Nov 24 2015

Liz Jammal

Leverage OEE for Continuous Improvement

Leverage OEEOverall Equipment Effectiveness (OEE) is probably one of the most widely recognized measures of manufacturing operations effectiveness. It is one of those metrics that is a “must calculate,” and can be a powerful method of evaluating the productivity of production systems to improve manufacturing operations. The OEE formula has become a significant KPI across many industries, influencing equipment and production improvements.

Unfortunately, often this measure is under-utilized. It isn’t enough to simply collect it, publish in reports and move on to the next reporting task. Taking a closer look at how OEE is measured, how it can be improved, and how it can become a central part of your continuous process improvement program, however, can be a way to unlock greater value.

Achieving bench-mark OEE is challenging, and requires the need to consistently follow a meticulous program. Although many production teams are already familiar with OEE and how to calculate it, they may be missing the mark on how to capitalise on the data collected.

The infographic at right visualizes the following points, which includes three steps to better take advantage of the power of OEE to improve your manufacturing operational excellence, and is summarised below:

1.   Automate OEE Data Collection

While many manufacturing processes are now automated, often the information that we could extract from machines is not. Ironically, we run around trying to capture information that is available from within our machines through other channels, such as relying on team members using manual methods. This is an ineffective use of time and often the statistics gathered doesn’t capture enough detail, therefore not representing the full situation.

Consider a team member that’s been asked to note down stoppages on a single line during a shift. The operator may not consider a very short stop necessary to note down because there was no need to call maintenance. When you analyse these numbers later to better understand downtime, you will not have the full details in order to address the causes of each stop.

Alternatively, OEE or manufacturing execution systems software relies directly on the machine to extract the data, and can plug straight into the PLC, HMI or OPC layer. This method is significantly more effective. The data we gather from our production environment is far more valuable when it’s accurate and collected in real-time.

2.   Apply OEE Metrics to Business Process Improvement

The value of OEE must be recognised from top management down to the plant floor to be successful in driving process improvement. Shared goals and objectives help to shape the direction and a common focus is required to improve business processes.

Assigning OEE champions with the power to make changes and solve problems as they occur is an essential part of enhancing the team effort. The assigning of responsibility and performing regular production reviews simultaneously empowers individuals and advances the plants’ performance.

This is where OEE begins to create value and inspire change – by identifying issues, engaging operations personnel and measuring against agreed goals and objectives.

Including OEE data in the planning and budgeting process additionally helps to build a business case around where money should be spent.

3.   Display the Data Visually

The quickest way to success is visually displaying real-time progress on the plant floor. Andon boards are a great example. This keeps the team focused and motivated. Big screen monitors, production progress bars and dashboards that support Lean can be enhanced when OEE data is clearly visible, enabling the required changes to be made quickly. Considering many people respond to visual stimuli, green, yellow and red status indicators bring an electric excitement to the shop floor.

Manufacturers that identify the most critical metrics, and deliver the right information to the right people at the right time will be empowered to make better decisions and become more competitive. Manufacturing operations management software can strengthen the effectiveness of automating and leveraging the benefits of measuring and analysing OEE at a strategic level.


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

Permanent link to this article:

Nov 19 2015

Do Manufacturers Need a New IIoT Platform?

Blog_11-19-15Virtually everyone agrees that the Industrial Internet of Things, or IIoT, is poised to have a huge impact on manufacturing. It will transform everything: how you design, manufacture, distribute and support products from cradle to grave.

The challenge is if you run a global manufacturing enterprise, where do you start? The technology and choices can be overwhelming. Should you develop enterprise manufacturing intelligence capabilities first? How will you integrate disparate legacy systems? What about closing the loop between design and production? What is the best way to manage an IIoT – is another platform required for the shop floor?

To help provide guidance and a path forward, the first thing you should be thinking about is what sort of “ecosystem” do you want? With that vision, it will be easier to plot a course to achieve. At a high level, I would propose the Industrial Internet of Things is all about collecting data, automating responses, converting that data into intelligence and then using the knowledge that ensues to streamline decision support, support continuous process improvement to enable sustained operational excellence.

If that sounds like a good vision to you, then here are four steps to accomplish it.


Four Steps to be Ready for the IIoT

  • Boost Data Acquisition. The most basic requirement for utilizing IIoT is to collect, process, and distribute data on manufacturing operations across the enterprise. This information can yield intelligence, help guide process improvement and support other enterprise operations, such as design and engineering, enterprise resource planning, and supply chain management. Data collection begins with making sure every part of your production process has a sufficient capacity to track every relevant performance metric. Then this data must be transferred to a central repository so it is visible for decision support and continuous improvement. Data trapped in silos won’t help – it must be readily available. Global Manufacturers are well-experienced with this type of data collection – the need has existed for over a decade.
  • Design-Production Integration. Most of today’s products are designed in the virtual world, digitally prototyped and then actually built. As production continues to be digitized, manufacturers want to take advantage of linking design with production. The Digital Twin concept is a popular example. A digital “as-built” can be a great way to quickly tell if products were built to plan, but is only possible if enough data is collected, such as with the IIoT . Once again, this need is not for those manufacturers with established global enterprise manufacturing systems. The issue and need for interoperability across enterprise business systems (ERP, PLM and MES) began many, many years ago.
  • Real-time Analytics. In the age of IIoT, applications will need to analyze and interact with manufacturing data constantly, in real time. This data then needs to be collected and aggregated in such a way it is readily accessible and visible to the rest of the organization – which means it will need to be stored somewhere. This type of capability can only occur when data is collected “clean,” so it can then be processed immediately. If data must always be normalized, then it won’t help improve decision making, automate responses, trigger alerts or support the voracious appetite process engineers (and others) now have for Descriptive, Diagnostic, Predictive and Prescriptive analytics. The driving force here is how to better harness the intelligence that can be gained from Predictive analytics. If equipment can predict when a future point-of-failure is most likely to occur, this information is very valuable to help plan accordingly to maximize uptime. Those manufacturers that gain access to accurate, predictive intelligence can really capitalize on this knowledge.
  • Global Traceability. Tracking a product’s genealogy is really all about consistent data collection, and making sure that data is readily available whenever needed. Today, that requirement is now extended to across the enterprise and out to supply chain partners and others. This is a role the IIoT can fulfil, by providing data from the production process for not only your operations, but potentially those of your partners, customers and suppliers. Unleashing the power of traceability as an enterprise function could let companies quickly identify and contain problems as they occur to limit financial damage and preserve brand equity. It can also help drive continuous improvement on a global scale.


Interestingly, many manufacturers have been pursuing an IIoT strategy already without actually realizing it. It might be the perfect way to help future-proof your manufacturing operations and provide a path to manage today’s manufacturing transformation – without calling it explicitly “IIoT.” What this means is that if your company has, or is now deploying an enterprise Manufacturing Execution System (MES), you are already well on your way to leveraging the IIoT. You have key processes established, trained knowledge workers and years of experience in pursuing operational excellence – the perfect framework to address new opportunities of an IIoT world.

With a MES in place, you have already begun your IIoT journey. And, if you already have some sort of Manufacturing or Business Intelligence systems in place, you can simply expand and deepen what you already have into a global solution for Manufacturing Operations Management (MOM).

On the other hand, if you’re one of the shrinking numbers of manufacturers that hasn’t yet taken the plunge, perhaps now is a good time to start? Put together a plan to manage global manufacturing operations from a single platform. Then add intelligence gathering and analysis capabilities. I see a convergence coming – a seamless integration between the MOM platform (ideally, BPM-based at the core) and an emerging analytics platform (with a modeling and inference engine at the core). This will become the platform of the 4th industrial age for manufacturers. The potential to generate significant business value and ROI will be huge, and you’ll have an IIoT platform for whatever comes next.

I welcome your feedback. What do you think?


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

Permanent link to this article:

Nov 17 2015

It’s a Wrap

Whether you like them or not, eggs, cheese, mushrooms or shrimp are likely to be part of your future shopping basket—as the raw materials in a new kind of plastic packaging.

New materials promise not only to reduce our reliance on petroleum products such as plastic, they also cut waste. Packaging accounted for more than 75 million tons (or 30%) of solid waste in the U.S. in 2013, while the European Union generates around 79 million tons of packaging waste annually.

However, waste from the agriculture industry is now being turned into biodegradable packaging materials. For example, Kirsi S. Mikkonen, a researcher at the University of Helsinki, is developing packaging films made from hemicelluloses, byproducts of the forestry industry and agriculture.

Cellulose, the part used by industry, makes up only 40% to 50% of wood, while hemicellulose and lignin each account for about 30%. Hemicelluloses can be retrieved from wood chips or, in thermo-mechanical mills, from wastewater.

Dr. Mikkonen converts the hemicelluloses into films that act as an effective barrier against oxygen. Edible films could protect food from drying out or spoiling, or even within food, to separate pizza crust from sauce. By coating paperboard with the films, she can make plastic-type containers.

Hemicelluloses and lignin can also be used in aerogels, which are porous and light but strong.

“When you put an aerogel in water, it acts like a sponge,” Dr. Mikkonen says. “It absorbs water and you can press it out, and it recovers its shape. We could make something like a soft pillow that could absorb moisture or drips from meat, or it could release active compounds and be used as active packaging.”

Blog_11-24-15_2Innovations in active packaging abound. The Fraunhofer Research Institution for Modular Solid State Technologies in Munich has developed a sensor film that detects molecules called amines that are released when meat or fish starts to spoil. As amines build up, the sensors turn from yellow to blue, indicating the level of spoilage. Many companies now sell labels and films that keep fruits and vegetables fresh by absorbing ethlyene.

Egg whites could provide another form of active packaging. Alexander Jones, a researcher at the University of Georgia in Athens, Georgia, mixed the egg-white protein albumin with glycerol to create a plastic with antibacterial properties.

Albumin plastic could be used for food packaging, to decrease spoilage. It could also be mixed with conventional plastic to add antibacterial properties to medical products, says Suraj Sharma, associate professor at the University of Georgia’s College of Family and Consumer Sciences.

Another reason to mix in conventional plastic is that albumin plastic is too brittle to be used alone for, say, a catheter tube, which needs flexibility, Dr. Jones says.

He also tested plastics made from soy and whey proteins. Soy proteins had no antibacterial properties—“it actually fed bacteria,” he says. Whey proteins mixed with glycerol made antibacterial plastic, but whey plastic minus glycerol acted like soy-based plastic, promoting bacteria growth.

The protein-based plastics have other advantages. They compost quickly, and the manufacturing process uses lower temperatures than for petroleum-based plastics, thereby saving energy. Whey, a byproduct of cheese processing, requires treatment before disposal, so diverting it into plastics would be a boon.

For now, egg whites are far more expensive than polyethelyne. But Dr. Jones believes that we might tap waste streams to get cheaper raw materials.

“Egg producers have eggs they don’t ship for various reasons,” Dr. Jones says. Using those “would reduce waste and also not compete with food as an end use.”


Shrimp shells are another waste source that can be turned into plastic. Harvard University researchers have turned chitin, a polysaccharide found in crustacean shells, into a strong, transparent material called shrilk, which can be used to make plastic bags, packaging and even diapers.

Meanwhile, Ecovative, a packaging company in Green Island, N.Y., uses mushrooms as the key ingredient in its compostable packaging. The root structure of a mushroom, called mycelium, acts like a glue. A mix of mycelium and agricultural byproducts is molded into different shapes, replacing styrofoam for example.

“Packaging today is essential for society to function,” Dr. Mikkonen says. “We need packaging to deliver food from the maker to the retailer and then to the consumer. But it produces lots of waste. It’s really important to develop some biodegradable alternatives.”


This article was originally posted on 3D PERSPECTIVES,


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

Permanent link to this article:

Nov 13 2015

Q&A Summary from Dr. Michael Grieves’ Webcast on the Digital Twin

blog_11-13-15Dr. Michael Grieves published a white paper in 2014 entitled: “Digital Twin: Manufacturing Excellence through Virtual Factory Replication.” This paper has generated considerable interest on how a digital mockup can be created based on what was actually produced, which can then be digitally compared to what was actually designed. In a perfect world, these two digital designs are “twins.” But, we don’t live in a perfect world. Discrepancies can be readily identified to perform root cause analysis to understand why, and then fix.

Dassault Systèmes had the opportunity to host a webcast featuring Dr. Grieves, which took a closer look at some of the concepts introduced in the white paper.

For those that might not have time to watch the entire webcast, we have summarized the questions and answers that immediately followed, and included below.

Do you see a future where the genealogy of products is digitally captured whereas “as-designed,” “as-built” and “as-maintained” data is all available for simulations, digital mockups and designs?

I am always reluctant to predict the future, and how quickly things change because it seems like technology still moves even faster than I had suggested. But, yes, I do think that the idea to design, manufacture and support products virtually is possible. Then, only when we get it all right do we actually physically move around some atoms. We want to trade off bits for atoms wherever we can. I think that at some point in time you will be able to inspect the virtual product, and you’ll be able to obtain all the characteristics that you would be able to obtain from having possession of the physical product. But I’m not going to predict the time frame.

Do commercial tools now exist that support the concept of a Digital Twin?

It all depends on what part of the Digital Twin concept you are referring to. Development models are now supported in CAD, which is a Digital Twin of what will be built. So, the realization of a physical product from a virtual product certainly exists. We really haven’t done as much as we need to do in terms of taking a manufactured product and creating a virtual or Digital Twin of that, although we do collect some information. I ought to caution that we may not need all the information about the physical product in the virtual product. It is really going to depend on what each use case is. For example, I’ll use a trivial one: if we don’t care what color the product is, we shouldn’t have it reflected in the virtual twin model. There is a cost to creating digital data. You have to figure out what has sufficient value to then make it available in the Digital Twin. With regards to the Digital Twin existing now, as a replication of an as-built product in a factory, I haven’t seen a representation, but I think people are working on making that happen. We have parts and pieces, and very nice factory simulations based on information collected from the factory floor. However, it is going to take a unified data repository to fully make a factory replication a reality.

How can a Digital Twin help with continuous improvement on a global scale?

What happens a lot of times with continual improvements today, Kaizen as an example, is you really have to move actual physical things around the factory floor in order to understand the improvement. If you could simulate those improvements virtually, and then send them off (after validating these improvements), you could ship to all factories and track them, making sure they were implemented in the proper fashion. Too often you see the same problems solved over and over again as each – both across locations and different generations of new products being rolled out across production facilities. The reason is that designers really have very little (if any) connection to the factory floor, so they don’t understand what problems have occurred or why a manufacturing process should be changed. Then, a new version of the product comes along with the same design bill of process from the manufacturing engineers, and the same problem gets solved over and over again. Sharing a Digital Twin from the factory floor with manufacturing engineering might help avoid this repeated and wasteful loop.

What role can connective technology (cloud or IOT) play with the Digital Twin?

One of the elements of the Internet of Things (IoT) is basically that physical products have intelligence in order to do two things:  sense and communicate. They need to sense what is happening, and then they need to be able to communicate that. In manufacturing we have the Industrial IoT (IIoT), which includes machines talking to each other. Let’s say I have machines that are changing their program or parameters in order to compensate for other things that are happening in the factory. You really need to understand what they’re changing. So having intelligence in machines is a good thing, but unless you have a Digital Twin that can effectively communicate with humans, to know exactly what is happening, I would contend that those kinds of systems can go out of control quickly and cascade failure.

What benefits does the Digital Twin bring to simulation?

If you are going to do product simulation, you have to get the physics right. For example, let’s assume you are building a building and need a beam that runs a little further. When you add length onto that beam, the simulation must reflect the additional weight at the same time, so when you wind up with the real building, it can hold the weight that it’s supposed to hold. That is a challenge we face in simulation: being able to simulate/replicate what occurs in the real world in the virtual world. That is where the simulation people are earning the real money. The Digital Twin is basically built into the concept of modeling and simulation.


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

Permanent link to this article:

Nov 10 2015

How to Make your Prototype a Winning Product

Blog_11-10-15With competition in the globalized marketplace intensifying more and more every day, Consumer Packaged Goods and Retail companies have been hit with increased demand for product innovation to enhance brand equity and differentiation and drive consumer loyalty.

But developing the next revolutionary product line isn’t as simple as televisions shows like Shark Tank make it seem. Behind these rare instances of success trail a preponderance of prototype failures. The rate of success is in fact so marginal that most products never even make it past the drawing board.  Did you know: More than 95% of prototype formulations end up failing?  This probably makes you wonder what those 5% of winning products did that the others didn’t. Let’s take a look at the key challenges responsible for keeping most products from achieving success – and discover how the winning 5% are able to power through them.

1) Avoiding Regulatory Setbacks while Shortening ‘Time-to-Market’

One of the key root causes of a company’s failure to deliver a product to market is its inability to sustain regulatory compliance. More often than not, brand and contract manufacturers manage compliancy using incongruent tools and isolated processes that make it nearly impossible to monitor developmental progress let alone adhere to evolving parameters. When it comes to regulation, successful product engineers understand the importance of being prepared for government inspection.

Failing to meet operating standards could mean delays in product release times or (in drastic cases) being forced to recall an entire product line. In an industry as fierce as CPG, manufacturers can’t afford to put up with costly launch delays. To avoid speed bumps along the innovation race, manufacturers must make every effort to prevent falling victim to non-compliance, or risk losing their pace.

Successful R&D organizations are equipped with tools like regulatory documentation management systems that perform continuous evaluation on material compliance and alert manufacturing teams to potential regulatory infringements. These systems ensure the quick adaptation of global formulas to protect against the negative ramifications of non-compliance. Proactive brand manufacturers using intelligent regulatory systems, can spend less time managing compliance data…and more time on product innovation.

2) Making Inaccessible Information, Accessible

Aside from regulatory failures, delayed product test results ultimately prevent many innovators from finding the right ingredients for their new product recipe. The changing landscape of regulation makes the accessibility to direct information no easy task for product engineers managing multiple development projects. An organization’s agility and capacity for collaboration are the deciding factors in determining who wins the race for new product delivery.

Creating the Perfect Product is a team effort – between marketing, R&D, legal, regulatory, purchasing and manufacturing teams. Effectively managing a collaborative joint effort across multiple silos and channels is where almost all product innovation organizations come up short and become disoriented. Smart innovators understand that the access to product information across all channels of the manufacturing organization is imperative for maximizing operational efficiency throughout the product development process.

To address this need, Consumer Packaged Goods and Retail companies rely on platforms with collaborative project management capabilities to increase productivity for new formulation creation by linking ongoing project and program execution with continuously-updated information in real-time. Maintaining relevancy and accuracy across all fields eliminates time-consuming protocols and formulation redundancy, provides stronger management over outcomes and leverages findings from research and testing.

Making strategic resource planning easier and more flexible empowers CPG brand and contract manufacturers to learn and implement practices most conducive to innovation. Equipped with the capability to recycle discoveries from past projects, this enhanced visibility of asset information allows product engineers to optimize time and resources as they continuously formulate and track their progress.

3) Expanding Formula Design Efforts without Increasing Resources

In an industry dealing with intricate recipes and powerful ingredients, R&D organizations in the CPG & Retail industry have one main focus when it comes to formula design – delivering the product promise which maximizes consumer satisfaction and drives usage and loyalty. A successful consumer product experience stems directly from a product’s performance, and hence, the product formula.

The delivery of an effective formula design calls for continuous innovation of product composition. Formulators need greater visibility from a material perspective – a robust management solution that manages the components required for the product formula while leveraging the most efficient use of resources at hand.

With the means to perform cost optimization on potential product compositions, CPG formulators can secure the knowledge to make optimal material choices during the early phases of the innovation process to satisfy the marketing requirements of ever-changing consumer trends.

Click here to view full blog post on 3D Perspectives


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

Permanent link to this article:

Older posts «