energy caps, proving that sustainability and profitability can go hand in hand.

2. Waste Reduction

Manufacturing waste is a double-edged sword—it pollutes the environment and represents inefficiencies in production. AI helps manufacturers minimize waste by enhancing production accuracy and enabling circular practices like recycling and reuse.

  • Procter & Gamble (P&G) uses AI-powered vision systems to detect defects in manufacturing lines, reducing waste caused by faulty products. This not only ensures higher quality but also significantly reduces raw material usage.
  • The European Union‘s circular economy mandates have inspired manufacturers in the steel and cement industries to adopt AI-driven waste recovery systems. For example, AI algorithms are used to identify recyclable materials from production waste streams, enabling closed-loop systems. 

Impact: AI helps companies cut down on waste while complying with mandates like the EU’s Fit for 55 package, making sustainability an operational advantage.

3. Sustainable Supply Chains

Supply chains in manufacturing are vast and complex, often contributing significantly to carbon footprints. AI-powered analytics enable manufacturers to monitor and optimize supply chain operations, from sourcing raw materials to final delivery.

  • Unilever uses AI to track and reduce the carbon emissions of its suppliers. By analyzing data across the supply chain, the company ensures that partners comply with sustainability standards, reducing overall emissions.
  • In Japan, automotive manufacturers are leveraging AI for supply chain optimization. AI algorithms optimize delivery routes and load capacities, cutting fuel usage and emissions while benefiting from tax incentives under Japan’s Green Growth Strategy.

Impact: By making supply chains more efficient, AI not only reduces emissions but also builds resilience, helping manufacturers adapt to global disruptions while staying sustainable.

4. Predictive Maintenance

Industrial machinery is a significant source of emissions and waste when it operates inefficiently or breaks down. AI-driven predictive maintenance ensures that equipment is operating at peak performance, reducing energy consumption and downtime.

  • General Electric (GE) uses AI-powered sensors to monitor the health of manufacturing equipment. These systems predict failures before they happen, allowing timely maintenance and reducing energy waste.
  • AI-enabled predictive tools are also being adopted under India’s PAT scheme, where energy-intensive industries leverage real-time equipment monitoring to enhance efficiency. (Source)

Impact: Predictive maintenance not only extends the lifespan of machinery but also ensures that energy-intensive equipment operates within sustainable parameters.

The Road Ahead

AI is no longer just a tool—it’s a critical partner in achieving sustainability. By addressing challenges in energy usage, waste management, and supply chain optimization, AI helps manufacturers not just comply with global mandates but thrive in a world increasingly focused on sustainability.

As countries continue to tighten regulations and push for decarbonization, manufacturers that embrace AI stand to gain a competitive edge while contributing to a cleaner, greener future.

Mantra Labs helps manufacturers achieve sustainable outcomes—driving efficiencies across the shop floor to operational excellence, lowering costs, and enabling them to hit ESG targets. By integrating AI-driven solutions, manufacturers can turn sustainability challenges into opportunities for innovation and growth, building a more resilient and responsible industry for the future.

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