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Cloud Computing Dominates Deloitte’s 2015 Global Venture Capital Confidence Survey

  • globeCloud computing is the strongest technology investment sector for the third year in a row.
  • Biopharmaceuticals and robotics are the two sectors that have gained the greatest venture capital confidence from 2014 to 2015.
  • U.S. technology hubs (Silicon Valley/San Francisco, New York, Boston, Los Angeles & Chicago), Israel and Canada dominate while confidence continues to fall in Brazil and other emerging markets.

These and other insights are from Deloitte’s 2015 Global Venture Capital Confidence Survey.  You can download a copy here (PDF, no opt-in, 70 pp.).  Deloitte has also produced and made available infographics of the key findings here (PDF, no opt-in, 4 pp.). Deloitte & Touche LLP and the National Venture Capital Association (NVCA) collaborated on the eleventh annual survey, which was conducted in May & June of this year. The study assesses investor confidence in the global venture capital environment, market factors shaping industries and investments on specific geographies and industry sectors.    Please see page 4 of the study for a description of the methodology.

Key take-aways include the following:

  • Global venture capital investors are most confident in cloud computing (4.18). Investors were asked to rate their confidence level in each sector. Confidence levels were measured on a scale of 1 to 5, with 5 representing the most confidence. Basis points indicate year-over-year changes. Mobile (4.05), Internet of Things (3.95) and enterprise software (3.82) are the top four sectors venture capitalists are the most confident in today. Biopharmaceuticals are experiencing the greatest increase in venture capital confidence today.  Please the the graphic below for additional details.

cloud growth

  • The United States (4.17), Israel (3.90) and Canada (3.60) dominate venture capital investors’ confidence while emerging markets including Brazil continues to fall. U.S. technology hubs including Silicon Valley/San Francisco, New York, Boston, Los Angeles and Chicago continue to retain and reinforce global venture capital investor confidence.  The following graphic illustrates global venture capital investor’s confidence by nation.

globe

  • Silicon Valley/San Francisco (4.28), New York (3.86) and Boston (3.77) are the top three U.S. metros global venture capital investors have the greatest confidence in.  Los Angeles (3.43) and Chicago (3.22) are the fourth and fifth most trusted U.S. metros that venture capitalists have confidence in.  $15.2B was invested by global venture capital investors in Silicon Valley/San Francisco according to the Deloitte study.  The following graphic compares venture capitalist confidence levels and venture capital investment dollars received in 2015 through Q2.

US Metro

  •  Immigration reform (61%) and patent demand reform (36%) are the top two  initiatives U.S.-based venture capitalists want addressed by policy leaders.  For non-U.S. venture capitalists, tax incentives/credits (50%), infrastructure and job creation (both 41%) are the top two initiatives they would like to see public policy leaders take on in their home country.

top two

  • Cloud computing continues across all sectors as the area global venture capital investors have the greatest confidence in.  Confidence in biopharmaceuticals grew the fastest of any sector measured by the survey between 2014 and 2015, and this is the first year Deloitte is tracking investor confidence in the Internet of Things (IoT).  A sector comparison is provided below.

sector investing

10 Ways Analytics Are Accelerating Digital Manufacturing

  • 42% of manufacturers say big data and analytics as their highest priority in 2015.
  • 56% of power distribution providers rank big data and analytics within their top three priorities for 2015.
  • 61% of aviation companies consider big data and analytics their highest priority this year.

Bottom line: Digital manufacturing strategies are gaining ground as manufacturers adopt big data and analytics to improve operational effectiveness, time-to-market, new product development and increase product quality and reliability.

Analytics Are Fueling Digital Manufacturing Growth

Big data and analytics adoption by manufacturers is the first step many are taking to create a galvanized, intelligent digital thread that unifies every aspect of their value chains. For aerospace manufacturers whose supply chains are exceptionally complex, big data and analytics are revolutionizing value chains starting with suppliers and progressing through all operations.

The majority of manufacturers are relying on analytics to improve order accuracy, shipment & cycle time performance, and product quality. Those excelling at digital manufacturing strategies are gaining additional analytical insights into how they can make decisions more accurately, quicker and with lower potential costs and risks.

The manufacturing industry generates more data than any other sector of the global economy on a consistent basis.   The more complex a given manufacturers’ operations are, the more valuable the insights gained from big data and analytics. The following comparison of big data analytics priorities by industry from a recent speech given by Jeff Immelt, CEO and President of General Electric illustrates this point:

analytics customer survey

Source: GE Minds and Machines Presentation, Jeff Immelt, CEO & President, General Electric.

10 Ways Analytics Are Accelerating Digital Manufacturing 

The ten ways analytics is accelerating digital manufacturing adoption globally include the following:

  • Providing real-time operator intelligence (70%), remote monitoring and diagnostics (66%), and condition-based maintenance (59%) are the three most valuable areas for analytics GE customers mentioned in a recent survey. GE’s industrial customers are looking to tailor pre-built applications that can deliver the eight different functional areas shown in the graphic below.  Manufacturers are looking to asset performance management as an integral part of their digital thread’s analytics and insight.

industrial customer perspective

Source: GE Minds and Machines Presentation, Jeff Immelt, CEO & President, General Electric.

  • Using data modeling to improve production workflows is improving Earnings Before Interest & Taxes (EBIT) by 55% for a chemical manufacturer.  Using analytics and data modeling to make more accurate,  efficient decisions encompassing making or buying ingredients, choosing to substitute an ingredient or not, optimizing equipment usage and/or reliability and gaining incremental sales through increased production capacity is leading to a significant improvement in EBIT for a leading chemical manufacturer on a consistent basis.  The following graphic provides insights into the contributions of each factor in improving EBIT performance.

EBIT Growth

Source: Taming manufacturing complexity with advanced analytics. McKinsey & Company by Patrick Briest, Valerio Dilda, and Ken Somers February 2015. 

  • Planning-execution integration in production centers and real-time production integration are two areas where analytics are having the greatest impact on manufacturers’ operating expenses (OPEX). When analytics are integrated as part of a digital manufacturing strategy, supply chains benefit when Web-EDI (Electronic Data Interchange) and real-time order conformation are implemented and analyzed for continual improvement.

Digital initaitves impact

Source: Operational Excellence through Digital in Manufacturing Industries. Capgemini Consulting.

  • Optimization tools (56%), demand forecasting (53%), integrated business planning (48%) and supplier collaboration & risk analytics (46%) are being rapidly adopted by manufacturers today, setting the foundation for digital manufacturing growth.  Deloitte recently interviewed supply chain executives regarding the thirteen fastest-moving technical capacities they are using today and expect to use in the future. The following graphic provides an overview of supply chain capabilities current in use and what percent of each they expect to use in the future.

use of supply chain capabilities

Source: Supply Chain Talent of the Future Findings from the third annual supply chain survey. Deloitte.  2015.

  • Analytics is integral to making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Analytics is extensively used in manufacturing centers who are in the process of reengineering their entire operations to attain Industrie 4.0 compliance. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are also relying on analytics extensively to guide their Industrie 4.0 journey. A recent Deloitte study of Industrie 4.0 adoption found that research and development (43%) will see the greatest transformational contribution from Industry 4.0.

Industry 4.0 areas

Source: Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies. Deloitte Consulting, 2015

  • Analytics is enabling manufacturers to also scale real time cloud-based operational intelligence, condition-based monitoring, monitoring & diagnostics and asset lifecycle management across global manufacturing centers.  Capturing, aggregating, analyzing and taking action on analytics across all production centers using the GE Predix Cloud will also accelerate digital manufacturing growth over time.  Integrating analytics, industrial and sensor data into a scalable series of data models and apps delivered as a Platform-as-a-Service (PaaS), GE will make this service commercially available in 2016.  The following graphic illustrates how complex manufacturers could use Predix Cloud to improve operational efficiency and quality.

horizontal capability controls

Source: Jeff Immelt Presentation on Pivot Strategy, December 16, 2014

  • Analytics is providing greater insights into product, process, program and service quality, forcing manufacturers to revamp existing production centers and make them more efficient.  Gaining greater insight into which production centers and factories are delivering the highest quality products and why is now possible.  The vision of unifying quality across an enterprise quality management and compliance (ECQM) framework is now a reality, driving greater digital manufacturing growth as a result. The following graphic from Tableau is an example of a manufacturing quality dashboard.

Mfg quality dashboard

Source: Manufacturing Analytics Quality Dashboard

  • Increasing production yields through the use of more effective supplier quality management and bill of material (BOM) planning integrated within production processes.  Analytics is extensively being used today for supplier audits, supplier quality management and traceability. Capitalizing on the full value of these analytics is a strong catalyst for manufacturers to move closer to digitizing their operations. Check out SelectHub’s Supply Chain Management (SCM) Buyer’s Guide and leaderboard to learn more about the SCM landscape.
  • Using analytics to predict machine failures before they occur reduces downtime, production costs and increase customer satisfaction.  In highly regulated industries production equipment is periodically audited and reviewed for conformance to specific standards.  Integrating even the simplest sensor into production equipment can deliver valuable insights into what factors cause it to fail.  Analytics are providing Failure Mode and Effects Analysis (FMEA) in real-time today, providing manufacturers with a glimpse into which equipment and machinery will most likely fail when. Knowing this can save literally millions of dollars in lost production time.
  • Adopting Pareto Analysis to continually improve schedule, quality and cost performance to the cell or production center level is driving digital manufacturing adoption.  Determining which factors are enhancing or reducing product, process and program quality is now possible using advanced manufacturing analytics. Differentiating between the many symptoms of a quality problem and its root cause is now becoming possible, especially for companies pursuing digital manufacturing strategies.

Additional sources of information on the impact of analytics on digital manufacturing:

 

10 Ways Big Data Is Revolutionizing Supply Chain Management

supply chain managementBottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck.  Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.

Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.

The ten ways big data is revolutionizing supply chain management include:

Figure 1 SCM Data Volume Velocity Variety

  • Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.  Big data is revolutionizing how supplier networks form, grow, proliferate into new markets and mature over time. Transactions aren’t the only goal, creating knowledge-sharing networks is, based on the insights gained from big data analytics. The following graphic from Business Ecosystems Come Of Age (Deloitte University Press) (free, no opt-in) illustrates the progression of supply chains from networks or webs, where knowledge sharing becomes a priority.

figure 1 big data scm

  • Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace. These are the top four supply chain capabilities that Delotte found are currently in use form their recent study, Supply Chain Talent of the Future Findings from the 3rd Annual Supply Chain Survey (free, no opt-in). Control tower analytics and visualization are also on the roadmaps of supply chain teams currently running big data pilots.

Figure 2 use of supply chain capabilities

  • 64% of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations.  SCM World’s latest Chief Supply Chain Officer Report provides a prioritization of the most disruptive technologies for supply chains as defined by the organizations’ members.  The following graphic from the report provides insights into how senior supply chain executives are prioritizing big data analytics over other technologies.

disruptive tech

  • Using geoanalytics based on big data to merge and optimize delivery networks.  The Boston Consulting Group provides insights into how big data is being put to use in supply chain management in the article Making Big Data Work: Supply Chain Management (free, opt-in). One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from the article. Combining geoanalytics and big data sets could drastically reduce cable TV tech wait times and driving up service accuracy, fixing one of the most well-known service challenges of companies in that business.

Figure 4 geoanalytics

figure 6 big data

 

figure 7 big data

  • Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.  Supply chain visibility often refers to being able to see multiple supplier layers deep into a supply network.  It’s been my experience that being able to track financial outcomes of supply chain decisions back to financial objectives is attainable, and with big data app integration to financial systems, very effective in industries with rapid inventory turns. Source: Turn Big Data Into Big Visibility.

figure 8 traceability

  • Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant. Big data has the potential to provide improved traceability performance and reduce the thousands of hours lost just trying to access, integrate and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted.
  • Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data. IBM has developed a quality early-warning system that detects and then defines a prioritization framework that isolates quality problem faster than more traditional methods, including Statistical Process Control (SPC). The early-warning system is deployed upstream of suppliers and extends out to products in the field.
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