• 42% of manufacturers say big data and analytics as their highest priority in 2015.
  • 56% of power distribution providers rank big data and analytics within their top three priorities for 2015.
  • 61% of aviation companies consider big data and analytics their highest priority this year.

Bottom line: Digital manufacturing strategies are gaining ground as manufacturers adopt big data and analytics to improve operational effectiveness, time-to-market, new product development and increase product quality and reliability.

Analytics Are Fueling Digital Manufacturing Growth

Big data and analytics adoption by manufacturers is the first step many are taking to create a galvanized, intelligent digital thread that unifies every aspect of their value chains. For aerospace manufacturers whose supply chains are exceptionally complex, big data and analytics are revolutionizing value chains starting with suppliers and progressing through all operations.

The majority of manufacturers are relying on analytics to improve order accuracy, shipment & cycle time performance, and product quality. Those excelling at digital manufacturing strategies are gaining additional analytical insights into how they can make decisions more accurately, quicker and with lower potential costs and risks.

The manufacturing industry generates more data than any other sector of the global economy on a consistent basis.   The more complex a given manufacturers’ operations are, the more valuable the insights gained from big data and analytics. The following comparison of big data analytics priorities by industry from a recent speech given by Jeff Immelt, CEO and President of General Electric illustrates this point:

analytics customer survey

Source: GE Minds and Machines Presentation, Jeff Immelt, CEO & President, General Electric.

10 Ways Analytics Are Accelerating Digital Manufacturing 

The ten ways analytics is accelerating digital manufacturing adoption globally include the following:

  • Providing real-time operator intelligence (70%), remote monitoring and diagnostics (66%), and condition-based maintenance (59%) are the three most valuable areas for analytics GE customers mentioned in a recent survey. GE’s industrial customers are looking to tailor pre-built applications that can deliver the eight different functional areas shown in the graphic below.  Manufacturers are looking to asset performance management as an integral part of their digital thread’s analytics and insight.

industrial customer perspective

Source: GE Minds and Machines Presentation, Jeff Immelt, CEO & President, General Electric.

  • Using data modeling to improve production workflows is improving Earnings Before Interest & Taxes (EBIT) by 55% for a chemical manufacturer.  Using analytics and data modeling to make more accurate,  efficient decisions encompassing making or buying ingredients, choosing to substitute an ingredient or not, optimizing equipment usage and/or reliability and gaining incremental sales through increased production capacity is leading to a significant improvement in EBIT for a leading chemical manufacturer on a consistent basis.  The following graphic provides insights into the contributions of each factor in improving EBIT performance.

EBIT Growth

Source: Taming manufacturing complexity with advanced analytics. McKinsey & Company by Patrick Briest, Valerio Dilda, and Ken Somers February 2015. 

  • Planning-execution integration in production centers and real-time production integration are two areas where analytics are having the greatest impact on manufacturers’ operating expenses (OPEX). When analytics are integrated as part of a digital manufacturing strategy, supply chains benefit when Web-EDI (Electronic Data Interchange) and real-time order conformation are implemented and analyzed for continual improvement.

Digital initaitves impact

Source: Operational Excellence through Digital in Manufacturing Industries. Capgemini Consulting.

  • Optimization tools (56%), demand forecasting (53%), integrated business planning (48%) and supplier collaboration & risk analytics (46%) are being rapidly adopted by manufacturers today, setting the foundation for digital manufacturing growth.  Deloitte recently interviewed supply chain executives regarding the thirteen fastest-moving technical capacities they are using today and expect to use in the future. The following graphic provides an overview of supply chain capabilities current in use and what percent of each they expect to use in the future.

use of supply chain capabilities

Source: Supply Chain Talent of the Future Findings from the third annual supply chain survey. Deloitte.  2015.

  • Analytics is integral to making the vision of Industrie 4.0 a reality. Industrie 4.0 is a German government initiative that promotes automation of the manufacturing industry with the goal of developing Smart Factories. Analytics is extensively used in manufacturing centers who are in the process of reengineering their entire operations to attain Industrie 4.0 compliance. Manufacturing value chains in highly regulated industries that rely on German suppliers and manufacturers are also relying on analytics extensively to guide their Industrie 4.0 journey. A recent Deloitte study of Industrie 4.0 adoption found that research and development (43%) will see the greatest transformational contribution from Industry 4.0.

Industry 4.0 areas

Source: Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies. Deloitte Consulting, 2015

  • Analytics is enabling manufacturers to also scale real time cloud-based operational intelligence, condition-based monitoring, monitoring & diagnostics and asset lifecycle management across global manufacturing centers.  Capturing, aggregating, analyzing and taking action on analytics across all production centers using the GE Predix Cloud will also accelerate digital manufacturing growth over time.  Integrating analytics, industrial and sensor data into a scalable series of data models and apps delivered as a Platform-as-a-Service (PaaS), GE will make this service commercially available in 2016.  The following graphic illustrates how complex manufacturers could use Predix Cloud to improve operational efficiency and quality.

horizontal capability controls

Source: Jeff Immelt Presentation on Pivot Strategy, December 16, 2014

  • Analytics is providing greater insights into product, process, program and service quality, forcing manufacturers to revamp existing production centers and make them more efficient.  Gaining greater insight into which production centers and factories are delivering the highest quality products and why is now possible.  The vision of unifying quality across an enterprise quality management and compliance (ECQM) framework is now a reality, driving greater digital manufacturing growth as a result. The following graphic from Tableau is an example of a manufacturing quality dashboard.

Mfg quality dashboard

Source: Manufacturing Analytics Quality Dashboard

  • Increasing production yields through the use of more effective supplier quality management and bill of material (BOM) planning integrated within production processes.  Analytics is extensively being used today for supplier audits, supplier quality management and traceability. Capitalizing on the full value of these analytics is a strong catalyst for manufacturers to move closer to digitizing their operations.
  • Using analytics to predict machine failures before they occur reduces downtime, production costs and increase customer satisfaction.  In highly regulated industries production equipment is periodically audited and reviewed for conformance to specific standards.  Integrating even the simplest sensor into production equipment can deliver valuable insights into what factors cause it to fail.  Analytics are providing Failure Mode and Effects Analysis (FMEA) in real-time today, providing manufacturers with a glimpse into which equipment and machinery will most likely fail when. Knowing this can save literally millions of dollars in lost production time.
  • Adopting Pareto Analysis to continually improve schedule, quality and cost performance to the cell or production center level is driving digital manufacturing adoption.  Determining which factors are enhancing or reducing product, process and program quality is now possible using advanced manufacturing analytics. Differentiating between the many symptoms of a quality problem and its root cause is now becoming possible, especially for companies pursuing digital manufacturing strategies.

Additional sources of information on the impact of analytics on digital manufacturing:

 


Filed under: Analytics, Cloud Computing, Louis Columbus' blog, SaaS ERP, SaaS Forecasts Tagged: 10 Ways Analytics Are Accelerating Digital Manufacturing, Analytics, Analytics and Big Data in Manufacturing, Big Data, Digital Manufacturing, Louis Columbus' blog, Manufacturing Intelligence, Software-as-a-Service

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