What does future-proof manufacturing plant floor software look like? Is it possible to have all the flexibility and choices each site needs and still adhere to enterprise standards? Yes; find out what modern MES architecture can promise for this uncertain era. Listen to the recording of this fireside chat to find out: Why plant floor…
- Why plant floor software must reach beyond functional fit into MES architecture
- How to be ready for anything: M&A, new plants, new regulations, moving products between plants
- The benefits of easily moving software to public or private cloud, on-prem, or hybrid
- Why containerization, orchestration, DevOps, and one-click deployment from a central CoE can avoid the MES trap of fit that fades over the years

- Transitioning to a data-driven process
- Digitalizing tech transfer data and processes
- Taking a holistic approach to product data modeling



Table of Contents
- Executive Overview
- The Importance of Effective Data Management
- Data Management Must Address Today's Challenges
- Data Management Challenges Reduce Productivity
- Evaluating the Practices of the Top Performers
- Data Management Practices of the Top Performers
- Enabling Top Performing Product Development
- Top Performers are More Likely to Embrace the Cloud
- Conclusions and Recommendations
- About the Research
- Acknowledgments
Leveraging Data Management to Enable Product Development
Analyzing Data Management Practices for Successful Product Development How do design data management practices impact product development performance? We surveyed 131 companies that design, engineer, and/or manufacture products to understand how they manage design data like CAD files, bills of material, and other product-related information to find out. We conducted this study as a follow-up to our previous studies to understand what has changed over time.Executive Overview
Survey Approach
- Find the data they need
- Share it with others
- Manage their design projects
- Provide the correct data to manufacturing
- Are more likely to use advanced features of their data management systems beyond simply storing and controlling CAD files
- Are 70% more likely to share data using the cloud for design and/or engineering
- Have 3 times as many people using their PLM system*
- Are 4.3 times as likely to have third parties such as customers and suppliers use their PLM systems *
Conclusions and Recommendations
Conclusions Effective design data management fundamentals enable better product development performance. The data shows, however, that companies face significant challenges in managing their critical design data due to product complexity. These challenges can lead to quality problems and inefficiency, and prevent companies from taking advantage of strategic opportunities such as design reuse. Addressing these challenges with effective design data management practices and technology leads to significant business advantages, including improved efficiency, quality, and time to market. Top Performers are more effective at data management and take a more collaborative approach to managing designs. To enable this, Top Performers leverage more mature use of their design data management systems. The fundamentals of design data management, supported by an effective data management solution, provide significant business value and provide a foundation that can be expanded on for future benefits. But to differentiate today, companies must go beyond controlling and accessing data. Recommendations Based on industry experience and research for this report, Tech-Clarity offers the following recommendations:- Ensure that your business has the basic fundamentals of data management in place, but expand the maturity of data management usage to drive higher value
- Improve design data management effectiveness to improve business performance in product design and development
- Explore the use of cloud collaboration to improve data management and product development performance

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- PepsiCo Senior Director of Global R&D Susan Hamel
- Kalypso Consumer Goods Global Director Colin Speakman
- Tech-Clarity President Jim Brown


- David Heiny, Co-Founder and CEO of SimScale
- David Weir-McCall, Industry Marketing Manager Architecture – Epic Games
- Eveart Foster, Director Technology Adoption of BuiltWorlds
- Igal Kaptsan, General Manager Software of GE Additive
- Igor Tsinman, Co-Founder and President of AMC Bridge
- Yuan Qinghui, Director Modeling and Data Science of Donaldson



Table of Contents
- Executive Summary
- Product Development Is Critical to Business Strategies
- The Time Wasters
- Implications of Time Wasters to the Business
- A Solution to Avoid Time Wasters
- Defining Product Complexity
- Business Value from PLM
- Extending PLM Use Results in Greater Satisfaction
- How Companies implement PLM
- Additional Values Due to the Cloud
- Conclusions
- Recommendations
- About the Research
- Acknowledgments
Executive Summary
Engineers Impact Business Success Exceptional products are critical to success, regardless of whether a company's business strategy is to grow or improve margins. Likewise, engineers are crucial to ensuring products have what it takes to succeed in the market. Therefore, empowering engineers is key to the successful execution of business strategies.
Conclusions
Reclaiming Lost Time Smaller and medium-sized companies prioritize their future growth and sustained success on winning in the marketplace with better, differentiated products. To support this, they can give their product development capabilities a significant boost by eliminating the time wasters that consume engineers' valuable time. Companies of all sizes find that PLM can empower their engineers to innovate by significantly reducing engineers' time on non-value-added tasks. As a result, they can enjoy a competitive advantage. In addition, technology advances, such as cloud-based offerings, can reduce implementation time, cost, and difficulty, making PLM more accessible to smaller and medium-sized companies.Recommendations
Next Steps
- Consider the business impact of engineering time wasters on your company and make investments to minimize them. By empowering engineers to focus more time on value-added work, you will get to market faster with better, more differentiated products.
- Consider how challenging it can be to find and recruit engineering talent in today’s business climate. Freeing engineers from time-wasting tasks can help take some pressure off your existing staff, improving their work environment and productivity, and reducing the need to add more.
- Look at PLM as a potential solution to reduce engineering time wasters. Smaller and medium-sized companies report that PLM offers benefits such as centralizing data, supporting multiple domains, managing processes, and improving collaboration. This frees engineers from tasks that waste their time so they can focus more on engineering and innovation.
- Use PLM for more than managing data. Those most satisfied with PLM also use it to manage engineering change processes, access control, requirements, and release processes.
- Extend the use of PLM to a broader audience beyond engineering. Those most satisfied with it include management, manufacturing, quality, and sales as users.
- Select a solution that has the flexibility to configure to your processes. An overwhelming 74% who found the implementation easy, identified this as helpful to the implementation.
- Consider a cloud solution. Interestingly, 78% of those who implemented a cloud solution considered the deployment easy and implemented it in half the time required by those using a non-cloud solution.

Table of Contents
- Executive Overview
- The Business Value of Systems Engineering
- Start with Process Definition
- Manage Requirements
- Design the System
- Design the System to be Modular
- Support Product Line Variants
- Enable Detailed Design
- Support Connectivity
- Verify and Validate the System
- Assess Service Requirements
- Consider Vendor Attributes
- Identify Specific Needs for Your Company
- Conclusion
- Recommendations
- About the Author
- Acknowledgments
Executive Summary
Fierce global competition continues to drive companies to seek new ways to competitively differentiate their products. Many achieve this differentiation through smarter and more connected products. This approach creates many innovative possibilities for new products and services.
While smarter products and connectivity create exciting opportunities for innovation, they also bring unique challenges and add new levels of complexity. Much of this complexity comes from requirements to integrate mechanical components, electronics, and software. Connectivity adds further complexity as you add sensors, streaming data, and an ecosystem of connected systems. Whether you are a systems engineer, product architect, or domain-specific engineer, addressing this complexity requires expert systems engineering practices.
To set the foundation for expert systems engineering practices, companies should focus on the entire systems engineering process, ensuring there are solutions for all aspects of systems engineering. Expert systems engineering practices will help companies become even more competitive in ways that will lead to higher growth and greater profitability.
The right technology is an essential part of implementing and supporting these expert systems engineering practices. The right technology will enable improved collaboration, better traceability with a digital thread, and earlier visibility to potential problems. This will result in a more efficient development process while reducing the risk of finding errors late in the process, helping to avoid delays and increased costs. This buyer’s guide will help manufacturers select the right software to support systems engineering.
This guide comprises four major sections covering systems engineering software tool functionality, service requirements, vendor attributes, and special company considerations (Figure 1). Each section includes a checklist of key requirements to support your selection process for systems engineering software tools.

Conclusion
Expert systems engineering practices are vital to take advantage of innovation available through embedded software and the Internet of Things. The opportunities to create smart, connected devices can help companies set their products apart from the competition, helping them win new customers and increase revenues. However, bringing together systems of mechanical components, electronics, and software is complex. That complexity grows exponentially as companies try to meet the various needs of customers with different configurations. Connected systems add even further complexity as you add sensors, streaming data, and connected ecosystems.
Complexity increases the risk that things will go wrong. The impact of these problems can have a significant business impact and hurt product profitability. Implementing expert systems engineering practices, with the right software tools to support them, can manage this complexity, making it easier to successfully bring profitable products to market. Even if today’s smart, connected devices are relatively simple, as they evolve and offer critical services such as those that impact safety, they will increase in complexity and need the same level of expert systems engineering practices.
These practices and the supporting solution are not just limited to systems engineers either. There are a variety of IoT-related roles involved with planning, designing, and architecting connected systems, such as IoT solution architects, who will struggle with the exact same challenges as systems engineers. Companies planning for growth should consider both current and future needs.
However, there are so many aspects of systems engineering; determining the right solution for your company can be very difficult. Using a high-level list of tool and process evaluation criteria such as the ones in this guide can help narrow down potential solutions. The lists provide a quick “litmus test” to determine if a solution and partner are a good fit before conducting detailed functional or technical reviews. In the end, it is crucial to ensure that functionality, service, vendor, and special requirements are all considered when selecting a solution.

Industry Disruption is the New Normal
Today’s global manufacturing industry is more susceptible to global disruptions than ever. In the past decade, we’ve had to recover from earthquakes, volcanoes, tsunamis, political uncertainty, and a global pandemic. These events have led to demand volatility, supply chain disruptions, and workforce impacts. Consequently, our research finds that about three-quarters of companies have experienced an increase in business risk and disruption over the prior five years.Responding to Adversity by Bouncing Forward
The manufacturing industry, however, did not give up. Instead, our research shows that:- 86% of companies increased their focus and attention on digital transformation as a response to disruption
- 49% of companies accelerated technology adoption.
Top Performers Get More Value by Acting on Digital Data
One of the keys to success is digitalizing data and processes. Our research shows that Top Performing companies are:- 50% more likely to have fully digital design data than poorer performing companies.
- Three times as likely to have fully digital design and development processes.
- Three times as likely to integrate IT and OT data and systems.
- More than three times as likely to be able to provide access to manufacturing data in time to impact performance.
Interested in More?
Don’t miss out on the opportunity to learn how to navigate your smart factory initiatives at our exclusive event on April 28th 12:00pm-4:30pm at the Great Lakes Brewery in Cleveland, OH. The jam-packed agenda includes industry expert panel, a networking Lunch, a deep dive roundtable session, and a happy hour (where the real secrets are revealed)! This is your opportunity to learn how industry leaders took advantage of the emerging technology and lowered costs while simplifying operations. Register now to network with industry peers and experts. Limited seating at this intimate event! For full event information and registration please visit the event sponsor, FactoryEye. Click here to view the full list of speakers. You can also view the event brochure here.

Table of Contents
- The Digital Transformation Imperative
- PLM Plays a Vital Role in Digital Transformation
- PLM Provides Multi-Faceted Business Value
- The Untapped Value of PLM
- Extending PLM Value Requires Vision
- PLM Implementations Must be Made Future Ready
- Identifying Top Performing CPG Product Developers
- Adopt More Digital Product Design Approaches
- Be More Strategic About PLM
- Utilize PLM More Broadly
- Take Advanced Approaches to Product Definition
- Leverage Advanced Product Formulation Techniques
- The State of PLM Implementations
- Move to the Cloud
- Conclusions
- About the Research
- Acknowledgments
Is PLM Meeting Its Potential?
PLM: Innovation Platform of Product Governance Utility?
PLM Plays a Vital Role in Digital Transformation
Support Digital Transformation PLM is the backbone of the digital enterprise. Respondents rated the importance of PLM to the strategic initiatives discussed above. A full 85% of companies see PLM as critical or important to digital transformation, and one-quarter say it is critical. On the other hand, only 3% see it as not important or relevant to supporting this strategic business imperative. Support Digital Threads, Twins The conclusion that PLM plays a vital role in digital transformation is supported by its importance for digital thread and digital twin initiatives. Over one-half of CPG companies say that PLM is important or critical to supporting the digital thread. Respondents report very similar views on the importance of PLM to support digital twin initiatives. The findings confirm our experience that PLM is an important part of digital transformation in the CPG industry.The Untapped Value of PLM
Achieve Top-Line Benefits CPG companies surveyed for this study were asked what goals they have achieved from PLM. The top two benefits, faster product development and improved product innovation, can lead to higher market share and greater revenue. Increased quality may also help improve sales and the top line. About half of the CPG companies polled report they receive each of these benefits. They also reported cost savings and operational benefits.
Conclusions
Top Performers Show That PLM Systems Can Deliver More Value PLM provides significant top-line, bottom-line, and operational value for CPG companies. Surveyed companies report multiple business benefits ranging from time-to-market improvements to increased compliance and traceability. Even with the high level of value realized, there is more to be achieved by extending PLM further. Top Performers set the stage to show how Others can get the most out of their current PLM investments. Top Performers, more than Others:- View PLM as a strategic platform for product innovation
- Recognize the value of PLM internally and in the value chain
- Extend PLM to more departments
- Digitalize data and processes
- Use more (and more advanced) PLM capabilities
- Use more advanced data modeling and simulation capabilities


Table of Contents
- The Performance Engineering Imperative
- Predicting Equipment Performance Virtually
- Create Holistic Equipment Models
- Predict Performance Through Simulation
- Improve Predictive Through Testing
- Improve Predictions with Field Data
- Improve Equipment Performance with Lower Overhead
- Acknowledgments
The Performance Engineering Imperative
Increasing Market Demands Engineers need to continuously improve and optimize equipment designs. Global competition is intense and the performance bar is constantly being raised. Equipment must meet increasingly challenging market demands for weight, fuel efficiency, sustainability, regulatory compliance, and more connected operations environments while still meeting aggressive time-to-market requirements. Some manufacturers are also evolving to new business models, such as product-as-a-service (PaaS), where profitability relies directly on equipment performance in the field.
- Holistically by managing tradeoffs
- Accurately by gaining a meaningful understanding of the impact of engineering decisions
- Efficiently by making it feasible to optimize early and often throughout the design process
Improve Equipment Performance with Lower Overhead
Adopt Predictive Performance Engineering Like predictive service, which has proven value, predictive performance engineering has tremendous business potential. The approach allows companies to unlock new levels of innovation and performance while simultaneously improving efficiency and reducing cost. Manufacturers that improve performance engineering maturity will likely be able to develop more innovative, high-performance equipment at a lower cost. At the same time, they will no longer have to trade off hitting their certification and market readiness objectives in order to reach performance goals. Improve Performance Engineering Maturity Companies can increase performance engineering maturity by improving the following four processes:- Create holistic equipment models
- Predict performance through simulation
- Improve predictions through testing
- Improve predictions with field data

- Jim Brown (keynote) - President and Founder, Tech-Clarity
- Bouncing Forward with Smart Manufacturing Solutions
- Mike Yost - Creator project IGBYS
- Technology Will Not Save Manufacturing. Manufacturers Will
- Jake Hall - Business Development Manager, Feyen Zylstra
- How to Attract the Next Workforce with Automation and Digital Solutions
- John Keyes - Director, Feyen Zylstra
- Sustainability & The Smart Manufacturing Imperative
- Kausik Dasgupta - Vice President of Technology, FactoryEye
- Data Lakes - Why Should I Care? They are not a nice-to-have but a must-have for manufacturers
- Jane Biddle - Moderator, FactoryEye
- Terri Ghio - President, North America, FactoryEye

Table of Contents
Executive Summary
- Importance of Concept and Industrial Design
- Identifying Top Performers
- Strategies to Reduce Non-Value-Added Work
- 1. Improve Efficiency with a Single Solution
- 2. Support Reuse by Leveraging the Concept during Detail Design
- 3. Enable Easy Iterations
- 4. Facilitate Internal Feedback
- 5. Solicit Customer Feedback
- 6. Select an Ideal Solution
- 7. Tie it Together on a Platform
- Recommendations
- About the Research
- Acknowledgments
Executive Summary
Reducing Non-Value-Added Work An overwhelming 76% of manufacturers agree that spending more time on concept and industrial design would improve products. Unfortunately, industrial designers waste significant time on non-value-added work, which holds them back. Further, detail designers waste even more time recreating industrial design details. Reducing this non-value-added work could help a company get to market faster, optimize profitability, maximize revenue potential, and offer more competitive offerings. Interestingly, 99% say that technology should play a role in limiting this non-value-added work. Moreover, the research identified key ways technology can help. Seven Practices This report reveals seven practices that will reduce non-value-added work in industrial design:- Use a single solution to support industrial and detail design.
- Leverage more of the concept model during detail design.
- Streamline design iterations between industrial and detail design.
- Facilitate internal feedback with more efficient design reviews.
- Involve customers as early as possible.
- Ensure your solution meets the unique needs of industrial designers.
- Tie everything together with a cloud platform.
- 95% of Top Performers who use a single solution for both industrial and detail design rate their remodeling process as ‘excellent’ or ‘very good.’
- 71% of industrial designers say it would save time if customers could provide more feedback early on.
- Industrial designers identify the top quality of an ideal industrial design solution as one that supports both subdivision and parametric modeling.

Recommendations
Recommendations and Next Steps Based on industry experience and research for this report, Tech-Clarity offers the following recommendations:- Investments to improve the efficiency of industrial and concept design can offer a competitive advantage.
- Consider a single solution to support industrial and detail design so that rather than wasting time importing, exporting, fixing, and recreating data, designers can spend more time on critical design work.
- Ensure that detail designers can reuse as much of the concept design as possible. Reinventing the wheel by recreating design details already in the concept model wastes their time. Instead, they could use that time to innovate, improve product quality, or beat deadlines to get to market faster.
- Reduce barriers to iterations so industrial and detail designers can remodel the design as needed to improve upon ideas, implement engineering changes, or fix manufacturability issues. When their work is siloed, iterations involve importing and exporting data, which wastes hours.
- Improve the timeliness of internal feedback with more streamlined ways to solicit feedback and more efficient design reviews. Real-time visibility to design progress on a single, integrated design collaboration platform can improve collaboration to efficiently collect feedback.
- Involve customers as early as possible to provide feedback to help ensure your product aligns with customer needs.
- When selecting a single solution for industrial and detail design, ensure it supports both subdivision and parametric modeling.
- Tie everything together with a cloud platform so that you have the infrastructure to support better collaboration, and designers can access the software capabilities they need on the hardware they prefer.

Table of Contents
- Executive Overview
- Taking Action to Get Value from a Flood of Data
- Building a Data Foundation
- Facet 1: Data Collection and Extraction
- Facet 2: Data Harmonization and Storage
- Facet 3: Contextualization
- Facet 4: Analytics
- Facet 5: Applications
- Consider a Complete Platform
- Recommendations
- References
- About the Author
Executive Overview
Urgency has never been greater for makers of biopharmaceuticals and consumer products to be faster, more efficient, and more confident in serving customers. As experts retire and regulators increase pressure, this need grows. Companies are look to Industry 4.0 technologies for digitalization and ideally predictive approaches to improve performance. At the heart of whether they succeed is their ability to manage operations data effectively. Many companies are experimenting with new technologies, particularly advanced analytics such as machine learning (ML), but most cannot build scalable and sustainable solutions. This raises a host of questions:- Why are so many projects failing to meet expectations?
- How can we get past proofs of concept (PoCs) and move on to enterprise-wide digital transformation?
- Beyond failing fast, can we finally move on from those experiments and succeed?
- Have a mix of digital and non-digital assets
- Operations data vary in provenance and usage
- Have to satisfy a wide variety of stakeholders by discipline and level in the organization
- Have many types, formats, cadences, and volumes of data to bring together: from operational technology (OT), IT, and business partners such as suppliers
- Need to contextualize a set of varied data streams differently for various stakeholders, such as asset, product, process, or time
- There are thousands of use cases and applications to satisfy, and even in a moderate-sized organization, many of these situations arise unexpectedly

Recommendations
Based on industry experience and research for this report, Tech-Clarity offers the following recommendations:- Focus on Value: Set your sights on how to stay focused on optimizing our business, allowing solution providers to deliver the industrial data management platform.
- Map It: Operations data management is a journey each piece building on the next. It pays to map it out in advance and get prerequisites in place at each step.
- Set Scope: Set small goals and craft projects that deliver business value and some piece of the foundation simultaneously. More than a one-off PoC, but not ”boiling the ocean,” and always with a long-term view.
- Commit Big: Don’t expect a silver bullet – this is hard work, just as ISA88 and 95 were; each facet is likely to be that much work
- Communicate: Educate everyone that getting to actionable insights requires all five facets of the data foundation to be robust and tightly integrated.
- Multi-level: Realize that this data foundation layer must include edge, fog, site, enterprise, and ecosystem layers.
- Ask Hard Questions: Don’t assume anything internally or with solution providers. Many projects make significant gains just in mapping the current reality and spotting issues to address immediately, even without new technology.
- Involve All: Form a team for the initiative with all stakeholders – IT and OT, production, quality, maintenance, site-level, enterprise-level.
- Partner: Don’t plan to do all the work yourself. You probably can’t afford the time, money, and problems of DIY experimentation.
- Test the Partner: If an industrial data management platform provider seems like a good fit, craft a proof of concept (PoC) in the context of a larger long-term strategy.
- Gain Benefits: Rather than fail fast, seek to succeed relatively quickly in getting to the next step with industrial data management

Table of Contents
- Why Digitalize Tech Transfer?
- Digitalize Data
- Automate Data Gathering
- Integrate Data and Processes
- Expand Collaboration
- Gain and Leverage Knowledge
- Adopt Best Practices to Improve Tech Transfer
- About the Research
- Acknowledgments
Why Digitalize Tech Transfer?
Tech Transfer Objectives The World Health Organization explains that “Technology transfer can be considered successful if there is documented evidence that the RU (receiving unit) can routinely reproduce the transferred product, process, or method against a predefined set of specifications as agreed with the SU (sending unit).” That may be true, but is that enough to define success from a business perspective? Companies have to be able to not just effectively transfer technology from early discovery to manufacturing. They have to do so efficiently, with quality, to compete. Identifying Best Practices Our report, Digitalizing Tech Transfer, benchmarked companies on their:- Speed in achieving steady-state, full-rate production
- Yield and product quality in early production, and
- Their overhead cost to support tech transfer.

- Digitalize Data
- Automate Data Gathering
- Integrate Data and Processes
- Expand Collaboration
- Gain and Leverage Knowledge
Adopt Best Practices to Improve Tech Transfer

- Digitalize Data
- Automate Data Gathering
- Integrate Data and Processes
- Expand Collaboration
- Gain and Capture Knowledge

Executive Summary
Endless Possibilities Recessions and the years following them often mark the birth of new businesses, making this a very exciting time for entrepreneurs. With expanded options for funding and opportunities for new technologies to enable innovation, there are endless possibilities for startups. As a result, we are now seeing more successful new companies than ever before. Unfortunately, for every success story, there are even more startups that do not make it. It is hard to start a new business from scratch as funding is limited, and competing with more established companies that have more resources can be challenging. The good news is that for many, modern software tools are closing the resource gap. New software solutions, supported by a modern infrastructure, allow even the smallest businesses access to resources that previously were available only to large companies with deep pockets. Cloud-Based Simulation A cloud-based simulation software platform is one such tool. Simulation software allows you to evaluate more options in less time, optimize the design, and catch problems early in the design process. In addition, startups can avoid significant upfront investments in software and hardware by using a cloud-based solution. Plus, companies do not need to worry about investing precious resources in an IT department to implement and maintain it. This Report This report discusses the most common reasons startups fail and explains how a cloud-based simulation platform can help. By taking advantage of the technology, it can allow you to:- Access more funding
- Improve profitability
- Get the product right
- Validate market needs
- Leverage the right talent

Table of Contents
- The Opportunities for Startups
- 1. Access More Funding
- 2. Improve Profitability
- 3. Get the Product Right
- 4. Validate Market Needs
- 5. Leverage the Right Talent
- Recommendations
- Acknowledgments
Recommendations
Recommendations and Next Steps
- Use simulation to demonstrate progress to investors to help justify additional funding.
- Leverage a cloud platform to preserve capital by avoiding high upfront costs for software licenses and investments in high-end hardware.
- Optimize your product for quality and cost to maximize profitability and allow you to price your product competitively.
- Support rapid iterations with simulation to develop a more innovative, competitive product.
- Take advantage of simulation in the cloud to validate your product early and often with potential customers to verify it will meet market needs.
- Tap into additional expertise as needed with a simulation cloud platform that will provide flexibility to easily collaborate with third parties.

Table of Contents
- Introducing the PDM Buyer's Guide
- Using the PDM Buyer's Guide
- Analyze PDM Capabilities: Control
- Analyze PDM Capabilities: Access
- Analyze PDM Capabilities: Share
- Assess Service Requirements
- Service Requirements: Implementation
- Service Requirements: User Adoption
- Service Requirements: Support
- Consider Vendor Requirements
- Identify Unique Company Needs
- Support the Digital Enterprise
- Conclusion
- Recommendations
- Acknowledgments
Introducing the PDM Buyer's Guide


Conclusion
Adopt Common Requirements for PDM PDM helps manufacturers address complexity and improve business performance. When evaluating PDM, manufacturers need to take into account:- Product requirements
- Implementation, adoption, and support requirements
- Vendor / business requirements
- Special requirements based on company size (particularly for very small or very large organizations)
- Special considerations to meet industry needs
- Future requirements as the manufacturing industry continues to digitally transform
Recommendations
Based on industry experience and research for this report, Tech-Clarity offers the following recommendations:- Identify and weigh PDM requirements based on company needs, company size, industry, and any unique company needs
- Use high-level requirements such as the ones in this guide to evaluate solutions based on business fit before engaging in detailed, technical evaluations
- Consider using the cloud or managed services solutions for companies that wish to move quickly, have limited IT resources, want to reduce overhead, or want to modernize their IT infrastructure
- Take user adoption into account, including simplified access, increased visualization, and task-specific apps for non-engineering resources
- Take into account long-term business and process growth needs including digital transformation, AR / VR, and IoT initiatives
- Consider the potential to expand to a more capable extended PDM or PLM system, but start small and get value along the way during implementation
How do you manage system family engineering?

Table of Contents
- New Semiconductor Imperatives
- Topic: Skyrocketing Demand Puts Pressure on Fabs
- Innovation Issues
- Meeting Demand
- Processing Challenges
- Progress Matters
- Quality Demands
- Empowering the Workforce
- IT Overwhelm
- Streamlining Information Systems
- MES Reimagined
- Reimagine and Reinvest in MES
- Acknowledgements
New Semiconductor Imperatives
Keeping up with rising demand, product portfolio growth, complex products, processing mix, and new application requirements creates tremendous challenges for semiconductor makers. Companies must increase their pace of both learning and ramping up new products and processes.
Production operations are a pinch point. To handle new demands, companies must optimize further. Thus, it is imperative to reimagine the manufacturing execution system (MES) for semiconductor to meet today’s needs.
MES must expand beyond work-in-process (WIP) and track and trace to become the data platform for production innovation, change, and efficiency. All types of facilities face this need, and modern MES can serve silicon, R&D, front-end fabs, back end, and even module production. While no longer isolated, front-end fabs face particular challenges and urgency due to their array of advanced capabilities.
Reimagine and Reinvest in MES
Expect More in Software Over the past several years, fab processes have changed, and your Manufacturing IT must as well. Open your minds to reimagine what MES is and does. Evaluate MES against customer requirements and strategic business needs. As this paper describes, modern MES solutions go far beyond track and trace and WIP management to support advanced semiconductor processing capabilities. Evaluate the Need Most semiconductor fabs have a vast “hidden fab” where workarounds have become the norm. To understand that count how many systems you use to support fab operations today measure how much time, effort, and energy your team spends finding and consolidating data for day-to-day operations understand how much time the Manufacturing IT team spends maintaining and customizing the MES. If you’re dissatisfied with the answers, look for software that supports your fab’s advanced capabilities. Reinvest in MES Push MES providers to get what you need. Some specifics you might need are in Figure 5. Beyond specifics, you need a smooth and coherent flow of data in the fab and into the enterprise. Speed of learning all you need to ramp up for NPI and continue improving yield and serving the market opportunities lies in that manufacturing data management. Investing is urgent – you can’t be as resilient nor as profitable as you want without data that’s in context and ready to use. *This summary is an abbreviated version of the research and does not contain the full content. Please visit our sponsor Critical Manufacturing for the full research (registration required). If you have difficulty obtaining a copy of the report, please contact us. [post_title] => Reimagining Semiconductor MES (eBook) [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => semiconductor-mes [to_ping] => [pinged] => [post_modified] => 2022-11-14 22:28:26 [post_modified_gmt] => 2022-11-15 03:28:26 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=11059 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [post_count] => 20 [current_post] => -1 [before_loop] => 1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 11416 [post_author] => 2574 [post_date] => 2022-05-13 09:36:21 [post_date_gmt] => 2022-05-13 13:36:21 [post_content] =>
- Why plant floor software must reach beyond functional fit into MES architecture
- How to be ready for anything: M&A, new plants, new regulations, moving products between plants
- The benefits of easily moving software to public or private cloud, on-prem, or hybrid
- Why containerization, orchestration, DevOps, and one-click deployment from a central CoE can avoid the MES trap of fit that fades over the years
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