Monthly Archives: October 2019

Implementing smart manufacturing – a competitive advantage

Implementing smart manufacturing – a competitive advantage

Category : Blogs

Implementing smart manufacturing – a competitive advantage

The innovations across the landscape of industrial machinery in manufacturing see a phenomenal overhaul of technological progressions. It is a formidable task to validate and manage modern manufacturing and assembly operations to achieve a first-class level of quality while optimizing cost.

Our first podcast in this series discussed how machinery manufacturers are gaining a competitive advantage by implementing smart manufacturing. We discussed how machine manufacturers are maximizing profits, and the industry trends driving the acceptance of this groundbreaking technology.

Our resident expert and guest is Bill Davis, who is the Director of Industrial Machinery and Heavy Equipment Solutions at Siemens, with over 30 years in the industry and 20 years as an engineer.

Below, is a transcript excerpt from our first audio podcast and blog.

Blake: Bill, before we dive into the topic, I thought it would be interesting to hear about your career, your background, and education.

Bill: I appreciate the opportunity to speak on this topic. My background and experience from industrial machinery and heavy equipment span 30 years. My career began as an engineer, working on highspeed machinery and equipment and progressed through the ranks in management, extending into operations. I have always been a driver of CAD, PLM, PDM, digital enterprise and the digital thread, and I’ve always been part of the driving force behind implementing these technologies at companies.  So, I’ve gained many skills with regard to CAD and PDM, as well as operations management. Thus, the topic of smart manufacturing is near and dear to my heart.

My education background includes a mechanical engineering degree, a bachelor’s in science degree from Milwaukee School of Engineering, with a master’s in Business Administration from Marquette University, specializing in operations, management, and strategic marketing.

Blake: That’s an impressive background. Within those 30 years of your career, how long have you been focused on smart manufacturing?

Bill: For about 20 of those 30 years, I’ve been an engineer. I’ve been either the liaison between engineering and operations or indirect operations management. So, throughout this timeline, I’ve seen a move towards digitalization, and leveraging that into smart manufacturing.

Blake: Right. So, in broaching the topic of smart manufacturing, let’s start at the 40,000-foot level and define it.

Bill: Okay, let’s begin by talking about a product that we all know and love, which is our cell phones. Think about what it takes to manufacture that device, and you begin to see how smart manufacturing plays a role from a product perspective. The cell phone has several different components, and each one of them has engineering requirements and content that needs to be manufactured by someone. Also, often there are regulations, rules, and requirements that the manufacturer or industry governs. For example, the FCC legislates the cell phone.

Smart manufacturing says, there’s a digital twin of this device, but that’s only a small portion of it. We want to extract that information and drive it into manufacturing operations, like CNC programming or inspection and drive assembly processes. So, we push that information back into the digital twin so that we have an executable digital twin of the cell phone at a product level.

The machinery we use to supply those cell phone manufacturers, industries and customers is asking us to provide additional content, including sensors and feedback about how the production and manufacturing process executes, and we leverage that feedback into the digital twin. So, an example would be if the housing for your cell phone is being machined on the CNC machine, the file needs to be generated from the CAD file and saved as a manufactured CAM file. However, at completion, we want to know that the housing is correct.

Therefore, an inspection process is going on and if we’re making the machine tool, we want to drive that back into the inspection data that we receive from our machine tool, and back into the digital twin of that cell phone. Thus, we’ve got traceability from the design to manufacturing, and a closed-loop process that provides a whole digital twin. We will be focusing more on industrial machinery in this process, and it’s essential to look at how the digital twin of the product and the digital twin of the machine are linked together.

Blake:  In focusing more on what Siemens Digital Industries is doing to position itself in the market, how are they implementing smart manufacturing into companies and manufacturers?

Bill: Firstly, Siemens is one of the largest corporations in the world, and we’re in a unique position on the software side, due to our association with Siemens AG. As one of the largest manufacturing companies on the planet, we’re always looking for opportunities to optimize manufacturing processes and earn more profit from every process that executes. So, we provide a good testbed for our manufacturing operations and execution management software solutions.

Secondly, Siemens is a leading provider of industrial automation for many years and the largest industrial automation supplier in the world. Therefore, we understand the need for technology integration into the machinery. We provide a high-quality function in the industrial automation process, and by manufacturing those components, we know what companies need from a machinery development perspective, and what is necessary to execute for staying ahead of the competition.

When you have size and experience, coupled with a world-class presence, Siemens is the only provider of the executable digital twin. We have the software to realize every step in the manufacturing process, from the creation of the idea to what we’re making from machinery, and how we’re going to be executing it through manufacturing and extending that into service life. This background is crucial for a machine builder because it allows machine manufacturers and designers to create more value, both in driving down cost and compressing delivery schedules. However, it also allows us to innovate faster by closing the loop between manufacturing operations and engineering.

Blake: Yes, a very comprehensive solution.

Bill Davis: Absolutely!

Blake: In looking at the trends in this industry, how do you see Siemens machine trends aligning with smart manufacturing? What is this process?

Bill: My background is primarily industrial machinery, although I spent some time in heavy equipment as well. There are some overarching themes and trends that we’ve been seeing in machinery development over the last several years and they circulate four topical areas.

The first one is connected. We want to have our customers expect that our machines are going to be communicating with other machinery in the plan. They’re going to be communicating with their overarching operating system and the machine OEM.

Secondly, it is a trend for adaptability.  This reflects how quickly the machine modifies its existing operating parameters and reacts to changing conditions. So, switching from one bottle size or one cell phone to another is a push-button affair. Currently, there’s much information, sensors, and capabilities available, so the machine can recognize when upstream products and processes have changed and automatically upgrade those changes to its operating conditions. It’s the beginning of artificial intelligence for machinery, but it’s spawned by all these sensors and capabilities, along with customer’s expectations for these machines to be more flexible.

The third trend is the predictability. With various operating conditions and unpredictable customer environments, we see an increased emphasis on simulation and the predictability of how a machine will perform in the field. It’s more complicated at higher speeds, with diverse use cases and enhanced complexity of machinery. It’s becoming more difficult to simulate all the aspects of a machine.

The last trend is extendability. We want to continue to be a provider of value to machinery. As a machine builder, I want to take my machine and extend its life, through the customer facility, and its entire lifespan. Also, from a financial perspective, I can create more value for my customers and be able to change some of the dynamics behind the cash flow to extend the life of the machine through predicted maintenance and adaptive performance. This is even more crucial as the workforce reduces.

In the past, experienced machine operators could tweak the machine and make it continue to operate in the field; however, that kind of experience is disappearing, with people retiring from the workforce. So, the OEM or machine builder needs to be more engaged with the customer throughout the machine life. This relationship is a value proposition for the customer and can transform their financial perspective.

Blake: In retouching on your statement about extending the life of a machine, are you seeing trends changing in the way a customer purchases a machine? Are they spending more money? Are they upgrading more frequently?

Bill: Historically, a company buys a machine from you and the machine operates throughout a certain lifespan and then you change the product, or they change the product. Currently, they either must buy something from you to adapt that machine to fit the new product, or they must replace it. So, we’re seeing machine complexity being incorporated with changing conditions, from a customer perspective, with shorter production runs. They need a more versatile machine.

Another occurrence is that customers are less willing to invest vast sums of money on machinery when they could use production as a service option, which allows that customer to buy machine capacity. This emerging trend is becoming popular with the heavy equipment manufacturers when you’re leasing machines. The difference is you’re paying for performance. So, if that machine isn’t running, you’re not paying for it.  The machine builder, or OEM, has a vested interest to ensure the device always works. Therefore, if it’s not meeting the production requirements – it needs service – then the OEM is financially responsible to rectify any performance problems or downtime issues and address them quickly to restore their cash flow.

Blake: Thank you, Bill, for this highly informative discussion on smart manufacturing and its competitive advantage.

To learn more about the competitive advantages of smart manufacturing for industrial machinery, listen to or read the first podcast or blog in this series.

This concludes the fifth blog in a series on Smart Manufacturing and the trends affecting the industry. Our future blogs will continue to spotlight transcript excerpts from the original podcasts.

By the Author: Bill Davis is the acting Industrial Machinery and Heavy Equipment Industry leader for Siemens Digital Industries Software.


The Future of AI part 1: early adopters and misconceptions

The Future of AI part 1: early adopters and misconceptions

Category : Blogs

The Future of AI part 1: early adopters and misconceptions

The future of artificial intelligence doesn’t look one single way. AI is poised to benefit a multitude of industries in a variety of different ways. What does artificial intelligence in the near-term look like? How is it impacting industries and what should companies know about AI to remain competitive over the next few years?

What are the early adopters of AI doing right now?

Early adopters of AI include everything from automotive to marketing.

Right now, AI is selling us more and more things through websites and social media ads. For example, “because you clicked or liked this, you might like this” or suggesting the next binge-worthy show.

AI is already in existence in smartphones too, such as with location services. AI can figure out where you usually go such as the grocery store, the office, your doctor, your gym, everywhere.

One of the biggest demonstrations of artificial intelligence is the self-driving car. Autonomous vehicles currently testing on the road are processing a gigabyte of data per second. This self-driving car must sense and interpret a massive amount of information from vehicles, traffic signals, weather, pedestrians and more, and make split-second decisions.

Finally, software engineering has adopted AI for the task of writing code; AI is contributing to writing spark code.

What are some misconceptions about AI?

The first iPhone was a revolution, but now the iPhone X is not.

Companies have started embracing AI to make a product or service slightly better but in five to seven years, the industries already at the forefront of using AI will reach a saturation point of cost reduction. They will likely embrace the innovation route and try to revolutionize the market by creating something which we can’t even imagine or envision yet.

The misconception is that this new technology will always seem new and maybe even a little foreign.

Artificial intelligence is both exciting and scary, but it will become as commonplace and mundane as the desktop computer or a dishwasher; neither of which talk about as the next big thing. In the near future, when artificial intelligence is seamlessly integrated and in wide use as a tool for businesses, home or even transportation, most won’t look at artificial intelligence as the next big thing.

By the end of the next decade, the hype will be gone. Once companies and industries accept the wider expectation, people will know what AI can and can’t do, and better leverage the technology.

What do the next 5-10 years look like for advancements in AI?

In five to seven years AI will still be in the cost-saving mode because there are a lot of benefits to come from markets, from system-level communications that will help move us toward powerful trends. But it will also mark the transition into innovation. Smart companies will envision what the trend for AI acceptance will be and have already made progress into their respective markets.

Data is consistently available. Industries will discover how to crunch the numbers, find patterns and implement those patterns in their specific industries like finance, insurance, manufacturing, and engineering.

It’s only a matter of time until different systems start talking to each other, such as a self-driving car communicating with other self-driving cars. That kind of intersystem communication, an internet-of-things, will come. Until then, it will be critical to embrace and enhance the capabilities of a human to machine conversation — a current struggle as anyone communicating with an Alexa or automated answering service can attest to.

Artificial intelligence will grow as humans learn how to converse with machines and vice versa.

The manufacturing industry will likely work very closely with the consumer industry in terms of AI. Drivers already receive notification in their car that their oil is low and it’s up to them to manage the maintenance. But technology installed in newer cars can be designed to sense the oil is low and schedule an appointment or signal to the dealership that maintenance is required. In this instance, a manufacturing company is working directly with a consumer product to enhance the quality of that product.

Read through our other blog posts to learn more about the future of AI and the possibilities this innovative new technology can offer.

Watch our video: The future of artificial intelligence: The keys to adopting AI.


Smart manufacturing for parts manufacturing

Category : Blogs

Smart manufacturing for parts manufacturing

Industrial machinery in manufacturing is witnessing astounding technological advancements based on smart manufacturing. However, with dynamic changes comes challenging steps to validating and managing modern manufacturing and assembly operations for achieving high-level quality while optimizing cost.

The third podcast of this series on Smart Manufacturing by Siemens Digital Industries Software, solutions for the industrial machinery industry by Siemens Digital Industries Software, is telling us how machine manufacturers are implementing smart manufacturing to improve the product of parts manufacturing.

We are learning the advantages that smart manufacturing brings to industrial machinery manufacturers. Also, we’re inspecting the improvements that smart manufacturing provides to manufacturing execution management, with discussions on the manufacturing of both the bill of process and bill of materials.

We welcome again our expert in this podcast series, Bill Davis, Director of Industrial Machinery and Heavy Equipment Solutions at Siemens, whose engineering expertise spans over 30-years.

Additive manufacturing – a combined, comprehensive approach

Our discussion begins with a differentiator to smart manufacturing and additive solutions.

learning approach solidifies what is a dynamic process for improving the philosophy of additive manufacturing. It’s a collaborative relationship with engineers and manufacturers to create a feedback loop of information that’s forward-looking.

Siemens follows this communicative flow so that manufacturers are continually seeking ways to take advantage of the latest innovative thinking. This methodology aims to profit from every aspect of the manufacturing process.

Manufacturing – low volume, highly complex

There are various scales of volume. So, when building PLCs, HMIs or motor controllers, there’s enormous bulk, and every second becomes an opportunity for saving money. Therefore, by having an operations control management execution or manufacturing execution management tool, it provides fine-grain analysis to assist in driving value into the manufacturing process.

So, manufacturing machinery, along with viewing the assembly, results in a detailed analysis that feeds information back into the engineering space to provide closed-loop, continuous improvement.

Bill of process and quality

Another piece of this process includes the bill of process or the directions for building a machine. There’s a methodology for building each part and rolling that part manufacturing up into optimizing the assembly process. This results in capturing what can be implemented into the schedule, while the assembly set-up is good-to-go for processing it.

Moreover, quality is integral to every process. To ensure quality requirements, all machines and fabricated parts must accommodate the conditions of the machine shop. It’s vital to use this information to qualify all methods for creating the part correctly. Also, there are crucial factors in using the appropriate machine, evaluating the sequence scheduling and incorporating flexibility into the process.

Benefits of smart manufacturing

Collaborative knowledge with a forward-thinking, innovative mindset serves to promote premium quality of product smart manufacturing while optimizing cost. This learning approach creates a competitive edge for solution providers to have an end-to-end solution, from manufacturing and design to service and back into the business system.


Smart manufacturing creating a smart factory

Smart manufacturing creating a smart factory

Category : Blogs

Smart manufacturing creating a smart factory

How do you make your factory smarter? With ubiquitous technological advancements, a company needs to stay in step with understanding how to improve machinery operations, which is dependent on a smart factory.

This process includes simulation – a crucial element to the smart manufacturing space, process, and machine.

This fourth podcast in a series on Smart Manufacturing by Siemens Digital Industries Software, solutions for the industrial machinery industry by Siemens Digital Industries Software, is stressing the importance of innovative manufacturing techniques for smart factory operations.

Insightful input from our resident expert in this podcast series is Bill Davis, Director of Industrial Machinery and Heavy Equipment Solutions at Siemens, whose engineering expertise spans over 30-years with 20 years as an engineer.

Smart manufacturing – where to begin

Smart manufacturing requires a smart assembly layout – getting the right parts and machines in the correct place on the assembly floor at the right time. A software solution requires simulating the positioning of the machines on the floor, eliminating high-traffic areas, removing bottle-necks due to slowness in receiving materials and optimizing work cell locations for optimum utilization.

Having everything in a relative arm’s reach improves efficiency; however, the factory also requires process simulation. Order and functionality are the standards for overall conflict resolutions to foster adaptability, predictability, and extendibility. This means finding the necessary tools, human or machine, to perform a quality job.

The shop floor and simulation

So, it’s not merely a focus on the shop layout capabilities, but also a need for process simulation at the human level to factor in any problematic issues, which usually don’t surface until complications occur in the field. Simulation of these issues is highly valuable for continual improvement of the design and development process. Also, it includes machine assembly and scheduling of machines on the product lines.

Ultimately, less time is spent in commissioning and debugging obvious known issues from the PLC code generation, thus getting the machine to the customer more rapidly via simulation of parts and kinematics. Moreover, this simulation gives back time in physical stress analysis and fatigue.

This result means a company generates more revenue, reducing the margin of erosion from errors and quality issues. However, none of these benefits could occur without the digital twin. Siemens has many examples of companies implementing the digitalization of machining or manufacturing centers, reaping more money, thus justifying any extra cost in this investment, resulting in faster machines via digital twin for software development and validation.

To learn to make your factory smarter and bringing quality to your parts and overall process, listen to this engaging podcast on Apple Podcasts, Stitcher, SpotifyCastboxTuneInGoogle and RSS Feed.

By the Author: Bill Davis


The possibilities of AI: an overview of AI in the workplace

The possibilities of AI: an overview of AI in the workplace

Category : Blogs

The possibilities of AI: an overview of AI in the workplace

Artificial intelligence is a bit like a car. If you don’t fill up the tank, it’s not going to go anywhere. Artificial intelligence is fueled by data; therefore, it needs to be fed a consistent amount of data or else it’s not going to learn or be effective.

Artificial intelligence is about teaching the computer to do something for you. It takes a monumental amount of information and data to build models and teach the computer to learn on its own. That’s why data, and the collection of data, is critical when embarking on working with AI.

Collecting more data means creating more accuracy when building models that will allow the machine to learn.

Step one is collecting as much data as you have access to. Then, you need to be able to cleanse, validate and make sense of that data ensuring it’s not biased or wrong. Inaccurate data will simply provide inaccurate results.

From here, companies can use the tools on the market, use open source technologies or algorithms, and start applying the machine learning and artificial intelligence techniques on the data.

Finally, businesses should seek the advice of experts. With so much information and data, companies don’t really know where to start. When they feed that much data into a system in search of results, they‘re overwhelmed by the vast amounts of different combinations and permutations of scenarios. Manually investigating each result is impossibly time-consuming and most companies don’t have the resources and personnel to dedicate to that level of analysis.

This information overload can lead to confusion and further hesitance in adopting AI.

Data scientists and consultants can help businesses determine how to sift through the vast amounts of data, take what’s necessary and ensure the artificial intelligence is maximizing its use.

AI and the business

Artificial intelligence is often thought of as computers thinking for themselves and being used as a tool to automate jobs. But it also offers implications in business to analyze industry trends, cost trends, understanding inventory and supply chain needs, shipping times and much more.

Having this technology available allows businesses to understand more factors that can influence their success. Artificial intelligence is thus a tool that can help organizations budget better, plan more efficiently and understand how to more effectively allocate resources.

This allows companies to make more informed decisions about where to invest research and development dollars, understand the way customers use their products and ensure they can optimize products and services before going into production.

AI can predict the trends of users and how they use the products, so businesses can keep up in an ever-changing market.

Businesses, especially manufacturers, are starting to see the benefits of using collaborative robots with AI embedded in them. These types of robots can work alongside the factory workers to do things like unloading goods from a conveyer belt, putting them into a pallet or moving them around. They can use computer vision to inspect the manufacturing of components coming off the production line. AI has the capability to spot defects and perform quality control beyond a human’s capacity.

Working with AI to predict the future

Both businesses and manufacturers alike can use machine learning to understand what’s happening with their products and make future predictions.

Any data, from sensor data to product data, can be used to teach the machines building the products. From this, companies can use this information to design products better and for predictive maintenance giving manufacturers the capability to prevent problems before they happen. As a simulation tool, artificial intelligence allows engineers and designers to fix issues before they become a costly problem to fix.

Predicting maintenance means a business’s operation can be proactive about scheduling. The earlier you can detect the potential failure, the more time and money that can be saved by reducing downtime.

At the same time, using artificial intelligence in the manufacturing process enables designers to see how varying components influence each other. This is critical especially as OEMs outsource more of the supply chain. Building products are pricey, AI can catch potential flaws in the making, and use all the collected data to predict if a product will pass the test.

Designing the best product possible

Working with artificial intelligence and machine learning allows businesses to have the computer do a lot of heavy lifting. When designing a product or developing software, it’s quicker and more effective to run multiple, different scenarios and simulations. The results offer their own collection of information, which is analyzed down to a potential set of useful data.

The machine learns as it narrows down all the possible scenarios providing a result that would otherwise take months and considerable cost to discover using traditional methods. Human beings don’t have time to look at every single possibility, so giving artificial intelligence this task frees up the engineers’ and designers’ time to be spent on more worthwhile endeavors.

Artificial intelligence won’t always give a specific answer; often it is a means for creating a more effective outcome by exploring a much larger set of possibilities, rather than a machine that will do all the work. it provides recommendations and some suggestions, freeing you to focus more time on solving the problem, rather than figuring out what the problem is.

AI helps analyze the data faster and allows users to establish an idea. But making the final decision is where the technology can’t completely replace a human. You don’t want to merely leave it in the hands of the machine and see what happens.

If you can apply artificial intelligence and machine learning techniques, along with automated analyses, you can receive those insights faster and design products better. AI closes that transitional loop as you’re creating a product design, running simulations, building prototypes, manufacturing the product and delivering those products to the market. The artificial intelligence gives you the tools and techniques to understand how that product is behaving so that you can design it better.

Working with AI in the future

Technology is rapidly evolving and accelerating. You can’t be afraid to try new things and innovate new ideas. You need to be looking at artificial intelligence and understand how to apply it within your specific field.

There’s almost no field that can’t benefit from the use of artificial intelligence. One of the most famous data science use cases for AI is the concept of the book-turned-movie Moneyball. The application of data science, which is a subcategory of artificial intelligence, helped build a more successful and cost-effective baseball team. It changed the industry.

Combining artificial intelligence with the digital enterprise—especially across manufacturing, industrializing and engineering—enables the integration of global data to create actionable insights.

There is untold value in data. But unless you can do something with that data, there is no value. You can have a car, but without fuel, it’s not going to go anywhere. A business using artificial intelligence to collect and use the right data can help position their products far ahead of its competitors.

Want to learn more about the future of AI and artificial intelligence in the workplace? Check out other blog posts about this innovative new technology.

Watch our video: Working with AI in the Next Industrial Revolution.