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Tips for Designers To Tackle Layoffs 

The Layoff Season…

SAP Lab Laid off 300 employees. Ericsson to fire 8500 people. Headlines like these have become very common these days. Over 340 organizations have laid off more than 1.10 lakh people so far across the world. Not only humans but even robots were fired by Google recently. Meta, Amazon, Twitter, Zoom, and Microsoft are some of the major companies to join the layoff bandwagon. These big tech companies have large teams with multiple people with the same skill set. Over the past few weeks, dozens of them have frozen hiring and made significant cutbacks to eliminate redundant positions in departments like HR, marketing, and design. This has led to high competition for jobs in these areas and skill sets and a decrease in job opportunities.

The value of a designer is not always acknowledged, especially in these times of economic uncertainty where cost-cutting becomes a priority. This has also resulted in a cut in the budget for design departments, which leaves designers with fewer employment options. The assumption that design is a luxury rather than a necessity also hinders people from appreciating the worth of designers. This article discusses some tips for designers to tackle layoffs and AI-based solutions that can assist them to stay relevant in UI/UX design.

Automation and technology advancements have led to an increase in the use of design software and tools, making it possible for non-designers to create designs and perform tasks that human designers previously did. This has led to a decrease in the need for human designers in specific industries such as graphic design and website design, where the use of templates and pre-designed elements has become more prevalent.

Who benefits during the recession?

Contract or Freelance Designers

Companies may have less money to spend on design work, which can lead to fewer opportunities for designers. However, designers who are willing to work on a contract or freelance basis may still be able to find jobs, as companies may look to save money by hiring contractors or freelancers rather than full-time employees.

Additionally, designers with a diverse set of skills and the ability to adapt to changing market conditions may be more likely to find work during a recession.

User Experience Designers & Researchers 

UX designers and researchers may still be able to find work, as companies may be looking to improve their online presence and user experience to stay competitive which is why improving their digital products and services may become their key focus area. This can lead to an increase in demand for UX designers and researchers.

Business leaders may also cut costs by streamlining their products and services during these tough times, which can increase demand for user research to understand customer needs and preferences.

It’s crucial to remember that the job market is extremely dynamic and subject to rapid change during a recession, making it challenging to forecast how the demand for designers will change.

Essential Product Companies

Companies that build business-essential products may see an increase in demand, as companies and organizations look to cut costs by investing in more efficient and cost-effective products. These types of products may include items such as software, hardware, and equipment that help companies streamline operations and improve productivity. Additionally, companies that specialize in cost-cutting solutions, such as supply chain optimization or cost-saving consulting, may also see an uptick in business during a recession. It’s important to note that not all product-based companies will benefit during a recession, it will depend on the type of product they produce and the industry they operate in.

How to stay relevant in designing amidst this economic uncertainty?

# Focussing on Soft Skills

While technical expertise is crucial, employers don’t hire only people who possess those skills. The workforce continues to place a high priority on soft skills, also known as employability or transferable skills, which are frequently influenced more by personality than by education or training. The soft skills that may be most important in an uncertain job market for 2023 and beyond, include:

1. Critical thinking skills

Critical thinking involves being able to think creatively and strategically, identify problems, and come up with innovative solutions. These skills are highly transferable across different industries and roles, making them an asset for any team.

2. Communication skills

Effective communication helps in building positive relationships, resolving conflicts, and promoting understanding. Employers value individuals with strong communication skills as they can work effectively with others, and can represent the company positively. It involves both verbal and written communication, and the ability to listen actively.

3. Mental Flexibility

Mental flexibility, also known as cognitive flexibility, is the ability to adapt to new situations, to think outside the box, and consider different perspectives. Employers value individuals with this type of cognitive ability that allows people to adjust their thinking and behavior to changing circumstances. 

4. Teamwork Ability

Teamwork is important in any profession, regardless of the industry or role. Employers value individuals with strong teamwork skills as they can work effectively with others and can contribute to the success of the team and the organization. Teamwork ability is a key skill for achieving common goals, fostering creativity, and promoting a positive work environment.

5. Self Leadership

Employers value individuals with strong self-leadership skills as it involves setting goals, making plans and taking action to achieve those goals, and being self-motivated, self-disciplined, and accountable for one’s own actions.

# Learning AI-based Tools to stay competitive

According to the Global AI Survey, three in four businesses (75%) are either exploring or implementing AI and are increasingly recognizing AI’s potential to transform their operations and create new business opportunities. The survey also revealed that the adoption of AI is still in its early stages, with many businesses facing challenges such as a lack of skilled talent to use these tools, difficulty integrating AI with existing systems, and concerns about data privacy and security.

If there’s one thing that can give designers a competitive edge, it is the use of AI generative tools. AI generative tools are designed to assist designers in creating new designs, patterns, and layouts using machine learning algorithms. These tools can generate a wide range of options based on a set of input parameters, allowing designers to quickly explore different possibilities and find new inspiration.

1. Natural Language Generation Tools (NLG)

NLG tools use algorithms to generate text based on predefined rules or templates. These tools are commonly used for report generation, news article writing, and chatbot interactions. For example GPT-3, Wordsmith, Quill, Articoolo, Textio, etc.

2. Content Ideation Tools

These tools use AI to generate ideas for content topics based on keyword analysis, social media trends, and other data sources. They can help the sales and marketing team identify new topics and angles to explore. For example BuzzSumo, SEMrush, ContentIdeator, Clearscope, etc.

3. Video and Image Generation Tools

There are various AI tools available for image and video generation, which use deep learning algorithms and computer vision techniques to create realistic and engaging visual content—for example Midjourney, DALL-E, Adobe Sensei, Lumen5, etc.

4. Music and Sound Generation Tools

AI can analyze existing music to create new compositions or generate realistic sound effects for video and gaming applications. For example Amper Music, AIVA, Jukedeck, etc.

The Road Ahead:

It’s important to note that while these tools can save time and resources, they also have limitations. They may lack the creativity and nuance that comes with human-generated content, and there is a risk of producing low-quality or biased content if the data inputs or algorithms used are flawed. So a designer who is familiar with these tools will have a better understanding of how to work with them and may be more in demand by companies and clients looking to integrate AI into their design processes. Embrace these opportunities and be open to new ways of thinking.

Disclaimer: I am not an economist. Everything mentioned in this article is supported by extensive research and is not my personal view. I hope this article gives you some clarity and confidence heading into these uncertain economic times. 

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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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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