Dec 05 2016

Megan Nichols

Lean Manufacturing Best Practices Help Save You Money

Developing lean inner-work practices has been the recent mindset of many industries, especially manufacturing. The concept “lean” stems from an organization’s desire to optimize workflow and eliminate unnecessary waste.

Companies across the world are reaping the benefits of standardizing their processes and procedures to minimize common mistakes and improve the overall value of the company.

Safety also plays a primary role in a company’s lean process. By incorporating safety into the lean manufacturing process, companies can improve their efficiency, workplace safety and employee buy-in of lean initiatives.

Understand the Benefits of Lean Manufacturing

In addition to eliminating waste, improving efficiency and increasing workplace safety, lean manufacturing also strives to produce products that are:

  • Simply made with few resources
  • Quick and on-time
  • Competitively priced
  • Better than competitors
  • Free of waste

The lean philosophy emphasizes crafting more value for customers by using fewer resources through an optimized workflow. Your outcome will be a more organized operation where your employees have access to the tools they need — and therefore, are more empowered to deliver quality service.

What does this mean for you? Your company will experience a more efficient process and workflow, which in turn, will produce profitable results.

Now that you know how advantageous lean manufacturing can be for your company, it’s time to be proactive. Lean isn’t a one-time project, and you’ll find there’s always room for improvement — more procedure refining, more ways to reduce waste and more ways to improve the return on your lean investments.

As with any new project, it’s crucial to establish initial goals for your lean transition, but keep in mind your goals don’t necessarily need to have an end point. Rather, this is a pledge to continual improvement.

Here are some of the key best practices to include in your lean manufacturing initiative.

Establish a Shared Vision

With any major company change, it’s of utmost importance to have all employees onboard, which can often be overlooked in the lean initiative process. It isn’t uncommon for your employees to be actively involved with a variety of internal and external programs such as family life, education or certification training so that they might view your new lean initiative as an inconvenience.

Adding a focus on safety into your approach may provide more value to the program in the eyes of your employees. Lean operations are critical to the jobs of employees. Improving safety and efficiency in their daily routines are essential to promoting a shared vision, achieving uniform goals and leading to the success of the program.

To get your employees on board and involved, here are some questions you should ask to understand necessary improvement areas:

  1. What additional procedures could be developed to meet employee needs and eliminate hazards?
  2. What are the types of hazards your employees encounter on a daily basis?
  3. What would make employees feel more valued and vested into the lean program?

Restructure a Sales Plan and Focus on Customer Service

It should come as no surprise that customer service needs to be at the heart of every successful organization, and with the addition of lean efforts, companies should not only strive to remove unnecessary waste from their customer-facing process but simultaneously deliver improved customer service. It’s quite simple, remove the waste from the customer service process, and you’ll improve delays, mistakes, inconveniences and reduce your overall costs.

So what can you do? Adjust scheduling, staffing and resources to match the schedule and wishes of your customers. After all, you can’t expect to provide excellent customer service if you do it on your own schedule, and only in ways that improve your own efficiency and goals, while ignoring those of your customer. By taking on a lean mindset, you’ll be speeding up service response times by removing wasted time your staff spends standing around doing nothing with no one to serve.

Properly Implement Lean Manufacturing in Your Company

Implementing lean process improvement has the potential to quickly become involved, detailed and a little overwhelming if you aren’t going about the process appropriately. It’s a good idea to have a proper lean program in the first place, and a vision of where you want your organization to be. The plan should be broken down into defined steps. Clearly defined performance targets should be set and monitored.

  • Focus on Your Customers: Your customers want value. Value creation occurs when the quality of services received is perceived as much higher compared to the cost. You know what your customers want. Now how can you provide it faster, better and cheaper?
  • Determine How Work Is Getting Done: As the manager or a principle in the company, you may have a lot of assumptions about how work is getting done. However, these assumptions might not actually mirror what is actually happening. It’s important to notate the steps in the process in an easy, laid out format so they could easily be repeated alone if need be. Try bringing in an observer to record the steps in the process.
  • Remove Waste and Inefficiencies: Once you become familiar with the workflow of your process, it’s time to determine how it’s directly creating value for your customers. If it isn’t, take a second look. Manage, improve and smooth the process to eliminate non-value-added activity time. Consider packaging and storage methods as well and choose materials that cost less and can save space. Examples of this could be wasted time and movement, excess inventory due to overproduction, customer delays, work batching delays, duplication of work and waiting around for approvals. Using bulky shipping methods can also waste valuable space in the factory or plant which can add additional expenses.
  • Empower Your Employees: The best people to improve your process are the people carrying out the process. You should be utilizing the full skill sets of your employees and determining if anyone could be giving more effort.
  • Track Numbers: Sometimes, what you think will work well, doesn’t. Test your process, collect data, highlight and eliminate errors and seek continuous improvement in value.

You should consider holding regular meetings with employees to follow-up the implementation of specific projects. Managers must seek to integrate lean principles into everyday business, rather than run it as a separate, temporary project on the side of operations.


If you liked this article, here are others you might also find interesting:

Permanent link to this article:

Nov 14 2016

What Augmented Reality Can Bring to the Industry and Why it Will Take Time

With Pokémon Go, augmented reality for gaming may have found its killer app. Gamers must locate, capture and battle Pokémon creatures that appear in their real-life environment.

But how long will it take for augmented reality applications to become standard in manufacturing? And, is Pokémon Go really an augmented reality app?

Virtual Reality vs Augmented Reality

You have probably heard of virtual reality (VR) and augmented reality (AR). But what is the difference?

Virtual reality creates a virtual world that users can interact with. The experience is immersive, thanks to the realistic simulation of vision and hearing. The applications of virtual reality are mainly found in gaming, entertainment and simulation. Virtual reality devices do not allow you to see the external world.

Augmented reality expands the real world with virtual artifacts, showing how you look with these beautiful earrings or highlighting the oil drain plug in your car engine. Augmented reality devices are see-through devices, with the potential to add virtual objects.


VR devices are immersive

AR devices are see-through displays









Key capabilities of AR devices are related to optics (resolution, field of view), environmental awareness (tracking of objects, spatial mapping) and user interactions (voice recognition, gesture input).

Ideally, AR applications layer information on top of real objects in a spatially intelligent way, which requires advanced technologies such as computer vision, depth sensing and object recognition.

For its part, the Pokémon Go app only relies on smart phone geo-location capabilities and camera to display flat Pokémons on top of real-life surroundings, sometimes with unrealistic results. This is why some consider Pokémon Go as a location-based game, and not as a true augmented reality app.

Augmented Reality Applications in Manufacturing

While VR is primarily for the entertainment market, business applications will drive a large part of the AR market, including industrial applications. The following scenarios within manufacturing are ideal and it will take time before they become common practice in the industry.

  • Assembly. The AR application assists the worker during the assembly process. For example, it can highlight existing parts involved, display virtual parts to assemble, show an animation illustrating the operation to perform, display an arrow pointing to the direction of an industrial closet, and highlight the bin containing the required fixtures when it becomes visible.
  • Maintenance and repair. In a similar way, the AR application helps users perform maintenance and repair operations on the shop floor. In the case of field service in remote areas, a remote expert can efficiently support the technician by actually seeing what the operator sees on-site and collaborating with him. In a consumer scenario, customers that need to perform a repair task at home, such as replacing the cooling fan for their laptop, are able to download instructions to their AR device. Looking at the laptop, they are then guided, step by step, on exactly how to disassemble the laptop then put it back together again with the new fan.
  • Training. The AR applications used for assembly or maintenance and repair can be adapted for hands-on training.
  • Quality control. The AR application assists the Quality expert during quality inspection procedures. For example, it allows the user to compare the actual geometry with the as-designed geometry or to check key characteristics at highlighted locations of complex products (e.g. to count the number of screws securing a lid). The user can also designate defective spots found directly on a part, then feed the location back to the Quality Management system or MES.
  • Warehouse management. The AR application assists warehouse pickers by showing arrows directing them to the items they are looking for and highlighting them once they are visible. Relevant inventory levels and KPIs are displayed on the screen.
  • Factory layout. The AR application helps manufacturing space designers assess the feasibility of changes in the factory layout, thanks to a realistic visualization of the current facilities mixed with additional virtual resources, such as new robots.

The devices supporting these applications can be handheld devices, such as tablets or smart phones, head-mounted devices, such as Microsoft’s HoloLens™, DAQRI Smart Helmet™, or projectors[1].

In most of these applications, integration with a PLM parts database as well as with various manufacturing processes (work instructions, quality, warehouse management, people skills management), is highly desirable. Coherence between 3D models (products, assets and factories) and manufacturing operations is also required.

Some important excepted benefits…

The benefits of AR for work instructions (our first scenario) were estimated by a Boeing study in 2014-2015. About 50 participants had to accomplish a series of tasks that were representative of a typical assembly process at Boeing. They were divided in 3 groups:

  • The first group used a touch monitor to read work instructions from a PDF document, as done routinely at Boeing. The monitor was at a fixed location and was not visible from the assembly workstation.
  • The second group read the same PDF document but from a tablet that was readily accessible and visible from the assembly workstation.
  • The third group also used a tablet, but with an experimental AR system showing immersive 3D work instructions in context.

Boeing measured the median number of assembly errors for first-time assembly (training) and for final assembly:

  • The first group made 8 errors at first, then 4 errors.
  • The second group made 1 error in both cases.
  • The third group (with the AR system) made 0.5 error at first and no error afterwards.

These results represent potential huge benefits for Boeing[2]!

Other qualitative benefits can be deduced from the scenarios above:  better and quicker training thanks to hands-on realistic experience, best practices captured from highly skilled users, reduced need for travel to the job site thanks to remote assistance, quicker warehouse picking with less errors, and a faster, more confident path to an optimal factory layout.

…But adoption rate is still slow

Given all the benefits above, one could expect to find a lot of testimonies of AR usage in the industry. But in fact, while many companies experiment with AR, it is difficult to find articles describing advanced AR implementations beyond pilot projects.

Lockheed-Martin has communicated about the use of AR for assembly and repair of the F-35, allowing engineers to work 30% faster, according to the company that built the software. But this still appears to be a trial.

At Volkswagen’s Wolfsburg plant, the deployment of smart glasses for order picking has started with 30 employees. However, the AR usage appears pretty simple, with glasses used as barcode readers and to display storage locations and part numbers directly in the field of vision, allowing workers to work hands-free.

BMW has shared a great video of a mechanic repairing a car engine with an overlay of virtual parts upon real ones, and in-context animations. But the video was released in 2007, and augmented reality is nowhere to be found on the BMW corporate Web site at the time this article was written.

It is worth noting that AR first appeared in Gartner’s technology hype cycle in 2004. More than 10 years later, AR is still in the “trough of disillusionment” section of the 2015 curve and mainstream adoption will not occur for 5 to 10 years, according to Gartner.

This is because AR requires a lot of advanced technologies and some of them are still maturing. Areas of much needed improvement include:

  • Hands-free light-weight devices with higher resolution, larger field-of-view and elegant and accurate optics
  • Lower latency— AR needs to respond quickly (in less than 15ms) as the user moves his eyes, head or body[3].
  • Smarter object recognition and depth-sensing technologies
  • Eye movement detection and other tracking of human input (gesture, voice-based interaction)
  • Emergence of standards for the development of AR applications across devices
  • High-speed connections allowing AR apps to integrate with the manufacturer’s back-end systems (Manufacturing Operations Management, PLM and ERP systems), and other devices.

How elaborate will your AR application be?

Does this mean that industries have to wait the 5 to 10 years for the technology to be mature, as estimated by Gartner?

Well, it depends on the kind of AR app we are talking about. We can identify 4 levels of sophistication for AR apps, as shown in the figure below:

AR maturity levels

AR maturity levels































What is the situation today?

  • Level 4 is still a dream.
  • Level 3 experimentations are becoming possible with devices such as Microsoft HoloLens™.
  • Level 2 pilot projects are still being conducted
  • Level 1 applications already exist in the industry and bring tangible benefits. These examples include facilitating field service in the oil and gas industry or, as we saw with the Volkswagen use case above, optimized order picking

According to Patrick Ryan from Index AR Solutions, the quality control, assembly and training applications are the most mature applications of AR. Assuming industrials target the low-hanging fruit, we can expect in the short term to hear more stories of location-based AR applications in these domains – even though some say this is not AR!

But, for Pokémon Go, who cares?


If you liked this article, here are other materials you might also find interesting:




[1] For example, to project a digital jig to stud welding.

[2] Assembly time measurements also showed a reduction of the build time, though not in the same proportion.

[3] Otherwise, the experience will not feel authentic and simulator sickness will likely occur




Permanent link to this article:

Sep 30 2016

Can the Fourth Industrial Revolution succeed without 100% reliable technical data?

Recently I wrote about the question of the human role in the Fourth Industrial Revolution. In this article, I want to address another common question: How important is data accuracy in a highly digitalized manufacturing enterprise? Can the Fourth Industrial Revolution work if data is not 100% accurate?

Obviously, low data quality is a serious problem in a highly automated environment. But that does not mean that data must be 100% accurate for the system to function. After all, today’s enterprises have nowhere near complete data accuracy, but they still manage to plan, execute, and produce products that generate a profit. Further, we need to be clear that 100% accuracy is unrealistic no matter how precise or complete our data collection may be. Perfection is a nice goal, but it is never truly achievable on a mass scale.

So the real question is, how accurate does data need to be in order to make the Fourth Industrial Revolution effective?

Planning vs actual data

The big difference in the Fourth Industrial Revolution, when compared with revolutions in the past such as those brought about by CIM (Computer Integrated Manufacturing) in the 80s and ERP in the 90s, is the emphasis on distributed intelligence among a network of participants, instead of on a centralized plan.

This points to two different levels of data. Thus, when it comes to data quality, I believe we need to take different approaches toward planning and actual data.

Planning data will include all the master data in the planning and modelling systems which may or may not be fully coherent or correct. This data is used by ERP and supply chain planning tools to help forecast future operations or by simulation tools to model logistics and manufacturing dynamics. Clearly, planning and modelling will never be completely accurate, and in fact, plans need to have a degree of flexibility built into them so that enterprises can react when the unexpected occurs—as it always does! As the saying goes, “The best laid plans of mice and men…”

Actual data, on the other hand, is collected during execution from as-built material BOM, as-built operating sequences, actual lead time, actual cost, intelligent sensors data, as well as unstructured data from social media. If the systems are designed correctly, the quality and accuracy of the actual data should be very high, at least on the local level. This means that the real-time by hour and by minute interactions of operations—such as between suppliers and factories, or between warehouse and production—should become more driven by actual data than planning data, and this is the critical factor in the Fourth Industrial Revolution. We see this happening now in some industries; for example, in the automotive industry, “Pull” manufacturing models are widely used, with production data being fed to the warehouse or suppliers to activate “just-in-time” movement of supplies.

However, in order to make sense of the ocean of actual data for real-time decision-making, one must compare them to an existing plan or model. So what does this all mean?

Continuous data improvement

I believe the conclusion is straightforward. Namely, that there is no need to aim for 100% reliable data from the onset but rather to take a pragmatic approach that is robust enough to handle data quality variation. It will be important to have an enterprise architectural strategy to minimize problems in data quality and integrity that come from too many disparate systems. But with that in place, enterprises should be able to take a gradual approach with clear business objectives that reflect the constraints on data quality, while using actual data collected from Fourth Industrial Revolution devices to improve planning data in the long term.

This feedback loop of continuous improvement on data quality sounds simple, but is rarely implemented by enterprises today. For example, standard cost is typically revised only once a year during the budget cycle. Standard lead times or safety stock levels are rarely revised, perhaps once every year. Although new technologies on Big Data analytics and optimization are available these days to quickly extract insights from actual data in a global level, rarely are such insights applied frequently to update planning data. With the Fourth Industrial Revolution, it will be become more imminent to automate this data quality feedback loop.

The right enterprise data strategy to ensure data integrity combined with a pragmatic approach in implementing continuous improvement should be to enable business to benefit from the Fourth Industrial Revolution without 100% reliable data. After all, the success of the Fourth Industrial Revolution does not depend on robots and automation, but rather on closing the gap between the cyber and physical world.


If you liked this article, here are other materials you might also find interesting:


Permanent link to this article:

Aug 02 2016

The 4th Industrial Revolution and the human touch: Why we need people in the loop

March 12, 2016 was a landmark in human history. Google’s AlphaGo artificial intelligence has beaten Lee Sedol, the world champion in the game of Go. The game is so complex that it was previously thought as impossible for any computer to beat human intuition. The most notable step occurred in move 37 of match 2. The computer was able to “create” a move that no human had thought of in the 2000 year history of the game. Many believe that move signifies the beginning of a new relationship between machine and human.

Other than Go, computers are now taking over many complex decision-making tasks. An example is the driverless car. The date is not far for such to become a commonplace reality on our roads and highways. With this in mind, I was asked recently about the role of humans in manufacturing. Is the driverless factory in our future?

My answer to this question is, “Perhaps someday, but not for a very long time.” Cars are one thing, but as complexity increases, the shortcomings of automation become apparent. Not only is manufacturing a highly complex and dynamic environment, involving multiple entities, materials, and processes, but it’s actually increasing in complexity as a result of the latest Industrial Revolution.

Here are three reasons why I think growing manufacturing complexity will keep humans in the loop for a long time to come.

Closer integration between design, engineering and manufacturing

Mass customization is driving down time-to-market while driving up product variety. To cope with such market demand, design, engineering and manufacturing can no longer be operated in silos. Instead, increasingly complex, multilateral and multi-directional interactions between these functions are required. So, while we might imagine an automated factory producing parts according to a fixed set of specifications, it’s clear that the interaction with other disciplines is beyond the capability of automation.

More complex value chains

Just as manufacturing interactions are becoming more complex, so are the value chains. Innovation used to occur largely behind the four walls of an enterprise. But in our interconnected world of today, this model is giving way to a more open approach to innovation that involves complex relationships of global players across enterprises, industries, and consumers.

What we’re really talking about here is creativity, a uniquely human ability. What’s more, in the realm of manufacturing innovation, we’re talking about collaborative creativity between people in different functions and organizations. It takes a human to understand the needs of human customers, and to develop innovative, practical ideas that can be acted upon by the extended enterprise. In fact, I would argue that instead of decreasing the need for human involvement, advancements in technology will drive a greater need for intelligent, connected, and informed human decision-making. Automation can drive processes and machinery. It can even drive cars. But it can’t drive innovation!

More rapid and continuous improvement cycle

Automation is good at delivering a fixed set of repeatable tasks. It is also good at producing data that can help reveal where improvements are needed. For example, analysis of process data can reveal a shortcoming in manufacturing. Customer feedback data can reveal problems with a design. Quality data can shed light on supplier issues.

But at the end of the day, data is static and unable to enact any improvements. Automation can help point the way to improvement, but it requires the creative and innovative human mind to envision, design, and enact those improvements in a global, extended enterprise.

Humans at the center

For all of the above reasons, I believe humans not only will be an essential part of this industrial Revolution, but will be at the very center of it. Manufacturers should be thinking of how automation will enhance the human role, not replace it.

As the 4th industrial Revolution advances, enterprises will increasingly need technology platforms that facilitate human interactions. This will be the only way to drive the necessary collaboration between science, system engineering, design, simulation, and manufacturing operations in the highly complex manufacturing environments of the future.

As for what happened on March 12. 2016, with move 37, the machine was able to “create” a move that was then beyond human capability. Interestingly, move 37 inspired move 78 in match 4, where Lee won the game with a move that again no human has thought of. The new relationship between human and machine is not one of replacement, but of mutual inspiration.

Technology will bring greater and greater automation, but far from replacing humans, the factory of the future will depend on the close collaboration between man and machine with people who are not just in the loop, but in the driver’s seat!


If you liked this article, here are other materials you might also find interesting:


Permanent link to this article:

Jul 26 2016

The secret to MES success: Learn from experience

Why do some MES initiatives succeed more than others? What can we learn from the most successful companies? Just as importantly, what can we learn from those companies that have not achieved all their goals?

These are important questions for manufacturers who are thinking of investing in IT systems that (they hope) will improve profitability and supply chain collaboration, and propel them closer to the Digital Factory of the future.

To find answers to these questions, Gartner, a global technology firm, partnered with the Manufacturing Enterprise Solutions Association (MESA) to survey more than 100 MES-user companies. It’s critical to note that all the companies in the survey have actually deployed manufacturing execution systems, thus providing real-world insights into benefits and challenges. The results of the “Business Value of MES Survey” are important for manufacturers who are making—or plan to make—substantial MES investments.

Good news, bad news

The good news is that most manufacturers are achieving significant benefits and ROI from their MES investments, especially in the short term. For example, the study found that 48% of MES projects resulted with improved quality within 3 months, and 80% in a year or less.

Of course short-term payback is good, but not if it comes at the expense of long-term success. And that’s the bad news: most manufacturers are failing to achieve all the benefits they expected. Specifically, slightly more than two-thirds said they achieved less than 75% of the intended business results, and 31% achieved less than half. As a result, these companies are having trouble planning the next step and getting the funding needed to continue transforming their manufacturing operations.

What exactly is going on?

Obstacles to success

Manufacturers do recognize the problem, at least to some extent. When asked what obstacles were in their way, they cited failure to fully understand the cost, business case and/or ROI of their MES investments.

This may seem surprising. How can an enterprise make this kind of investment without fully understanding it? There are at least two reasons.

One is the low-hanging fruit mentioned above. Manufacturers can see rapid ROI in the short-term from capabilities such as improved visibility and quality, and that is enough to justify the first wave of MES technology. After that, manufacturers often find they don’t have a business case for going further, and this can lead to problems down the road.

As the survey analysis notes: “While there are some ‘quick hit’ benefits achieved in implementing MES, lasting improvements (reduced labor cost, and improved inventory and cash flow) still take time and require a long-term focus on people and process as well as technology.”

A second reason enterprises have struggled is that global manufacturing is enormously complex and touches every other system in the organization, and many outside it as well. Enterprise MES intersects literally every activity, from process, materials, and equipment, to planning, people, and partners. Developing an IT strategy for managing this involves much more than inserting technology into factories. It requires a long-term strategy for change and transformation.

Applying lessons learned

Perhaps the most critical lesson is that manufacturers cannot afford to underestimate the scope of the project. “Value buckets” are typically used to scope the most important and impactful areas for the company to focus on when implementing a MES system. Most manufacturers look at less than 20 buckets focusing on the near term success. In contrast, Dassault Systèmes DELMIA recommends assessing over 75 high value areas when deploying an MES system to ensure both short and long term goal achievement.

A second lesson is that successful MES implementations are not just about technology, they are also about people, processes, and organizational transformation. Understanding this difference, and how to address it, is a key to success in enterprise MES.

Gartner concludes that manufacturers need to “shift the focus from IT projects prioritized on short-term ROI to a formal application strategy for manufacturing operations.” This strategy needs to include centralized management of MES technology and a broad view of the role of MES beyond the four walls in order to meet the enterprise goal of greater supply chain collaboration.


In the rush to modernize, many manufacturers are missing the opportunity to make significant, long-lasting impact on their organization. Instead of going it alone, or viewing MES as an application that can be plugged into their operations, manufacturers would be well served to find a partner that has a proven process and “path through the MES forest” that delivers a comprehensive ROI plan capable of delivering the desired impact that comes from a well-planned MES strategy.

With a broad business case, realistically planned and based on measurable ROI expectations, manufacturers can achieve more in less time with their MES deployments. And critically, they can lay the foundation for continued long-term business improvement, supply chain collaboration, profitability, and competitive advantage.

In a future blog, I’ll take a closer look at the Gartner/MESA survey and what we can learn from it.


If you liked this article, here are other materials you might also find interesting:

Permanent link to this article:

Older posts «