Saturday, September 19, 2026

Creating value from AI and digital capabilities in logistics operations

As AI and digital capabilities become more widely adopted in logistics operations, the companies seeing returns from their investments are doing three things differently.


Companies have invested heavily in AI and digital logistics capabilities in recent years, as they grapple with network disruptions, capacity volatility, labor constraints, and regulatory and geopolitical uncertainty—alongside the constant pressure to improve productivity. Transportation is becoming more digitized and warehouses are increasingly automated, with AI-enabled tools, digital twins, and visibility platforms moving from edge technology into the core of how logistics functions operate today.

With AI and digital capabilities becoming a standard feature of logistics, rather than a source of differentiation, attention needs to turn now to translating these capabilities into measurable operational and financial impact.

Read more...

https://www.mckinsey.com/capabilities/operations/our-insights/creating-value-from-ai-and-digital-capabilities-in-logistics-operations

McKinsey Technology Trends Outlook 2026

Which frontier technologies matter most for companies in 2026? Our annual report highlights the latest technology innovations, developments, and talent trends and their potential impact on business and society.


McKinsey Technology Trends Outlook 2026 - Full Report (143 pages)


The technology story of 2026 has moved off the screen and into the physical world. Innovation is accelerating in the power grids and chips that underpin the data center boom; in the intelligent robots that embody AI; in the agentic systems discovering new chemical compounds; and in the launch pads sending thousands of satellites into orbit.


AI needs energy to scale. That’s one reason energy technologies alone drew nearly $200 billion in investment in 2025, among the highest capital influx in any technology domain. And spending on AI infrastructure doubled in a single year. These developments show that the defining questions today are not only about what technology can do. They are also about who can build the hardware and assemble the skilled workforce to deploy AI in the real world. At the same time, huge leaps were made in cybersecurity and software development—illustrating that AI is accelerating the digital frontier, too.


McKinsey’s Technology Trends Outlook 2026 examines 14 technology trends that define 2026, expanding our coverage from last year to include two new fast-emerging domains: agentic software development and AI for scientific discovery and engineering. For easier navigation, we group the trends into three broader categories: AI revolution, compute and connectivity frontiers, and cutting-edge engineering. The lines between these domains are blurring, and much of the innovation is happening in the gaps.


Read more ....

https://www.mckinsey.com/capabilities/tech-and-ai/our-insights


Tuesday, January 27, 2026


A new survey of manufacturing COOs shows high hopes for scaling AI—and high budgets. But some companies may be underinvesting in the enablers needed for AI to generate lasting value.

The vision of AI in manufacturing is seductive: “lights out” factories that are so heavily automated that they almost run themselves, with human workers monitoring operations from an off-site control center. Indeed, a few of the most advanced robotics factories have already passed a crucial line, with robots building robots.

That’s the future that so many COOs desire, according to our global survey of more than 100 COOs at manufacturers with at least $1 billion in revenues (see sidebar, “Our methodology”). Companies are raising their bets on digital and AI technologies.

Read more in A new survey of manufacturing COOs shows high hopes for scaling AI—and high budgets. But some companies may be underinvesting in the enablers needed for AI to generate lasting value.

Thursday, December 18, 2025

Merry Christmas and Happy New Year!

 


Dear Clients and Friends of Amancio Quality Consulting, 

In this special period, we want to express our gratitude for the trust and partnership throughout 2025. 

May Christmas be filled with peace, love and happy moments with those you love the most. 

We wish that 2026 brings new opportunities, achievements and many achievements. May the New Year be prosperous, full of health, success and prosperity for all of you! 

We thank you for being part of our history. May an extraordinary 2026 come! 

Merry Christmas and Happy New Year! 

Amancio Quality Consulting Team


Thursday, November 20, 2025

What is an operating model?


An operating model is the backbone of any organization. It outlines how the company delivers value to its customers, operates on a day-to-day basis, and achieves its strategic objectives.

McKinsey research shows that even top-performing companies achieve only about 70 percent of their strategies’ full potential, due in no small part to shortcomings in their operating models. But in today’s fast-paced business landscape, having an effective and well-defined operating model is crucial for closing this strategy-to-performance gap, adapting to changing market conditions, and achieving long-term success. 

A robust operating model serves as a guiding framework for decision-making, resource allocation, innovation, and many other critical activities and practices in the business—all in the service of improving efficiency and generating sustainable growth.

To learn more about operating models and how they can enable organizations to realize their full potential, read on ...

McKinsey-explainers/what-is-an-operating-model

Monday, November 3, 2025

5 takeaways from the world’s largest dataset on industrial transformation

  • Insights from over 1,000 industrial transformations prove progress happens when processes advance together – not through isolated pilots.
  • Convergence is the new rule. Companies combining AI, internet of things and automation achieve greater productivity impact than those relying on single tools.
  • People and technology advance together: 75% of sites that invest in workforce capabilities – from safety and skills to employee experience – achieve above-median performance.

Industrial transformation has been occurring piecemeal over the last few years, with emerging technologies, new business models and data-driven processes deployed to improve the efficiency and capability of operations and supply chains across sectors.


However, industrial transformation is no longer a series of one-off experiments. Lumina, the World Economic Forum’s new AI-powered platform for lighthouse transformation, developed by the Centre for Advanced Manufacturing and Supply Chains, unites eight years of data from the Global Lighthouse Network – a community of the world's most advanced operational sites.


Drawing from more than 1,000 real-world cases across 32 countries, the evidence is clear: companies are moving beyond pilots, deploying multiple technologies together and delivering measurable impact. Factories are now tech companies.


What these cases reveal is not just the scale of change but the patterns behind it. Why do some organizations break through while others remain stuck? The answer lies less in single technologies and more in how processes, people and systems evolve together.

Read more, clicking here

Friday, October 10, 2025

Unlocking Innovation: Implementing Design Sprints in Hardware Manufacturing

Imagem Freepik
In today's fast-paced market, hardware manufacturing companies face intense pressure to innovate quickly while managing complex supply chains and physical production constraints. One powerful methodology that's gaining traction is the Design Sprint—a structured process originally popularized by Google Ventures. This blog post explores what Design Sprints are, their core concepts, essential tools, key characteristics, best practices for application in hardware settings, and the common difficulties encountered during implementation.


What is a Design Sprint?

A Design Sprint is a time-constrained, five-day process designed to solve critical business problems through rapid ideation, prototyping, and user testing. It condenses months of work into a single week, allowing teams to validate ideas before committing significant resources. Developed by Jake Knapp at Google Ventures, it's particularly useful for reducing risks in product development by focusing on user-centered solutions.

While traditionally applied to software and digital products, Design Sprints are increasingly being adapted for hardware manufacturing, where they help teams tackle challenges such as product redesign or process optimization. For instance, companies like Lego have scaled Design Sprints to physical product innovation, running over 150 sprints in a year to accelerate toy development.


Core Concepts of Design Sprints

At its heart, a Design Sprint revolves around five phases: Understand (mapping the problem), Sketch (ideating solutions), Decide (selecting the best ideas), Prototype (building a testable version), and Test (validating with users). These phases emphasize collaboration, creativity, and iteration, drawing from design thinking principles.

In hardware manufacturing, these concepts must account for physical realities. For example, the "Prototype" phase might involve 3D modeling or mock-ups rather than fully functional hardware to fit the sprint's timeline. The goal is to foster a mindset of rapid experimentation, even in industries where changes can be costly.


Main Tools for Design Sprints

Effective Design Sprints rely on a mix of analog and digital tools to facilitate collaboration and visualization. Common ones include:

  • Whiteboards and Post-it Notes: For brainstorming and mapping ideas during the Understand and Sketch phases.
  • Digital Collaboration Platforms: Tools like Miro or Mural for virtual whiteboarding, especially useful in remote teams common in global manufacturing.
  • Prototyping Software: Figma or Sketch for quick digital mocks; in hardware contexts, CAD tools like SolidWorks or 3D printing software for physical simulations.
  • Engineering-Specific Tools: For hardware firms, platforms like Valispace integrate requirements management and system modeling to track Agile progress in real-time, linking hardware specs to prototypes.

These tools enable cross-functional teams—engineers, designers, and stakeholders—to work efficiently without needing advanced setups.


Characteristics of Design Sprints

Design Sprints are defined by several standout traits:

  • Time-Bound Intensity: Typically five days, promoting focused effort and quick decisions.
  • Collaborative and Inclusive: Involves diverse team members to bring multiple perspectives, reducing silos in manufacturing environments.
  • User-Centric Focus: Emphasizes testing with real users early, ensuring hardware designs meet market needs.
  • Risk-Reduction Oriented: By prototyping and testing rapidly, sprints minimize the financial risks associated with hardware production, where tooling and materials are expensive.

In hardware manufacturing, a key characteristic is adaptability—sprints may extend slightly for physical prototyping but retain the core emphasis on iteration over perfection.


Best Practices for Implementing Design Sprints in Hardware Manufacturing

To succeed in hardware contexts, companies should adapt standard practices to physical constraints. Here are some proven strategies:

  • Assemble Cross-Functional Teams: Include engineers, manufacturers, and supply chain experts alongside designers. For example, Volkswagen used a Design Sprint to redesign customer service for car sales, involving multi-stakeholder workshops that led to higher sales and customer loyalty.
  • Start Small and Scale: Begin with minimal preparation, as Lego did by halting production abruptly and preparing day-by-day, allowing teams to learn on the fly.
  • Incorporate Rapid Prototyping Techniques: Use digital twins or low-fidelity models to simulate hardware. Extend sprints if needed for physical tests, but limit to avoid losing momentum.
  • Validate Early and Often: Test prototypes with end-users or stakeholders to catch manufacturing issues like component integration early.
  • Foster Agile Mindset: Integrate tools like Kanban for workflow visualization and daily standups to maintain adaptability in hardware's longer cycles.

These practices can reduce development time by up to 30%, as seen in hardware teams using integrated platforms.


Difficulties in Implementation and Application

Despite their benefits, applying Design Sprints in hardware manufacturing isn't without hurdles:

  • Physical Prototyping Constraints: Unlike software, building hardware prototypes takes time and resources, often requiring specialized equipment. This can extend the traditional five-day timeline, leading to frustration.
  • Interlinked Hardware-Software Dependencies: Changes in hardware design impact embedded software, complicating iterative processes.
  • Resistance to Change: Manufacturing cultures rooted in waterfall methods may resist the sprint's rapid, failure-embracing approach, as seen in traditional hardware paradigms with lengthy cycles.
  • Scalability and Coordination Issues: In large firms, coordinating across global teams and time zones can cause deadlocks, as noted in remote workshops.
  • Cost and Risk Management: Early errors in prototypes can be expensive due to materials and tooling, making stakeholders hesitant to experiment.

Overcoming these requires strong leadership buy-in and gradual integration, starting with pilot sprints on non-critical projects.


Design Sprints offer hardware manufacturers a pathway to faster innovation, but success hinges on tailoring the process to industry specifics. By addressing these challenges head-on, companies can turn ideas into viable products more efficiently than ever before. If your team is considering a sprint, start with a small challenge and build from there!


Joao F Amancio de Moraes - Amancio Quality Consulting