Published on Oct 31, 2023

Technology leaders such as Apple, IMB, Google, and Atlassian rely on design systems to codify and scale design efforts across entire organizations. However, it’s important to note that design systems are not exclusive to well-known brands; in fact, a study conducted by Forrester in 2020 revealed that 65% of the companies surveyed have integrated design systems into their workflows.

What is a design system?

A design system is a comprehensive collection of design guidelines, principles, components, and assets that are created and maintained to ensure consistency and cohesion in the visual and user interface design of a product or brand. It serves as a centralized resource that helps design and development teams create a unified and coherent user experience across various platforms and devices.

A design system consists of:

Pattern library
Design tokens
Components
Brand guidelines
Documentation

Why is having a design system important?

A design system provides numerous benefits to organizations, design and development teams, and end users. Here are some key reasons why having a design system is essential:

Consistency: Design systems ensure a consistent and cohesive look and feel across a product or brand. This consistency builds trust with users and creates a recognizable and professional identity.
Efficiency: Design systems save time and resources by providing pre-defined design components and guidelines. Designers and developers can reuse established elements, reducing the need to recreate design assets from scratch.
Productivity: With reusable components and clear guidelines, design and development teams can work more efficiently, reducing the time required for decision-making and development iterations.
Scalability: Design systems allow for easy scaling as a product or brand grows. New features, pages, or products can be created while maintaining a consistent design, saving time and effort.
Improved Collaboration: Design systems promote collaboration between designers and developers. By speaking a common design language and using shared components and guidelines, teams can work together more effectively.
Accessibility: Design systems often include accessibility guidelines, ensuring that products are designed and built with inclusivity in mind, making them usable by a broader range of people.
User Experience: A well-designed system leads to a better user experience. Consistency and familiarity make it easier for users to navigate and interact with a product or brand.
Brand Identity: Design systems help maintain a strong and coherent brand identity. This is crucial for branding and marketing efforts, as it reinforces brand recognition and loyalty.
Rapid Prototyping: Design systems facilitate quick prototyping and testing, as designers can focus on the overall experience and functionality, knowing that the visual design is consistent.
Adaptability: Design systems can be updated to accommodate changes in design trends, new technology, or user feedback, allowing products and brands to evolve without losing their core identity.
Documentation: Clear and detailed documentation explaining how to use the design system’s components and guidelines. This is crucial for ensuring that designers and developers can easily implement the system.
Version Control: A system for managing changes and updates to the design system to ensure that all team members are using the most current version.
Cost Savings: By reducing design and development redundancy, design systems can save money in the long run, making projects more cost-effective.
Maintainability: A well-maintained design system ensures that design elements are updated consistently, reducing the risk of visual and functional inconsistencies.

Examples of Design systems:

Conclusion

Design systems are a cost-effective solution for organizations, as they formalize design choices that can be easily replicated on a larger scale. There are specific ways in which these systems expedite the design and development processes, and maximize the benefits of your design system. For businesses, it streamlines workflows, ensures consistency, enhances efficiency and productivity, reduces costs, and contributes to increased ROI and revenue. Furthermore, it forms the foundation for the successful execution of marketing initiatives and overall brand development.

However, there are challenges associated with the implementation of a design system. It requires a commitment to making it a top priority, comprehensive planning, active involvement of all departments across various domains, and thorough testing to meet current standards. Additionally, it’s essential to recognize that this is an ongoing project that will continually need fine-tuning, regular maintenance, governance, and the addition of new essential elements and features to stay current and aligned with industry trends.

In the end, the effort is well worth it. Many companies that have established their brand design systems report accelerated growth and a substantial increase in revenue due to embracing this concept.

About the Author: Ashish is a Lead Designer at Mantra Labs. He helps clients make better decisions on their digital products with his expertise in UI/UX design.

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Building a layered approach with AI

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.

Importance of Design Systems: Enhancing Product Quality and Speed

What is a design system?