Bottom Line: In order to make strides towards operational excellence, medical device manufacturers need to gain greater insights into their Cost of Quality (CoQ).
Cost of Quality
If medical device manufacturing wasn’t already complex enough with strict FDA regulations and industry standards, Cost of Quality adds another dimension to its complexity. Since the breadth of information and data associated with CoQ can be overwhelming, we put this eBook together to provide clarity and simplify the process for those who want to learn more about where and how they can improve processes today.
This eBook helps you fully understand and calculate Cost of Quality so you can start making process improvement plans today.
From this eBook, you will learn:
Why cost of quality matters
Cost of quality’s impact on the value chain
The true cost of poor quality
How to calculate the costs of good and poor quality
The purpose of the index is to understand how business users perceive, plan for and utilize four key technologies: cloud, mobility, security and big data. Dell released the first wave of its results this week and will be publishing several additional chapters throughout 2016. You can download Chapter 1 of the study here (PDF, no opt-in, 18 pp.).
Key take-aways from the study include the following:
Orchestrating big data, cloud and mobility strategies leads to 53% greater growth than peers not adopting these technologies. Midmarket organizations adopting big data alone have the potential to grow 50% more than comparable organizations. Effective use of Bring Your Own Device (BYOD) mobility strategies has the potential to increase growth by 53% over laggards or late adopters..
73% of North American organizations believe the volume and complexity of their data requires big data analytics apps and tools. This is up from 54% in 2014, indicating midmarket organizations are concentrating on how to get more value from the massive data stores many have accumulated. This same group of organizations believe they are getting more value out of big data this year (69%) compared to last year (64%). Top outcomes of using big data include better targeting of marketing efforts (41%), optimization of ad spending (37%), and optimization of social media marketing (37%).
54% of an organization’s security budget is invested in security plans versus reacting to threats.Dell & TNS Research discovered that midmarket organizations both in North America and Western Europe are relying on security to enable new devices or drive competitive advantage. In North America, taking a more strategic approach to security has increased from 25% in 2014 to 35% today. In Western Europe, the percentage of companies taking a more strategic view of security has increased from 26% in 2014 to 30% this year.
IT infrastructure costs to support big data initiatives (29%) and costs related to securing the data (28%) are the two greatest barriers to big data adoption. For cloud adoption, costs and security are the two biggest barriers in midmarket organizations as is shown in the graphic below.
Cloud use by midmarket companies in France increased 12% in the last twelve months, leading all nations in the survey. Of the 11 countries surveyed, France had the greatest increase in cloud adoption within midmarket companies. French businesses increased their adoption of cloud applications and platforms from 70% in 2014 to 82% in 2015.
Hybrid cloud computing, machine learning and enterprise 3D printing are predicted to reach mainstream adoption within two to five years.
By 2020, 75% of businesses will already have transitioned to or will be in the process of becoming entirely digital businesses.
Enterprise 3D printing is at the low end of 5%-to-20% market penetration range today.
Seven new categories of technologies have been added for the first time this year. These include Advanced Analytics with Self-Service Delivery, Citizen Data Science, Cryptocurrency Exchange, Digital Dexterity, Micro Data Centers, People-Literate Technology and Software-Defined Security.
It’s clear that Gartner is targeting enterprise leaders most influential in new technology adoption, including digital, business and market strategists in this specific hype cycle given the pragmatic focus on business outcomes.
The Gartner Hype Cycle for Emerging Technologies, 2015 is shown below:
More and more manufacturers rely on paperless Manufacturing Execution Systems (MES) today to maintain quality, compliance, visibility and control. Compared to last year 2014, analysts are predicting more MES growth with IoT and Enterprise applications in the next few years according to the following roundup of Manufacturing Execution Systems forecasts and market estimates.
In 2018 the global market for MES is predicted to be sized at around $3.8 Trillion, compared to $2.7 Trillion in 2013. Source: Statista.
The Global Manufacturing Execution System (MES) and Enterprise Manufacturing Intelligence (EMI) market is expected to grow at a CAGR of 15.6% and 16.25% during the period of 2015-2020, respectively. The discrete industry segment is growing at a CAGR of 18.2% and 14.5%, followed by the process industry growing at a CAGR of 16.1% and 11.2%, respectively, during the period of 2015-2020. Source: Research and Markets
The Manufacturing Execution System market in process and discrete industries is estimated to reach USD 12.6 Billion by 2020, at a CAGR of 10.85% between 2015 and 2020. The discrete industry sectors comprise automotive, medical devices, aerospace & defense, and FMCG. The application of MES in the automotive sector accounted for a large share of the manufacturing execution system market. The emerging impact of cloud-based MES is propelling the adoption of manufacturing execution systems at the production stage. The market for hybrid cloud-based MES is expected to grow at a high CAGR of 15.54% between 2015 and 2020. Source: Markets and Markets
North America dominates the manufacturing execution system market with the highest market share of ~30% in 2014; it is expected to grow at a CAGR of 9.59% between 2015 and 2020. However, the Asia-Pacific and Europe markets for manufacturing execution system are expected to grow at high CAGR of 12.93% and 10.65%, respectively, between 2015 and 2020. Source: Markets and Markets
The global MES market is expected to grow two fold from 2014 to 2020, at a CAGR of 10-12%. Europe and North America are the largest contributors in the global MES market, capturing almost equal amount of share. APAC on the other hand is expected to register higher growth as it is attracting investors to manufacture. Growth in the manufacturing industries along with government policies to increase the foreign direct investment for emerging nations such as India are expected to accelerate the growth of manufacturing execution system market and provide a promising future in this industry. Source: Future Market Insights
Technavio’s analysts forecast the MES market in APAC to grow at a CAGR of 16.7% over the period 2015-2019. Source: Technavio
The worldwide Internet of Things (IoT) market is expected to grow 19% in 2015, led by digital signage. The IoT market in manufacturing operations will grow from $42.2 billion in 2013 to $98.8 billion in 2018, a five-year compound annual growth rate (CAGR) of 18.6%. Growth will be driven by ongoing efforts to increase efficiency and link islands of automation. Source: IDC Research
Enterprise application is forecast to grow at a rapid pace (with a 6.6% CAGR, the market is expected to reach $201.7 billion by 2019), driven by demand for public cloud offerings and modern intuitive user experiences. Demand for public cloud (at a CAGR of 15.6%) is clearly outpacing on-premises/other software market (at a CAGR of 3.9%), but the on-premises market remains significant because medium-sized and large enterprises are choosing to maintain existing software rather than disrupt the critical business processes surrounding the management of finance, order management, payroll, procurement, projects, strategic assets, engineering, manufacturing, and supply chain management. Source: IDC Research
Key take-aways from the report include the following:
Software & computing (18%), financial (11.6%), manufacturing (10.9%) and retail (9.8%) industries have the highest percentage of programmers creating big data and analytics applications today. Additional industries where big data app development is active and growing include entertainment (7.7%), telecommunications (7.5%), utilities & energy (6.6%) and healthcare (4.6%). The following graphic provides an overview of the industries addressed.
Capturing more information than traditional database practices (22.60%), capturing and analyzing unstructured data (21.10%) and the potential for visualizing or analyzing data differently (20.70%) are the three top use cases driving app development today. Evans Data found that capturing more information than traditional database practices allow increased 6% since last year, making it the top use case in 2015. The following graphic provides the distribution of responses by use cases from the developers surveyed.
Total size of the data being processed (40.8%), complex, unstructured nature of the data (38.1%) and the need for real-time data analysis (17.7%) are the top three factors driving big data adoption over traditional database solutions. Evans Data found that the size and complexity of structured and unstructured data is the catalyst that gets enterprises moving on the journey to big data adoption. The ability to gain greater insights into their data with descriptive, predictive and contextually-driven analytics is the fuel that keeps big data adoption moving forward in all companies.
33.2% of all big data and advanced analytics developers are concentrating on the software & computing industry. Of these developers, 36.7% are working in organizations of 101 to 1,000 employees, 32.9% are in enterprises of 1,000+ employees, and 30.1% are in organizations of 100 employees or less. 42.6% of all big data software development in manufacturing begins in enterprises (1K+ employees).
Enterprises competing in the software & computing industry (17.5%), manufacturing (15.8%) and financial industry (14%) are investing the heaviest in big data and analytics app development. Overall, 32% of big data and analytics projects are custom-designed and produced by system integrators and value-added resellers (SI, VAR). 70% of big data and advanced analytics apps for manufacturing are created by enterprise and system integrator/value-added reseller (SI/VAR) development teams. The following graphic provides an overview of industries targeted by big data, segmented by developer segment.
Sales and customer data (9.6%), IT-based data analysis (9.4%), informatics (8.7%) and financial transactions (8.4%) are the most common big data sets app developers are working with today. In addition marketing, system management, production and shop floor data, and web & social media-generated data are also included. Evans Data found that informatics data sets grew the fastest in the last six months, and scientific computing is now competing with transaction processing systems as a dominant data set developers rely on to create new apps.
Marketing departments have quickly become the most common users of big data and advanced analytics apps (14.4%) followed by IT (13.3%) and Research & Development (13%). Evans Data asked developers which departments in their organizations are putting big data and advanced analytics apps to use, regardless of where they were created. 38.2% of all big data use in organizations today are in customer-facing departments including marketing, sales, and customer service.
Availability of relevant tools (10.9%), storage costs (10.2%) and siloed business, IT, and analytics/data science teams (10.0%) are the top three barriers developers face in building new apps. It’s interesting to note that compliance and having to transition from legacy systems did not score higher in the survey, as these two areas are inordinately more complex in more regulated, older industries. For big data and advanced analytics to accelerate across manufacturing and financial industries, compliance and legacy systems integration barriers will need to first be addressed.
Quality of data (19.2%), relevance of data being acquired (13.5%), volume of data being processed (12.6%) and ability to adequately visualize big data (11.7%) are the four biggest problem areas faced by big data developers today. Additional problem areas include the volume of data in storage (10.5%), ability to gain insight from big data (10.1%) and the high rate of data acquisition (7.6%). The remainder of problem areas are shown in the graphic below.
Providing real-time correlation and anomaly detection of diverse security data (29.9%) and high-speed querying of security intelligence data (28.1%) are the two most critical areas vendors can assist developers with today. Big data and analytics app developers are looking to vendors to also provide more effective security algorithms for various use case scenarios (17.6%), flexible big data analytics across structured and unstructured data (14.2%) and more useful graphical front-end tools for visualizing and exploring big data (5.1%).
Enterprises are realizing only 35% of the value from their workloads already in the cloud.
Leading enterprise cloud adopters have migrated nearly two-thirds of their workloads to the cloud, yet the average company has only 18% there.
Up to 50% of the value of cloud investments is predicated on streamlining and improving company operations.
These and other insights are from the recently published Bain & Company study, Tapping Cloud’s Full Potential. A downloadable version of the study is available here (8 pp, free no opt-in). Bain & Company interviewed 428 companies and found that enterprises that are focused on getting strategic value from their cloud investments aim to migrate at least 50% of their workloads to the cloud.
Key take-aways from the study include the following:
Only 35% of the value from workloads in the cloud today is being realized, leaving 65% of their value untapped. Enterprises have the potential to gain much greater value from the workloads they already have in the cloud. The following figure from the study shows current realized value versus potential value.
Bain advises enterprises who want to lead their markets to take a “cloud first” approach to initiating new IT workloads. Taking a cloud first approach requires redefining how new IT operational models scale to support and accelerate emerging and existing business models. The following graphic illustrates this point.
Bain found that putting workloads on public or hybrid clouds delivers more value in the form of bigger cost savings, greater flexibility, more scalability and better built-in services. For example, adopting the Salesforce1 development platform has enabled some companies to launch 80% more applications annually and speed time to market by 70%. Bain also cites enterprises that moved development to IaaS and PaaS clouds from Amazon Web Services (AWS) reducing downtime by 72% and improving application availability by 3.9 hours per user per year.
Change management is essential for any enterprise to realize the full value of their cloud investment. Bain found that no matter how much effort is put into planning, piloting and implementing cloud technologies, up to 50% of the value of cloud investment is predicated on streamlining company operations first. Change management strategies endorsed and evangelized by senior executives is critical for cloud-based systems and platform to succeed.
Enterprises that take an Agile development approach and tighten the integration between development and infrastructure with DevOps practices increase the probability of cloud project success. Bain sees enterprises who standardize their development platforms, leverage automation tools and take advantage of self-service options for stakeholders succeed with their cloud initiatives.
Investments in virtualization pay off. Bain also found that well-run IT organizations with 60% to 75% of their traditional server environments virtualized are able to migrate faster than those that are less reliant on virtualization.
At the Gartner Symposium/ITxpo held October 4 – 8th in Orlando, David Cearley, Vice President and Gartner Fellow, released the company’s Top Ten Strategic Technology Trends for 2016. What makes this latest series of top ten strategic technology trends so fascinating for manufacturers is the emergence of Digital Mesh, Smart Machines and an entirely new IT reality comprised of an Adaptive Security Architectures, Advanced System Architectures, Mesh App and Service Architectures and Internet of Things (IoT) Architecture and Platforms. These three strategic areas unify the ten trends Gartner defines as significant for 2016.
How Algorithms And Smart Machines Will Revolutionize Manufacturing
Gartner predicts that algorithms will have a galvanizing effect across and within enterprises and implies this will extend to value chains, defining the future of business from an interconnection and relationship standpoint. Algorithms will increasingly be used for automating background tasks that are completed by smart machines. General Electric’s Predix platform, IBM’s IoT Foundation and several other cloud-based IoT platforms are already making progress on transforming the vision of algorithm-based or smart machines into a reality in global manufacturing. Taken in this context, Gartner’s top ten strategic technology trends for 2016 are directly applicable to the future of digital manufacturing. A graphic illustrating the top ten strategic technology trends for 2016 is shown below.
Gartner Top 10 Strategic Technology Trends For 2016
A summary of the top 10 strategic technology trends for 2016 are:
The Device Mesh – Gartner defines the device mesh as including mobile devices, wearable, consumer and home electronic devices, automotive devices and environmental devices — such as sensors in the Internet of Things (IoT). What’s so compelling about this trend from a manufacturing standpoint is the potential to capture quality, cost, time-to-market and most importantly – customer feedback – during each phase of a product or service’s journey through the value chain of a business. Accuracy, agility, time-to-market and quality will all drastically improve as a result of the device mesh becoming more commonplace.
Ambient User Experience – Having a digital mesh as a foundation, the next step is to create a unified, seamless user experience across all applications. This is partially attainable today with cloud-based platforms. Digital manufacturing’s growth will be predicated on how well an ambient user experience can be delivered to the shop floor.
3D Printing Materials – The growing range of 3D-printable materials will drive a compound annual growth rate of 64.1 percent for enterprise 3D-printer shipments through 2019. These advances will necessitate a rethinking of assembly line and supply chain processes to exploit 3D printing. “3D printing will see a steady expansion over the next 20 years of the materials that can be printed, improvement in the speed with which items can be printed and emergence of new models to print and assemble composite parts,” said Mr. Cearley.
Information of Everything – Everything in the digital mesh produces, uses and transmits information. This information goes beyond textual, audio and video information to include sensory and contextual information. Information of everything addresses this influx with strategies and technologies to link data from all these different data sources. Capturing this data and putting it into a manufacturing strategy context can save thousands of hours a year by better optimizing manufacturing operations.
Advanced Machine Learning – In advanced machine learning, deep neural nets (DNNs) move beyond classic computing and information management to create systems that can autonomously learn to perceive the world, on their own. The explosion of data sources and complexity of information makes manual classification and analysis infeasible and uneconomic. DNNs automate these tasks and make it possible to address key challenges related to the information of everything trend. DNNs (an advanced form of machine learning particularly applicable to large, complex datasets) is what makes smart machines appear “intelligent.”
Autonomous Agents and Things – Machine learning gives rise to a spectrum of smart machine implementations — including robots, autonomous vehicles, virtual personal assistants (VPAs) and smart advisors — that act in an autonomous (or at least semiautonomous) manner. While advances in physical smart machines such as robots get a great deal of attention, the software-based smart machines have a more near-term and broader impact.
Adaptive Security Architecture – The complexities of digital business and the algorithmic economy combined with an emerging “hacker industry” significantly increase the threat surface for an organization. Relying on perimeter defense and rule-based security is inadequate, especially as organizations exploit more cloud-based services and open APIs for customers and partners to integrate with their systems. Gartner says IT leaders must focus on detecting and responding to threats, as well as more traditional blocking and other measures to prevent attacks. Application self-protection, as well as user and entity behavior analytics, will help fulfill the adaptive security architecture.
Advanced System Architecture – The digital mesh and smart machines require intense computing architecture demands to make them viable for organizations. Providing this required boost are high-powered and ultraefficient neuromorphic architectures. Fueled by field-programmable gate arrays (FPGAs) as an underlining technology for neuromorphic architectures, there are significant gains to this architecture, such as being able to run at speeds of greater than a teraflop with high-energy efficiency.
Mesh App and Service Architecture – Monolithic, linear application designs (e.g., the three-tier architecture) are giving way to a more loosely coupled integrative approach: the apps and services architecture. Enabled by software-defined application services, this new approach enables Web-scale performance, flexibility and agility. Microservice architecture is an emerging pattern for building distributed applications that support agile delivery and scalable deployment, both on-premises and in the cloud. Containers are emerging as a critical technology for enabling agile development and microservice architectures. Bringing mobile and IoT elements into the app and service architecture creates a comprehensive model to address back-end cloud scalability and front-end device mesh experiences.
Internet of Things Platforms – IoT platforms complement the mesh app and service architecture. The management, security, integration and other technologies and standards of the IoT platform are the base set of capabilities for building, managing and securing elements in the IoT. IoT platforms constitute the work IT does behind the scenes from an architectural and a technology standpoint to make the IoT a reality. “Any enterprise embracing the IoT will need to develop an IoT platform strategy, but incomplete competing vendor approaches will make standardization difficult through 2018,” said Mr. Cearley.
Key take-aways from the study include the following:
64% of enterprises are making mobility a critical or high priority on their technology agendas today. 69% of B2C organizations say mobile technologies to their business/technology agendas versus 57% of B2B organizations. The following graphic compares the levels of priority enterprises are assigning to mobility on their technology agendas today.
Improving internal communication (68%), customer retention (62%), and increasing speed of decision-making (60%) are the top three factors driving mobile tech investment today. 82% of financial services firms are relying on mobile-based technologies to sustain and improve customer retention strategies. Enterprises are beginning to see just how critical it is to align selling and services strategies with how prospects and customers prefer to buy, which is increasingly through mobile channels. The following graphic provides an overview of internal and external factors driving mobile tech investments.
49% are planning to add additional Wi-Fi network capability to accommodate mobile devices, 48% are increasing spending on tablets, and 43% on smartphones. 42% are planning to increase spending on custom mobile app development (both in-house and outsourced) and 40% are increasing their investments in enterprise mobility management.
CRM (46%), Sales Force Automation (SFA) and Field Force Automation (FFA) (both 39%) are the top apps enterprises are investing the most heavily in. CRM leads all app areas, with 29% of enterprises extending mobile capabilities of existing apps, and 18% deploying new mobile apps. 21% of enterprises are extending the mobile capabilities of their SFA apps and 18% are doing the same with their FFA apps. The accelerating adoption of cloud Enterprise Resource Planning (ERP) systems including Acumatica, Plex Systems and others is a factor in 15% of enterprises choosing to deploy new mobile applications.
13% of enterprises are piloting IoT projects and 6% are using them in production today. 14% of SMBs surveyed have IoT pilots in progress today and 2% have integrated IoT into their production workflows. The following graphic compares IoT adoption maturity by size of business.
48% of all enterprises predict that IoT will require new mobile security strategies and/or investments. 44% of the largest enterprises with over 1,000 employees predict that IoT will require new IT skill sets and expertise as well. The following graphic provides an overview of the eight most significant areas where IoT is predicted to most impact enterprise mobility strategies.
When evaluating mobile vendors, 90% of enterprises with over 1,000 employees expect potential vendor partners’ products and systems will integrate well with existing infrastructure and applications. In aggregate, 85% of all enterprises expect mobile vendors to provide products and systems that integrate well with existing infrastructure and apps already in use. Additional factors include ability to meet security requirements and ease of deployment and management (both 83%). Support and services is the 3rd most important attribute with a 78% aggregate rating and 84% in enterprises with over 1,000 employees.
Integration, security and ease of deployment are the top three areas enterprises are using to evaluate mobile vendors. Integration with existing infrastructure and applications is most important in education and government sectors (92%), followed by Financial Services (91%) and Healthcare (89%). Security dominates as an evaluation factor in two of the most regulated industries in the survey, Financial Services (96%) and Healthcare (92%).