Deutsche Bank estimates AWS derives about 15% of its total revenue mix or has attained a $1.5B revenue run rate in Europe.
AWS is now approximately 6x the size of Microsoft Azure globally according to Deutsche Bank.
These and other insights are from the research note published earlier this month by Deutsche Bank Markets Research titled AWS/Cloud Adoption in Europe and the Brexit Impact written by Karl Keirstead, Alex Tout, Ross Sandler, Taylor McGinnis and Jobin Mathew. The research note is based on discussions the research team had with 20 Amazon Web Services (AWS) customers and partners at the recent AWS user conference held in London earlier this month, combined with their accumulated research on public cloud adoption globally.
These are the five ways Brexit will accelerate AWS and public cloud adoption:
The proliferation of European-based data centers is bringing public cloud stability to regions experiencing political instability. AWS currently has active regions in Dublin and Frankfurt, with the former often being used by AWS’ European customers due to the broader base of services offered there. An AWS Region is a physical geographic location where there is a cluster of data centers. Each region is made up of isolated locations known as availability zones. AWS is adding a third European Union (EU) region in the UK with a go-live date of late 2016 or early 2017. Microsoft has 2 of its 26 global regions in Europe, with two more planned in the UK. Google’s Cloud Platform (GCP) has just one region active in Europe. The following Data Center Map provides an overview of data centers AWS, Microsoft Azure and GCP have in Europe today and planned for the future.
Brexit is making data sovereignty king. European-based enterprises have long been cautious about using cloud platforms to store their many forms of data. Brexit is accelerating the needs European enterprises have for greater control over their data, especially those based in the UK. Amazon’s planned third EU region based in London scheduled to go live in late 2016 or early 2017 is well-timed to capitalize on this trend.
Up-front costs of utilizing AWS are much lower and increasingly trusted relative to more expensive on-premise IT platforms. Brexit is having the immediate effect of slowing down sales cycles for managed hosting, enterprise-wide hardware and software maintenance agreements. The research team found that the uncertainty of just how significant the economic impact Brexit will have on the European economies is making companies tighten capital expense (CAPEX) budgets and trim expensive maintenance agreements. UK enterprises are reverting to OPEX spending that is already budgeted.
CEOs are pushing CIOs to get out of high-cost hardware and on-premise software agreements to better predict operating costs faster thanks to Brexit. The continual pressure on CIOs to reduce the high hardware and software maintenance costs is accelerating thanks to Brexit. Because no one can quantify with precision just how Brexit will impact European economies, CEOs, and senior management teams want to minimize downside risk now. Because of this, the cloud is becoming a more viable option according to Deutsche Bank. One reseller said that public cloud computing platforms are a great answer to a recession, and their clients see Brexit as a catalyst to move more workloads to the cloud.
Brexit will impact AWS Enterprise Discount Program (EDP) revenues, forcing a greater focus on incentives for low-end and mid-tier services. Deutsche Bank Markets Research team reports that AWS has this special program in place for its very largest customers. Under an EDP, AWS will give price discounts to large customers that commit to a full year (or more) and pay upfront, in many cases with minimum volume increases. One AWS partner told Deutsche Bank that they’re aware of one EDP payment of $25 million. In the event of a recession in Europe, it’s possible that such payments could be at risk. These market dynamics will drive AWS to promote further low- and mid-tier services to attract new business to balance out these larger deals.
Internet of Things (IoT) sensors and devices are expected to exceed mobile phones as the largest category of connected devices in 2018, growing at a 23% compound annual growth rate (CAGR) from 2015 to 2021.
By 2021 there will be 9B mobile subscriptions, 7.7B mobile broadband subscriptions, and 6.3B smartphone subscriptions.
Worldwide smartphone subscriptions will grow at a 10.6% CAGR from 2015 to 2012 with Asia/Pacific (APAC) gaining 1.7B new subscribers alone.
These and other insights are from the 2016 Ericcson Mobility Report (PDF, no opt-in). Ericcson has provided a summary of the findings and a series of interactive graphics here. Ericcson created the subscription and traffic forecast baseline this analysis is based on using historical data from a variety of internal and external sources. Ericcson also validated trending analysis through the use of their planning models. Future development is estimated based on macroeconomic trends, user trends (researched by Ericsson ConsumerLab), market maturity, technology development expectations and documents such as industry analyst reports, on a national or regional level, together with internal assumptions and analysis.In addition, Ericsson regularly performs traffic measurements in over 100 live networks in all major regions of the world. For additional details on the methodology, please see page 30 of the study.
Key takeaways from the 2016 Ericcson Mobility Report include the following:
Internet of Things (IoT) sensors and devices are expected to exceed mobile phones as the largest category of connected devices in 2018, growing at a 23% compound annual growth rate (CAGR) from 2015 to 2021. Ericcson predicts there will be a total of approximately 28B connected devices worldwide by 2021, with nearly 16B related to IoT. The following graphic compares cellular IoT, non-cellular IoT, PC/laptop/tablet, mobile phones, and fixed phones connected devices growth from 2015 to 2021.
400 million IoT devices with cellular subscriptions were active at the end of 2015, and Cellular IoT is expected to have the highest growth among the different categories of connected devices, reaching 1.5B connections in 2021. Ericcson cites the growth factors of 3GPP standardization of cellular IoT technologies and cellular connections benefitting from enhancements in provisioning, device management, service enablement and security. The forecast for IoT connected devices: cellular and non-cellular (billions) is shown
Global mobile broadband subscriptions will reach 7.7B by 2021, accounting for 85% of all subscriptions. Ericcson is predicting there will be 9B mobile subscriptions, 7.7B mobile broadband subscriptions, and 6.3B smartphone subscriptions by 2021 as well. The following graphic compares mobile subscriptions, mobile broadband, mobile subscribers, fixed broadband subscriptions, and mobile CPs, tablets and mobile routers’ subscription growth.
Worldwide smartphone subscriptions will grow at a 10.6% compound annual growth rate (CAGR) from 2015 to 2012. Ericcson predicts that the Asia/Pacific (APAC) region will gain 1.7B new subscribers. The Middle East and Africa will have smartphone subscription rates will increase more than 200% between 2015–2021. The following graphic compares growth by global region.
Mobile subscriptions are growing around 3% year-over-year globally and reached 7.4B in Q1 2016. India is the fastest growing market regarding net additions during the quarter (+21 million), followed by Myanmar (+5 million), Indonesia, (+5 million), the US (+3 million) and Pakistan (+3 million). The following graphic compares mobile subscription growth by global region for Q1, 2016.
90% of subscriptions in Western Europe and 95% in North America will be for LTE/5G by 2021. The Middle East and Africa will see a dramatic shift from 2G to a market where almost 80% of subscriptions will be for 3G/4G. The following graphic compares mobile subscriptions by region and technology.
Mobile video traffic is forecast to grow by around 55% annually through 2021, accounting for nearly 67% of all mobile data traffic. Social networking traffic is predicted to attain a 41% CAGR from 2015 to 2021. The following graphic compared the growth of mobile traffic by application category and projected mobile traffic by application category per month.
Ericcson also provided mobile subscription, traffic per device, mobile traffic growth forecast, and monthly data traffic per smartphone. The summary table is shown below:
Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.
Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.
The ten ways machine learning is revolutionizing manufacturing include the following:
Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.
Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.
Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.
Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent. By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown
Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post, Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.
Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale. For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning. Machine learning is extending what enterprise-level price optimization apps provide today. One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.
Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.
Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.
Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).
Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.