Worldwide end-user spending on public cloud services is forecast to grow 23.1% in 2021 to total $332.3 billion, up from $270 billion in 2020.
Garter predicts worldwide end-user spending on public cloud services will jump from $242.6B in 2019 to $692.1B in 2025, attaining a 16.1% Compound Annual Growth Rate (CAGR).
Spending on SaaS cloud services is predicted to reach $122.6B this year, growing to $145.3B next year, attaining 19.3% growth between 2021 and 2022.
These and many other insights are from Gartner Forecasts Worldwide Public Cloud End-User Spending to Grow 23% in 2021. The pandemic created the immediate need for virtual workforces and cloud resources to support them at scale, accelerating public cloud adoption in 2020 with momentum continuing this year. Containerization, virtualization, and edge computing have quickly become more mainstream and are driving additional cloud spending. Gartner notes that CIOs face continued pressures to scale infrastructure that supports moving complex workloads to the cloud and the demands of a hybrid workforce.
Key insights from Gartner’s latest forecast of public cloud end-user spending include the following:
36% of all public cloud services revenue is from SaaS applications and services this year, projected to reach $122.6B with CRM being the dominant application category. Customer Experience and Relationship Management (CRM) is the largest SaaS segment, growing from $44.7B in 2019 to $99.7B in 2025, attaining a 12.14% CAGR. SaaS-based Enterprise Resource Planning (ERP) systems are the second most popular type of SaaS application, generating $15.7B in revenue in 2019. Gartner predicts SaaS-based ERP sales will reach $35.8B in 2025, attaining a CAGR of 12.42%.
Desktop as a Service (DaaS) is predicted to grow 67% in 2021, followed by Infrastructure-as-a-Service (IaaS) with a 38.5% jump in revenue. Platform-as-a-Service (PaaS) is the third-fastest growing area of public cloud services, projected to see a 28.3% jump in revenue this year. SaaS, the largest segment of public cloud spending at 36.9% this year, is forecast to grow 19.3% this year. The following graphic compares the growth rates of public cloud services between 2020 and 2021.
In 2021, SaaS end-user spending will grow by $19.8B, creating a $122.6B market this year. IaaS end-user spending will increase by $22.7B, the largest revenue gain by a cloud service in 2021. PaaS will follow, with end-user spending increasing $13.1B this year. CIOs and the IT teams they lead are investing in public cloud infrastructure to better scale operations and support virtual teams. CIOs from financial services and manufacturing firms I’ve recently spoken with are accelerating cloud spending for three reasons. First, create a more virtual organization that can scale; second, extend the legacy systems’ data value by integrating their databases with new SaaS apps; and third, an urgent need to improve cloud cybersecurity.
CIOs and the organizations they serve are prioritizing cloud infrastructure investment to better support virtual workforces, supply chains, partners, and service partners. The CIOs I’ve spoken with also focus on getting the most value out of legacy systems by integrating them with cloud infrastructure and apps. As a result, cloud infrastructure investment starting with IaaS is projected to see end-user spending increase from $82B this year to $223B in 2025, growing 38.5% this year alone. End-user spending on Database Management Systems is projected to lead all categories of PaaS through 2025, increasing from $31.2B this year to $84.8B in 2025. The following graphic compares cloud services forecasts and growth rates:
Sage Intacct, Oracle ERP Cloud, and Microsoft Dynamics 365 ERP are the three highest-rated ERP systems by their users.
86% of Unit4 ERP users say their CRM system is the best of all vendors in the study. The survey-wide satisfaction rating for CRM is 73%, accentuating Unit4 ERP’s leadership in this area.
85% of Ramco ERP Suite users say their ERP systems’ analytics and reporting is the best of all 22 vendors evaluated.
These and many other insights are from SoftwareReview’s latest customer rankings published recently in their Enterprise Data Quadrant Report, Enterprise Resource Planning, April 2021. The report is based entirely on attitudinal data captured from verified owners of each ERP system reviewed. 1,179 customer reviews were completed, evaluating 22 vendors. SoftwareReviews is a division of the world-class IT research and consulting firm Info-Tech Research Group. Their business model is based on providing research to enterprise buyers on subscription, alleviating the need to be dependent on vendor revenue, which helps them stay impartial in their many customer satisfaction studies. Key insights from the study include the following:
Sage Intacct, Oracle ERP Cloud, Microsoft Dynamics 365 ERP, Acumatica Cloud ERP, Unit4 ERP and FinancialForce ERP are most popular with their users. SoftwareReview found that these six ERP systems have the highest Net Emotional Footprint scores across all ERP vendors included in the study. The Net Emotional Footprint measures high-level user sentiment. It aggregates emotional response ratings across 25 questions, creating an indicator of overall user feeling toward the vendor and product. The following quadrant charts the results of the survey:
80% of Acumatica Cloud ERP users say their system helps create more business value, leading all vendors on this attribute. How effective an ERP system is at adapting to support new business and revenue models while providing greater cost visibility is the essence of how they deliver business value. The category average for this attribute is 75%. Of the 22 vendors profiled, 12 have scores at the average level or above, indicating many ERP vendors are focusing on these areas to improve the business case of adopting their systems.
86% of Sage Intacct ERP users say their system excels at ease of implementation, leading all vendors in the comparison by a wide margin. Implementing a new ERP system can be a costly and time-consuming process as it involves extensive training, change management, and integration. Ease of Implementation received a category score of 75% across the 22 vendors, indicating ERP vendors are doubling down investments to improve this area. Just 11 of the 22 ERP vendors scored above the category average.
Bottom Line: Customer revenue lifecycles are the lifeblood of any services business, making FinancialForce’s Spring 2021 release timely given the services-first revenue renaissance happening today.
The essence of an excellent services business is that it can consistently create expectations clients trust and the business regularly exceeds. Orchestrating the best people for a given project at the right time, tracking costs, revenue, and margin across all services revenue, including those associated with a client’s assets, is very challenging. Customer revenue lifecycles are in the data, yet no one can get to them because they’re hidden across multiple systems that aren’t integrated. Knowing how efficient a services business is at turning customer engagement into cash is what everyone needs to know, but no one can find. The challenge is equally as daunting for long-established services providers and those rushing into new services businesses to redefine themselves in the hope of profits that are more consistent and fewer price wars.
How Much Is Customer Engagement Is Worth?
Services businesses face the paradox of exceeding client expectations with every engagement but not knowing if extra time, resources, and staff invested are paying off with more revenue and profit. FinancialForce’s Spring 2021 release looks to solve this problem. What galvanizes the ERP, PSA, and platform announcements is a fresh intensity on customer centricity, both for the services business adopting the Spring 2021 release and the customers it’s intended to serve.
Knowing if and by how much a given customer engagement and its revenue lifecycle generate cash, and its potential is one of the core focus areas of the Spring 2021 release. It’s badly needed as many services are flying blind today, overcommitting resources for little return and too often losing control of client engagement and paying the price in lost margin and profits. FinancialForce sees that pain and wants to alleviate it with better financial visibility on all aspects of customer services revenue. FinancialForce aims to provide customer-centric financial reporting down to the revenue stream and costing measure level.
Key Takeaways From The Spring 2021 Release
Customer centricity seen through a financial lens is the cornerstone of FinancialForce’s latest release. One of the primary goals of this release is to update more applications to Salesforce Lightning to provide FinancialForce users with a more consistent user experience across all applications. Salesforce has been doubling down for years on Lightning and its user experience technologies, with FinancialForce reaping the benefits for over a decade. FinancialForce is transitioning their core Professional Services Automation (PSA), Billing, Accounting & Finance and Procurement, Order and Inventory Management to Lightning in this release in response to their customers wanting a consistent user experience across the entire FinancialForce suite of applications. The Spring 2021 release reflects how FinancialForce strives to provide a real-time understanding of customer lifetime value for their ERP and PSA customers.
Additional key takeaways include the following:
FinancialForce sees reducing days to close as one of the highest priorities they need to address today. The majority of new feature announcements center on how the days to close cycles can be streamlined, especially across multi-company and multisite locations across geographic and currency-specific regions of the world. Multi-company currency revaluation will help FinancialForce customers who operate across multiple geographies that operate in different currencies and will be especially useful for those clients creating new global channels and considering foreign acquisitions. Further showing the high priority they are putting on reducing days to close, the Spring 2021 release also includes automated eliminations, multi-company period close for software closes, which are designed to temporarily close out a financial report and revenue schedules that can provide a future view in revenues – a key factor in knowing customer revenue lifecycles.
New features and a new Lightning interface for Accounting, Billing Central, and Inventory Management simplifies complex transactions for users. FinancialForce has one of the most customer-driven product management teams in enterprise software. The depth of features they have added to inventory management, transactional and reconciliation processes for accounting, drop-ship use cases, and enhancements for adding products to billing contracts show how much FinancialForce is listening to customers.
AI-enhanced financial reporting that works with any Einstein data set. FinancialForce leads the Salesforce partner ecosystem when it comes to integrating Tableau CRM (formerly known as Einstein Analytics) into its platform. Now thirteen releases in, FinancialForce’s Spring 2021 release reflects the intuitive, adaptive intelligence that the product management team aims to achieve by integrating Einstein into their financial reporting workflows.
Professional Services Automation (PSA) Applications Including Resource Management, Project Management, and Time & Expense upgraded to Lightning. Transitioning three of the core PSA applications to Lightning will help broaden adoption and make them easier to upsell and cross-sell across the FinancialForce customer base. It will also help existing customers using these applications get new employees up to speed faster on them, given how much more streamlined Lightning is as an interface compared to previous versions.
Intelligent Staffing solves the complex challenges resource managers face when assigning the best possible associates to a given project. Designed to filter and intelligently rank potential resources based on region, practice, group skill sets, and availability, Intelligent Staffing is designed to get resource managers as close to an ideal match as possible for a given project’s requirements. This is a much-welcomed new feature by FinancialForce customers who are large-scale services providers as they’re facing the challenges of assigning the right person to the right project at the right time to ensure project success.
Integration of Salesforce AI’s Next Best Action (NBA) will raise the level of project expertise at scale across customers. Part of the customer centricity focus in Spring 2021 is focused on providing customers with new technologies and applications to share expertise and knowledge at scale. Next Best Action provides prescriptive guidance for the project manager and will see heavy use in new associate onboarding across services businesses and achieve greater corporate-wide learning at scale. This is consistent with the focus in the Spring 2021 release on bringing greater space and speed to mid-size and larger services customers.
FinancialForce defines customer engagement and centricity from a financial standpoint in the Spring 2021 release. Too often, services businesses commit to large-scale projects without a clear idea of the customer revenue lifecycle. With FinancialForce, they can stop and ask if the level of customer engagement they’re committing to is worth it or not – and if it isn’t, what needs to be done. FinancialForce is doubling down on user experience and accelerating time-to-close, two areas their customers want innovation to and look to them to deliver. Look for FinancialForce to scale out with more MuleSoft and Tableau integration scenarios, all aimed at capitalizing on their expertise developing on the Salesforce platform. There’s a bigger challenge to customer engagement on the horizon, and that’s providing a real-time view of financials across all customers with all available data across a business, making MuleSoft integration key to FinancialForce’s future growth.
99% of mid-market manufacturing executives are familiar with Industry 4.0, yet only 5% are currently implementing or have implemented an Industry 4.0 strategy.
Investing in upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES) are manufacturers’ top three priorities based on interviews with them.
Mid-market manufacturers getting the most value out of Industry 4.0 excel at orchestrating a variety of technologies to find new ways to excel at product quality, improve shop floor productivity, meet delivery dates, and control costs.
Real-time monitoring is gaining momentum to improve order cycle times, troubleshoot quality problems, improve schedule accuracy, and support track-and-trace.
These and many other fascinating insights are from Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver Value. BDO is a leading provider of assurance, tax, and financial advisory services and is providing the report available for download here (PDF, 36 pp., no opt-in). The survey was conducted by Market Measurement, Inc., an independent market research consulting firm. The survey included 230 executives at U.S. manufacturing companies with annual revenues between $200M and $3B and was conducted in November and December of 2018. Please see page 2 of the study for additional details regarding the methodology. One of the most valuable findings of the study is that mid-market manufacturers need more evidence of Industry 4.0, delivering improved supply chain performance, quality, and shop floor productivity.
Insights from the Shop Floor: Machine Upgrades, Smart Machines, Real-Time Monitoring & MES Lead Investment Plans
In the many conversations I’ve had with mid-tier manufacturers located in North America this year, I’ve learned the following:
Their top investment priorities are upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES).
Manufacturers growing 10% or more this year over 2018 excel at integrating technologies that improve scheduling to enable more short-notice production runs, reduce order cycle times, and improve supplier quality.
Key Takeaways from BDO’s Industry 4.0 Study
Manufacturers are most motivated to evaluate Industry 4.0 technologies based on the potential for growth and business model diversification they offer. Building a business case for any new system or technology that delivers revenue, even during a pilot, is getting the highest priority by manufacturers today. Based on my interviews with manufacturers, I found they were 1.7 times more likely to invest in machine upgrades and smart machines versus spending more on marketing. Manufacturers are very interested in any new technology that enables them to accept short-notice production runs from customers, excel at higher quality standards, improve time-to-market, all the while having better cost visibility and control. All those factors are inherent in the top three goals of business model diversification, improved operational efficiencies, and increased market penetration.
For Industry 4.0 technologies to gain more adoption, more use cases are needed to explain how traditional product sales, aftermarket sales, and product-as-a-service benefit from these new technologies. Manufacturers know the ROI of investing in a machinery upgrade, buying a smart, connected machine, or integrating real-time monitoring across their shop floors. What they’re struggling with is how Industry 4.0 makes traditional product sales improve. 84% of upper mid-market manufacturers are generating revenue using Information-as-a-Service today compared to 67% of middle market manufacturers overall.
Manufacturers who get the most value out of their Industry 4.0 investments begin with a customer-centric blueprint first, integrating diverse technologies to deliver excellent customer experiences. Manufacturers growing 10% a year or more are relying on roadmaps to guide their technology buying decisions. These roadmaps are focused on how to reduce scrap, improve order cycle times, streamline supplier integration while improving inbound quality levels, and provide real-time order updates to customers. BDOs’ survey results reflect what I’m hearing from manufacturers. They’re more focused than ever before on having an integrated engagement strategy combined with greater flexibility in responding to unique and often urgent production runs.
Industry 4.0’s potential to improve supply chains needs greater focus if mid-tier manufacturers are going to adopt the framework fully. Manufacturing executives most often equate Industry 4.0 with shop floor productivity improvements while the greatest gains are waiting in their supply chains. The BDO study found that manufacturers are divided on the metrics they rely on to evaluate their supply chains. Upper middle market manufacturers are aiming to speed up customer order cycle times and are less focused on getting their total delivered costs down. Lower mid-market manufacturers say reducing inventory turnover is their biggest priority. Overall, strengthening customer service increases in importance with the size of the organization.
By enabling integration between engineering, supply chain management, Manufacturing Execution Systems (MES) and CRM systems, more manufacturers are achieving product configuration strategies at scale. A key growth strategy for many manufacturers is to scale beyond the limitations of their longstanding Make-to-Stock production strategies. By integrating engineering, supply chains, MES, and CRM, manufacturers can offer more flexibility to their customers while expanding their product strategies to include Configure-to-Order, Make-to-Order, and for highly customized products, Engineer-to-Order. The more Industry 4.0 can be shown to enable design-to-manufacturing at scale, the more it will resonate with senior executives in mid-tier manufacturing.
Manufacturers are more likely than ever before to accept cloud-based platforms and systems that help them achieve their business strategies faster and more completely, with analytics being in the early stages of adoption. Manufacturing CEOs and their teams are most concerned about how quickly new applications and platforms can position their businesses for more growth. Whether a given application or platform is cloud-based often becomes secondary to the speed and time-to-market constraints every manufacturing business faces. The fastest-growing mid-tier manufacturers are putting greater effort and intensity into mastering analytics across every area of their business too. BDO found that Artificial Intelligence (AI) leads all other technologies in planned use.
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.
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
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.
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. By 2021, 75% of enterprises with a positive IoT ROI will use tactical analytics applications to reduce operating costs, but the 25% that successfully invest strategically in a decision architecture will increase their revenue share. Source: IDC FutureScape: Worldwide IoT 2018 Predictions.
The global IoT market is projected to grow to $661.74B by 2021. The Industrial IoT market is expected to grow to $123.8B by 2021, and the IoT Cloud Market is estimated to grow to $7.15B by Source: IoT Growth: A Forecast.
WiFi and Bluetooth low energy (BLE) are top contenders as preferred IoT connectivity mechanisms. However, long-range, wide-area networks (LoRaWAN) and narrowband IoT (NB-IoT) are equally poised to give a tough fight to WiFi and BLE vendors. Data analytics, correlation, and pattern recognition capabilities at point-of-data creation prove to be a key decision factor in vendor evaluation. Source: IDC Survey Reveals Significant Impact of Internet of Things Initiatives on IT Infrastructure.
According to IDC, worldwide spending on the Internet of Things (IoT) is forecast to reach $772.5B in 2018, an increase of 14.6% over the $674B that will be spent in 2017. IoT hardware will be the largest technology category in 2018 with $239B going largely toward modules and sensors along with some spending on infrastructure and security. Services will be the second largest technology category, followed by software and connectivity. Source: IDC Forecasts Worldwide Spending on the Internet of Things to Reach $772 Billion in 2018.
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. FinancialForce, Kenandy 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.
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%).
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.
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.
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.
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%).
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.
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.
93% 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:
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.
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.
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.
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.
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.
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
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.
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.
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.