Lean Article

Etienne Nichols’s picture

By: Etienne Nichols

At one point in my career, after managing design controls and risk management documentation, I decided to move on.

When the day came to put in my two-week notice, I walked over to another engineer’s cubicle with the news. “From now on,” I said, “design controls are yours.”

I’ll never forget the way their smile faded when they heard that. Managing and updating the design controls documentation for this product line was done in an Excel spreadsheet, and that spreadsheet was turning into a full-time job.

It’s unfortunate, but managing design controls (and the general documentation load) that way is one of the reasons talented engineers leave the medtech industry. But it doesn’t have to stay that way.

What are design controls for, anyway?

Before I get into why Excel causes engineer attrition in medtech, I want to talk a little about why we have design controls in the first place. Because design controls are, at the most basic level, what good engineers do naturally.

Chris Caldwell’s picture

By: Chris Caldwell

As the growth in fulfillment warehouses, e-commerce, and third-party logistics skyrockets, and unique customer demands evolve, more companies are exploring the concept of dark warehouses—fully automated, “lights-out” facilities that use intelligent, interconnected devices to operate without human labor. Due to labor availability, cost-benefit analysis, technological advancements, and other factors, the shift toward these facilities continues.

Dark warehouse drivers

Although the reasons for advanced technology integration, including robotic automation, will vary from one enterprise to another, there are multiple key concepts and innovations prompting the gradual move toward “darker” warehouses.

Refined robotic tools

More affordable, robust, and energy-efficient robots can now be easily integrated into a wider variety of operations for increased capacity. Intelligent peripherals—including sophisticated vision, sensor technology, and flexible end-of-arm tooling (EOAT)—all work together to facilitate fluid product flow with improved quality.

Eric Whitley’s picture

By: Eric Whitley

Historically, manufacturing processes have often involved substantial waste. From the early days of industrialization, companies have prioritized production speed and volume over efficient resource use. As resources seemed abundant and environmental consciousness was low, excessive waste became an accepted cost of business.

In recent years, there’s been a notable shift in manufacturing practices. Driven by global awareness of climate change and dwindling resources, companies have begun to prioritize sustainability. This shift not only reflects a commitment to ethical operations but also a response to consumer demands for environmentally responsible products.

The consequences of waste in manufacturing

Waste in the manufacturing sector leads to severe environmental consequences. Excessive material waste contributes to landfill overfill, while energy waste increases greenhouse gas emissions. Furthermore, water pollution from untreated industrial waste affects aquatic life and human health.

Donna McGeorge’s picture

By: Donna McGeorge

Nano Tools for Leaders—a collaboration between Wharton Executive Education and Wharton’s Center for Leadership and Change Management—are fast, effective tools that you can learn and start using in less than 15 minutes, with the potential to significantly improve your success.

The goal

Invest your energy strategically to improve productivity and results.

Nano tool

Many of our productivity problems manifest because we’re operating on autopilot. We don’t think about what, when, or even why we’re doing things; we just do them in the order in which the tasks come to us, or how they’re written on our to-do list. Add to that a near-constant inflow of information and problems to solve, and it can feel like time evaporates without you ever getting to the tasks that matter most.

Multiple Authors
By: Scott A. Hindle, Douglas C. Fair

We are one year away from the 100th anniversary of the creation of the control chart: Walter Shewhart created the control chart in 1924 as an aid to Western Electric’s manufacturing operations. Since it’s almost prehistoric, is it now time to leave the control chart technique—that started out using pen and paper—to the past? Or, in this digital era, is the control chart still relevant to enable manufacturers to improve their competitive position by improving quality and productivity, and reducing waste?

Read on to see the story of two plants. Some key words to look out for:
• Predictable process
• Actionable insights
• Improvement
• Cost savings
• Waste

The annotated control chart above is from the Tokai Rikka plant in Japan more than 40 years ago.

Multiple Authors
By: Douglas C. Fair, Scott A. Hindle

Today’s manufacturing systems have become more automated, data-driven, and sophisticated than ever before. Visit any modern shop floor and you’ll find a plethora of IT systems, HMIs, PLC data streams, machine controllers, engineering support, and other digital initiatives, all vying to improve manufacturing quality and efficiencies.

That begs these questions: With all this technology, is statistical process control (SPC) still relevant? Is SPC even needed anymore? Some believe manufacturing sophistication trumps SPC technologies that were invented 100 years ago. But is that true? 

We the authors believe that SPC is indeed relevant today and can be a vitally important aid to manufacturing. (SPC can be used outside of manufacturing, and to great benefit, but we keep our focus on manufacturing.)

As quality professionals and statisticians, are we biased in our view? Possibly. After visiting hundreds of manufacturing plants around the globe, though, and witnessing their unending manufacturing challenges and opportunities, the evidence is overwhelming: SPC is an important strategic tool in the quest for improved quality and reduced costs. We also postulate that SPC has more potential uses and benefits today than ever before.

John Davis’s picture

By: John Davis

Over the past decade, one of the biggest advances in enterprise resource planning (ERP) has been the ability to communicate and integrate with machines and external software programs to lower costs and increase efficiency. For example, BOM Compare software can reduce engineering costs and get jobs into production much faster by expediting the design-to-production process. Integrating ERP with nesting software can significantly lower material and labor costs, and reduce scrap, by automatically determining the most efficient way to cut parts on a piece of metal.

Ramūnas Berkmanas’s picture

By: Ramūnas Berkmanas

Imagine a manufacturing world where machines seamlessly collaborate with artificial intelligence (AI) to ensure flawless quality inspection. It’s a future that holds immense potential for revolutionizing the industry.

Major manufacturers like FANUC, ABB, and KUKA AG, alongside specialized cobot producers such as Techman Robot, Automata, Franka Emika, and Universal Robots, are driving this innovation.

According to a Grand View Research report, the collaborative robot market was valued at $1.23 billion in 2022 and projected to exceed $11.04 billion by 2030.

Let’s delve deeper into the future of manufacturing, where cobots and AI converge for unparalleled quality inspection.

Why quality inspection matters

Quality inspection is crucial in manufacturing. It ensures that products meet the desired standards and customer expectations. Automated inspection, driven by advancements in inspection technology and machine learning integration, plays a vital role in achieving high-quality control and defect detection.

Harish Jose’s picture

By: Harish Jose

Today I’m looking at some practical suggestions for reducing sample sizes for attribute testing. A sample is chosen to represent a population. The sample size should be sufficient to represent the population parameters such as mean and standard deviation. Here we’re looking at attribute testing, where a test results in either a pass or a fail.

The common way to select an appropriate sample size using reliability and confidence level is based on the success run theorem. The often-used sample sizes are shown below. The assumptions for using binomial distribution hold true here.

The formula for the success run theorem is given as:

n = ln(1 – C)/ ln(R), where n is the sample size, ln is the natural logarithm, C is the confidence level, and R is reliability.

Multiple Authors
By: Chandrakant Isi, Francesco Rivalta

You’re in an early-stage hardware startup or a tinkerer in a toolshed with a product design set to shake up the market. Not sure how to turn your idea into a product? Here’s a step-by-step guide to the product development journey. From the initial design sketched out on paper to the final product in your hand, every decision counts. This article will help you avoid costly mistakes and accelerate the process of bringing your product to market.

Instead of just listing the steps, I’ll explain the entire process through a case study of my motorized robot grip project. A gripper is what lets robots grab, hold, and shift stuff around. The journey to create one includes steps like designing, prototyping, making the actual product, checking its quality, and of course, putting it to the test. But remember, making a product isn’t always a straight path. For example, after designing you build a prototype. Feedback from that prototype might prompt you to rethink and tweak your design. This cycle might repeat several times before reaching the final product.

1. Design

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