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:

Jul 14 2016

Megan Nichols

How Technology Can Optimize Warehouse Efficiency

Warehouses, much like any other competitive industry, are beginning to utilize emerging technologies in new and cost-effective ways. These technological breakthroughs make warehouses more efficient, safer and generally more sophisticated than the warehouses of the past.

The types of technology are varied and serve different purposes, so here are examples of some of the best technological developments being used in warehouses today.

Software Solutions

Not many types of businesses can expect to compete if they’re not utilizing software to help streamline their work. Warehouses are big beneficiaries of software, as the plethora of warehouse management systems makes life easy. The software helps track inventory, acts on orders and aids in just about anything else that needs to be monitored. The days of pen and paper are mercifully over. Warehouses can now use hand scanners, barcodes and other tools in order to improve efficiency.

Prices vary, and there are a number of different software solutions out there to choose from. If you’re not totally happy with the software you’re currently using, then and see if some better options are out there. Using the right software can have a big effect on the business. Just make sure you’ve tested the software before switching over to an entirely new system.

Smartly Tracking Equipment’s Condition

Monitoring equipment, whether it’s forklifts, conveyor belts or cranes, is a critical component for any warehouse. However, it’s also a time-consuming and labor-intensive process. Whether equipment is brand-new or purchased second-hand, many warehouses have begun digitally tracking the condition of their equipment.

Some businesses use QR codes that can be scanned by smartphones so that technicians can instantly see the equipment’s history and report back on any repairs conducted. This saves time and also ensures that potential problems don’t fall through the cracks or get lost in the paperwork shuffle. A system like that is especially important for ensuring the safety of the people using the equipment.

Automation in the Warehouses

The warehouses used by online retailer are so advanced that they’ve attracted mainstream attention from people who have likely never set a foot in a warehouse. What makes Amazon’s warehouses so interesting is their advanced use of automation to streamline tasks (although Amazon definitely isn’t alone in utilizing this form of technology).

By using robots, Amazon is able to fulfill millions of orders with relatively low overhead. Their warehouses are still operated by human workers, but you can expect Amazon to transition more towards automation in the near future. It saves time and money, which are two resources businesses need most. Other companies are expected to invest more in automation than in previous years. The technology keeps getting better and cheaper, making it more attractive to businesses.

Internet of Things Technology

The Internet of Things (IoT) has changed everything from how people live in their homes to how cars are connected to the internet. It’s still a fairly new technology with plenty of potential (and problems to iron out), yet it’s especially promising for improving warehouse efficiency.

Here’s an example of how it works:

Itamco, a precision parts manufacturer, utilized IoT by hooking up its forklifts with a machine monitoring systems. Drivers would spend ages looking for forklifts scattered around the massive facility, while fully loaded forklifts would sometimes sit around for hours. Now, drivers scan a barcode on the product they’re loading. The company’s communications system ensures that they always know where the forklifts are, what’s being loaded and when a delivery has been completed.

IoT can also have a big impact on the building that houses the warehouse operation. Heating, ventilation and lighting can all be automatically handled by the IoT in order to ensure efficiency and keep utility bills down. For many businesses, the investment in IoT can quickly pay off if it’s used effectively.

Geo-Fencing for Efficiency

With geo-fencing technology, messages can be sent to users in a pre-defined geographical area. Marketers have experimented with geo-fencing technology for a while to target potential customers, but it’s even more useful in warehouses. Geo-fencing ensures that you can keep track of inventory by setting up digital boundaries within your warehouse.

Geo-fencing technology can be used to create automatic alerts whenever a product has entered or left an area. It’s extremely useful for inventory purposes and to track other processes. When this technology is up and running, you won’t have to worry about forgetting a task or losing an item.

Finding What Works

Technology is never totally foolproof, and it’s not uncommon to encounter some technical glitches when implementing new systems. Despite those headaches, though, the effort is worthwhile for warehouses hoping to improve their efficiency.

Some of these technologies work better than others depending on how your warehouse is structured. To find out what technology is worth investing in, take a close look at your operations and how processes could be improved. Once you do that, you’ll know what sort of technology is the best fit for your company.


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


Permanent link to this article:

Jul 12 2016

7 Challenges to a Wider Adoption of Additive Manufacturing in the Industry – Part 2

Previously, we discussed three of the biggest challenges manufacturers face in the adoption of Additive Manufacturing for functional parts: getting the right shape, meeting requirements for part qualification and determining the appropriate machine, raw material and process parameters. Let us continue with the remaining four identified challenges.

4.     Is it really worth it?

At some point, the decision to produce additively manufactured parts will likely boil down to return on investment (ROI).  AM can bring significant economic advantages when:

  • The cost of raw materials is high (e.g. titanium). Unused powder can be recycled (up to a point), while classical subtractive manufacturing can machine away up to 90% of the material to produce the final component.
  • Lighter parts bring high value. AM makes it easy to produce light parts whose layout has been optimized using topology optimization. Weight considerations are of course especially important in the Aerospace & Defense industry.
  • Parts require complex assemblies. A single additively manufactured part can sometimes replace complex multi-part assemblies imposed by traditional manufacturing techniques.
  • Custom parts or low volumes are needed. Building custom parts with AM can be done at no extra cost, while traditional manufacturing requires designing molds or numerical control programs.

However, AM is a costly process by itself:

  • Machines are still expensive. According to the Senvol database, prices range from $100,000 to more than $1,000,000 for machines that can print metallic parts.
  • Raw materials are also expensive, from $110 to $200 per kilogram for steel and aluminum powder and around $600 per kilogram for titanium according to this source.
  • Post-processing operations can be expensive and are often time-consuming. The printed part must be separated from the build platform[1] and the support structures must be removed. Extra steps must be taken to meet quality requirements: For example, heat treat to remove internal stress of metallic parts, abrasive finishing (polishing, sandpapering, machining operations) and application of coating.
  • Slow fabrication speed limits the ROI one can expect from the usage of an AM machine in large series. It can take hours or even days to print a large part, so traditional techniques can continue to outperform on speed and efficiency. Nesting parts in the build chamber allows printing of many parts in a single build, thereby reducing overall production times. However, this approach raises concerns about traceability and certification in regulated industries[2].

Cost analysis and modeling of AM processes is a hard subject. A Roland Berger study took up the challenge and estimated the cost of additive manufacturing to €3.14 per cm3, a pretty high value. However, Roland Berger expects a large increase of build rates[3] in the future and a decline in powder prices.

5.     Are operators being put at risk?

The AM process presents health hazards and manufacturers need to make sure operators are safe from these risks.

Powders are the main concern. They can cause irritation to eyes and skin and should not be inhaled. Metal powders are flammable and potentially explosive, especially aluminum powders. Specific procedures should be enforced when retrieving material from stock, loading powder for a build and removing extra powder after a build.

Operators at Risk

Wet separator being used to prevent the formation of a metal powder cloud during
a build cleanup. Note the use of a respirator to avoid inhaling metal powder. Source: NIST.

Other risks are related for example to the high temperature of the build chamber or the important weight of build trays, which must therefore be handled with care.

This article lists 15 Standard Operating Procedures that have been applied at NIST Metal AM Laboratory to prevent these risks. As with any health hazard, manufacturers should make sure that safety procedures are enforced via proper equipment, training, signs on the shop floor and software control.

6.     Tracking Parts to Ensure Regulatory Compliance

Additive Manufacturing presents new opportunities and new challenges in terms of traceability.

On the opportunities side, serial numbers can be directly printed on the part, at no additional cost. This is much simpler, quicker and reliable than for example printing and applying a barcode sticker.

By Bcn0209 at English Wikipedia [Public domain], via Wikimedia Commons

A miniature 3D printed turbine. Characters (and
therefore serial numbers) can easily be integrated in the part.

On the challenges side, remote fabrication for maintenance becomes an important application of AM. For example, on warships or on offshore platforms, instead of waiting days for spare parts to be shipped, they can be printed on site. More generally, goods can be printed locally via a distributed network of AM printers, thereby reducing lead times and transport costs.

However, all these parts still need to be tracked, linking serial numbers with machines, raw materials used, locations and operators. A global traceability solution, enclosing multiple AM and supply chain locations will be needed more than ever.

7.     Part size and material limitation

The size of the build chamber inevitably limits the size of producible parts. A workaround is to cut the part into smaller blocks so that they fit the build space. Blocks will then have to be assembled by mechanical joining or welding.

US government, academia, and industry have put resources into overcoming this hurdle. The Big Area Additive Manufacturing (BAAM) machine, built by Cincinati Inc. in collaboration with the Department of Energy’s Oak Ridge National Laboratory (ORNL) and Lockheed Martin was demonstrated to print a roadster.

An increasing variety of materials are now available. For metals alone, aluminum, bronze, copper, nickel, iron, steel, titanium or even silver or gold materials can be used.

However, the range of alloys is still too limited to meet all industry needs. Non weldable metals or difficult-to-weld alloys are generally not suited to AM.

Additive Manufacturing has the potential to revolutionize design, manufacturing processes and supply chain. However, its expansion in the industry is currently hindered by a number of challenges. A tight collaboration between academies, manufacturers, machine vendors, material providers and industrial software solution vendors probably represents the best opportunity to meet these challenges.


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


[1] For example with a wire-EDM system or a band saw

[2] For example, consider several parts printed with a Selective Laser Sintering machine in a single batch. Because the parts are at different locations of the build tray, the laser will not have exactly the same impact. Parts will not be identical.

[3] HP claims that its future Multi Jet Fusion 3D printer will reduce the build time down to a factor of 10 (comparison made with Selective Laser Sintering and Fused Deposition Modeling printer solutions from $100,000 to $300,000, as of April 2016).

Permanent link to this article:

Older posts «

» Newer posts