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10 Ways Machine Learning Is Revolutionizing Manufacturing

machine learningBottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.

Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

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

  • Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

  • Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.

factory and demand analytics

Source:  GE Global Research Stifel 2015 Industrials Conference

  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

  • Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent.  By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

  • Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post,     Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.

Salesforce AI Acquisitions

Salesforce revenue sources

  • Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  • Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
  • The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
  • Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale.  For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
  • Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning.   Machine learning is extending what enterprise-level price optimization apps provide today.  One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.

Additional reading:

Cisco Blog: Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes

Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft 

Focus Group: Big Data Analytics for Smart Manufacturing Systems

GE Predix: The Industrial Internet Platform

IDC Manufacturing Insights reprint courtesy of Cisco: Designing and Implementing the Factory of the Future at Mahindra Vehicle Manufacturers

Machine Learning: What It Is And Why It Matters

McKinsey & Company, An Executive’s Guide to Machine Learning

MIT Sloan Management Review, Sales Gets a Machine-Learning Makeover

Stanford University CS 229 Machine Learning Course Materials
The Economist Feature On Machine Learning

UC Berkeley CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes

University of Washington CSE 446 – Machine Learning – Winter 2014

Sources:

Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.

Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.

Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).

Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.

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Salesforce On The State Of Analytics, 2015

  • analytics predictions 2015Between 2015 and 2020, the number of data sources analyzed by enterprises will jump 83%.
  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes.
  • 54% of marketers say marketing analytics is absolutely critical or very important to creating a cohesive customer journey.
  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data.

These and many other interesting insights are from the 2015 State of Analytics study from Salesforce Research. Salesforce conducted the study in mid-2015, generating 2,091 responses from business leaders from enterprises (not limited to Salesforce customers). Geographies included in the study include the U.S., Canada, Brazil, U.K., France, Germany, Japan, and Australia.  While Salesforce is a leading provider of analytics, the report strives to deliver useful insights beyond just endorsing their product direction.

10 insights and predictions on the state of analytics include the following:

  • Between 2015 and 2020, the number of data sources analyzed will jump 83%. Salesforce Research found that the number of data sources actively analyzed by businesses has grown just 20% in the last five years. This is projected to accelerate rapidly, attaining a compound annual growth rate of 120% in the 10-year forecast period. High performing enterprises will be relying on a projected 50 different data sources by 2020, leading all performance categories tracked in the study.

data explosion

  • Relying on manual processes to get all the data in one view (53%) is one of the greatest challenges enterprises face today. Additional factors driving enterprises to integrate more data sources into their analytics applications include finding that too much data is left unanalyzed (53%), spending too much time updating spreadsheets (52%), and analysis is performance by business analysts, not end users of the data (50%).  All of these factors and those shown in the graphic below form the catalyst that is driving greater legacy, 3rd party and broader enterprise data integration into analytics applications.

lack of automation

  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes. In addition, 84% of high performers are projecting that the importance of analytics will increase substantially or somewhat in the next two years. 65% of all business leaders surveyed are predicting that the importance of analytics will increase substantially or somewhat in the next two years.

analytics is critical to driving business strategy

  • High performing enterprises are 4.6x more likely than underperformers to agree that data is driving their business decisions. In addition, 60% of high performing enterprises’ leaders agree with the statement that their organizations have moved beyond numbers keeping score to data driving business decisions. Salesforce Research also found that 43% of high performers rely on empirical data, developing hypotheses and then experimenting and observing the outcomes before making a decision.

data drives decisions

  • Driving operational efficiencies and facilitating growth (both 37%) are the two areas enterprises are initially focused on with analytics today.  Once analytics apps are delivering insights and are part of daily workflows, enterprises expand their use into optimizing operational processes (35%), identifying new revenue streams (33%) and predicting customer behavior (32%). The following graphic provides a comparison of the top ten use cases.

analytics every corner

  • High performance enterprises consistently analyze more than 17 different kinds of data across their analytics apps.  In contrast, underperforming organizations only analyze 10 different data sources, and moderate performers, 15. The following graphic provides an overview of the top ten most-used sources of data.

companies track a wide variety of data

  • High performers are 3.5x more likely than underperformers to extensively use mobile reporting tools to analyze data wherever they are. 55% of high performing enterprises are more likely to be extensively using mobile reporting tools to analyze data.  The following graphic compares mobile analytics adoption across high, medium and low performing enterprises.

top teams tap mobile analytics

  • Speed of deployment (68%), ease of use for business users (65%) and self-service and data discovery tools (61%) are the three top three priorities leaders place on selecting new analytics apps.  Mobile capabilities to explore and share data (56%) and cloud deployment (54%) are the fourth and fifth factors leaders mentioned.  The following graphic compares the decision factors that go into selecting an analytics app.

decision factor analytics app

  • Industries who have the greater analytics adoption today (over 50% of users active on apps and tools) include high tech (36%) and financial services (32%). Automotive (30%) and media & communications (30%) also have attained significant adoption.

adoption

  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data. Leaders in high performance enterprises see the value of Big Data (76%) to a much greater extent than their lower performing counterparts (14%).   High performing enterprises are 3.1x more likely than underperformers to be confident in ability to manage data from internal systems, customers, and third parties.

Why Salesforce Is Winning The Cloud Platform War

300px-Salesforce_Logo_2009The future of any enterprise software vendor is being decided today in their developer community.

Alex William’s insightful thoughts on Salesforce Is A Platform Company. Period. underscores how rapidly Salesforce is maturing as a cloud platform.  And the best measure of that progress can be seen in their developer community.

(To be clear, Salesforce and the other companies mentioned in this post are not clients and never have been.  I track this area out of personal interest.)

DevZone force.com

The last four years I’ve made a point at every Salesforce Dreamforce event to spend the majority of my time in the developer area.  Watching mini hacks going on in the DevZone, mini workshops, the Salesforce Platform and Developer keynotes over the last few years has been a great learning experience.  An added plus: developers are often skeptical and want to see new enhancements help streamline their code, extend its functionality, and push the limits of the Force.com platform. This healthy skepticism has led to needed improvements in the Force.com platform, including a change to governor limits on Application Programmer Interface (APIs) performance and many other enhancements.  Despite the criticisms of Force.com being proprietary due to Apex and SOQL, the crowds at developer forums continue to grow every year.

I’ve started to look at the developer area as the crucible or foundry for future apps.  While the Cloud Expo shows how vibrant the partner ecosystem is, the developer area is where tomorrow’s apps are being coded today. The Force.com Workbook, an excellent reference for Force.com developers, was just released October 1 and DeveloperForce shows how far the developer support is matured in Salesforce.  In addition a new Force.com REST API Developer’s Guide is out just last month.

The Journey From Application To Platform

In visiting the developer area of Dreamforce over the last four years I’ve seen indications that Salesforce is successfully transforming itself into a cloud platform business:

  • Significant jump in the quantity and quality of developer attendees from 2010 to 2012.  The depth of questions, sophistication of code samples, calls for more flexibility with governor limits, and better mobile support typified these years.
  • Steady improvement to visual design tools, application development environment and support for jQuery, Sencha and Apache Cordova.
  • The steady maturation of Salesforce Touch as a mobile development platform and launch of Salesforce Platform Mobile Services Launched in 2011, this platform continues to mature, driven by developer’s requirements that reflect their customers’ needs for mobility support.  HTML 5 is supported and the apps I’ve seen written on it are fast, accurate and ideal for customer service.  ServiceMax has created exceptional mobile apps including their comprehensive ServiceMax for iPad app on the Force.com platform.
  • 2012: Rise of the Mobile Enterprise Developer.  Salesforce’s enterprise customers in 2009 weren’t nearly as active as they were last year with questions on legacy systems integration and how to create web services capable of integrating customer data.  2011 was a breakout year in mobile app development with 2012 showing strong momentum on mobile web services development.  I expect this year’s Dreamforce developer community to reflect the rapidly growing interest in mobile as well.

How Enterprise Applications Make The Salesforce Platform Work For Them

In speaking with Salesforce developers over the years one of my favorite questions continues to be “what is the real payoff of having a native Force.com application in your company?”  Initially I thought this was marketing spin from enterprise software vendors attempting to use features as benefits, however after a closer look it is clear that the platform has significant advantages, especially for any solution requiring global deployments or large numbers of users.  Here is what I found out:

  • The investments Salesforce.com has made in their cloud infrastructure over several years (and continue to make) has resulted in a platform that developers  are leveraging to rapidly deliver enterprise applications that deliver world-class performance, reliability, and security.
  • Of the many native Force.com applications that extend Salesforce beyond CRM, it’s been my experience the most challenging are Configure-Price-Quote (CPQ) and contract management.  Creating a single system of record across these two areas is challenging even outside of Force.com, which is why many companies in this space have two entirely different product strategies.  Apttus is the exception as they have successfully created a unified product strategy on Force.com alone.  I recently had the chance to speak with Neehar Giri, President and Chief Solutions Architect.  “Apttus’ strategic decision to deliver our enterprise-class applications natively on the Salesforce platform has allowed us to focus on our customer needs, meeting and exceeding their expectations in both functionality and speed of innovation,” said Neehar Giri, president and chief solutions architect, Apttus.  “We’ve seen the platform evolve rapidly in its capabilities and global scalability.  Apttus’ customers have and continue to benefit from the true multi- tenancy, world class security, reliability and performance of the Salesforce Platform.”
  • Salesforce.com’s multi-tenant architecture allows for optimization of computing resources resulting in savings and significant gains in efficiency for global enterprises even over applications deployed on private clouds.
  • Native Force.com applications share the same security model as Salesforce apps.  Financialforce.com chose to develop their accounting, ordering and billing, professional services automation and service resource planning entirely on the Force.com architecture due to shared master data, multi- tenancy, world class security, reliability and performance.  This shared architecture also benefits enterprise consumers of native applications by providing best-in-class uptime.
  • Native Force.com applications are contributing to greater return on investment (ROI). IT often does not need to manage data integration or sync issues, upgrades to even large numbers of users are easily deployed, and users can remain in a familiar interface.   These benefits support faster and easier deployment as well as rapid user adoption both of which are critical to success and a high ROI for any solution. Enterprise developers have often mentioned the familiar interface and ease of deployment have led to higher rates of adoption than any other approach to delivering new application functionality.
  • Advanced APIs to support integration of legacy applications not on the Force.com platform.
  • Proven ability of Salesforce.com to support global deployments.  The company has expanded its global support centers.  Salesforce.com also publishes real-time statistics on system status: http://trust.salesforce.com/trust/.
  • A continuing acceleration of new capabilities resulting from increasing numbers of developers driving the advancement of the platform through their collective input, suggestions and requirements.
  • Ability to design applications that respond with greater customer insight and intelligence across mobile devices.  ServiceMax has an impressive series of mobile applications that do this today.  I had a chance to speak with David Yarnold, their CEO about his vision for the company.  He wants to give ServiceMax’s customers the ability to deliver flawless field service where every interaction is perfect.  By building on the Force.com architecture he explained how each service customers’ contextual intelligence can be seen in real-time by everyone involved in serving customers.  Clearly ServiceMax is capitalizing on the mobile development platform area of Force.com as well.

Bottom Line: Enabling developers to attain greater revenue growth, while creating an extensive mobile app development platform is further proof Salesforce has turned the corner from being an application company to a platform provider.

The Best Cloud Companies and CEOs to Work For in 2013

???????????????Hiring great people and creating a culture of achievement that is fun, focused and able to get challenging tasks done is not an easy task.

Keeping that culture strong and focused on the customer takes a unique leader that consistently earns trust and respect.  Those are the qualities I think of whenever I’m asked to recommend the best cloud computing companies to work for.  Using the scores from Glassdoor.com I’ve put together the table below comparing cloud computing companies and when available, the percentage of employees who approve of their CEO.

If you’re not familiar with Glassdoor, it’s a website that gives employees the chance to rate their companies and CEOs anonymously, along with reporting salaries.  Friends in the Human Resources community tell me it’s an effective recruitment site as well.

Cloud computing companies are sorted based on the percentage of employees would recommend their company to a friend.  I added in CEO scores to get a sense of which companies have a significant gap between morale and the perception of the CEO.  As of today according to employee rankings, Microsoft has the largest gap between percentage of employees who would recommend the company to a friend (77%) and  CEO rating (48%).

Glassdoor rankings for cloud computing

The highest rated CEOs you’d want to work for based on their Glassdoor ratings are as follows, with their ratings shown as of today:

Jyoti Bansal of AppDynamics (100%)

Drew Houston, Dropbox (100%)

Aneel Bhursi, Workday (100%)

Scott Scherr, Ultimate Software (97%)

Jim Whitehurst, Red Hat (97%)

Larry Page, Google (95%)

Aaron Levie, Box (94%)

Marc Benioff, Salesforce (93%)

Tom Georgens, NetApp (92%)

Mark Templeton, Citrix Systems (91%)

Bill McDermott & Jim Hagemann Snabe, SAP (90%)

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