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Posts tagged ‘SAP’

How To Make Complex CPQ Selling Simple With Visual Configurators

Bottom Line: Realizing visual configurators’ full potential starts by enabling engineering, production, and sales to become real-time collaborators in creating new products.

2D, 3D, Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) visual configurators are proliferating across the Configure, Price, and Quote (CPQ) landscape today. Manufacturing marketing teams say they are the most effective lead generation technology they have, responsible for 40%+ growth in Marketing Qualified Leads (MQLs) this year alone. Sales VPs and Chief Revenue Officers (CROs) are seeing from 9% to 30% improvements in deal close rates and over 90% increases in quote accuracy. Visual configurators deliver shock-and-awe to prospects and drive more leads and deals.

Product Models Need To Scale, Driving Greater Collaboration

The good test of any product configurator is whether it can scale from assemble-to-order (ATO) to Engineer-To-Order (ETO) while enabling real-time collaboration between engineering, production, and sales. A given products’ many attributes and options defined by engineering in their PLM system need to be consistent with manufacturing’s work instructions and Bill of Materials (BOM) in their ERP system. And the visual configurator sales & marketing is using needs to reflect, in real-time, what engineering defined in PLM and what manufacturing’s ERP system can build. Product models serve as the master data that enables real-time collaboration between engineering, manufacturing, and sales.

Visual configurators need to push beyond the veneer of delivering shock-and-awe and enable real-time collaboration between PLM, ERP, and CRM & CPQ systems to achieve their full potential. Visual configuration providers need to pursue the goal of enabling engineering, manufacturing, and sales to be collaborators in creating accurate products and challenge themselves to deliver the following:

  • Improve sales performance while increasing margin per deal by providing only the options that are the most buildable at the lowest cost.
  • Eliminate disconnects between what engineering designed and what manufacturing can produce leads to more sales at higher gross margins.
  • Close product configuration gaps and improving fulfillment speed and product quality, creating greater customer loyalty and follow-on sales.
  • Automatically propagate product and design changes across all functional areas to accelerate new products to market while improving product quality.
  • Real-time fine-tuning of new product features to the model level that specific customers want becomes possible when engineering, manufacturing, and sales are collaborating in real-time.
  • Update work instructions and BOMs in real-time based on changes customers make in product visualizations.
  • Improve the balance of revenue across configurable products to sell higher-margin models based on real-time collaboration between PLM, ERP, CRM, and CPQ systems.
  • See in real-time how changes in product design, Bill of Materials (BOM), and delivery dates impact the financial performance of a manufacturer.

Predicting Visual Configuration’s Future

Shortening cycle times from product concept to completed product is the secret to succeeding with visual configuration. And when each manufacturing cycle time has its cadence or speed depending on how little or much a customer wants a product customized, visual configurators need to flex and deliver what customers want when.

Companies defining the future of visual configuration today include CDSDERWID, and SAP Visual Enterprise. These three companies are defining the future of visual configuration by enabling real-time integration between PLM, ERP, CRM, and CPQ systems.  I recently spoke with John Major, CEO of CDS to get his insights into what’s driving visual configuration’s success today.  “What we’re seeing in the marketplace now are two things. One is the clients want to understand how our visual configuration solution is going to fit into their change management as it’s rooted in PLM, because to any manufacturer, PLM reigns supreme,” he said. He continued, “The second is about staffing. When you’re a manufacturing company, and you buy a visual configurator toolkit that requires you to create your app, a few things happen. You need to staff up a software team to now run that toolkit and write development. So your long-term cost is fairly significant versus a company that can deliver an entire solution at scale.” 

CDS is partnering with eLogic, who is regarded as the leading system integration partner in CPQ and product configuration and is considered a global leader in delivering business solutions for manufacturers across SAP configuration technologies and Microsoft Dynamics 365, Power Platform & Azure. Together they are delivering next-generation visual configuration solutions for their shared clients. Examples of the work they are doing are shown below:

  • Real-time model updates keep engineering, manufacturing, and sales in sync. When customers are designing a new product in a CPQ session, the model is updated in real-time and saved, so engineering, manufacturing, and sales can see how their changes affect the product. An example of this is shown below:

  • When the product is configured “to scale,” 2D proposal drawings are automatically generated, and the product model is updated in real-time, making augmented reality visualizations possible. 3D models are also made available in a variety of CAD formats. Additionally, an Augmented Reality model is created that can be placed in any virtual environment. What’s noteworthy is that while the model’s appearance is changing, all relevant changes to the work instructions and BOM are happening in real-time using the SAP Visual Enterprise

  • When product models are the catalyst enabling real-time collaboration between engineering, manufacturing, and sales, selling into the aftermarket becomes profitable. Aftermarket selling has a complexity all its own. Taking on the challenge of shortening cycle times from product concept to completed products in the market is what’s needed today. The example below shows a piece of equipment selected in CPQ, then rotated, zoomed in, and exploded to see the internal components. Internal parts can now be selected, quoted, ordered and delivered for replacement.

Conclusion

Visual configurators are capable of so much more than they are delivering today. It’s time to graduate beyond the shock-and-awe stage, which has been very successful in driving leads, generating MQLs, and closing deals. It’s time to get down to the hard work of making all those impressive models buildable at scale and profitable. And that comes by doubling down efforts at shortening cycle times from product concept to completed product. That’s the true north of this market and the secret to succeeding. Getting engineering, manufacturing, and sales collaborating using product models as a single source of truth is the best place to start.

The Best IoT Companies To Work For In 2019 Based On Glassdoor

Employees would most recommend the following companies to their friends looking for an IoT job:  IGELSAPARMFortinetGoogleMicrosoftBoschSamsaraSchneider ElectricSiemensDell TechnologiesRed HatCisco Systems and Trend Micro. These 14 companies are the highest rated by employees working for them based on a comparison of Computer Reseller News’ Internet of Things 50, 2019  with their respective Glassdoor scores as of today, Sunday, August 18, 2019.

Forbes readers’ most frequent requests center on which companies are the best to work for in emerging technology fields, including IoT. The Computer Reseller News’ Internet of Things 50, 2019 list of companies is used to complete the analysis as it is an impartial, independent list created by CRN. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Comparing the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the following analysis was completed. 14 IoT companies on the list have very few (less than 20) or no Glassdoor reviews, so they are excluded from the rankings. In 2017 I did a factor analysis and found that companies who flood Glassdoor with fake reviews hit a wall around ten posts. With those findings in mind, an IoT company would need a minimum of 20 current employee interviews to be included in the final recommended list. Please find the full data set available for download here. The best IoT companies to work for are shown below and please click on the graphic to expand for easier reading:

The highest-rated IoT CEOs on Glassdoor as of August 18, 2019, include the following:

CEO Company Name  % of employees who approve of the CEO as of August 18, 2019, on Glassdoor 2019 CRN Internet of Things Categories
Jed Ayres, CEO, North America IGEL 100% IoT Software and Services
Bill McDermott, CEO (Glassdoor Top CEOs of 2019) SAP 96% IoT Software and Services
Satya Nadella, CEO (Glassdoor Top CEOs of 2019) Microsoft 96% IoT Software and Services
Sanjit Biswas, Founder, CEO Samsara 96% IoT Hardware
James Whitehurst, President, CEO Red Hat 96% IoT Software and Services
Volkmar Denner, CEO Bosch 94% IoT Hardware
Simon Segars, CEO ARM 93% IoT Hardware
Jean-Pascal Tricoire, CEO (Glassdoor Top CEOs of 2019) Schneider Electric 93% Industrial Internet of Things (IoT) Providers
Ken Xie, Founder, Chairman, CEO Fortinet 92% IoT Security
Thomas Kurian, CEO Google Cloud 92% IoT Software and Services
Michael Dell, Chairman, CEO Dell Technologies 92% IoT Hardware
Eva Chen, CEO Trend Micro 92% IoT Security
Joe Kaeser, CEO Siemens 91% Industrial Internet of Things (IoT) Providers
Chuck Robbins, CEO (Glassdoor Top CEOs of 2019) Cisco Systems 91% IoT Hardware

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

The Best Cloud Computing Companies And CEOs To Work For In 2019 Based On Glassdoor

  • SysdigFivetranNuxeoCloudianMendixStreamSetsZscalerZohoSAPOutSystemsKony, and Netskope are the most likely to be recommended by their employees to friends looking for a cloud computing company to work for in 2019.
  • Cloud platform and development companies dominate the highest rated cloud businesses when indexed by the percent of employees who would recommend their company to a friend.
  • Taken together, the 12 CEOs leading the top-rated cloud computing companies are approved by 98% of employees as of March 3, 2019, on Glassdoor. CEOs in this group include Thomas Hogan of Kony, Paulo Rosado of OutSystems, Bill McDermott of SAP, and Sridhar Vembu of Zoho.

These and many other insights are from an analysis completed today comparing Computer Reseller News’ 100 Coolest Cloud Computing Vendors of 2019 by their respective Glassdoor scores. The Computer Reseller News annual list of the 100 coolest cloud computing vendors is an impartial, 3rd party benchmark of the fastest-growing and most likely to hire cloud businesses expanding today.  By far the most common request from Forbes readers is which cloud computing companies are the best to work for. The goal of this analysis is to provide readers with insights into which cloud computing companies best fit their skills and at the same time have a strong reputation based on feedback from existing employees.

Indexing the most interesting and fastest growing cloud computing companies by their Glassdoor scores and reputations is a great way to begin defining a long-term career growth strategy. One factor not quantified is how well of a fit an applicant is to company culture. Take every opportunity for in-person interviews, read Glassdoor ratings often and observe as much as possible about daily life in companies of interest to see if they are a good fit for your skills and strengths.

Using the 2019 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the table below is provided. You can find the original dataset here. There are 15 companies on the CRN list that don’t have that many or any entries on Glassdoor, and they are excluded from the rankings shown below. You can find their mention in the original dataset. If the image below is not visible in your browser, you can view the rankings here.

The highest rated CEOs on Glassdoor as of March 3, 2019, include the following. Please click on the graphic and dataset to expand for easier reading.

The original dataset is shown below:

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.

The Best Big Data Companies And CEOs To Work For In 2018

Forbes readers’ most common requests center on who the best companies are to work for in analytics, big data, data management, data science and machine learning. The latest Computer Reseller News‘ 2018 Big Data 100 list of companies is used to complete the analysis as it is an impartial, independent list aggregated based on CRN’s analysis and perspectives of the market. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Using the 2018 Big Data 100 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the following analysis was completed today. 25 companies on the list have very few (less than 15) or no Glassdoor reviews, so they are excluded from the rankings. Based on analysis of Glassdoor score patterns over the last four years, the lower the number of rankings, the more 100% scores for referrals and CEOs. These companies, however, are included in the full data set available here. If the image below is not visible in your browser, you can view the rankings here.

 

The highest rated CEOs on Glassdoor as of May 11, 2018 include the following:

Dataiku Florian Douetteau 100%
StreamSets Girish Pancha 100%
MemSQL Nikita Shamgunov 100%
1010 Data Greg Munves 99%
Salesforce.com Marc Benioff 98%
Attivio Stephen Baker 98%
SAP Bill McDermott 97%
Qubole Ashish Thusoo 97%
Trifacta Adam Wilson 97%
Zaloni Ben Sharma 97%
Reltio Manish Sood 96%
Microsoft Satya Nadella 96%
Cloudera Thomas J. Reilly 96%
Sumo Logic Ramin Sayar 96%
Google Sundar Pichai 95%
Looker Frank Bien 93%
MongoDB Dev Ittycheria 92%
Snowflake Computing Bob Muglia 92%
Talend Mike Tuchen 92%
Databricks Ali Ghodsi 90%
Informatica Anil Chakravarthy 90%

 

The Best Software Companies To Work For In 2018, According To Glassdoor

These and other findings are based on an analysis of Glassdoor rankings of Software Magazine’s 2017 Software 500 list of the leading software companies globally. An Excel spreadsheet was first created using the 2017 Software 500 list as the basis of the Glassdoor company comparisons. Rankings from Glassdoor were added today for the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO.The Software 500 list was used to preserve impartiality in the rankings.  The original data set the analysis is based on is available for download here in Microsoft Excel format.

To gain greater insights into the data sets a series of cross-tabulations and correlation analyses were done using IBM SPSS Statistics Version 25. The analysis shows CEOs have an even greater impact on improving their company’s recommendation scores, rising to 82% this year from 70% in 2015. The analysis also showed that companies who flood Glassdoor with fake reviews hit a wall around 10 posts, down from 15 in 2015. This doesn’t stop some companies from offering cash, prizes, and merchandise to their employees in exchange for positive reviews. Relying on Glassdoor and ideally in-office visits to see how a company culture is and how your potential boss treats others is ideal.

The following are the highest rated software companies to work for in 2018, based the (%) of employees who would recommend the company to a friend:

The following companies scored between 80% and 89% on the rating % of employees who would recommend this company to a friend:

Please see the entire data set for the rankings of all companies included in the Software Magazine 500 here in Microsoft Excel format.

6 Ways Cloud ERP Is Revolutionizing How Services Deliver Results

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

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

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

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

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

Why IT Projects Fail

There are many reasons why IT integration projects fail.  From the lack of senior management support to imprecise, inaccurate goals, IT integration projects fail more often than they have to. Based on consulting I’ve done with system integrators, distribution providers, financial services firms, logistics providers and manufacturers, five core lessons emerge.  One of the most innovative companies taking on these challenges is enosiX, whose customer wins at Yeti Coolers, Vera Bradley, BUNN and others provide a glimpse into the future of real-time integration.

  • Middleware forces IT integration projects to focus only on moving data instead of improving business processes.
  • Not having a clear idea of the goals the integration needs to attain in the first place.
  • Sacrificing application response times, data accuracy and user experience in never-ending middleware projects.

Five Lessons Learned From IT Integration Failures

The following lessons learned are based on my experiences and work with IT departments, Vice Presidents of Infrastructure, Enterprise Systems, Cloud Platforms, CIOs, and CFOs. The lessons learned from them are helping current and future IT integration projects increase the odds of success.

  1. Selecting middleware or an integration platform not capable of offline, mobile use with the ability to synchronize in real-time once connected. The fastest growing areas of Customer Relationship Management (CRM) are being fueled by the real-time availability of data on mobile devices. In Configure-Price-Quote (CPQ) and Quote-to-Cash (QTC) workflows, tethered and untethered use cases dominate. To be competitive, any company relying on these two strategies to sell must have an integration framework capable of delivering data in real-time that enables quick app response times, higher performance, and a better user experience. IT integration projects that don’t take this requirement into account nearly always fail.
  1. Selecting an integration solution that requires time-consuming, expensive training and has a steep learning curve. When a given middleware, integration technology or framework is too difficult for IT to learn and use, projects fail fast. The middleware landscape is littered with companies whose marketing is covering up products that have non-existent to mediocre documentation and learning materials. One of the primary factors behind Salesforce’s exceptional growth is their commitment to making the user experience on their platform immediately scalable to each application developed and launched on it. Within 30 minutes, sales teams are often up and running with new apps, successfully selling as a result. Integration frameworks that don’t force system users to change how they work are the new gold standard and are driving the market forward.
  1. Using middleware for business process logic integration when it is designed for data only. Attempting to use middleware for business process logic workflows can get complex and costly fast. It’s one of the main reasons IT integration projects don’t deliver results. In reality, the most valuable aspects of any integration project are the business processes and supporting logic that is automated, streamlined and tailored to a businesses’ unique needs, revolutionizing it in the process. This point of failure happens when IT architects push middleware beyond its limits and attempt to do what more streamlined integration frameworks are designed to accomplish. Business process logic is core to the future of any IT integration project. It is surprising that more organizations don’t look for integration frameworks that have this capability designed into the core architecture.
  1. Failing to consider how data transfers can be minimized or eliminated in the planning and deployment of an integration project. The more customer-centric a project, the more the variety and depth of data transfers required for the integration to be complete. Data transfers grow exponentially and can challenge the scale of a middleware platform quickly. The most successful IT integration projects aren’t data transfer-intensive, they are business strategy driven. One of the most effective best practices of integration is not having to move the data at all. Using an SOA-based framework as a means to enable data consumption without having to perform lengthy ETL processes is the future of integration. By definition, middleware relies on a series of tightly-coupled integration points designed to move data asynchronously. In contrast, SOA-based frameworks are designed to enable real-time synchronous communication through the use of loosely-coupled connections that can flex in response to business process requirements.
  1. Failure to plan and anticipate how a change in one cloud platform or enterprise application including those running on Salesforce’s Force.com, a SAP R/3 system and other platforms impact the entire company’s IT stability. The VP of Infrastructure for a globally-based gaming and hospitality chain told me he and his team often are given the challenging task of bringing up new casino and hotel operations offices globally in two weeks. He sends in an advance team to determine how best to integrate with any legacy on-premise systems. The team also works to integrate any unique Salesforce apps that need to be included into the main Salesforce instance at the tab level, and to determine how best to integrate into the SAP R/3 procurement system. System security is the highest priority during the integration pilot and go-live work.  The company has standardized on a series of network adapters and connectors that are designed to shield all traffic across the network. He told me that just one API change in the IT stack supporting their SAP R/3 integration would cause all adapters to quit working, report an error condition and force debugging to the line level.  They learned this during a go-live with a Reno property. Today all changes to middleware are run in a pilot mode in a sandbox first, and the company is looking to get away from middleware entirely as a result.

From the enosiX blog post, Why IT Integration Projects Fail.

Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2014

NYC SkylineFrom manufacturers looking to gain greater insights into streamlining production, reducing time-to-market and increasing product quality to financial services firms seeking to upsell clients, analytics is now essential for any business looking to stay competitive.  Marketing is going through its own transformation, away from traditional tactics to analytics- and data-driven strategies that deliver measurable results.

Analytics and the insights they deliver are changing competitive dynamics daily by delivering greater acuity and focus.  The high level of interest and hype surrounding analytics, Big Data and business intelligence (BI) is leading to a proliferation of market projections and forecasts, each providing a different perspective of these markets.

Presented below is a roundup of recent forecasts and market estimates:

  • The Advanced and Predictive Analytics (APA) software market is projected from grow from $2.2B in 2013 to $3.4B in 2018, attaining a 9.9% CAGR in the forecast period.  The top 3 vendors in 2013 based on worldwide revenue were SAS ($768.3M, 35.4% market share), IBM ($370.3M, 17.1% market share) and Microsoft ($64.9M, 3% market share).  IDC commented that simplified APA tools that provide less flexibility than standalone statistical models tools yet have more intuitive graphical user interfaces and easier-to-use features are fueling business analysts’ adoption.  Source: http://www.idc.com/getdoc.jsp?containerId=249054
  • A.T. Kearney forecasts global spending on Big Data hardware, software and services will grow at a CAGR of 30% through 2018, reaching a total market size of $114B.  The average business expects to spend $8M on big data-related initiatives this year. Source: Beyond Big: The Analytically Powered Organization.
  • Cloud-based Business Intelligence (BI) is projected to grow from $.75B in 2013 to $2.94B in 2018, attaining a CAGR of 31%.  Redwood Capital’s recent Sector Report on Business Intelligence  (free, no opt in) provides a thorough analysis of the current and future direction of BI.  Redwood Capital segments the BI market into traditional, mobile, cloud and social business intelligence.   The following two charts from the Sector Report on Business Intelligence  illustrate how Redwood Capital sees the progression of the BI market through 2018.

redwood capital global intelligence market size

  • Enterprises getting the most value out of analytics and BI have leaders that concentrate more on collaboration, instilling confidence in their teams, and creating an active analytics community, while laggards focus on technology alone.  A.T. Kearney and Carnegie Mellon University recently surveyed 430 companies around the world, representing a wide range of geographies and industries, for the inaugural Leadership Excellence in Analytic Practices (LEAP) study.  You can find the study here.  The following is a graphic from the study comparing the characteristics of leaders and laggards’ strategies for building a culture of analytics excellence.

leaders and laggards2

  • The worldwide market for Big Data related hardware, software and professional services is projected to reach $30B in 2014.  Signals and System Telecom forecasts the market will attain a Compound Annual Growth Rate (CAGR) of 17% over the next 6 years.  Signals and Systems Telecom’s report forecasts Big Data will be a $76B market by 2020.  Source: http://www.researchandmarkets.com/research/s2t239/the_big_data
  • Big Data is projected to be a $28.5B market in 2014, growing to $50.1B in 2015 according to Wikkbon.  Their report, Big Data Vendor Revenue and Market Forecast 2013-2017 is outstanding in its accuracy and depth of analysis.  The following is a graphic from the study, illustrating Wikibon’s Big Data market forecast broken down by market component through 2017.

Big Data Wikibon

  • SAPIBMSASMicrosoftOracle, Information Builders, MicroStrategy, and Actuate are market leaders in BI according to Forrester’s latest Wave analysis of BI platforms.  Their report, The Forrester Wave™: Enterprise Business Intelligence Platforms, Q4 2013 (free PDF, no opt in, courtesy of SAS) provides a thorough analysis of 11 different BI software providers using the research firm’s 72-criteria evaluation methodology.
  • Amazon Web Services, Cloudera, Hortonworks, IBM, MapR Technologies, Pivotal Software, and Teradata are Big Data Hadoop market leaders according to Forrester’s latest Wave analysis of Hadoop Solutions.  Their report, The Forrester Wave™: Big Data Hadoop Solutions, Q1 2014 (free PDF, no opt in, courtesy of MapR Technologies) provides a thorough analysis of nine different Big Data Hadoop software providers using the research firm’s 32-criteria evaluation methodology.
  • IDC forecasts the server market for high performance data analysis (HPDA) will grow at a 23.5% compound annual growth rate (CAGR) reaching $2.7B by 2018.  In the same series of studies IDC forecasts the related storage market will expand to $1.6B also in 2018. HPDA is the term IDC created to describe the formative market for big data workloads using HPC. Source: http://www.idc.com/getdoc.jsp?containerId=prUS24938714
  • Global Big Data technology and services revenue will grow from $14.26B in 2014 to $23.76B in 2016, attaining a compound annual growth rate of 18.55%.  These figures and a complete market analysis are available in IDC’s Worldwide Big Data Technology and Services 2012 – 2016 Forecast.  You can download the full report here (free, no opt-in): Worldwide Big Data Technology and Services 2012 – 2016 Forecast.

big data analytics by market size

  • Financial Services firms are projected to spend $6.4B in Big Data-related hardware, software and services in 2015, growing at a CAGR of 22% through 2020.  Software and internet-related companies are projected to spend $2.8B in 2015, growing at a CAGR of 26% through 2020.  These and other market forecasts and projections can be found in Bain & Company’s Insights Analysis, Big Data: The Organizational Challenge.  An infographic of their research results are shown below.

Big-Data-infographic-Bain & Company

potential payback of big data initiatives

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