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Posts tagged ‘ERP’

Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022

Gartner Predicts Public Cloud Services Market Will Reach $397.4B by 2022
  • 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.

Conclusion

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:

Which ERP Systems Are Most Popular With Their Users In 2021?

Which ERP Systems Are Most Popular With Their Users In 2021?
  • 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.
Which ERP Systems Are Most Popular With Their Users In 2021?
  • 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.
Which ERP Systems Are Most Popular With Their Users In 2021?

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.

Professional Services’ Revenue Challenges Are Complex

Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:

  • Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
  • When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
  • Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
  • Revenue leakage gains momentum and drains margins when the following happens:
    • Un-forecasted delays and timescale creep
    • Reduced utilization rates across each key resource required for the project to be completed
    • Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
    • Incorrect pricing versus the costs of sales & service often leads to customer churn.
    • Revenue leakage gains momentum as each of these factors further drains margin

Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:

How FinancialForce Is Using AI To Fight Revenue Leakage

Why Professional Services Are Especially Vulnerable To Revenue Leakage 

Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.

The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.

Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.

By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.

How FinancialForce Is Using AI To Fight Revenue Leakage

Conclusion

FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay.  Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.

COVID-19’s Impact On Tech Spending This Year

COVID-19's Impact On Tech Spending This Year

The human tragedy the COVID-19 pandemic has inflicted on the world is incalculable and continues to grow. Every human life is priceless and deserves the care needed to sustain it. COVID-19 is also impacting entire industries, causing them to randomly gyrate in unpredictable ways, directly impacting IT and tech spending.

COVID-19’s Impact On Industries

Computer Economics in collaboration with their parent company Avasant published their Coronavirus Impact Index by Industry that looks at how COVID-19 is affecting 11 major industry sectors in four dimensions: personnel, operations, supply chain, and revenue. Please see the Coronavirus Impact Index by Industry by Tom Dunlap, Dave Wagner, and Frank Scavo of Computer Economics for additional information and analysis.  The resulting index is an overall rating of the impact of the pandemic on each industry and is shown below:

Computer Economics and Avasant predict major disruption to High Tech & Telecommunications based on the industry’s heavy reliance on Chinese supply chains, which were severely impacted by COVID-19. Based on conversations with U.S.-based high tech manufacturers, I’ve learned that a few are struggling to make deliveries to leading department stores and discount chains due to parts shortages and allocations from their Chinese suppliers. North American electronics suppliers aren’t an option due to their prices being higher than their Chinese competitors. Leading department stores and discount chains openly encourage high tech device manufacturers to compete with each other on supplier availability and delivery date performance.

In contrast to the parts shortage and unpredictability of supply chains dragging down the industry, software is a growth catalyst. The study notes that Zoom, Slack, GoToMyPC, Zoho Remotely, Microsoft Office365, Atlassian, and others are already seeing increased demand as companies increase their remote-working capabilities.

COVID-19’s Impact On IT Spending  

Further supporting the Coronavirus Impact Index by Industry analysis, Andrew Bartels, VP & Principal Analyst at Forrester, published his latest forecast of tech growth today in the post, The Odds of a Tech Market Decline In 2020 Have Just Gone Up To 50%.

Mr. Bartels is referencing the market forecasts shown in the following forecast published last month, New Forrester Forecast Shows That Global Tech Market Growth Will Slip To 3% In 2020 And 2021 and shown below:

Key insights from Forrester’s latest IT spending forecast and predictions are shown below:

  • Forrester is revising its tech forecast downward, predicting the US and global tech market growth slowing to around 2% in 2020. Mr. Bartels mentions that this assumes the US and other major economies have declined in the first half of 2020 but manage to recover in the second half.
  • If a full-fledged recession hits, there is a 50% probability that US and global tech markets will decline by 2% or more in 2020.
  • In either a second-half 2020 recovery or recession, Forrester predicts computer and communications equipment spending will be weakest, with potential declines of 5% to 10%.
  • Tech consulting and systems integration services spending will be flat in a temporary slowdown and could be down by up to 5% if firms cut back on new tech projects.
  • Software spending growth will slow to the 2% to 4% range in the best case and will post no growth in the worst case of a recession.
  • The only positive signs from the latest Forrester IT spending forecast is the continued growth in demand for cloud infrastructure services and potential increases in spending on specialized software. Forrester also predicts communications equipment, and telecom services for remote work and education as organizations encourage workers to work from home and schools move to online courses.

Conclusion

Every industry is economically hurting already from the COVID-19 pandemic. Now is the time for enterprise software providers to go the extra mile for their customers across all industries and help them recover and grow again. Strengthening customers in their time of need by freely providing remote collaboration tools, secure endpoint solutions, cloud-based storage, and CRM systems is an investment in the community that every software company needs to make it through this pandemic too.

Securing Multi-Cloud Manufacturing Systems In A Zero Trust World

Securing Multi-Cloud Manufacturing Systems In A Zero Trust World

Bottom Line: Private equity firms are snapping up manufacturing companies at a quick pace, setting off a merger and acquisition gold rush, while leaving multi-cloud manufacturing systems unprotected in a Zero Trust world.

Securing the Manufacturing Gold Rush of 2019

The intensity private equity (PE) firms have for acquiring and aggregating manufacturing businesses is creating an abundance of opportunities for cybercriminals to breach the resulting businesses. For example, merging formerly independent infrastructures often leads to manufacturers maintaining — at least initially — multiple identity repositories such as Active Directory (AD), which contain privileged access credentials, usernames, roles, groups, entitlements, and more. Identity repository sprawl ultimately contributes to maintenance headaches but, more importantly, security blind spots that are being exploited by threat actors regularly. A contributing factor is a fact that private equity firms rarely have advanced cybersecurity expertise or skills and therefore don’t account for these details in their business integration plans. As a result, they often rely on an outdated “trust but verify” approach, with trusted versus untrusted domains and legacy approaches to identity access management.

The speed PE firms are driving the manufacturing gold rush is creating a sense of urgency to stand up new businesses fast – leaving cybersecurity as an afterthought, if even a consideration at all. Here are several insights from PwC’s Global Industrial Manufacturing Deals Insights, Q2 2019 and Private Equity Trend Report, 2019, Powering Through Uncertainty:

  • 39% of all PE investors rate the industrial manufacturing sector as the most attractive for acquiring and rolling up companies into new businesses.
  •  The manufacturing industry saw a 31% increase in deal value from Q1 2019 to Q2 2019 with industrial manufacturing megadeals driving deal value to $27.4B in Q2, 2019, on 562 deals.
  • Year-to-date North American manufacturing has generated 184 deals worth $15.2B in 2019.
  •  Worldwide and North American cross-sector manufacturing deal volumes increased by 32% and 30% in Q2, 2019 alone.

PE firms are also capitalizing on how many family-run manufacturers are in the midst of a generational change in ownership. Company founders are retiring, and their children, nearly all of whom were raised working on the shop floor, are ready to sell. PE firms need to provide more cybersecurity guidance during these transactions to secure companies in transition. Here’s why:

How To Secure Multi-Cloud Manufacturing Systems in a Zero Trust World

To stop the cybercriminals’ gold rush, merged manufacturing businesses need to take the first step of adopting an approach to secure each acquired company’s identity repositories, whether on-premises or in the cloud. For example, instead of having to reproduce or continue to manage the defined rights and roles for users in each AD, manufacturing conglomerates can better secure their combined businesses using a Multi-Directory Brokering approach.

Multi-Directory Brokering, such as the solution offered by Privileged Access Management provider Centrify, empowers an organization to use its existing or preferred identity directory as a single source of truth across the organization, brokering access based on a single identity rather than having to manage user identities across multiple directories. For example, if an organization using AD acquires an organization using a different identity repository or has multiple cloud platforms, it can broker access across the environment no matter where the “master” identity for an individual exists. This is particularly important when it comes to privileged access to critical systems and data, as “identity sprawl” can leave gaping holes to be exploited by bad actors.

Multi-Directory Brokering is public cloud-agnostic, making it possible to support Windows and Linux instances in one or multiple Infrastructure-as-a-Service (IaaS) platforms to secure multi-cloud manufacturing systems. The following diagram illustrates how Multi-Directory Brokering scales to support multi-cloud manufacturing systems that often rely on hybrid multi-cloud configurations.

Manufacturers who are the most negatively impacted by the trade wars are redesigning and re-routing their supply chains to eliminate tariffs, so they don‘t have to raise their prices. Multi-cloud manufacturing systems are what they’re relying on to accomplish that. The future of their business will be heavily reliant upon how well they can secure the multi-cloud configurations of their systems. That’s why Multi-Directory Brokering makes so much sense for manufacturers today, especially those looking for an exit strategy with a PE firm.

The PE firms driving the merger and acquisition (M&A) frenzy in specific sectors of manufacturing need to take a closer look at how Identity and Access Management (IAM) is being implemented in the manufacturing conglomerates they are creating. With manufacturing emerging as a hot industry for PE, M&A, and data breaches, it’s time to move beyond replicating Active Directories and legacy approaches to IAM. One of the most important aspects of a successful acquisition is enabling administrators, developers, and operations teams to access systems securely, without massive incremental cost, effort, and complexity.

Conclusion

The manufacturing gold rush for PE firms doesn’t have to be one for cybercriminals as well. PE firms and the manufacturing companies they are snapping up need to pay more attention to cybersecurity during the initial integration phases of combining operations, including how they manage identities and access. Cybercriminals and bad actors both within and outside the merged companies are lying in wait, looking for easy-exploitable gaps to exfiltrate sensitive data for monetary gain, or in an attempt to thwart the new company’s success.

Sources:

Global industrial manufacturing deals insights: Q2 2019, PwC, 2019. A PDF of the study is accessible here (6 pp., no opt-in).

Private Equity Trend Report, 2019, Powering Through Uncertainty, PwC, February 2019, 80 pp., PDF, no opt-in.

Industry 4.0’s Potential Needs To Be Proven On The Shop Floor

  • 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 ValueBDO 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.

How To Improve Supply Chains With Machine Learning: 10 Proven Ways

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  • Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

  • The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning. Applying machine learning algorithms and techniques to improve supply chains starts with data sets that have the greatest variety and variability in them. The most challenging issues supply chains face are often found in optimizing logistics, so materials needed to complete a production run arrive on time. Source: KPMG, Supply Chain Big Data Series Part 1

  • Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

  • Reducing forecast errors up to 50% is achievable using machine learning-based techniques. Lost sales due to products not being available are being reduced up to 65% through the use of machine learning-based planning and optimization techniques. Inventory reductions of 20 to 50% are also being achieved today when machine learning-based supply chain management systems are used. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

  • DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models. DHL’s research team continually tracks and evaluates the impact of emerging technologies on logistics and supply chain performance. They’re also predicting that AI will enable back-office automation, predictive operations, intelligent logistics assets, and new customer experience models. Source: DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

  • Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. The greatest growth barrier is the lack of skilled labor available. Using machine learning and advanced analytics manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour, 2018

  • Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. Inspectorio is a machine learning startup to watch in this area. They’re tackling the many problems that a lack of inspection and supply chain visibility creates, focusing on how they can solve them immediately for brands and retailers. The graphic below explains their platform. Source: Forbes, How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility, January 23, 2019

  • Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before. Combining multi-enterprise commerce networks for global trade and supply chain management with AI and machine learning platforms are revolutionizing supply chain end-to-end visibility. One of the early leaders in this area is Infor’s Control Center. Control Center combines data from the Infor GT Nexus Commerce Network, acquired by the company in September 2015, with Infor’s Coleman Artificial Intelligence (AI) Infor chose to name their AI platform after the inspiring physicist and mathematician Katherine Coleman Johnson, whose trail-blazing work helped NASA land on the moon. Be sure to pick up a copy of the book and see the movie Hidden Figures if you haven’t already to appreciate her and many other brilliant women mathematicians’ many contributions to space exploration. ChainLink Research provides an overview of Control Center in their article, How Infor is Helping to Realize Human Potential, and two screens from Control Center are shown below.

  • Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains. By taking a least privilege access approach, organizations can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and the costs of operating a modern, hybrid enterprise. CIOs are solving the paradox of privileged credential abuse in their supply chains by knowing that even if a privileged user has entered the right credentials but the request comes in with risky context, then stronger verification is needed to permit access.  Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment.  Centrify is a leader in this area, with globally-recognized suppliers including Cisco, Intel, Microsoft, and Salesforce being current customers.  Source: Forbes, High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019, November 28, 2018.
  • Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs. McKinsey found that predictive maintenance enhanced by machine learning allows for better prediction and avoidance of machine failure by combining data from the advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible and overall maintenance costs may be reduced by up to 10%. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

References

Accenture, Reinventing The Supply Chain With AI, 20 pp., PDF, no opt-in.

Bendoly, E. (2016). Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics. Journal Of Business Logistics37(1), 6-17.

Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

CPQ Needs To Scale And Support Smarter, More Connected Products

  • For smart, connected product strategies to succeed they require a product lifecycle view of configurations, best attained by integrating PLM, CAD, CRM, and ERP systems.
  • Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.
  • In 2018, $985B will be spent on IoT-enabled smart consumer devices, soaring to $1.49B in 2020, attaining a 23.1% compound annual growth rate (CAGR) according to Statista.
  • Industrial manufacturers will spend on average $121M a year on smart, connected products according to Statista.

Succeeding with a smart, connected product strategy is requiring manufacturers to accelerate their IoT & software development expertise faster than they expected. By 2020, 50% of manufacturers will generate the majority of their revenues from smart, connected products according to Capgemini’s recent study. Manufacturers see 2019 as the breakout year for smart, connected products and the new revenue opportunities they provide.

Industrial Internet of Things (IIoT) platforms has the potential of providing a single, unified data model across an entire manufacturing operation, giving manufacturers a single unified view of product configurations across their lifecycles. Producing smart, connected products at scale also requires a system capable of presenting a unified view of configurations in the linguistics each department can understand. Engineering, production, marketing, sales, and service all need a unique view of product configurations to keep producing new products. Leaders in this field include Configit and their Configuration Lifecycle Management approach to CPQ and product configuration.

Please see McKinsey’s article IIoT platforms: The technology stack as a value driver in industrial equipment and machinery which explores how the Industrial Internet of things (IIoT) is redefining industrial equipment and machinery manufacturing. The following graphic from the McKinsey explains why smart, connected product strategies are accelerating across all industries. Please click on the graphic to expand it for easier reading.

CPQ Needs To Scale Further To Sell Smart, Connected Products

Smart, connected products are redefining the principles of product design, manufacturing, sales, marketing, and service. CPQ systems need to grow beyond their current limitations by capitalizing on these new principles while scaling to support new business models that are services and subscription-based.

The following are the key areas where CPQ systems are innovating today, making progress towards enabling the custom configuration of smart, connected products:

  • For smart, connected product strategies to succeed they require a product lifecycle view of configurations, best attained by integrating PLM, CAD, CRM, and ERP systems. Smart, connected product strategies require real-time integration between front-end and back-end systems to optimize production performance. And they also require advanced visualization that provides prospects with an accurate, 3D-rendered view that can be accurately translated to a Bill of Materials (BOM) and into production. The following graphic is based on conversations with Configit customers, illustrating how they are combining PLM, CAD, CRM and ERP systems to support smart, connected products related to automotive manufacturing. Please click on the graphic to expand it for easier reading.

  • CPQ and product configuration systems need to reflect the products they’re specifying are part of a broader ecosystem, not stand-alone. The essence of smart, connected products is their contributions to broader, more complex networks and ecosystems. CPQ systems need to flex and support much greater system interoperability of products than they do today. Additional design principles include designing in connected service options, evergreen or long-term focus on the product-as-a-platform and designed in support for entirely new pricing models.
  • Smart, connected products need CPQ systems to reduce physical complexity while scaling device intelligence through cross-sells, up-sells and upgrades. Minimizing the physical options to allow for greater scale and support for device intelligence-based ones are needed in CPQ systems today. For many CPQ providers, that’s going to require different data models and taxonomies of product definitions. Smart, connected products will be modified after purchase as well, evolving to customers’ unique requirements.
  • After-sales service for smart, connected products will redefine pricing and profit models for the better in 2019, and CPQ needs to keep up to make it happen. Giving products the ability to send back their usage rates and patterns, reliability and performance data along with their current condition opens up lucrative pricing and services models. CPQ applications need to be able to provide quotes for remote diagnostics, price breaks on subscriptions for sharing data, product-as-a-service and subscription-based options for additional services. Many CPQ systems will need to be updated to support entirely new services-driven business models manufacturers are quickly adopting today.

How Blockchain Can Improve Manufacturing In 2019

  • The business value-add of blockchain will grow to slightly more than $176B by 2025, then exceed $3.1T by 2030 according to Gartner.
  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain.
  • Combining blockchain and IoT will revolutionize product safety, track-and-traceability, warranty management, Maintenance, Repair & Overhaul (MRO), and lead to new usage-based business models for smart, connected products.
  • By 2023, 30% of manufacturing companies with more than $5B in revenue will have implemented Industry 4.0 pilot projects using blockchain, up from less than 5% today according to Gartner.

Blockchain’s greatest potential to deliver business value is in manufacturing. Increasing visibility across every area of manufacturing starting with suppliers, strategic sourcing, procurement, and supplier quality to shop floor operations including machine-level monitoring and service, blockchain can enable entirely new manufacturing business models. Supply chains are the foundation of every manufacturing business, capable of making use of blockchain’s distributed ledger structure and block-based approach to aggregating value-exchange transactions to improve supply chain efficiency first. By improving supplier order accuracy, product quality, and track-and-traceability, manufacturers will be able to meet delivery dates, improve product quality and sell more.

Capgemini Research Institute’s recent study, Does blockchain hold the key to a new age of supply chain transparency and trust? provide valuable insights into how blockchain can improve supply chains and manufacturing. A copy of the study is available here (PDF, 32 pp., no opt-in). Capgemini surveyed 731 organizations globally regarding their existing and planned blockchain initiatives. Initial interviews yielded 447 organizations who are currently experimenting with or implementing blockchain. Please see pages 25 & 26 of the study for additional details regarding the methodology.

Key takeaways of the study include the following:

  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain. Capgemini found that there was 456 food recalls alone in the U.S. last year, costing nearly $3.5B. Blockchain’s general ledger structure provides a real-time audit trail for all transactions secured against modifications making it ideal for audit and compliance-intensive industries.

  • Gaining greater cost savings (89%), enhancing traceability (81%) and enhancing transparency (79%) are the top three drivers behind manufacturer’s blockchain investments today. Additional drivers include increasing revenues (57%), reducing risks (50%), creating new business opportunities (44%) and being more customer-centric (38%). The following graphic from the study illustrates the manufacturer’s priorities for blockchain. Capgemini finds that improving track-and-traceability is a primary driver across all manufacturers, consistent with the broader trend of manufacturers adopting software applications that improve this function today. That’s also understandable given how additional regulatory compliance requirements are coming in 2019 and those manufacturers competing in highly regulated industries including aerospace & defense, medical devices, and pharma are exploring how blockchain can give them a competitive edge now

  • Digital marketplaces, tracking critical supply chain parameters, tracking components quality, preventing counterfeit products, and tracking asset maintenance are the five areas Capgemini predicts blockchain will see the greatest adoption. Based on interviews with industry experts and startups, Capgemini found 24 blockchain use cases which are compared by level of adoption and complexity in the graphic below. The use cases reflect how managing supplier contracts is already emerging as one of the most popular blockchain use cases for manufacturing organizations today and will accelerate as compliance becomes even more important in 2019.

  • Manufacturers have the most at-scale deployments of blockchain today, leading all industries included in the study. Blockchain adoption is still nascent across all industries included in the study, with 6% of manufacturers having at-scale implementations today. Customer products manufacturers lead in pilots, with 15% actively [purusing blockchain in limited scope today. And retailers trail all industries with 91% having only proofs of concept.

  • Combining IoT and blockchain at the shipping container level in supply chains increases authenticity, transparency, compliance to product and contractual requirements while reducing counterfeiting. In highly regulated industries including Aerospace & Defense (A&D), Consumer Packaged Goods (CPG), medical devices, and pharma, combining IoT and blockchain provides real-time data on the shipping container conditions, tamper-proof storage, each shipment’s locational history and if there have been changes in temperature and product condition. Capgemini sees use cases where a change in a shipment’s temperature as measured by a sensor change sends alerts regarding contractual compliance of perishable meats and produce, averting the potential of bad product quality and rejected shipments once they reach their destination.

  • Capgemini found that 13% of manufacturers are Pacesetters and are either implementing blockchain at scale or have pilots in at least one site. Over 60% of Pacesetters believe that blockchain is already transforming the way they collaborate with their partners. Encouraged by these results, Pacesetters are set to increase their blockchain investment by 30% in the next three years. They lead early stage experimenters and all implementers on three core dimensions of organizational readiness. These include end-to-end visibility across functions, detailed and defined supportive processes, and availability of the right talent to succeed.

  • Lack of a clear ROI, immature technology and regulatory challenges are the top three hurdles Pacesetter-class manufacturers face in getting blockchain initiatives accepted and into production. All implementations face these three challenges in addition to having to overcome the lack of complementary IT systems at the partner organizations. The following graphic compares the hurdles all manufacturers face in getting blockchain projects implemented by the level of manufacturers adoption success (Pacesetter, early-stage experimenters, all implementers).

Source: Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust? October, 2018

10 Ways To Improve Cloud ERP With AI And Machine Learning

Capitalizing on new digital business models and the growth opportunities they provide are forcing companies to re-evaluate ERP’s role. Made inflexible by years of customization, legacy ERP systems aren’t delivering what digital business models need today to scale and grow.

Legacy ERP systems were purpose-built to excel at production consistency first at the expense of flexibility and responsiveness to customers’ changing requirements. By taking a business case-based approach to integrating Artificial Intelligence (AI) and machine learning into their platforms, Cloud ERP providers can fill the gap legacy ERP systems can’t.

Closing Legacy ERP Gaps With Greater Intelligence And Insight

Companies need to be able to respond quickly to unexpected, unfamiliar and unforeseen dilemmas with smart decisions fast for new digital business models to succeed. That’s not possible today with legacy ERP systems. Legacy IT technology stacks and the ERP systems they are built on aren’t designed to deliver the data needed most.

That’s all changing fast. A clear, compelling business model and successful execution of its related strategies are what all successful Cloud ERP implementations share. Cloud ERP platforms and apps provide organizations the flexibility they need to prioritize growth plans over IT constraints. And many have taken an Application Programming Interface (API) approach to integrate with legacy ERP systems to gain the incremental data these systems provide. In today’s era of Cloud ERP, rip-and-replace isn’t as commonplace as reorganizing entire IT architectures for greater speed, scale, and customer transparency using cloud-first platforms.

New business models thrive when an ERP system is constantly learning. That’s one of the greatest gaps between what Cloud ERP platforms’ potential and where their legacy counterparts are today. Cloud platforms provide greater integration options and more flexibility to customize applications and improve usability which is one of the biggest drawbacks of legacy ERP systems. Designed to deliver results by providing AI- and machine learning insights, Cloud ERP platforms, and apps can rejuvenate ERP systems and their contributions to business growth.

The following are the 10 ways to improve Cloud ERP with AI and machine learning, bridging the information gap with legacy ERP systems:

  1. Cloud ERP platforms need to create and strengthen a self-learning knowledge system that orchestrates AI and machine learning from the shop floor to the top floor and across supplier networks. Having a cloud-based infrastructure that integrates core ERP Web Services, apps, and real-time monitoring to deliver a steady stream of data to AI and machine learning algorithms accelerates how quickly the entire system learns. The Cloud ERP platform integration roadmap needs to include APIs and Web Services to connect with the many suppliers and buyer systems outside the walls of a manufacturer while integrating with legacy ERP systems to aggregate and analyze the decades of data they have generated.

  1. Virtual agents have the potential to redefine many areas of manufacturing operations, from pick-by-voice systems to advanced diagnostics. Apple’s Siri, Amazon’s Alexa, Google Voice, and Microsoft Cortana have the potential to be modified to streamline operations tasks and processes, bringing contextual guidance and direction to complex tasks. An example of one task virtual agents are being used for today is guiding production workers to select from the correct product bin as required by the Bill of Materials. Machinery manufacturers are piloting voice agents that can provide detailed work instructions that streamline configure-to-order and engineer-to-order production. Amazon has successfully partnered with automotive manufacturers and has the most design wins as of today. They could easily replicate this success with machinery manufacturers.

  1. Design in the Internet of Things (IoT) support at the data structure level to realize quick wins as data collection pilots go live and scale. Cloud ERP platforms have the potential to capitalize on the massive data stream IoT devices are generating today by designing in support at the data structure level first. Providing IoT-based data to AI and machine learning apps continually will bridge the intelligence gap many companies face today as they pursue new business models. Capgemini has provided an analysis of IoT use cases shown below, highlighting how production asset maintenance and asset tracking are quick wins waiting to happen. Cloud ERP platforms can accelerate them by designing in IoT support.

  1. AI and machine learning can provide insights into how Overall Equipment Effectiveness (OEE) can be improved that aren’t apparent today. Manufacturers will welcome the opportunity to have greater insights into how they can stabilize then normalize OEE performance across their shop floors. When a Cloud ERP platform serves as an always-learning knowledge system, real-time monitoring data from machinery and production assets provide much-needed insights into areas for improvement and what’s going well on the shop floor.

  1. Designing machine learning algorithms into track-and-traceability to predict which lots from which suppliers are most likely to be of the highest or lowest quality. Machine learning algorithms excel at finding patterns in diverse data sets by continually applying constraint-based algorithms. Suppliers vary widely in their quality and delivery schedule performance levels. Using machine learning, it’s possible to create a track-and-trace application that could indicate which lot from which supplier is the riskiest and those that are of exceptional quality as well.
  2. Cloud ERP providers need to pay attention to how they can help close the configuration gap that exists between PLM, CAD, ERP and CRM systems by using AI and machine learning. The most successful product configuration strategies rely on a single, lifecycle-based view of product configurations. They’re able to alleviate the conflicts between how engineering designs a product with CAD and PLM, how sales & marketing sell it with CRM, and how manufacturing builds it with an ERP system. AI and machine learning can enable configuration lifecycle management and avert lost time and sales, streamlining CPQ and product configuration strategies in the process.
  3. Improving demand forecasting accuracy and enabling better collaboration with suppliers based on insights from machine learning-based predictive models is attainable with higher quality data. By creating a self-learning knowledge system, Cloud ERP providers can vastly improve data latency rates that lead to higher forecast accuracy. Factoring in sales, marketing, and promotional programs further fine-tunes forecast accuracy.
  4. Reducing equipment breakdowns and increasing asset utilization by analyzing machine-level data to determine when a given part needs to be replaced. It’s possible to capture a steady stream of data on each machine’s health level using sensors equipped with an IP address. Cloud ERP providers have a great opportunity to capture machine-level data and use machine learning techniques to find patterns in production performance by using a production floor’s entire data set. This is especially important in process industries where machinery breakdowns lead to lost sales. Oil refineries are using machine learning models comprise more than 1,000 variables related to material input, output and process perimeters including weather conditions to estimate equipment failures.
  5. Implementing self-learning algorithms that use production incident reports to predict production problems on assembly lines needs to happen in Cloud ERP platforms. A local aircraft manufacturer is doing this today by using predictive modeling and machine learning to compare past incident reports. With legacy ERP systems these problems would have gone undetected and turned into production slowdowns or worse, the line having to stop.
  6. Improving product quality by having machine learning algorithms aggregate, analyze and continually learn from supplier inspection, quality control, Return Material Authorization (RMA) and product failure data. Cloud ERP platforms are in a unique position of being able to scale across the entire lifecycle of a product and capture quality data from the supplier to the customer. With legacy ERP systems manufacturers most often rely on an analysis of scrap materials by type or caused followed by RMAs. It’s time to get to the truth about why products fail, and machine learning can deliver the insights to get there.
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