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Posts from the ‘ERP’ Category

10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations is achievable with machine learning.
  • Reducing supply chain forecasting errors by 50% and lost sales by 65% with better product availability is achievable with machine learning.
  • Automating quality testing using machine learning is increasing defect detection rates up to 90%.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level.

Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

The ten ways machine learning is revolutionizing manufacturing in 2018 include the following:

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations are is achievable with machine learning. Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimization are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

  • Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.

  • Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC. Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

  • McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

  • Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

  • Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%. AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimization across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

  • Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more. Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

  • A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning. The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

  • Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE). OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Additional reading:

Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi

Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute

Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services

Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)

Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone

Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge

Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Get started with the Connected factory preconfigured solution, Microsoft Azure

Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum

Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group

Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell

Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK

Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated reviewAi Edam28(1), 83-97.

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company

Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney

The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press

The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017

The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applicationsProduction & Manufacturing Research4(1), 23-45.

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Roundup Of Internet Of Things Forecasts And Market Estimates, 2018

 

  • According to IDC, worldwide spending on the IoT is forecast to reach $772.5B in 2018. That represents an increase of 15% over the $674B that was spent on IoT in 2017.
  • The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%.
  • Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each.
  • Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector.
  • Internet Of Things Market To Reach $267B By 2020 according to Boston Consulting Group.
  • According to IDC FutureScape: Worldwide IoT 2018 Predictions, By the end of 2020, close to 50% of new IoT applications built by enterprises will leverage an IoT platform that offers outcome-focused functionality based on comprehensive analytics capabilities.

The last twelve months of Internet of Things (IoT) forecasts and market estimates reflect enterprises’ higher expectations for scale, scope and Return on Investment (ROI) from their IoT initiatives. Business benefits and outcomes are what drives the majority of organizations to experiment with IoT and invest in large-scale initiatives. That expectation is driving a new research agenda across the many research firms mentioned in this roundup. The majority of enterprises adopting IoT today are using metrics and key performance indicators (KPIs) that reflect operational improvements, customer experience, logistics, and supply chain gains. Key takeaways from the collection of IoT forecasts and market estimates include the following:

  • The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%. According to GrowthEnabler & MarketsandMarkets analysis, the global IoT market share will be dominated by three sub-sectors; Smart Cities (26%), Industrial IoT (24%) and Connected Health (20%). Followed by Smart Homes (14%), Connected Cars (7%), Smart Utilities (4%) and Wearables (3%). Source: GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in.

  • Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector. Advisory firm Bain predicts the most competitive areas of IoT will be in the enterprise and industrial segments. Bain predicts consumer applications will generate $150B by 2020, with B2B applications being worth more than $300B. Globally, enthusiasm for the Internet of Things has fueled more than $80B in merger and acquisition (M&A) investments by major vendors and more than $30B in venture capital, according to Bain’s estimates. Source: Bain Insights: Choosing The Right Platform For The Internet Of Things

  • The global IoT market is growing at a 23% CAGR of 23% between 2014-2019, enabling smart solutions in major industries including agriculture, automotive and infrastructure. ― Key challenges to growth are the security and scalability of all-new connected devices and the adherence to open standards to facilitate large-scale monitoring of different systems. Source: Export opportunities of the Dutch ICT sector to Germany (25-04-17), PDF, 95 pp., no opt-in

  • According to  Variant Market Research, the Global Internet of Things (IoT) market is estimated to reach $1,599T by 2024, from $346.1B in 2016, attaining a CAGR of 21.1% from 2016 to 2024. Asia-Pacific is predicted to grow at the fastest CAGR over the forecast period 2016 to 2024. The growth is attributed to increasing adoption of IoT in emerging countries such as India and China, high rate of mobile and internet usage, and development of next-generation technologies. Source: Global Internet of Things (IoT) Market: Rising Adoption of Cloud Platform Noticed by Variant Market Research. 

  • Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each. Improving the accuracy, speed, and scale of supply chains is an area many organizations are concentrating on with IoT. IoT has the potential to redefine quality management, compliance, traceability and Manufacturing Intelligence. Business-to-Consumer (B2C) companies are projected to spend $25B on IoT in 2020, up from $5B in 2015. The following graphic compares global spending by vertical between 2015 and 2020. Source: Statista, Spending on the Internet of Things worldwide by vertical in 2015 and 2020 (in billion U.S. dollars).

 

  • By 2020, 50% of IoT spending will be driven by discrete manufacturing, transportation, and logistics, and utilities BCG predicts that IoT will have the most transformative effect on industries that aren’t technology-based today. The most critical success factor all these use cases depend on secure, scalable and reliable end-to-end integration solutions that encompass on-premise, legacy and cloud systems, and platforms.Source: Internet Of Things Market To Reach $267B By 2020.

  • The hottest application areas for IoT in manufacturing include Industrial Asset Management, Inventory and Warehouse Management and Supply Chain Management. In high tech manufacturing, Smart Products, and Industrial Asset Management are the hottest application areas. The following Forrester heat Map for 2017 shows the fastest growing areas of IoT adoption by industry. Source: IoT Opportunities, Trends, and Momentum Robert E Stroud CGEIT CRISC.

  • B2B spending on IoT technologies, apps and solutions will reach €250B ($296.8B) by 2020 according to a recent study by Boston Consulting Group (BCG). IoT Analytics spending is predicted to generate €20B ($23.7B) by 2020. Between 2015 to 2020, BCG predicts revenue from all layers of the IoT technology stack will have attained at least a 20% Compound Annual Growth Rate (CAGR). B2B customers are the most focused on services, IoT analytics, and applications, making these two areas of the technology stack the fastest growing. By 2020, these two layers will have captured 60% of the growth from IoT. Source: Internet Of Things Market To Reach $267B By 2020.

  • Manufacturers most relied on the Industrial Internet of Things (IIoT) in 2017 to help better understand machine health (32%) on the shop floor, leading to more accurate Overall Equipment Effectiveness (OEE) measurements. Changing how plant maintenance personnel will work and interact with all levels of operation (29.5%) and helping to better prevent and predict shutdowns (27.1%) are the top three use cases of IIoT according to Plant Engineering and Statista. 

  • Improving customer experiences (70%) and safety (56%) are the two areas enterprises are using data generated from IoT solutions most often today. Gaining cost efficiencies, improving organizational capabilities, and gaining supply chain visibility (all 53%) is the third most popular uses of data generated from IoT solutions today. 53% of enterprises expect data from IoT solutions to increase revenues in the next year. 53% expect data generated from their IoT solutions will assist in increasing revenues in the next year. 51% expect data from IoT solutions will open up new markets in the next year. 42% of enterprises are spending an average of $3.1M annually on IoT. Source: 70% Of Enterprises Invest In IoT To Improve Customer Experiences.

  • McKinsey Global Institute estimates IoT could have an annual economic impact of $3.9T to $11.1T by 2025. Their forecast scenario includes diverse settings and use cases including factories, cities, retail environments, and the human body. Factories alone could contribute between $1.2T to $3.7T in IoT-driven value. Source: McKinsey & Company, What’s New With The Internet of Things?

  • Business Intelligence Competency Centers (BICC), R&D, Marketing & Sales and Strategic Planning are most likely to see the importance of IoT. Finance is considered among the least likely departments to see the importance of IoT. The study also found that sales analytics apps are increasingly relying on IoT technologies as foundational components of their core application platforms.These and many other insights are from Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study. The study defines IoT as the network of physical objects, or “things,” embedded with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. The study examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. Please see page 11 of the study for details regarding the methodology.

  • Manufacturing, Consulting, Business Services and Distribution/Logistics are IoT industry adoption leaders. Conversely, Federal Government, State & Local Government are least likely to prioritize IoT initiatives as very important or critical. IoT early adopters are most often defining goals with clear revenue and competitive advantages to drive initiatives. Manufacturing, Consulting, Business Services and Distribution/Logistics are challenging, competitive industries where revenue growth is often tough to achieve. IoT initiatives that deliver revenue and competitive strength quickly are the most likely to get funding and support. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • IoT advocates or early adopters say location intelligence, streaming data analysis, and cognitive BI to deliver the greatest business benefit. Conversely, IoT early adopters aren’t expecting to see as significant of benefits from data warehousing as they are from other technologies. Consistent with previous studies, both the broader respondent base and IoT early adopters place a high priority on reporting and dashboards. IoT early adopters also see the greater importance of visualization and end-user self-service. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • Business Intelligence Competency Centers (BICC), Manufacturing and Supply Chain are among the most powerful catalysts of BI and IoT adoption in the enterprise. The greater the level of BI adoption across the 12 functional drivers of BI adoption defined in the graphic below, the greater the potential for IoT to deliver differentiated value based on unique needs by area. Marketing, Sales and Strategic Planning are also strong driver areas among IoT advocates or early adopters. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • IoT early adopters are relying on growing revenue and increasing competitive advantage as the two main goals to drive IoT initiatives’ success. The most successful IoT advocates or early adopters evangelize the many benefits of IoT initiatives from a revenue growth position first. IoT early adopters are more likely to see and promote the value of better decision-making, improved operational efficiencies, increased competitive advantage, growth in revenues, and enhanced customer service when BI adoption excels, setting the foundation for IoT initiatives to succeed. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • The most popular feature requirements for advanced and predictive analytics applications include regression models, textbook statistical functions, and hierarchical clustering. More than 90% of respondents replied that these three leading features are “somewhat important” to their daily use of analytics. Geospatial analysis (highly associated with mapping, populations, demographics, and other Web-generated data), recommendation engines, Bayesian methods, and automatic feature selection is the next most required series of features. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • 74% of IoT advocates or early adopters say location intelligence is critical or very important. Conversely, only 26% of the overall sample ranks location intelligence at the same level of importance. One of the most promising use cases for IoT-based location intelligence is its potential to streamline traceability and supply chain compliance workflows in highly regulated manufacturing industries. In 2018, expect to see ERP and Supply Chain Management (SCM) software vendors launch new applications that capitalize on IoT location intelligence to streamline traceability and supply chain compliance on a global scale. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

Sources:

10 Predictions For The Internet Of Things (IoT) In 2018

2017 Internet Of Things (IoT) Intelligence Update

Bain Insights, Three Ways Telcos Can Win On The Internet Of Things [Infographic]

Bain Insights: Choosing The Right Platform For The Internet Of Things

Big Data & Analytics Is The Most Wanted Expertise By 75% Of IoT Providers

Cambridge Consultants, Review of latest developments in the Internet of Things, 7 March 2017, 143 pp., free, no opt-in.

Cognizant Trend Study: Digital Industrial Transformation with the Internet of Things: How can European companies benefit from IoT?

Ernst & Young,  Internet of Things Human-machine interactions that unlock possibilities –  Media & Entertainment. 24 pp., PDF, no opt-in.

GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in

IDC, Worldwide Spending on the Internet of Things Forecast to Reach Nearly $1.4 Trillion in 2021, According to New IDC Spending Guide

IHS Markit IoT Trend Watch 2017, pdf, 26 pp., free, no opt-in

Internet Of Things Market To Reach $267B By 2020

Internet Of Things Will Revolutionize Retail

PwC, Leveraging the Upcoming Disruptions from AI and IoT, 20 pp., PDF, free, no opt-in

McKinsey & Company, Beyond The Supercycle: How Technology Is Reshaping Resources

McKinsey & Company,  Digital machinery: How companies can win the changing manufacturing game

McKinsey & Company, Taking the pulse of enterprise IoT

McKinsey & Company, What’s New With The Internet of Things?

IoT: Landscape and Nasscom Initiatives, May 2017. 36 pp., PDF, free, no opt-in

Stanford University Course EE392B, Industrial IoT: Applications Overview April 4, 2017, Dimitry Gorinevsky

Verizon, State of the Market: Internet of Things 2017 Making way for the enterprise

What Makes An Internet Of Things (IoT) Platform Enterprise-Ready?

Woodside Capital Partners, The Industrial Internet of Things: Making Factories “Smart” For The Next Industrial Revolution, PDF, 126 pp., free, no opt-in

THE INTERNET OF THINGS 2017 REPORT: How the IoT is improving lives to transform the world

The IoT Platforms Report: How software is helping the Internet of Things evolve

 

 

 

 

6 Ways Cloud ERP Is Revolutionizing How Services Deliver Results

  • Cloud ERP is the fastest growing sector of the global ERP market with services-based businesses driving the majority of new revenue growth.
  • Legacy Services ERP providers excel at meeting professional & consulting services information needs yet often lack the flexibility and speed to support entirely new services business models.
  • Configure-Price-Quote (CPQ) is quickly emerging as a must-have feature in Services-based Cloud ERP suites.

From globally-based telecommunications providers to small & medium businesses (SMBs) launching new subscription-based services, the intensity to innovate has never been stronger. Legacy Services ERP and Cloud ERP vendors are responding differently to the urgent needs their prospects and customers have with new apps and suites that can help launch new business models and ventures.

Services-based Cloud ERP providers are reacting by accelerating improvements to Professional Services Automation (PSA), Financials, and questioning if their existing Human Capital Management (HCM) suite can scale now and in the future. Vertical industry specialization is a must-have in many services businesses as well.  Factoring all these customer expectations and requirements along with real-time responsiveness into a roadmap deliverable in 12 months or less is daunting.  Making good on the promises of ambitious roadmaps that includes biannual release cycles is how born-in-the-Cloud ERP providers will gain new customers including winning many away from legacy ERP providers who can’t react as fast.

The following key takeaways are based on ongoing discussions with global telecommunications providers, hosters and business & professional services providers actively evaluating Cloud ERP suites:

  • Roadmaps that reflect a biyearly release cadence complete with user experience upgrades are the new normal for Cloud ERP providers. Capitalizing on the strengths of the Salesforce platform makes this much easier to accomplish than attempting to create entirely new releases every six months based on unique code lines. FinancialForceKenandy and Sage have built their Cloud ERP suites on the Salesforce platform specifically for this reason. Of the three, only FinancialForce has provided detailed product roadmaps that specifically call out support for evolving services business models, multiple user interface (UI) refreshes and new features based on customer needs. FinancialForce is also one of the only Cloud ERP providers to publish their Application Programming Interfaces (APIs) already to support their current and next generation user interfaces.
  • Cloud ERP leaders are collaborators in the creation of new APIs with their cloud platform provider with a focus on analytics, integration and real-time application response. Overcoming the challenges of continually improving platform-based applications and suites need to start with strong collaboration around API development. FinancialForce’s decision to hire Tod Nielsen, former Executive Vice President, Platform at Salesforce as their CEO in January of this year reflects how important platform integration and an API-first integration strategy is to compete in the Cloud ERP marketplace today. Look for FinancialForce to have a break-out year in the areas of platform and partner integration.
  • Analytics designed into the platform so customers can create real-time dashboards and support the services opportunity-to-revenue lifecycle. Real-time data is the fuel that gets new service business models off the ground. When a new release of a Cloud ERP app is designed, it has to include real-time Application Programming Interface (API) links to its cloud platform so customers can scale their analytics and reporting to succeed. What’s most important about this from a product standpoint is designing in the scale to flex and support an entire opportunity-to-revenue lifecycle.
  • Having customer & partner councils involved in key phases of development including roadmap reviews, User Acceptance Testing (UAT) and API beta testing are becoming common.  There’s a noticeable difference in Cloud ERP apps and suites that have gone through UAT and API beta testing outside of engineering.  Customers find areas where speed and responsiveness can be improved and steps saved in getting workflows done. Beta testing APIs with partners and customers forces them to mature faster and scale further than if they had been tested in isolation, away from the market. FinancialForce in services and IQMS in manufacturing are two ERP providers who are excelling in this area today and their apps and suites show it.
  • New features added to the roadmap are prioritized by revenue potential for customers first with billing, subscriptions, and pricing being the most urgent. Building Cloud ERP apps and suites on a platform free up development time to solve challenging, complex customer problems. Billing, subscriptions, and pricing are the frameworks many services businesses are relying on to start new business models and fine-tune existing ones. Cloud ERP vendors who prioritize these have a clear view of what matters most to prospects and customers.
  • Live and build apps by the mantra “own the process, own the market”. Configure-Price-Quote (CPQ) and Quote-to-Cash (QTC) are two selling processes services and manufacturing companies rely on for revenue daily and struggle with. Born-in-the-cloud CPQ and QTC competitors on the Salesforce platform have the fastest moving roadmaps and release cadences of any across the platform’s broad ecosystem. The most innovative Services-focused Cloud ERP providers look to own opportunity-to-revenue with the same depth and expertise as the CPQ and QTC competitors do.

Business Intelligence And Analytics In The Cloud, 2017

  • 78% are planning to increase the use of cloud for BI and data management in the next twelve months.
  • 46% of organizations prefer public cloud platforms for cloud BI, analytics and data management deployments.
  • Cloud BI adoption increased in respondent companies from 29% to 43% from 2013 to 2016.
  • Almost half of organizations using cloud BI (46%) use a public cloud for BI and data management compared to less than a third (30%) for hybrid cloud and 24% for private cloud.

These and many other insights are from the BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends published January 2017 (39 pp., PDF, no opt-in). Business Application Research Center (BARC) is a research and consulting firm that concentrates on enterprise software including business intelligence (BI), analytics and data management. Eckerson Group is a research and consulting firm focused on serving the needs of business intelligence (BI) and analytic leaders in Fortune 2000 organizations worldwide. The study is based on interviews completed in September and October 2016. 370 respondents participated in the survey globally. Given the size of the sample, the results aren’t representative of the global BI and analytics user base. The study’s results provide an interesting glimpse into analytics and BI adoption today, however. For a description of the methodology, please see page 31 of the study.

Key insights from the study include the following:

  • Public cloud is the most preferred deployment platform for cloud BI and analytics, and the larger the organization toe more likely they are using private clouds. 46% of organizations selected public cloud platforms as their preferred infrastructure for supporting their BI, analytics, and data management initiatives in 2016. 30% are relying on a hybrid cloud platform and 24%, private clouds. With public cloud platforms becoming more commonplace in BI and analytics deployments, the need for greater PaaS- and IaaS-level orchestration becomes a priority. The larger the organization, the more likely they are using private clouds (33%). Companies with between 250 to 2,500 employees are the least likely to be using private clouds (16%).

grouped-bi-cloud-platform-graphic

  • Dashboard-based reporting (76%), ad-hoc analysis and exploration (57%) and dashboard authoring (55%) are the top three Cloud BI use cases. Respondents are most interested in adding advanced and predictive analytics (53%), operational planning and forecasting (44%), strategic planning and simulation (44%) in the next year. The following graphic compares primary use cases and planned investments in the next twelve months. SelectHub has created a useful Business Intelligence Tools Comparison here that provides insights into this area.

cloud-bi-use-cases

  • Power users dominate the use of cloud BI and analytics solutions, driving more complex use cases that include ad-hoc analysis (57%) and advanced report and dashboard creation (55%). Casual users are 20% of all cloud BI and analytics, with their most common use being for reporting and dashboards (76%). Customers and suppliers are an emerging group of cloud BI and analytics users as more respondent companies create self-service web-based apps to streamline external reporting.

cloud-bi-power-users

  • Data integration between cloud applications/databases (51%) and providing data warehouses and data marts (50%) are the two most common data management strategies in use to support BI and analytics solutions today. Respondent organizations are using the cloud to integration cloud applications with each other and with on-premises applications (46%).  The study also found that as more organizations move to the cloud, there’s a corresponding need to support hybrid cloud architectures. Cloud-based data warehouses are primarily being built to support net new applications versus existing apps on-premise. Data integration is essential for the ongoing operations of cloud-based and on-premise ERP systems. A useful comparison of ERP systems can be found here.

cloud-data-integration

  • Data integration between on-premises and cloud applications dominates use cases across all company sizes, with 48% of enterprises leading in adoption. Enterprises are also prioritizing providing data warehouses and data marts (48%), the pre-processing of data (38%) and data integration between cloud applications and databases (38%). The smaller a company is the more critical data integration becomes. 63% of small companies with less than 250 employees are prioritizing data integration between cloud applications and databases (63%).

use-cases-of-cloud-management-by-company-size

  • Tools for data exploration (visual discovery) adopted grew the fastest in the last three years, increasing from 20% adoption in 2013 to 49% in 2016. BI tools increased slightly from 55% to 62% and BI servers dropped from 56% to 51%. Approximately one in five respondent organizations (22%) added analytical applications in 2016.

bi-tools-growth

  • The main reasons for adopting cloud BI and analytics differ by size of the company, with cost (57%) being the most important for mid-sized businesses between 250 to 2.5K employees. Consistent with previous studies, small companies’ main reason for adopting cloud BI and analytics include flexibility (46%), reduced maintenance of hardware and software (43%), and cost (38%). Enterprises with more than 2.5K employees are adopting cloud BI and analytics for greater scalability (48%), cost (40%) and reduced maintenance of hardware and software (38%). The following graphic compares the most important reason for adopting cloud BI, analytics and data management by the size of the company.

most-important-reason-for-adopting-cloud-bi-and-data-management

5 Ways Integration Is Enabling The Factory Of The Future

  • factory-of-the-future-report93% of global product leaders say that predictive maintenance combined with real-time equipment monitoring enabled by integration is a must-have for factory planning today.
  • 75% of global product leaders plan to implement factory of the future initiatives and programs in the next five years or less, starting with Industry 4.0
  • 67% of automotive executives expect that new technologies enabled by real-time integration will enable their teams to reach and exceed lean management and continuous improvement goals starting this year and accelerating through 2030.

Boston Consulting Group’s recent article, The Factory of the Future provides insights into a recent global survey the consulting firm conducted of more than 750 manufacturing product leaders from leading companies in three industrial sectors: automotive (which includes suppliers and original equipment manufacturers, or OEMs), engineered products, and process industries. The survey’s objective is to define the vision for the factory of the future in 2030.  Determining long-term benefits and the roadmap to implementation are also goals of the study Boston Consulting Group (BCG) and its research partner, the Laboratory for Machine Tools and Production Engineering at RWTH Aachen University, achieved. The Factory of the Future is a vision for how manufacturers should enhance production by making improvements in three dimensions: plant structure, plant digitization, and plant processes.

5 Ways Integration Fuels The Factory Of The Future’s Growth

Real-time integration based on intelligent objects that connect diverse enterprise systems including SAP, Salesforce and others is the foundation that manufacturing companies must adopt to excel in their Factory of the Future efforts. These real-time objects illustrate the future of Application Programmer Interfaces (API).  APIs that will fuel and drive the Factory of the Future will enrich each real-time integration points across manufacturing networks. Intelligent Objects pervasively used today are the precursors to the most valuable APIs that will enable Factories of the Future tomorrow. With APIs continually improving and gaining the capability to provide insight and intelligence, the essential role of real-time integration in all factories of the future becomes clear.

The following are the five ways integration is enabling the Factory of the Future today:

  1. Real-time integration enables the value chains supporting the Factories of the Future to continually accelerate, excel and improve with additional insight that drives future growth strategies. Bringing greater intelligence into each integration point across the value chains supporting the Factories of the Future leads to new technologies delivering greater lean management benefits. Real-time integration will deliver strong benefits in the areas of lean management, predictive maintenance, modular line setups, and the orchestration and collaboration of smart robots.

factory-of-the-future-1

  1. The Implementation Roadmap for the Factory of the Future shows how critical real-time integration is to the Factory of the Future’s vision being attained. Multidirectional layouts, modular line setups, sustainable production, the orchestration of smart and collaborative robotics and attainment of big data and analytics plans all are dependent on real-time integration. The following graphic from the study illustrates just how central integration is to the optimizing of plant structure and plant digitization.

factory-of-the-future-2

  1. By integrating large-scale enterprise systems including those from SAP, Salesforce and others with legacy, 3rd party and homegrown systems, every area of production quality will improve. The most urgent need global manufacturers have is finding new ways to improve product, process and service quality without raising costs. Improving the quality of these three dimensions makes any manufacturer more trusted and successful in selling next-generation products.  By aggregating data using real-time integration so that Big Data and advanced analytics can be used to find new patterns, some of the world’s most well-known manufacturers are excelling on product quality. To produce cylinder heads at its plant in Untertürkheim, Germany, Mercedes-Benz uses predictive analytics to examine more than 600 parameters that influence quality. Mercedes-Benz is an early adopter of using Big Data and advanced analytics to improve quality management and bring high precision to engineering. Bosch has implemented software that analyzes data about its production of fuel injectors in real time. The software monitors process adherence and recognizes trends. It automatically transmits information about deviations to operators, allowing them to improve the process accordingly.
  1. Real-time integration across and within manufacturing systems enables multi-directional layouts of production workflows. The Audi R8 manufacturing facility in Heilbronn, Germany, does not have a fixed conveyor so the teams there has greater multidirectional flexibility in building customized vehicles.  Real-time integration across the Audi factory floor is essential to provide R8 production teams with the specifics of how they can best collaborate and deliver the highest quality vehicles in the shortest amount of time. Real-time integration is enabling driverless transport systems, guided by a laser scanner and radio frequency identification technology in the floor, which moves the car bodies through the assembly process. These systems enable assembly layout changes quickly with no impact on existing production. Enabling real-time integration often involves extensive field mapping between different systems, which is a lengthy and error-prone process. Integration technology provider enosiX has developed a unique, real-time integration technology that obsoletes the need for field mapping and supports bi-directional data updates.
  1. Enabling the Factory of the Future’s production operations to flex in response to rapidly changing customer requirements is entirely dependent on real-time, reliable integration of production and customer-facing systems. The implications of the study on the future of manufacturing underscore just how critical it is for manufacturers to be agile enough to create entirely new business models while gaining insight and intelligence into how they can continually improve lean manufacturing. When real-time integration unifies a value chain for any manufacturer, their speed, scale and ability to simplify the complex processes required to serve customers turns into a formidable competitive advantage.

 

3 Ways To Improve Selling Results With SAP Integration


sap-integration
The more integrated the systems are supporting any selling strategy, the greater the chances sales will increase. That’s because accuracy, speed, and quality of every quote matter more than ever. Being able to strengthen every customer interaction with insight and intelligence often means the difference between successful upsells, cross-sells and the chance to bid and win new projects. Defining a roadmap to enrich selling strategies using SAP integration is delivering results across a variety of manufacturing and service industries today.

Getting more value out of the customer data locked in legacy SAP systems can improve selling results starting with existing sales cycles. Knowing what each customer purchased, when, at what price, and for which project or location is invaluable in accelerating sales cycles today. There are many ways to improve selling results using SAP integration, and the following are the top three based on conversations with SAP Architects, CIOs and IT Directors working with Sales Operations to improve selling results. These five approaches are generating more leads, closing more deals, leading to better selling decisions and improving sales productivity.

 3 Ways SAP Integration Is Improving Selling Results

  1. Reducing and eliminating significant gaps in the Configure-Price-Quote (CPQ) process by integrating Salesforce and SAP systems improves selling and revenue results quickly. The following two illustrations compare how much time and revenue escape from the selling process. It’s common to see companies lose at least 20% of their orders when they rely on manual approaches to handling quotes, pricing, and configurations. The greater the complexity of the deal is the more potential for lost revenue.  The second graphic shows how greater system integration leads to lower costs to complete an order, cycle time reductions, order rework reductions, and lead times for entire orders dropping from 69 to 22 days.

3 Ways To Improve Selling Results With SAP Integration

3 Ways To Improve Selling Results With SAP Integration

  1. Having customer order history, pricing, discounts and previously purchased bundles stored in SAP ERP systems integrated into Salesforce will drive better decisions on which customers are most likely to buy upsells, cross-sells and new products when. Instead of having just to rely on current activity with a given customer, sales teams can analyze sales history to find potential purchasing trends and indications of who can sign off on deals in progress. Having real-time access to SAP data within Salesforce gives sales teams the most valuable competitive advantage there is, which is more time to focus on customers and closing deals.  enosiX is taking a leadership role in the area of real-time SAP to Salesforce integration, enabling enterprises to sell and operate more effectively.
  1. Improving Sales Operations and Customer Service productivity by providing customer data in real-time via Salesforce to support teams on a 24/7 basis worldwide. The two departments who rely on customer data more than sales need to have real-time access to customer data on a 24/7 basis from any device at any time, on a global scale. By integrating customer data held today in SAP ERP and related systems to Salesforce, Sales Operations, and Customer Service will have the visibility they’ve never had before. And that will translate into faster response times, higher customer satisfaction and potentially more sales too.

Additional Reading:

Accenture, Empowering Your Sales Force

Aberdeen Group, Configure-Price-Quote: Best-In-Class Deployments that Speed The Sale

Aberdeen Group, Configure/Price/Quote: Better, Faster Sales Deals Enabled

Aberdeen Group, Sales Enablement Advances In Configure/Price/Quote Solutions

Forbes, What’s Hot In CRM Applications, 2015: Why CPQ Continues To Accelerate

Forbes,  Cloud-Based CPQ Continues To Be One Of The Hottest Enterprise Apps Of 2016

Forbes, Five Ways Cloud-Based CPQ Increases Sales Effectiveness And Drives Up CRM Adoption

The Sales Management Association,  The Impact of Quoting Automation: Enabling the Sales Force, Optimizing Profits, and Improving Customer Engagement

Five Ways Cloud Service Providers Are Making Manufacturers More Competitive

  • manufacturing-execution-systemsEnterprises are only realizing 35% of the total potential value of their cloud deployments according to a recent Bain & Company study.
  • Companies that moved development to IaaS and PaaS clouds from Amazon Web Services (AWS) reduced downtime by 72% and improved application availability by 3.9 hours per user per year.

These and other key take-aways are from the recent Bain & Company study, Tapping Cloud’s Full Potential. The full report PDF is available for download here (free, no opt-in). The following graphic from the report illustrates the currently realized value of cloud deployments in enterprises today according to Bain & Company.

Capturing only one-third of the value of their workloads

The researchers found several critical drivers of cloud value with one of the most important being the strengthening and clarifying of a product and service focus. The following graphic illustrates the critical drivers of cloud value.

getting the most value

Cloud Service Providers Give Manufacturers The Ability To Stay Competitive

Cloud-first strategies designed to accelerate and strengthen shifts in emerging business models is paying off according to Bain’s research results.

Manufacturers choosing to pursue a cloud-first strategy are focusing on evolving their business models, processes, systems and performance quickly to stay in step with customers’ needs. For many manufacturers, their customers’ pace is faster than internal IT organizations can anticipate and react to.  CSPs are helping to close that gap.

Here are five ways CSPs are making manufacturers more competitive:

  • Bringing industry expertise to the shop floor level. The best CSPs serving manufacturers today have management teams that have decades of combined manufacturing experience in specific industries. The CEO of a specialty tools manufacturer remarked that his company’s cloud strategy was more focused on accelerating plant floor performance first.  Working with a CSP that had expertise in their industry, this manufacturer was able to gain greater supply chain visibility and improve forecast accuracy, all with cloud-based apps.
  • Solving legacy and 3rd party system integration problems so that cloud-based ERP, CRM, supply chain management (SCM) systems can scale quickly. When a rust-belt based manufacturer of heating, ventilation and air conditioning (HVAC) systems had the opportunity to grow their business by expanding into build-to-order customized products, their CSP partner made it possible to integrate an entirely new product configurator and cloud-based ERP system module to manage quote-to-cash. Today, 30% of corporate-wide profits are from build-to-order selling strategies.
  • Knowledge-sharing supplier networks are becoming more attainable for manufacturers thanks to cloud technologies and CSPs. All manufacturers have strategic plans that include greater integration of their supplier networks, with many seeking to create knowledge-sharing networks. One of the best studies of how to create a knowledge-sharing network is from Dr. Jeffrey Dyer and Dr. Kentaro Nobeoka based on their intensive work with Toyota. Their study, Creating And Managing A High Performance Knowledge-Sharing Network: The Toyota Case is a great read. The following graphic from the study illustrates the evolution of a knowledge-sharing network. Manufacturers are relying on cloud platforms and CSPs to enable shifts in network structures and nurture change management to create self-sustaining systems.

Evolution of network

  • Two-tier ERP adoption in manufacturing is growing as CSPs master cloud ERP systems. CSPs are moving beyond providing basic services, specializing in cloud ERP, CRM, SCM, pricing, services and legacy system integration to keep pace with manufacturers’ demands. In one high tech manufacturer, their CSP partner orchestrated the procuring and launch of their cloud-based two-tier ERP system integrated to an SAP instance in their headquarters. Today they operate production centers in Asia, North America and Australia, all coordinated through the main SAP instance in the U.S. headquarters.
  • Making Service Level Agreements (SLAs) more relevant to manufacturing business models. Instead of just getting SLAs for uptime, security and system stability, manufacturers are getting advanced manufacturing intelligence dashboards that provide visibility to the plant or production center level.

Bottom Line:  Manufacturers are increasingly relying on CSPs’ cloud, industry and integration expertise to support the transition many are making to new business models and get greater than 35% of the value from their cloud investments.

Additional resources on Cloud ERP systems:

Why Cloud ERP Adoption Is Faster Than Gartner Predicts

200393880-001A recent study completed by Gartner titled Survey Analysis: Adoption of Cloud ERP, 2013 Through 2023 published on January 24, 2014, written by Nigel Rayner advises CIOs and application leaders of financial services institutions to “consider cloud ERP as a potential replacement for aging core ERP systems that are out of support or running on an old technology platforms (such as mainframes).“

The methodology is based on a survey of Gartner Research Circle members from North America, EMEA, APAC and Latin America from companies that range in size from $10M to $10B.

Key take-aways of the study including the following:

  • Including the 2% that already have core ERP in the cloud, a total of 47% of organizations surveyed plan to move their core ERP systems to the cloud within five years. This is because their ERP requirements tend to be focused around administrative ERP (financials, human capital management and procure-to-pay) where there is a wider range of cloud options (compared with manufacturing).
  • In aggregate, 30% of respondents say that the majority of their ERP systems will be on-premises for the foreseeable future as can be seen from the following graphic.

cloud adoption pie chart

  • 30% of organizations surveyed said they planned to keep the majority of their ERP systems on-premise for the foreseeable future.  Manufacturing organizations dominated this survey segment.

Why Cloud ERP Is Accelerating Faster Than Gartner Predicts

Two-tier ERP is the Trojan Horse of cloud ERP.  If Gartner had asked their respondents about if and how cloud-based ERP systems are being considered and used in two-tier ERP strategies globally, their survey and previous forecasts would have been significantly different.

From researching and working with manufacturers where two-tier ERP strategies make perfect sense for extending their legacy ERP systems to move into new markets, the following key take-aways emerge:

  • Achieving faster time-to-market while reducing cost of quality.  This is quickly turning into a year of transition for many supply chains, with the shift most noticeable in aerospace and defense.  Tighter project schedules driven by reduced budgets, coupled with more aggressive launch schedules is making this the year of the agile supplier.  Cloud-based ERP systems are essential to suppliers in this industry especially.
  • Legacy ERP systems lack scalability to support 21rst century compliance. One CIO who is a good friend jokingly refers to the legacy ERP systems populating each division of the manufacturing company he works for as fuel for his silos of excellence.  His point is that legacy ERP systems don’t have the data models to support the current quality management and compliance requirements corporate-wide and are relegated to siloed roles in his organization.  Cloud-based applications, specifically designed for ISO 9100, AS9100 Rev. C can do what legacy systems can’t, which is span across the aerospace manufacturer’s entire operations.
  • SaaS-based manufacturing and distribution software will increase from 22% in 2013 to 45% by 2023.  According to MintJutras, a leading research and advisory firm tracking ERP trends, a survey completed in 2013 shows SaaS-based applications will steadily grow from 22% of all manufacturing and distribution software installed to 45% within ten years.  The catalyst for much fo this growth will be two-tier ERP system adoption.
  • Microsoft’s New CEO knows the enterprise and cloud’s role in it. Satya Nadella has the daunting task of bringing innovation back into Microsoft.  As Anshu Sharma writes in his blog post today Satya Nadella: Microsoft, Coffee and the Relevance Question provides an excellent analysis of the challenges and paradoxes faced by the new Microsoft CEO.  It’s common knowledge in the Microsoft Partner community that the company runs one of the largest two-tier ERP system architectures in IT today, with an SAP R/3 instance in headquarters and Microsoft Dynamics AX running in each subsidiary.
  • All cloud ERP providers including Microsoft intend to monetize two-tier as much as they possibly can, architecting their respective Cloud OS strategies and enterprise suites to capitalize on it. Microsoft released an overview of their Cloud OS strategies in the following presentation, which provides a thorough overview of their perspective of the hosting market and how it relates to their apps business. Also included is the following graphic, Cloud OS: Innovation at Scale.  All of the factors taken together will drive up adoption of Microsoft Dynamics AX 2012 and streamline two-tier enterprise sales across all cloud ERP providers.  Last year at Microsoft Worldwide Partner Conference the announcement was made that Microsoft Dynamics AX 2012 would be available on Windows Azure in July, 2014.

cloud scale

  • Mobility is unifying the manufacturing shop floor to the top floor faster than anyone thinks.  In traditional ERP systems mobile platforms are most often used for material handling, warehouse management, traceability, quality management, logistics and service tracking. From the discussions I’ve had with CIOs and a few CEOs of manufacturing companies, there’s a high level of interest in analytics, alerts and approvals on Android and Apple tablets.  These apps and the speed of results they deliver are the new corporate bling. Intuitive, integrated and fast, these mobile apps make it possible for senior managers to check up on operations for wherever they are globally, in addition to approving contracts and being notified of events via alerts.  For Gartner’s assessment of cloud ERP to have been complete in this survey, mobility also needed to be covered

Best- And Worst-Performing Cloud Computing Stocks In The First Half Of 2013

Cloud computing stocks continue to show wide variation in performance throughout the first half of this year.

Ten of the twenty companies in the Cloud Computing Stock Index delivered returns to shareholders with NetSuite leading with a 37.30% share gain, delivering $13,730 on $10,000 invested on January 2, 2013.

To more fully define the stock performance of these companies, I’ve added Earnings Per Share (EPS), Price/Earnings Ratio, Year-To-Date (YTD) Total Gains or Loss, Annualized Gain or Loss, and Total Dollar Value of $10,000 invested on January 2, 2013.  You can download the latest version of the Cloud Computing Stock Index here.  The filter applied to these companies is that 50% or more of their revenues are generated from cloud-based applications, infrastructure and services.  Additional details of the index are provided at the end of this post.

 

Best Performing

Name

Symbol

(1/2/13 – 7/5/13)Total Gain or Loss

Annualized Gain or Loss

Total Dollar Value of $10K invested in this stock on Jan. 2, 2013 as of July 5th:

NetSuite Inc

N

37.30%

87.55%

$13,730.00

Keynote Systems, Inc.

KEYN

36.18%

84.53%

$13,618.00

CA, Inc.

CA

26.67%

59.83%

$12,667.00

Workday Inc

WDAY

23.81%

52.77%

$12,381.00

Cisco Systems, Inc.

CSCO

22.60%

49.82%

$12,260.00

Symantec Corporation

SYMC

18.84%

40.84%

$11,884.00

Amazon.com, Inc.

AMZN

11.10%

23.23%

$11,110.00

 

Worst Performing

Name

Symbol

(1/2/13 – 7/5/13)Total Gain or Loss

Annualized Gain or Loss

Total Dollar Value of $10K invested in this stock on Jan. 2, 2013 as of July 5th:

Rackspace Hosting, Inc.

RAX

-46.78%

-71.39%

$5,322.00

Fusion-IO, Inc.

FIO

-41.21%

-65.13%

$5,879.00

F5 Networks, Inc.

FFIV

-31.57%

-52.88%

$6,843.00

VMware, Inc.

VMW

-29.94%

-50.63%

$7,006.00

Riverbed Technology…

RVBD

-24.91%

-43.34%

$7,509.00

Red Hat, Inc.

RHT

-11.47%

-21.46%

$8,853.00

Key Take-Aways:

  • NetSuite leads the index with a 37.3% gain in their stock price, and $10K invested in their stock on January 2nd of this year would be worth $13,730 as of July 5th.  Cloud-based Enterprise Resource Planning (ERP) systems acceptance is accelerating, evidenced by the success NetSuite is having with their two-tier ERP strategy and recent announcement they are moving into manufacturing.  Their recent alliance with Oracle also shows upside potential.   A cloud-based ERP provider leading the index is good news for Acumatica and Plex Systems especially, the leader in cloud-based ERP systems for manufacturing and one of the most enthusiastic customer bases in enterprise software.  Both of these companies are privately held or they would have been included in the index.
  • The 20 companies that comprise the Cloud Computing Stock Index attained a 29.6% return from July 10, 2012 to July 5, 2013.  The Dow Jones Industrial Average (DJIA) gained 18.83%;  Microsoft, 14.02%; Oracle, 7.17%; and SAP, 27.51%.  The following chart compares the performance of each. Please click on the index to expand it for easier viewing.

  • Widespread adoption of Amazon Web Services, success using the Kindle series of tablets as customer acquisition tools for digital content, market leadership of the online retail landscape, and successful pilots of the AmazonFresh online grocery business in Los Angeles and Seattle are all fueling Amazon’s stock performance this year.

Specifics on the Cloud Computing Stock Index

I used The Cloud Times 100 as the basis of the index, and included the 20 following companies, all of which are publically traded.  The latest edition of the Cloud Computing Stock Index is shown here.  Please click on the index to expand it for easier viewing.

 Note: I do not hold equity positions or work for any of the companies mentioned in this blog post or included in the Cloud Computing Stock Index.  

Gartner Predicts CRM Will Be A $36B Market By 2017

CRM in 2017The latest enterprise software forecast from Gartner shows Customer Relationship Management (CRM) increasing to a $36.5B worldwide market by 2017, a significant increase from the $20.6B forecasted in Q1 of this year.  CRM also leads all enterprise software categories in projected growth, showing a 15.1% CAGR from 2012 to 2017, also revised up from 9.7% in the Q1 forecast.

The latest round of forecasts published in the report,  Gartner Forecast: Enterprise Software Markets, Worldwide, 2012-2017, 2Q13 Update shows CRM eclipsing ERP in worldwide market size in 2017.  The following graph compares the relative growth of CRM, ERP, Business Intelligence (BI), Supply Chain Management and Web Conferencing, Collaboration/Social Software Suites.  Source: Gartner Forecast: Enterprise Software Markets, Worldwide, 2012-2017, 2Q13 Update.  Please click on the image to increase its size for easier reading.

Figure 1 Forecast

Key Take-Aways

Figure 2 Forecast

  • Worldwide enterprise software spending is projected to be $304B in 2013 in the latest forecast, up from $279B in the Q1 forecast. Gartner claims stronger demand for CRM, supply chain management and security are leading to accelerating market growth.
  • ERP spending worldwide is projected to grow from $26.03B in 2013 to $34.3B in 2017, attaining a CAGR in the forecast period 2012 – 2017 of 7%.
  • Business Intelligence (BI) worldwide is projected to grow from $14B in 2013 to $18.6B in 2017, attaining a CAGR in the forecast period 2012 – 2017 of 7.3%.
  • Supply Chain Management (SCM) worldwide is projected to grow from $9.16B in 2013 to $13.6B in 2017, attaining a CAGR in the forecast period 2012 – 2017 of 10.4%.
  • Data Integration Tools and Data Quality Tools worldwide are projected to grow from $4B in 2013 to $6B in 2017, attaining a CAGR in the forecast period 2012 – 2017 of  10.3%.

 Bottom Line:  Gartner’s latest forecasts show that enterprises are realizing the most valuable assets they have are solid, long-term customer relationships.  Trust really is the new currency, as my friend Michael Krigsman often says.

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