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Posts from the ‘Manufacturing’ Category

Feb 17

IT And Marketing Show Strongest Interest In Adopting Gen AI First

IT, Marketing Show Strongest Interest In Adopting Gen AI First

  • Currently, 16% of organizations have implemented generative AI in production, while 44% are piloting it for potential applications.
  • Interest in deploying generative AI for production applications saw a fivefold increase from the first to the fourth quarter of 2023.
  • Healthcare, manufacturing, and education are the three industries most actively pursuing generative AI adoption.
  • A majority of organizations, 63%, deem CRM data critical to their generative AI initiatives.

These and many other insights are from Dresner Advisory Services‘ recent Generative AI Report. The advisory firm surveyed its research community of over 8,000 organizations and vendors’ customer communities. The study is global in scope, with 50% of respondents from North America, 26% from EMEA, 19% from Asia/Pacific and 6% from Latin America. Dresner’s report stands out for its in-depth and nuanced analysis of gen AI adoption across global organizations.

News about generative AI has captivated technology leaders. Demand for gen AI-related news and insights dominates many organization leaders’ time. 29% are following gen AI news updates constantly, and 30% say they often check in and see what’s new in gen AI, 24% regularly check the news. Overall, 72% of analytics and business intelligence (BI) professionals have made gen AI news a priority. North American respondents are the most diligent with constantly checking gen AI news, reflecting the region having the highest production use of gen AI.

Key takeaways from the report include the following:

Professionals in IT and marketing report plans to be the first adopters of generative AI, with 44% of IT and 36% of marketing professionals saying adopting gen AI is a primary focus. Operations/ production, sales, and C-level executives also show significant interest in adopting gen AI early. Dresner’s report states that “finance and human resources least often indicate overall interest, exceeding a majority only when aggregating their primary, secondary, and tertiary responses.”

gen ai

63% of organizations consider CRM data as critical or very important to generative AI. Finance and accounting data is considered the next most important, followed by call center and supply chain data. Dresner’s analysis found that respondents least often expect generative AI to leverage workforce (HR) data. Organizations are wary of using HR data due to privacy concerns combined with the stringent standards and safeguards on data security and its use across regulated industries today.

gen ai

Gen AI adoption across organizations accelerated rapidly in 2023. From 1Q23 to 4Q23, production use increased nearly fivefold, experimenting increased by 70%, and planned use in 12 months increased by 157%. Dresner’s research results reflect a major shift in generative AI prioritization last year. The report’s authors contend that implementation activity and funding were primarily from autonomous, decentralized sources, not from C-level mandates or sponsors, as it occurred late in fiscal years and into annual budget cycles.

gen ai

Consumer services lead gen AI production levels at 43%. Technology, business services, and healthcare have the next three highest levels of gen AI production in use today. The education industry reports the highest experimentation rate at 67%, closely followed by healthcare at 62%, while government trails at 50%. The report notes that the government also reports the highest levels for planned use beyond 12 months and no planned use.

gen ai

40% of organizations consider it critical to achieve productivity and efficiency gains from gen AI. One in three (30%) say improving customer experience and personalization is the next most critical priority, followed by improved search quality and decision-making (26%).

gen ai

Data privacy concerns are considered critical to 46% of organizations pursuing gen AI initiatives today. Legal and regulatory compliance, the potential for unintended consequences, and ethics and bias concerns are also significant. Less than half of respondents—46% and 43%, respectively—consider costs and organizational policy important to generative AI adoption.

gen ai

 

Nov 14

1 Comment

5 Proven Ways Manufacturers Can Get Started With Analytics

5 Proven Ways Manufacturers Can Get Started With Analytics

Going into 2020, manufacturers are at an inflection point in their adoption of analytics and business intelligence (BI). Analytics applications and tools make it possible for them to gain greater insights from the massive amount of data they produce every day. And with manufacturing leading all industries on the planet when it comes to the amount of data generated from operations daily, the potential to improve shop floor productivity has never been more within reach for those adopting analytics and BI applications.

Analytics and BI Are High Priorities In Manufacturing Today

Increasing the yield rates and quality levels for each shop floor, machine and work center is a high priority for manufacturers today. Add to that the pressure to stay flexible and take on configure-to-order and engineer-to-order special products fulfilled through short-notice production runs and the need for more insight into how each phase of production can be improved. Gartner’s latest survey of heavy manufacturing CIOs in the 2019 CIO Agenda: Heavy Manufacturing, Industry Insights, by Dr. Marc Halpern. October 15, 2018 (Gartner subscription required) reflects the reality all manufacturers are dealing with today. I believe they’re in a tough situation with customers wanting short-notice production time while supply chains often needing to be redesigned to reduce or eliminate tariffs. They’re turning to analytics to gain the insights they need to take on these challenges and more. The graphic below is from Gartner’s latest survey of heavy manufacturing CIOs, it indicates the technology areas where heavy manufacturing CIOs’ organizations will be spending the largest amount of new or additional funding in 2019 as well as the technology areas where their organizations will be reducing funding by the highest amount in 2019 compared with 2018:

Knowing Which Problems To Solve With Analytics

Manufacturers getting the most value from analytics start with a solid business case first, based on a known problem they’ve been trying to solve either in their supply chains, production or fulfillment operations. The manufacturers I’ve worked with focus on how to get more orders produced in less time while gaining greater visibility across production operations. They’re all under pressure to stay in compliance with customers and regulatory reporting; in many cases needing to ship product quality data with each order and host over 60 to 70 audits a year from customers in their plants. Analytics is becoming popular because it automates the drudgery of reporting that would otherwise take IT team’s days or weeks to do manually.

As one CIO put it as we walked his shop floor, “we’re using analytics to do the heavy data crunching when we’re hosting customer audits so we can put our quality engineers to work raising the bar of product excellence instead of having them run reports for a week.” As we walked the shop floor he explained how dashboards are tailored to each role in manufacturing, and the flat-screen monitors provide real-time data on how five key areas of performance are doing. Like many other CIOs facing the challenge of improving production efficiency and quality, he’s relying on the five core metrics below in the initial roll-out of analytics across manufacturing operations, finance, accounting, supply chain management, procurement, and service:

  • Manufacturing Cycle Time – One of the most popular metrics in manufacturing, Cycle Time quantifies the amount of elapsed time from when an order is placed until the product is manufactured and entered into finished goods inventory. Cycle times vary by segment of the manufacturing industry, size of manufacturing operation, global location and relative stability of supply chains supporting operations. Real-time integration, applying Six Sigma to know process bottlenecks, and re-engineering systems to be more customer-focused improve this metrics’ performance. Cycle Time is a predictor of the future of manufacturing as this metric captures improvement made across systems and processes immediately.
  • Supplier Inbound Quality Levels – Measuring the dimensions of how effective a given supplier is at consistently meeting a high level of product quality and on-time delivery is valuable in orchestrating a stable supply chain. Inbound quality levels often vary from one shipment to the next, so it’s helpful to have Statistical Process Control (SPC) charts that quantify and show the trends of quality levels over time. Nearly all manufacturers are relying on Six Sigma programs to troubleshoot specific trouble spots and problem areas of suppliers who may have wide variations in product quality in a given period. This metric is often used for ranking which suppliers are the most valuable to a factory and production network as well.
  • Production Yield Rates By Product, Process, and Plant Location – Yield rates reflect how efficient a machine or entire process is in transforming raw materials into finished products. Manufacturers rely on automated and manually-based approaches to capture this metric, with the latest generation of industrial machinery capable of producing its yield rate levels over time. Process-related manufacturers rely on this metric to manage every production run they do. Microprocessors, semiconductors, and integrated circuit manufacturers are continually monitoring yield rates to determine how they are progressing against plans and goals. Greater real-time integration, improved quality management systems, and greater supply chain quality and compliance all have a positive impact on yield rates. It’s one of the key measures of production yield as it reflects how well-orchestrated entire production processes are.
  • Perfect Order Performance – Perfect order performance measures how effective a manufacturer is at delivering complete, accurate, damage-free orders to customers on time. The equation that defines the perfect order Index (POI) or perfect order performance is the (Percent of orders delivered on time) * (Percent of orders complete) * (Percent of orders damage free) * (Percent of orders with accurate documentation) * 100. The majority of manufacturers are attaining a perfect order performance level of 90% or higher, according to The American Productivity and Quality Center (APQC). The more complex the product lines, configuration options, including build-to-order, configure-to-order, and engineer-to-order, the more challenging it is to attain a high, perfect order level. Greater analytics and insights gained from real-time integration and monitoring help complex manufacturers attained higher perfect order levels over time.
  • Return Material Authorization (RMA) Rate as % Of Manufacturing – The purpose of this metric is to define the percentage of products shipped to customers that are returned due to defective parts or not otherwise meeting their requirements. RMAs are a good leading indicator of potential quality problems. RMAs are also a good measure of how well integrated PLM, ERP and CRM systems, resulting in fewer product errors.

Conclusion

The manufacturers succeeding with analytics start with a compelling business case, one that has an immediate impact on the operations of their organizations. CIOs are prioritizing analytics and BI to gain greater insights and visibility across every phase of manufacturing. They’re also adopting analytics and BI to reduce the reporting drudgery their engineering, IT, and manufacturing teams are faced with as part of regular customer audits. There are also a core set of metrics manufacturers rely on to manage their business, and the five mentioned here are where many begin.

Mar 30

10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations is achievable with machine learning.
  • Reducing supply chain forecasting errors by 50% and lost sales by 65% with better product availability is achievable with machine learning.
  • Automating quality testing using machine learning is increasing defect detection rates up to 90%.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level.

Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

The ten ways machine learning is revolutionizing manufacturing in 2018 include the following:

  • Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations are is achievable with machine learning. Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimization are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

  • Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.

  • Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC. Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

  • McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

  • Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

  • Automating inventory optimization using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%. AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimization across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

  • Combining real-time monitoring and machine learning is optimizing shop floor operations, providing insights into machine-level loads and production schedule performance. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more. Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

  • A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning. The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

  • Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE). OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Additional reading:

Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi

Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute

Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services

Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)

Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone

Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge

Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Get started with the Connected factory preconfigured solution, Microsoft Azure

Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum

Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group

Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell

Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK

Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. Ai Edam, 28(1), 83-97.

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company

Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney

The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press

The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017

The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Transform the manufacturing supply chain with Multi-Echelon inventory optimization, Microsoft, March 1, 2018.

Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45.

Jan 1

5 Comments

Roundup Of Internet Of Things Forecasts And Market Estimates, 2018

 

  • According to IDC, worldwide spending on the IoT is forecast to reach $772.5B in 2018. That represents an increase of 15% over the $674B that was spent on IoT in 2017.
  • The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%.
  • Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each.
  • Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector.
  • Internet Of Things Market To Reach $267B By 2020 according to Boston Consulting Group.
  • According to IDC FutureScape: Worldwide IoT 2018 Predictions, By the end of 2020, close to 50% of new IoT applications built by enterprises will leverage an IoT platform that offers outcome-focused functionality based on comprehensive analytics capabilities.

The last twelve months of Internet of Things (IoT) forecasts and market estimates reflect enterprises’ higher expectations for scale, scope and Return on Investment (ROI) from their IoT initiatives. Business benefits and outcomes are what drives the majority of organizations to experiment with IoT and invest in large-scale initiatives. That expectation is driving a new research agenda across the many research firms mentioned in this roundup. The majority of enterprises adopting IoT today are using metrics and key performance indicators (KPIs) that reflect operational improvements, customer experience, logistics, and supply chain gains. Key takeaways from the collection of IoT forecasts and market estimates include the following:

  • The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%. According to GrowthEnabler & MarketsandMarkets analysis, the global IoT market share will be dominated by three sub-sectors; Smart Cities (26%), Industrial IoT (24%) and Connected Health (20%). Followed by Smart Homes (14%), Connected Cars (7%), Smart Utilities (4%) and Wearables (3%). Source: GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in.

  • Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector. Advisory firm Bain predicts the most competitive areas of IoT will be in the enterprise and industrial segments. Bain predicts consumer applications will generate $150B by 2020, with B2B applications being worth more than $300B. Globally, enthusiasm for the Internet of Things has fueled more than $80B in merger and acquisition (M&A) investments by major vendors and more than $30B in venture capital, according to Bain’s estimates. Source: Bain Insights: Choosing The Right Platform For The Internet Of Things

  • The global IoT market is growing at a 23% CAGR of 23% between 2014-2019, enabling smart solutions in major industries including agriculture, automotive and infrastructure. ― Key challenges to growth are the security and scalability of all-new connected devices and the adherence to open standards to facilitate large-scale monitoring of different systems. Source: Export opportunities of the Dutch ICT sector to Germany (25-04-17), PDF, 95 pp., no opt-in

  • According to  Variant Market Research, the Global Internet of Things (IoT) market is estimated to reach $1,599T by 2024, from $346.1B in 2016, attaining a CAGR of 21.1% from 2016 to 2024. Asia-Pacific is predicted to grow at the fastest CAGR over the forecast period 2016 to 2024. The growth is attributed to increasing adoption of IoT in emerging countries such as India and China, high rate of mobile and internet usage, and development of next-generation technologies. Source: Global Internet of Things (IoT) Market: Rising Adoption of Cloud Platform Noticed by Variant Market Research. 

  • Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each. Improving the accuracy, speed, and scale of supply chains is an area many organizations are concentrating on with IoT. IoT has the potential to redefine quality management, compliance, traceability and Manufacturing Intelligence. Business-to-Consumer (B2C) companies are projected to spend $25B on IoT in 2020, up from $5B in 2015. The following graphic compares global spending by vertical between 2015 and 2020. Source: Statista, Spending on the Internet of Things worldwide by vertical in 2015 and 2020 (in billion U.S. dollars).

 

  • By 2020, 50% of IoT spending will be driven by discrete manufacturing, transportation, and logistics, and utilities BCG predicts that IoT will have the most transformative effect on industries that aren’t technology-based today. The most critical success factor all these use cases depend on secure, scalable and reliable end-to-end integration solutions that encompass on-premise, legacy and cloud systems, and platforms.Source: Internet Of Things Market To Reach $267B By 2020.

  • The hottest application areas for IoT in manufacturing include Industrial Asset Management, Inventory and Warehouse Management and Supply Chain Management. In high tech manufacturing, Smart Products, and Industrial Asset Management are the hottest application areas. The following Forrester heat Map for 2017 shows the fastest growing areas of IoT adoption by industry. Source: IoT Opportunities, Trends, and Momentum Robert E Stroud CGEIT CRISC.

  • B2B spending on IoT technologies, apps and solutions will reach €250B ($296.8B) by 2020 according to a recent study by Boston Consulting Group (BCG). IoT Analytics spending is predicted to generate €20B ($23.7B) by 2020. Between 2015 to 2020, BCG predicts revenue from all layers of the IoT technology stack will have attained at least a 20% Compound Annual Growth Rate (CAGR). B2B customers are the most focused on services, IoT analytics, and applications, making these two areas of the technology stack the fastest growing. By 2020, these two layers will have captured 60% of the growth from IoT. Source: Internet Of Things Market To Reach $267B By 2020.

  • Manufacturers most relied on the Industrial Internet of Things (IIoT) in 2017 to help better understand machine health (32%) on the shop floor, leading to more accurate Overall Equipment Effectiveness (OEE) measurements. Changing how plant maintenance personnel will work and interact with all levels of operation (29.5%) and helping to better prevent and predict shutdowns (27.1%) are the top three use cases of IIoT according to Plant Engineering and Statista. 

  • Improving customer experiences (70%) and safety (56%) are the two areas enterprises are using data generated from IoT solutions most often today. Gaining cost efficiencies, improving organizational capabilities, and gaining supply chain visibility (all 53%) is the third most popular uses of data generated from IoT solutions today. 53% of enterprises expect data from IoT solutions to increase revenues in the next year. 53% expect data generated from their IoT solutions will assist in increasing revenues in the next year. 51% expect data from IoT solutions will open up new markets in the next year. 42% of enterprises are spending an average of $3.1M annually on IoT. Source: 70% Of Enterprises Invest In IoT To Improve Customer Experiences.

  • McKinsey Global Institute estimates IoT could have an annual economic impact of $3.9T to $11.1T by 2025. Their forecast scenario includes diverse settings and use cases including factories, cities, retail environments, and the human body. Factories alone could contribute between $1.2T to $3.7T in IoT-driven value. Source: McKinsey & Company, What’s New With The Internet of Things?

  • Business Intelligence Competency Centers (BICC), R&D, Marketing & Sales and Strategic Planning are most likely to see the importance of IoT. Finance is considered among the least likely departments to see the importance of IoT. The study also found that sales analytics apps are increasingly relying on IoT technologies as foundational components of their core application platforms.These and many other insights are from Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study. The study defines IoT as the network of physical objects, or “things,” embedded with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. The study examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. Please see page 11 of the study for details regarding the methodology.

  • Manufacturing, Consulting, Business Services and Distribution/Logistics are IoT industry adoption leaders. Conversely, Federal Government, State & Local Government are least likely to prioritize IoT initiatives as very important or critical. IoT early adopters are most often defining goals with clear revenue and competitive advantages to drive initiatives. Manufacturing, Consulting, Business Services and Distribution/Logistics are challenging, competitive industries where revenue growth is often tough to achieve. IoT initiatives that deliver revenue and competitive strength quickly are the most likely to get funding and support. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • IoT advocates or early adopters say location intelligence, streaming data analysis, and cognitive BI to deliver the greatest business benefit. Conversely, IoT early adopters aren’t expecting to see as significant of benefits from data warehousing as they are from other technologies. Consistent with previous studies, both the broader respondent base and IoT early adopters place a high priority on reporting and dashboards. IoT early adopters also see the greater importance of visualization and end-user self-service. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • Business Intelligence Competency Centers (BICC), Manufacturing and Supply Chain are among the most powerful catalysts of BI and IoT adoption in the enterprise. The greater the level of BI adoption across the 12 functional drivers of BI adoption defined in the graphic below, the greater the potential for IoT to deliver differentiated value based on unique needs by area. Marketing, Sales and Strategic Planning are also strong driver areas among IoT advocates or early adopters. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • IoT early adopters are relying on growing revenue and increasing competitive advantage as the two main goals to drive IoT initiatives’ success. The most successful IoT advocates or early adopters evangelize the many benefits of IoT initiatives from a revenue growth position first. IoT early adopters are more likely to see and promote the value of better decision-making, improved operational efficiencies, increased competitive advantage, growth in revenues, and enhanced customer service when BI adoption excels, setting the foundation for IoT initiatives to succeed. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • The most popular feature requirements for advanced and predictive analytics applications include regression models, textbook statistical functions, and hierarchical clustering. More than 90% of respondents replied that these three leading features are “somewhat important” to their daily use of analytics. Geospatial analysis (highly associated with mapping, populations, demographics, and other Web-generated data), recommendation engines, Bayesian methods, and automatic feature selection is the next most required series of features. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • 74% of IoT advocates or early adopters say location intelligence is critical or very important. Conversely, only 26% of the overall sample ranks location intelligence at the same level of importance. One of the most promising use cases for IoT-based location intelligence is its potential to streamline traceability and supply chain compliance workflows in highly regulated manufacturing industries. In 2018, expect to see ERP and Supply Chain Management (SCM) software vendors launch new applications that capitalize on IoT location intelligence to streamline traceability and supply chain compliance on a global scale. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

Sources:

10 Predictions For The Internet Of Things (IoT) In 2018

2017 Internet Of Things (IoT) Intelligence Update

Bain Insights, Three Ways Telcos Can Win On The Internet Of Things [Infographic]

Bain Insights: Choosing The Right Platform For The Internet Of Things

Big Data & Analytics Is The Most Wanted Expertise By 75% Of IoT Providers

Cambridge Consultants, Review of latest developments in the Internet of Things, 7 March 2017, 143 pp., free, no opt-in.

Cognizant Trend Study: Digital Industrial Transformation with the Internet of Things: How can European companies benefit from IoT?

Ernst & Young,  Internet of Things Human-machine interactions that unlock possibilities –  Media & Entertainment. 24 pp., PDF, no opt-in.

GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in

IDC, Worldwide Spending on the Internet of Things Forecast to Reach Nearly $1.4 Trillion in 2021, According to New IDC Spending Guide

IHS Markit IoT Trend Watch 2017, pdf, 26 pp., free, no opt-in

Internet Of Things Market To Reach $267B By 2020

Internet Of Things Will Revolutionize Retail

PwC, Leveraging the Upcoming Disruptions from AI and IoT, 20 pp., PDF, free, no opt-in

McKinsey & Company, Beyond The Supercycle: How Technology Is Reshaping Resources

McKinsey & Company,  Digital machinery: How companies can win the changing manufacturing game

McKinsey & Company, Taking the pulse of enterprise IoT

McKinsey & Company, What’s New With The Internet of Things?

IoT: Landscape and Nasscom Initiatives, May 2017. 36 pp., PDF, free, no opt-in

Stanford University Course EE392B, Industrial IoT: Applications Overview April 4, 2017, Dimitry Gorinevsky

Verizon, State of the Market: Internet of Things 2017 Making way for the enterprise

What Makes An Internet Of Things (IoT) Platform Enterprise-Ready?

Woodside Capital Partners, The Industrial Internet of Things: Making Factories “Smart” For The Next Industrial Revolution, PDF, 126 pp., free, no opt-in

THE INTERNET OF THINGS 2017 REPORT: How the IoT is improving lives to transform the world

The IoT Platforms Report: How software is helping the Internet of Things evolve

 

 

 

 

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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.