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COVID-19 has likely had a greater impact on healthcare than on any other industry sector, said Glenn Snyder, Principal and Lead Analyst for MedTech at Deloitte, and a featured speaker at a recent SEMI webinar that offered a glimpse into the Future of MedTech in the run-up up to the SEMI Global Smart MedTech Symposium, kicking off tomorrow and running through August 5th. Snyder said medtech growth may appear muted in its early years but is poised to begin a steep climb as innovation continues, harkening back to the super-charged growth of circuitry on a wafer (aka Moore’s Law), which also saw a seemingly slow, flat start. Medtech enjoy its own exponential growth powered by 5G implementations, consumer demand, and the development of a robust ecosystem of bio-sensors, data standards, and regulatory improvements. Consumer-Driven Future and COVID-19 Impact Snyder noted that the future of medtech will be consumer-driven – enabled by open, highly interoperable data and secure platforms geared toward end users. A case in point: Detecting disease early through sensor systems will rely on not only on-body and environmental sensors, marking a fundamental shift from the today’s today’s hospital-centric system to improve health outcomes. Telemedicine growth during the pandemic is a notable example. In one case study of a health system, Snyder noted that telehealth usage skyrocketed from 1% to 60% of all patient visits over the early months of the pandemic but has since dropped to 10% due to the lack of charting, billing and other support systems needed to sustain the high rate of telehealth visits. Even so, hospitals expect to see a steady rise in consumers’ use of telehealth in the coming years. One driver are pilot programs for healthcare-at-home services for post-surgical patients. The programs have delivered better health outcomes and are more personalized and family-friendly than medical clinic or hospital visits. They also cost less. Digital monitoring using remote biometrics sensors are one key to driving the long-term success of these programs. Health Systems Changing Their Business Model In the medtech sector, changes in health system business models lag consumer adoption. What’s more, policy changes aren’t keeping pace with new models for medtech products. For medtech products to thrive, a solid foundation of data gathering, transmission and management capabilities that tie into traditional healthcare systems must be formed. Companies considering a vertically integrated approach to the medtech market can steer clear of healthcare providers – but only at the risk of having less access to patients and their historical healthcare data. Snyder said companies that control vertically integrated healthcare products and patient data can make support systems more efficient and robust but may struggle to deepen their market penetration. Companies such as Intuitive Surgical have found success with this model by offering highly differentiated products. Supply Chain Alarmingly Thin for Medtech Devices In a recent Deloitte survey of medtech companies, 60% reported that at least half of their products are powered by semiconductors, yet 70% noted pointed to high supply chain risks with most of their products because they have only a single source. Risk management and creating a resilient supply chain will remain key for medtech providers to adapt on a global scale. Partnerships and Collaborations During the event roundtable, Snyder mentioned that bio and pharma companies have partnered successfully to grow their businesses. Doug Kiehl of Eli Lilly, the moderator of the discussion, added that traditional healthcare providers should look outside of their usual business circles for medtech innovation. COVID-19 highlighted how new multi-disciplinary healthcare partnerships risk assessment processes have opened several paths to innovation previously unexplored. Both Snyder and Kiehl expect to see more collaboration between health systems and medtech innovators as they uncover synergistic business models. SEMI Global Smart MedTech Symposium Kicks Off Today Explore the gaps in the supply chain at the Global Smart MedTech Symposium and join the conversation with medtech device companies and health systems providers. Sessions include: Realtime Continuous Diagnostics and Monitoring Decentralized DNA Sequencing and Molecular Diagnostics Data Science and Infrastructure – AI/Data Fusion Applications in Rural and Decentralized Healthcare in the Digital Age The four-day symposium features three sessions at different times each day to cater to participants in Taiwan/Asia, Europe and North America. Register today! Heidi Hoffman is senior director of Technology Communities marketing at SEMI.
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Connecting product development to manufacturing to the field in the semiconductor equipment industry by becoming a Model-Based Enterprise Manufacturers across many industries, including the semiconductor equipment industry, are facing pressure to dramatically reduce product development cycle and production ramp times while also enhancing product quality and reliability. This challenge is complicated when multiple configurations are deployed and then maintained, enhanced and upgraded in the field. Buzzwords like digital thread, digital twin, smart manufacturing, Industry 4.0, and digital supply networks point toward a fusion of process, technology, data, and talent that promise the game-changing outcomes needed to address these challenges. Yet, there remains uncertainty about how these various elements come together into a cohesive approach across the product lifecycle. One such approach involves establishing a Model-Based Enterprise (MBE), with alignment across organizational silos, processes, and technologies. Too often, however, an effort to transform to an MBE is frustrated by “random acts of digital” that pursue implementation of certain digital technologies but fall far short of delivering value. What is needed instead is a deeper understanding of the characteristics of a true MBE and the tools and approaches to transform the organization, processes, and data to an MBE and thrive in the marketplace. Elements of a Model-Based Enterprise Modeling in engineering is not new, but what’s emerging are MBEs that comprise digital models connected upstream and downstream over the entire product lifecycle, from a product’s conception and development through its production and end market installation and use. At the heart of such MBEs are digital threads—integrated sets of processes executed within an interconnected technology ecosystem that drive the end-to-end product lifecycle and provide MBE data traceability front to back. A digital thread in the MBE environment includes all of the process, data, and system capabilities that enable digital representations of the product lifecycle stages, or digital twins, of which there are three primary types: The product digital twin is a virtual or simulated representation of the product and each of its components and configurations. While most manufacturers manage engineering models with CAD/CAE solutions, just 15% use product digital twins. Leaders in this space have seized a competitive advantage in product development and accelerated engineering. Yet, becoming a true MBE requires more than product digital twins. The process digital twin is a model of the manufacturing equipment, processes, and the workforce required to carry out related operations. The process digital twin represents the operation of the physical factory floor and its assets, complete with workflows and instructions that describe how the manufacturing processes are performed. A process digital twin relies on data from the product digital twin and allows the enterprise to build according to a product plan and predict what may happen on the factory floor. About 5% of enterprises use process digital twins. After production, a service digital twin represents the installation, use, maintenance, and repair of each product operating in the end market. The service digital twin is informed by product and process digital twins to facilitate adjustments and enhancements based on real-world data. Less than 5% of manufacturers use service digital twins, which is perhaps expected due to their reliance on the existence of the product and process digital twins. When underlying data (e.g., models, specifications, and configurations) are standardized and integrated across a digital thread, an enterprise has the capacity to monitor and refine a product over the span of the thread while also injecting insights and improvements back into the thread. The result is that engineering and manufacturing and end customer usage feedback is continuous and efficient, achieving in weeks what once took months. Visibility into materials, costs, suppliers, and more enable the enterprise to pivot and keep production moving, even when dealing with unforeseen challenges. Real-time monitoring that synthesizes live data also helps reveal performance insights and end market issues (e.g., installation issues, quality issues, etc.) that allow improvements to the offerings and operations of the enterprise. While the manufacturing and product development benefits of an MBE may be clear, the path to becoming an MBE and achieving these benefits can be challenging. Figure 1. Representative MBE end-to-end digital thread that connects product development, manufacturing, and the field (Source: Deloitte Development LLC, 2021) Accelerating Transformation New and emerging manufacturers have an opportunity to build toward their MBE vision without the historic data constraints legacy systems can impose. For more established manufacturers, such is more typical in the semiconductor equipment space, legacy technology and processes can present obstacles to an MBE transformation path. Stakeholders who have invested time and resources implementing certain enterprise platforms (e.g., CAD, PLM, ALM, MES, ERP, etc.) often look for how these can be used to enable a digital thread and digital twins over the enterprise. This limiting view frustrates a broader, more holistic opportunity to transform to an MBE and thrive in the marketplace. There is thereby a dual imperative to define a modernized and scalable end-to-end technology architecture for managing the MBE product data while also establishing a capabilities implementation roadmap that rapidly leads to MBE maturity. To be successful in your MBE transformation, four core functional capabilities that enable a digital thread must be considered: digital engineering, industrial simulation, manufacturing execution, and real-time monitoring. Addressing each with optimized tools allows a manufacturer to rapidly move from strategy to reality. Recognizing the complexity of addressing these functional needs, Deloitte has developed preconfigured solutions to help expedite and enhance transformation across each of these core areas. Design with D-PLM Simulate with D-Sim Execute with D-MES Monitor with D-IoT Accelerates product and application lifecycle management transformations with a multi-phased approach, including a phased, multi-year PLM/ALM roadmap and business case. Facilitates the ability to test and refine processes in a virtual environment, rapidly revealing the most efficient and effective industrial processes more quickly than is possible in a real-world environment. Integrates pre-defined processes related to production planning, execution, tracking and tracing, quality management, data collection, and visualization, with integrations to PLM and ERP. Delivers fast implementation of IoT capabilities that connect, collect, and analyze a broad scope of production data to drive quicker returns for high-impact areas while cultivating digital adoption. While many enterprises have various initiatives in model-based systems and manufacturing, few have tied them together with an end-to-end digital thread and set of data standards over the entire product life cycle. The foregoing preconfigured solutions can help enterprises transform to a true MBE that can typically achieve: 15% – 20% better development efficiency 30% – 50% faster time to market 8% – 20% product cost reduction 10% – 30% cost of quality reduction With such improvement potential, this could be the right time to map out and accelerate your MBE transformation to support your evolving business models and products. Deloitte Consulting LLP Co-Authors Kevin Prendeville Principal, Product Strategy Lifecycle Management [email protected] Vijay Santhanam Managing Director, Product Strategy Lifecycle Management [email protected] Kenneth Norton Senior Manager, Product Strategy Lifecycle Management [email protected] Dan Hamling Specialist Master, Technology Semiconductor [email protected] As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte USA LLP, Deloitte LLP and their respective subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication. Copyright © 2021 Deloitte Development LLC. All rights reserved.
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