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smart-manufacturing

Implementing smart manufacturing for a smart factory

Category : Blogs

Implementing smart manufacturing for a smart factory

With new strides and technological advancements propelling the manufacturing industry to greater innovations, it’s essential to understand how improving machinery operations is dependent upon making your factory smarter.

A substantial contributor to this series is our resident expert, Bill Davis, who is the Director of Industrial Machinery and Heavy Equipment Solutions at Siemens Digital Industries Software. Bill’s expertise in this industry expands over 30 years, with 20 years as an engineer.

We continue this topic by having him explain the dynamics of smart manufacturing and smart factories.

Below, is a transcript excerpt from the final portion of this podcast series:

This podcast series stresses the importance of simulation for both the manufacturing environment and the machine build.

Blake (interviewer): A part of taking advantage of being smarter in manufacturing and operations is the assembly layout. For example, you’ve got five machines in one location, ten machines somewhere else, and they’re all very similar, but they also have variance. So, how do you ensure that the correct parts are in the right location on the assembly floor at the appropriate time?

Bill: You need to have a software solution that lets you simulate the machine location on the floor and how to get materials to them. It’s process simulation, which is a novel idea to ensure eliminating high-traffic zones to areas of delay in receiving materials. There’s a need to improve optimization in how the work cell is located, and in the machine builder. However, how do you ensure you’re putting the right materials needed and defined by the manufacturing bill of materials? Also, how do you ensure the materials are arranged on the floor for optimization assembly?

Everything needs to be within an arm’s reach, so it’s not just the shop layout and simulation, but also process simulation. What’s often overlooked are conflicts in the order of operation. Machinery is about putting ten pounds of functionality into a five-pound bag. So, the assembly process is a compromise that’s a result of the design. However, as we look at all the trends towards adaptability, predictability, and extendibility, it’s vital to view the human factor for the assembly process and decipher the necessary tools to achieving high quality.

For example, we’ve got a machine assembly that’s inside a frame, and you can’t physically move the torque wrench to get to the right spot, so that’s a quality problem. However, being able to simulate that in the early part of the design process will help us get first-time quality with the ability to make changes when it costs less. So, you can move an element of that frame two or three inches to the right or left and torque the bolt properly.

We need the shop layout capability and process simulation at the human factor level to be aware of problems. Sometimes these problems don’t manifest themselves until there’s a problem in the field. For example, a bolt that seems like it was appropriately torqued can loosen up and cause other failures. However, the root cause is the capability of the assembly process. So, being able to simulate it as part of the design process is crucial for continuous improvement into the development process. And, it’s not just looking at the individual assembly and machine assembly process but scheduling multiple machines and multiple product lines at the same time.

In my previous role, we had high variability in shop floor requirements. One machining center would require a 20 x 20-foot space, and another a
10 x 30-foot space, creating a jigsaw puzzle in terms of the process. There was a physical scheduling challenge throughout the shop in backing it through the part manufacturing. The assembly process for scheduling software capability is vital to know the number of users on each machine and the floor space utilization.

This information culminates in knowing factor acceptance or a side acceptance test. Routinely, a customer comes in and checks out the machine and is elated with its performance, thus signing on the bottom line for us to deliver it. However, simulation can involve the programmer sitting in front of the machine and testing many operating conditions in a stressful environment. The more complex the machine, the higher the probability of not having time to test everything, including the assurance that the machine PLC code meets all requirements.

You need to have software that allows simulating the upfront design process in the software development piece. Thus, simulating the PLC code on the machine floor, running through the use-cases effectively to validate that the codes perform in the way the simulation indicates. You can focus on areas of challenge that you may not be able to simulate, to bring that machine into a commissionable state quickly.

Every time a machine is sitting idle or being tested, it allows time for potential cash. So, the less time in commissioning and debugging, the more rapidly you can get the machine to the customer and perform simulation for movement of parts and kinematics.

These are items performed from an assembly management operations perspective, to drive value for the company and efficiency in generating more cash flow, reducing margin erosion by errors and quality problems. And, none of this could occur without the executable digital twin.

Blake: Do you have examples or use-cases where companies have implemented the simulation and seen a reduction in errors or a reduction in time while gaining an increase in cash flow? Without mentioning company names, do we have examples of improved metrics?

Bill: Absolutely! So, there’s a company that’s a Siemens customer and software user that is embracing the digital twin in digitalization of their machining and manufacturing centers. Also, there’s a packaging machinery company that builds bags or boxes in packaging a product, whether it’s chips or bags, and putting them into boxes using the digital twin for the design to increase their production speed-building the world’s fastest packaging machinery.

The extra-cost they spent is justified by having the world’s fastest machine and embracing the digital twin for software development, virtual commissioning and software validation. The programmer evaluated the machine, testing it out to ensure it didn’t collide and then hit the go button, moving the pick head to the right, then to the left, colliding with the frame. So, by leveraging our software from an automation perspective, they’re able to:

  • Validate that the machine is functioning to perform the required tasks.
  • Increase the speed of the machine due to the servo-motor, driving it to greater capabilities.

Therefore, at higher speeds and greater reliability they understand the limits of what the program should be accomplishing and align with the statement from the VP of Engineering that, “they would rather the machines collide in the digital twin than in the real one.”

Therefore, driving that validation verification through the design perspective creates greater efficiency on the shop floor and allows for executing more machines, witnessing a 20 percent improvement in the overall capacity of their machine shop and operations due to minimizing the time previously spent in validation, verification and commissioning. Though that time was not eliminated entirely, it was minimized, so the gains translated to an increase in machine sales.

Blake: That’s a great example, Bill. Let’s move into the quality of parts manufacturing and how Siemens ensures quality through its portfolio of offerings.

Bill: When we look at Siemens Digital Industries Software portfolio of software products, we’re offering design and simulation products, data management of the lifecycle, operations management software and manufacturing engineering software. So, it includes both the CAM piece and the scheduling piece from shop floor management. However, what’s crucial is that we have closed-loop quality. So, we take the information from the management of the digital twin and the component quality, whether it’s a supplier piece or an actual physical piece, and we build that into our manufacturing process. This is merely one portion of the closed-loop operations management quality inspection process.

Also, we can automate the inspection process by linking the coordinate measuring machines to the database so that we can program the inspection points which are critical to quality measurements, translating those back and retaining them into statistical process control documentation.  We can handle that from a part process in quality measurements so that incoming material inspections can be handled in the same way as our software. Then, we have software capability to ensure the validation of the process to confirm people are trained in executing it. So, the hallmarks of quality are people, process and products.

It’s important that we talk about these in three contexts – the product itself, the process defined (assembly or machining) and to ensure that people can execute the process and its capability. Therefore, when you have an error, is it an error in the machine’s capability of making the part to the required specifications or is it the inabilities of the person executing the machine in hindering the quality?  We need software capabilities in both areas: machine capability and qualified people.

To increase the IQ of your quality process, a closed-loop manufacturing process is what we’re discussing, and it ensures consistency by establishing the digital twin that encompasses the full manufacturing process. This eliminates the risks because it identifies where your critical failure points are to know the critical quality points in the product, trace it back to the process and certify people.

The digital twin provides flexibility and increased efficiency to know that you can’t execute the same thing in the same way every time. Also, you need adaptable alternatives based on research constraints. The digital twin includes the manufacturing process which is crucial for quickly evaluating your alternatives, determining the impact on the schedule and adapting it to the changing conditions on the floor.

Blake: We thank Bill for his contribution to this series. It has been a pleasure to gain his insight and expertise throughout this entire conversation on smart manufacturing and its effect on the manufacturing industry.

We are looking forward to hearing Bill Davis’ expertise on an upcoming podcast series slated for January 2020 on
advanced machine engineering solutions.

To learn more about the competitive advantages of smart manufacturing for industrial machinery, listen to all of our smart manufacturing podcasts or begin reading the first blog in this series.

This concludes the final blog in this current series on smart manufacturing and the trends impacting the industry.

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


Implementing smart manufacturing – leveraging the digital twin

Implementing smart manufacturing – leveraging the digital twin

Category : Blogs

Implementing smart manufacturing – leveraging the digital twin

Industrial machinery in manufacturing is witnessing dynamic technological advancements. It is a monumental mission to manage this new wave of advanced manufacturing and assembly operations for achieving the highest standard of quality while optimizing cost.

Transcription of our second podcast in this series teaches us how machine manufacturers are leveraging the digital twin to make their manufacturing smarter. We are discovering the significance of digital twin for machine builders and examining the digital twin process from design through manufacturing, operations management, commissioning and service life.

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

Below, is the transcription from our Thought Leadership Community’s second audio podcast on this topic. For the full audio podcasts or blog series, refer to the links at the bottom of this page.

Blake (interviewer): Welcome to the Siemens Digital Industries Software podcast series on smart manufacturing brought to you by the Siemens Thought Leadership team! Bill, in the manufacturing industry we hear about 3D printing or additive manufacturing. Just briefly touch on how that comes into play with smart manufacturing?

Bill: Certainly. 3D printing is one of the newer trends in the manufacturing process. It is what CNC machining calls subtractive manufacturing. So, the additive is building up the part from base materials of powder or liquid product base raw material. The interesting aspect from a machinery perspective is it allows the machine designer to take advantage of the complex machine parts (15-20 parts) that would be built up, so they were expensive, highly-precise and requiring much assembly time. Currently, the engineer can start the process looking at the outcome or output from a performance parameter and discover how that additive part can be made less costly, with greater reliability and durability.

There are some ramifications from a smart manufacturing perspective and the digital twin of that part usually requires more than just additive manufacturing. It requires post-process machining, and another handling of that part to make it ready for the industrial machinery assembly. So, an additive is sparking a whole new class of machinery that includes both the additive side as well as the subtractive machining side in the same machining center. That’s the significant change that we see from a machinery manufacturing perspective. We’ve got the design piece of it, but we also must handle the manufacturing perspective in the machining centers. Therefore, it’s causing our machine shops to reinvest in more complicated machinery.

Blake: Right. And you mentioned digital twin. Can we discuss that topic in the context of machinery and how Siemens Digital Industries Software is impacting digitalization?

Bill: When we look at the digital twin, the conventional discussion is around digital twin design. Everyone claims that they have the digital twin of the machine, and it’s true – they have 3D models and representations of it. However, the digital twin is much more than just the design parts. Conventionally, people think about the digital twin as being primarily the mechanical component of the machine. What’s happening is more adaptability is being driven and introduced in the machine by means other than mechanical. We’re using more servo-motor drives and more software to create a flexible and adaptable machine.

So, you can’t have a digital twin unless that digital twin reflects the electrical part  the software and PLC programming.

Within the design realm, multiple disciplines must be incorporated into the true digital twin and Siemens Digital Industries Software has the software that manages all aspects. Also, there’s a need for simulation to be connected to the digital twin. Historically, this is performed after the design is worked on and released. If a problem is found in the field, then the simulation figures out what was wrong, and an engineering change is made. We then need to integrate that simulation into the digital twin upfront, as part of the creation of the digital twin in the engineering space; otherwise, we don’t have a complete digital twin.

Let’s take that into the manufacturing space because we’re talking about smart manufacturing here, so we take all those parts, whether mechanical, electrical or software and drive them down into specific manufacturing disciplines. Then from our part manufacturing process, the CNC, CAM programs, CMM or inspection files are generated so that we can automate the process system manufacturer for those parts.

On the software side, we simulate the execution of that machine in its digital twin, so if the machine has a rack that’s supposed to extend 200 mm in one direction and 200 mm in another direction, we simulate that with kinematics, so we can see what’s happening to the machine as the slide accelerates back and forth. Thus, we’re simulating the software, execution, and physical domain for that engineering piece.

On the electrical side, we deal with a tremendous amount of variability. One of the topics rarely discussed from an industrial machinery perspective is that we often build machines that are a one-off, and we want to tie them back to a central digital twin machine’s origin of product families and variant configurations. So, from an electrical perspective, you may have manufacturing requirements that for one customer is Siemens hardware, and for another a customer, such as Rockwell hardware. And, those machines, while their functionality is the same, their configuration from an electrical and mechanical purchasing requirement is entirely different.

Therefore, the variability where customers and machinery builders find themselves is reflected in that digital twin. Also, it’s essential to realize how manufacturing execution occurs as a reflection of the digital twin. So, it’s not just the parts that need to be produced, and the program that needs to be executed, but how to manage the delivery of parts, the manufacturing of those parts from an operations management perspective and the quality control.

From my operations management background, I see one of the biggest challenges is having multiple machines that you’re trying to build simultaneously and coordinating all these activities to deliver the correct parts to the right place, at the right time and reflect that in the manufacturing operation. So, we need to incorporate the designed bill of materials, configure it to fit the manufacturing and assembly operation, deliver the finished machine to the customer expeditiously and visualize that process again for the manufacturing process  juxtapose to other customers in this same space.

So, the digital twin spans this whole gambit from design through manufacturing, operations, management, into commissioning and the service life.

Blake: That’s useful information. I think people have a misconception about the digital twin because of hearing so much about it. They only think of it in terms of product, and I think you really outlined to us all the disciplines that are involved.

So, in looking at smart manufacturing for the corporation or manufacturing company that is wanting to improve their process reduction process, could you cover some of the benefits of smart manufacturing? How it’s going to help them at a general level?

Bill: Let’s talk about it from the context of what needs to be delivered by the OEM or machine builder when we begin to have conversations with customers about smart manufacturing. Some factors play into the complexity of the machinery process, and how to create value from the digital twin. How do I address the digital twin’s complexities in both my manufacturing operations, supply chain requirements and regulatory quality requirements? So, the multiple facets of the machinery build are important to leverage that common backbone or framework of the digital twin to integrate digital engineering and regulatory quality requirements in closing the loop.

I can use machinery builders, typically serving many different industries, whether food processing machinery or high-tech electronics. Regulations are a significant part of that and the single source profitability loss. This is what we call margin erosion, which comes from not complying with regulations. So, having that traceability between the digital twin through the execution and proving that we met the regulatory requirements is a crucial part of smart manufacturing.

The manufacturing of the machinery is an elaborate dance between supply chain, internal manufacturing, and assembly. So, we need to have a united knowledge management piece that ties everything together. Smart manufacturing allows us to have the portability to take a design and move it across the border to a different facility, and retain the quality and reliability for the manufacturer’s product. So, this is a significant piece of smart manufacturing that’s tying into the design, extension, manufacturing operations and operations management.

BlakeThank you, Bill, for another enlightening conversation on leveraging the digital twin in smart manufacturing.

We learned that the digital twin in smart manufacturing is more than merely a mechanical component or 3D model of a product. The digital twin is representing the electrical part, software and PLC programming. Also, multi-disciplines are being implemented into the digital twin via software, managing all aspects, including the integrating of simulation to proactively find problems and solve them.

To learn more about the competitive advantages of smart manufacturing for industrial machinery, listen to all of our smart manufacturing podcasts or begin reading the first blog in this series.

This concludes the sixth blog in a series on smart manufacturing and the trends affecting the industry. Our future blogs will continue to spotlight excerpts from the transcribed conversation of the original podcasts.

By the expert: Bill Davis


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.


Artificial intelligence development is changing how industry works

Artificial intelligence development is changing how industry works

Category : Blogs

Artificial intelligence development is changing how the industry works

Many industries are going to benefit from artificial intelligence development. It’s hard to say which ones in the long term will find the highest level of success, but we can already see significant benefits in a host of industries.

At its core, artificial intelligence is a tool that can acquire, organize and analyze vast amounts of data to create and parameterize models to recognize patterns and make predictions. AI is delivering many benefits and its continued use is the key to making a business more competitive. By automating some of the repetitive, basic tasks, a company can increase productivity, reduce mistakes and enable quicker, better decisions. In insurance, for example, companies are using AI to automate claims processing. The entertainment industry uses AI to optimize streaming services and suggest content based on an individual’s previous choices and comparing it to the choices of others.

Leveraging AI Today

If you’re a business or a company wondering about what to do about AI, whether to use it or even when to use it, then the answer is, Yes. Businesses must think about using AI. Artificial intelligence is a practical tool, and just like banks use it to prevent fraud or healthcare uses its algorithms to scan X rays, companies should look to solve problems and challenges with AI.

In engineering and manufacturing, artificial intelligence is already enhancing scheduling in a factory by improving downtime and conducting predictive maintenance scheduling. Artificial intelligence saves companies money by reducing costs, for example by collecting data from running machines in the factory and feed it into training for predictive maintenance AI models.

Manufacturers can use these models to detect signs that maintenance is needed, such as changes in vibration signals which might indicate there is a developing problem. They can then schedule a maintenance session at the downtime of their choosing, perhaps overnight on a Saturday where there could be minimal or no loss of production. Naturally, it’s more economical to perform maintenance at the company’s discretion than having an expensive machine offline for several days, while possibly waiting for delivery of replacement parts from somewhere on the other side of the world.

Companies like Electricity Generators that use wind turbines are using AI-trained models to do maintenance and optimize their operations. Without this knowledge, they would continue guessing about how to evolve their operations to greater efficiency. They would also maintain the turbines based on a schedule and not whether the turbine required it, or ad hoc when issues arose. With the help of the data from sensors mounted on those turbines, they can better predict maintenance needs and how to minimize downtime. This makes a significant financial impact and can be the difference between being competitive and not being competitive.

Additive manufacturing, or 3D printing, and simulation are other areas in manufacturing where AI will play a more significant role. Manufacturers will need to simulate and validate to check that an additive manufacturing process will produce a quality part and will integrate within a complex product. As this industry grows, there will be more and more manufacturing done via additive manufacturing. It will open horizons for components and systems that are just not possible to traditional manufacturing techniques. Additive manufacturing needs AI to help improve the design, its validation and the planning of the manufacturing components.

The pioneers in 3D printing are the companies producing software and the components to go into the additive manufacturing machines. This includes using artificial intelligence to improve the digital twins to help manufacturers of everything from cars and planes to the factory or smaller components to make better predictions about their performance. AI can simplify the set-up of simulations, speed up those simulations and detect problems earlier without the need to conduct detailed analyses. AI truly plays a part in all these types of analyses and can help companies use the digital twin more effectively.

Today we’re seeing the finance, advertising and consumer industries benefiting the most from AI, but that’s likely because they have access to data. With lots of information, the leaders in these industries are feeding continuous data into their analytics algorithms, improving their models, and getting deeper insights than before.

As more data becomes available, especially in data-sensitive industries where proprietary technology is subject to privacy and confidentiality, companies will struggle to divulge their data. Healthcare and manufacturing industries are particularly concerned about the confidentiality of their data as it’s part of their competitive edge. For those offering services involving AI, it is imperative to address these concerns and information regarding how it will be used and how protection will be provided.

Three industries where AI is already succeeding

There are countless examples as to the importance of AI and its abilities to help businesses maintain their competitiveness and increase both growth and profits. AI is already being used for data collection, assisting companies to become more efficient and productive.

AI in banking

AI is already being used in the financial industry to help companies reduce fraud, assess risk, improve trade and minimize the costs of making credit decisions. But AI in banking is moving a little deeper. Deutsche Bank, for example, already uses this technology to monitor customer phone calls where they can pick up potential fraudulent cases and alert the operators. It is also detecting possible fraudulent transactions before they happen. This reduces financial loss from fraud, which is often publicly embarrassing for a brand.

AI in healthcare

Artificial intelligence is being used with great benefit in the healthcare industry as well. In the United Kingdom, there’s a serious shortage of radiologists. What AI in healthcare is accomplishing is the automation of analyzing the scans, helping to determine symptoms and even suggesting diagnoses. This both speeds up the diagnostic processes and allows radiologists to better use their time and make smarter decisions by automating preliminary scanning and analysis to eliminate easily detected negative results and focus their attention on the cases where decisions require their expertise.

At Stanford University’s Center for Artificial Intelligence in Medicine & Imaging, researchers continue developing artificial intelligence technology to scan mammograms and reduce false negatives and false positives. Ross Shachter, associate professor of management science and engineering at Stanford University, does not see radiologists being replaced but believes AI can assist in determining breast cancer cases more effectively and efficiently while ensuring professionals can better manage their time with patients.

Artificial intelligence developments are also being used to build medical devices that aid in patient health. Implementing a predictive algorithm that gathers data during the development and production of a medical device requires data and performance records to determine the probability of that device working to the standards necessary for government approval and healthcare acceptance. This gives engineers more time to develop medical devices and manufacturers greater confidence in product introduction.

Artificial intelligence in insurance

Insurance companies have access to an insurmountable level of data – a key ingredient to the successful implementation of artificial intelligence. Using this information makes processing claims, underwriting and even customer service more efficient. If a customer wants to take out a policy or make a claim, they’ll likely interact with a form of artificial intelligence like a chatbot that uses algorithms to address customer concerns. Insurance companies can free up their employees to work on the more complex cases requiring a higher level of human judgment, whereas AI deals with straightforward cases.

With the adoption of machine learning and artificial intelligence, along with the data from the Internet of Things environments, manufacturers and engineers across a variety of industries can increase productivity and reduce operational risk. Predictive and prescriptive maintenance and optimized asset performance management are just some of the ways every industry from healthcare and entertainment, to banking and manufacturing, are utilizing this innovative solution.

Click here to learn more about the future of AI and its uses.


Integrated Quality Tools for Supplier Quality Management

Integrated Quality Tools for Supplier Quality Management

Category : Blogs

Integrated Quality Tools for Supplier Quality Management

Increasing global competition over the past decade has forced automotive OEMs and their suppliers to improve quality and efficiency. There is a growing need for tools that make it possible to measure the quality processes used by each member of the supply chain since the OEMs are committed to providing quality products for their customers. Quality issues arising anywhere in the supply chain may put the OEM’s prestige at risk. Because of this, having quality guarantees with regard to the materials and components provided by suppliers can determine the success or failure of a product.

A structured approach to quality, based on an effective Quality Management System (QMS), is of crucial importance for any organization committed to achieving product and customer satisfaction excellence. Supply chain and quality management are two significant aspects of ensuring a company’s leadership in a dynamic and globally competitive environment. QMS capabilities are particularly important in the automotive supplier network, where requirements should be defined and then transferred through the supply chain. In recent years, the relationship between an OEM and its suppliers has changed. To achieve competitive advantages, the main strategy is to change from a sometimes adversarial relationship to a fully cooperative collaboration between customers and suppliers.

Moreover, suppliers need to be third-party certified to ISO9001 and IATF 16949. The scope of this requirement affects subassembly, sequencing, sorting, rework and calibration services in addition to direct material suppliers. Potential suppliers are released internally after a quality process audit and/or a technical process audit depending on the perceived risks. A proactive approach to Supplier Quality is demanding and decisive in all product life cycle stages, such as:

  • Development Phase. To release and validate the supplier and the bought-out part for the production phase.
  • Production Phase. To inspect the supplied parts by incoming goods control.
  • Production Check Phase. To assess the suppliers and their parts and provide a ranking.
  • Utilization Phase. To track the problem-solving process of the supplier (short-term) and to audit the results of it (long-term).

How does QMS Professional support supplier management?

QMS Professional provides automotive OEMs and tier suppliers with the integrated quality management tools needed to ensure the quality of materials and components provided by suppliers.

Two key modules of QMS Professional enable manufacturers to manage the quality of materials and components from their suppliers. First, QMS Supplier Assessment Management (SAM) helps manage your supplier base for optimal product quality control. With QMS SAM, you will have access to real-time quality data relating to all your suppliers and their incoming goods. A strong and viable supplier network can also help you minimize risks and associated costs while bringing a better quality product to the market.

Second, QMS Professional includes a Supplier Portal within its Concern and Complaint Management (CCM) software. This capability enables you to quickly manage product defect complaints in situations in which the supplier is considered responsible. The designated supplier can access and handle complaints using the eight disciplines (8D) problem-solving methodology. Throughout the process, the customer has access to the current handling status, including changes and updates. With the implementation of the QMS Supplier Portal, you can replace time-consuming manual communication with a controlled, automated system. This automated approach allows you to save valuable time by providing immediate notification of handling status to your customer.


ERP - Making the Investment Work using APS

ERP – Making the Investment Work using APS

Category : Blogs

ERP – Making the Investment Work using APS

The materials war has been won (via MRP and/or Kanban control), the capacity war has just started. Companies with a high cost of raw materials and/or high value of end products are trying to move towards make to order and away from making to stock but in doing this many are finding that a knowledge of the capacity constraints of their plant is key to answering the question ‘when can we deliver?’

What these companies need is a tool that will show them visually what the current load is, what the impact of unexpected events will be, and to be able to ‘what if’ solutions to deliver performance across all orders. They need the equivalent of a crystal ball for their manufacturing operations.

Of course, there are different types of production and ways to supply the demand and for each case, there is a key process and IT considerations for moving to better, leaner and more efficient operations.

For example, companies that are looking to techniques such as Kanban to control the flow of products and materials through a manufacturing process and in many companies, this is very effective for some or all of their plants. The requirement for IT is thus much lower but for many companies perhaps 80% of the parts can be handled in this way while the other 20% cannot so there is often a requirement to handle control differently depending on the part.

However, in an MTO, engineer-to-order, and mass customization company with many products and common resources that make them, scheduling becomes more important. We have seen cases where significant variations in cycle times from one product to another for each stage in a classic pull flow of an assembly line has made it difficult to predict the makespan for a particular batch. This was dependent on what other products shared the same line, their batch sizes and their sequence.

Companies are also increasingly turning to out-sourcing of all the components and just assemble to order. They may have a long order book stretching months ahead but still find it difficult to predict the consequences of a sudden change because they do not have the capacity information of their suppliers to provide accurate answers.

Outsourcing is fine but it can lead to an erosion of a company’s visibility and control of its operations.

Measurement of the benefits achieved from a software implementation is often difficult because the objectives were not explicitly spelled out at the beginning of the project.

Traditionally companies would seek to control their operations better and along the way increase their stock turns and reduce work in process which leads to shorter delivery times. Certainly, delivery performance is becoming more important but sometimes it is difficult to put a quantitative value against it. How many extra orders did we get because of better on-time performance?

Accountants don’t really help either because they will focus on resource utilization and ‘overhead recovery’ rather than on delivery performance. The old saying of Accountants ‘knowing the price of everything but the value of nothing’ comes to mind.

Turning data into information and that into knowledge is a powerful argument and if the ERP company can’t provide what users want then a ‘best of breed’ product must be a good way forward for many companies.

It’s just another way of giving Visibility in what tends to be a ‘black box’ of data that is difficult to get at.

Today we know that most ERP systems have very limited functionality in detailed operational scheduling and APS tools have a proven track record in providing companies with an operational crystal ball to see what is happening and what will happen based on the constraints of their factory.

Knowing what the problem is can be halfway to solving it using the ‘what-if’ analysis available to APS users and knowing what will happen in advance gives the planner a considerable advantage in making timely and cost-effective decisions.

The benefits are again well documented in case studies. These include lower raw material stocks and work-in-process while obtaining increased operational efficiency and on-time delivery performance.

APS brings with it additional tools that allow, for example, both material and resource constraints to be considered and some of these tools include a memory-resident BOM exploder for carrying out ad-hoc capable of promise inquiries in seconds. For many companies, this is the ‘Holy Grail’ that they want to add to their existing ERP installation.

On the other hand, collecting data but then doing nothing with it is just giving you an insight into what happened and maybe why. But using the information to help close the loop between what has happened and what will happen is a more powerful argument for its use.

Right now, in many companies we have ‘islands of control’ whereby a cell, a department even a factory within a supply chain seeks to meet specific objectives that have been given to them and even more important, they are measured against those objectives as key performance indicators, KPIs.

However, what may be a KPI for that unit, cell or department may not actually be in the best interest of the entire business. Companies need to take a holistic view of their business and see what is best and apply those to the KPIs they provide the individual units.

By the Author – Marco Antonio Baptista is the MOM Channel Leader in Americas at Siemens Digital Industries Software.