Lean Article

Doug Devereaux’s picture

By: Doug Devereaux

The premise for the NIST MEP Digital Supply-Chain Network project is familiar to MEP centers—many small and medium-sized manufacturers (SMMs) are often not ready for Industry 4.0 and don’t know how to implement it. Manufacturers with fewer than 50 employees often lag in digital supply-chain areas such as setting cybersecurity policies and leveraging data and information analytics.

The digital supply chain in manufacturing refers to the consistent and sustainable connectivity between the manufacturer and the lowest-level suppliers to the delivery of the product to the customers. It includes capturing operational data from sensors, machines, and other connected assets, but it also includes ERPs, sourcing, finance, and cybersecurity. A manufacturer that efficiently manages its digital supply chain has a head start on optimizing performance with better demand forecasting and automated inventory management, improved time to market, and lower-cost sources of raw materials.

Benjamin Kessler’s picture

By: Benjamin Kessler

Suddenly, supply chains are in the spotlight. The practical details of how products arrive on supermarket shelves, for example, gained unwelcome relevance amid last year’s wave of panic buying caused by Covid-19 disruption. At the same time, the environmental damage wrought by wasteful industrial processes came under intensifying criticism from consumers, civil society, and regulators. Businesses have stepped up their search for “zero waste” or circular economy solutions.

You could say that Luk Van Wassenhove, INSEAD emeritus professor of technology and operations management, has spent most of his 40-year career inadvertently preparing for this moment. A pioneer in sustainability research, Van Wassenhove worked closely with Xerox during the 1990s as it became one of the first companies to remanufacture and sell a new “green line” of copying machines.

Edmund Andrews’s picture

By: Edmund Andrews

Seems everybody has a horror story about health insurance: Kafkaesque debates with robotic agents about what is and isn’t covered. Huge bills from a doctor you didn’t know was “out of network.” Reimbursements that take months to process.

It’s no secret that healthcare in the United States is tangled in wasteful red tape. A study in 2019 estimated that administrative complexity was the single biggest source of waste in healthcare—bigger even than fraud or over-pricing—and imposes an annual cost of $265 billion.

The true extent of that waste, according to a new study led by Jeffrey Pfeffer at the Stanford Graduate School of Business, is even more shocking. Pfeffer and his colleagues found that administrative “sludge” in healthcare insurance costs employers and the economy billions of dollars in squandered work time, employee stress, absenteeism, and reduced productivity.

Scott Heide’s picture

By: Scott Heide

During the last several decades, the ability to manufacture customized products for customers has become increasingly attractive to a growing number of companies. However, customization has led to manufacturers drowning in a sea of increasingly complex bills of material (BOM).

Standard products are great when product changes are minimal, when identical products can be put into identical boxes hour after hour. The custom world, on the other hand, is always dynamic and ever-changing. Common tools that work well for standard design, engineering, and manufacturing resist adaptation into solutions for customization. An early symptom of looming problems is the need for huge repositories of parts masters or BOMs to be maintained.

Issues with the ‘150-percent BOM syndrome’

There are solutions that draw from established technologies and are designed from the ground up for dynamic, generative methodologies. Before outlining these, we must dig deeper into why standard solutions cause problems.

The drive to somehow repurpose standard practices into a custom infrastructure has led to the well-known “150-percent BOM,” also known as “master BOM,” “variant structure,” and “configurable BOM.”

Quality Digest’s picture

By: Quality Digest

Digital transformation is the integration of technology into all areas of a business, which fundamentally changes how organizations operate and deliver value to their customers. But what does success look like in a digital transformation? Project is on time and budget? Stakeholders are engaged early and often? Business objectives are met by implementing a digital solution? Stakeholders and end users are able to function in their jobs after go-live? Answer: All of the above!

All too often, change management is limited to communications or training. Although these activities are vital to a successful transformation, they are only components of a change management strategy that is focused on creating awareness, surfacing barriers to change, and achieving and sustaining end user adoption. The transformation is not complete at the time a digital solution is implemented. In many respects, the change is just beginning. A comprehensive change management program will continue to measure user adoption by monitoring quantitative and qualitative success metrics defined in the strategy.

Adam Conner-Simons’s picture

By: Adam Conner-Simons

Laser cutting is an essential part of many industries, from car manufacturing to construction. However, the process isn’t always easy or efficient. Cutting huge sheets of metal requires time and expertise, and even the most careful users can still produce huge amounts of leftover material that go to waste. The underlying technologies that use lasers to cut edges aren’t actually all that cutting-edge: Users are often in the dark about how much of each material they’ve used, or if a design they have in mind can even be fabricated.

With this in mind, researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have created a new tool called Fabricaide that provides live feedback on how different parts of the design should be placed onto their sheets—and can even analyze exactly how much material is used. 


Fabricaide: a tool for less wasteful laser-cutting

Ayman Jawhar’s picture

By: Ayman Jawhar

Product management as we’ve known it up until now—as a limited function or role—is effectively dead. However, viewed as a culture, product management is thriving. I predict “product culture” will be central to the future of work in digital economies. Yet knowledge workers, executives, and business educators unfortunately remain indebted to the old paradigms of product. They’re lagging far behind.

That was the argument I made in my previous article, to which quite a few readers took offense, with comments like:
“IT folks should stop complicating product management as if they were the first people to discover it!”
• “Disingenuous. Product function is an evolution, not a revolution.”
• “This is a good example of the nonsense published about the product.”

These strong sentiments were welcome because they’re a reminder that, in scientifically rationalizing work, we have forgotten how deeply personal and subjective it is. We also limit the power of collective work if we treat it only as a virtual assembly line between functions, roles, and organizational matrices.

Andrew Schutte’s picture

By: Andrew Schutte

Industrial engineers design, develop, test, and evaluate integrated systems for managing industrial production processes. Functions include quality control, human work factors, inventory control, logistics and material flow, cost analysis, and production coordination. These and other facets are usually part of the job description when being hired.

Although the Bureau of Labor Statistics estimates a 10-percent growth rate among industrial engineers from 2019 to 2029, the attrition rate is anecdotally just as high; that equates to 100-percent attrition in a decade. Nowhere is the dissatisfaction and attrition of industrial engineers as great as in the engineer-to-order manufacturing space.

Yoav Kutner’s picture

By: Yoav Kutner

Like business-to-consumer (B2C) ecommerce, business-to-business (B2B) ecommerce allows customers to purchase parts and supplies via an online portal. The difference is that in B2B ecommerce, both the customers and suppliers are businesses, and the customers may or may not be the end users of the product being purchased. In addition, a B2B solution needs to let customers submit a request for quote (RFQ), negotiate, and do more of the back-and-forth that occurs in business transactions.

Despite the fact that purchasing is done online—a digital solution for many B2B online platforms—a lot of the back-end processes are still done manually, not much differently than in a brick-and-mortar business. An online order might need to be copied and pasted into an Excel spreadsheet or even an enterprise resource planning (ERP) system, for instance. Ditto for getting customer information into a customer relationship management (CRM) system or generating quotes. This manual back-end work keeps both customer and supplier from operating efficiently, introducing errors into orders, or even delaying orders.

Andrew Peterson’s picture

By: Andrew Peterson

Manufacturing robotics is to some extent following a similar path of advances to those in machining and fixed automation systems. Though the ROI is most easily measured in efficiency and cost savings, manufacturers are looking for robotic technology to help them resolve a pain point in their operation or to create new opportunities. It might be to link processes more efficiently or eliminate the need to outsource a specific function or two.

The growth path for small and medium-sized manufacturers (SMMs) with robotics is therefore increasingly focused on applications and added capabilities, not just efficiency and continuous improvement. The key to increasing adoption of robotics in SMMs is making the robots easier to use and reuse.

In essence, adoption is dependent upon robots having more human-like dexterity and self-control.

NIST Labs has designs on making robots easier to use

Scientists and engineers at NIST Labs are working to close a significant gap between cutting-edge technology and what is currently deployed on many manufacturing shop floors. This is largely due to the lack of measurement science to verify and validate emerging novel research and thus reduce the risk of adoption.

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