Industrial Roadmap for Connected Machines. Sal Spada Research Director ARC Advisory Group

Transcription

1 Industrial Roadmap for Connected Machines Sal Spada Research Director ARC Advisory Group

2 Industrial Internet of Things (IoT) Based upon enhanced connectivity of this stuff Connecting intelligent physical entities People, sensors, devices, machines, assets, and products, to each other, to Internet services, and to applications. IoT connected applications Data acquisition, aggregation, analysis, and visualization. IoT architecture Mobile and intelligent devices, wired and wireless networks, cloud computing, Big Data, analytics, and visualization tools. Business Value of Information Lower costs, optimize processes, and transformative applications, services, or business models 2

3 IoT is a Source of Trusted Data Digitizing the Sources of Data Transforms Assumptions into Facts People/Operators Social, connected, better decisions, etc. Empower operators with knowledge Intelligent Assets Technology-enhanced with sensors, processors, memory, communications Software-defined behavior Reactive, predictive, and social Potentially self-aware and autonomous Data Communications & Infrastructure Cloud-based Big Data storage Analytics and Software Asset Optimization and System Optimization PredictiveAnalytics Intelligent Information when and where people need it 3

4 Industrial IoT Ecosystem is Transformative Connectivity provides the ability to enhance products and services Industrial IoT and the Connected Asset Value Network: Digital Umbilical Cord Securely share selected asset performance information with asset manufacturer or partners Asset manufacturers can provide better support services Greater uptime, fewer disruptions Reduce asset lifecycle cost Supports Innovation Enables Product as a Service or other new business models Improve product design Leverage New Sensors as well as Data From Existing Systems to Improve Operating Performance 4

5 IoT-A Model for Industrial Companies IoT-A (Internet of Things Architecture) project in Europe established an architectural reference model for IoT Reference for compliant IoT architectures. View and perspective on different architectural aspects of concern to IoT stakeholders. Functional View developed with seven main areas 5

6 Connected Device Management Platform Models Often Referred to as M2M Extends Device Connectivity Solutions From To Open-loop monitoring Closed-loop solutions Alarming, field service SIM card management Remotely resolve issues 6

7 Internet of Things Current State No standard systems/networks/interfaces Multiplicity of communications, embedded intelligence, and sensor & actuator technologies Multiplicity of concurrent IoT research and development activities underway Multiplicity of separate but sometimes overlapping IoT domains Health Care, Smart Manufacturing, Smart Cities, Logistics, Smart Houses, Smart Energy, Retail, Smart Transport, etc. 7

8 Business Case for Industrial IoT in Metal Working Operations

9 Machine Tools in Operation Today Valued at $352 Billion Average machine tool life= 15 to 25 years. At least 2.8 million Machine Tools installed in the last 13 years Historical Annual Unit Shipments of Computer Numerical Controls (Unit Shipments) (x000) Average Selling Prices (x000) Metal Cutting 2013 Milling $118.9 Laser $450.8 Plasma/OxyFuel $142.6 Electron Beam $142.6 Waterjet $146.8 Other* $115.6 Total $123.6 Source: ARC Advisory Group 9

10 Industry Production Value = $453 billion Assuming that companies in this industry are profitable, lets assume a Gross Margin of 22%. Industry Total Industry Production Value ($Billions) Total Industry Production Value (%) Industry Production Value of Products Produced by Machine Tools ($Billions) Aerospace & Defense $ % $26.0 Automotive $1, % $95.9 Cement & Glass $ % $2.0 Chemical $1, % $2.5 Petrochemical $ % $0.7 Oil & Gas $1, % $15.2 Refining $ % $1.8 Electric Power Generation $ % $12.3 Electronics & Electrical $ % $1.8 Semiconductors $ % $1.3 Fabricated Metals $ % $14.4 Food & Beverage $1, % $7.7 Furniture & Wood Products $ % $0.7 Machinery Manufacturing $1, % $207.5 Medical Products $ % $2.0 Mining & Metals $1, % $15.2 Pharmaceutical & Biotech $ % $2.3 Plastics & Rubber $ % $9.0 Printing & Publishing $ % $2.9 Pulp & Paper $ % $0.3 Textiles $ % $22.9 Water & Wastewater $ % $0.4 Other $ % $8.6 Total $15, % $453.6 Source: ARC Advisory Group Industry Total Industry Production Value ($x000) Production Value $453,000, % Operating Costs $353,340, % Gross Margin $99,660, % 10

11 Annual Operating Costs Exceed $353 billion Overall Industry Machine Utilization = 45% Margin and Production Output Scenario Analysis Industry Production Costs Industry ($x000) Production Value $453,000, % Operating Costs $353,340, % Gross Margin $99,660, % Cost Item Annual Cost ($billion) Operating Machine $167.9 Labor $143.1 Tooling $16.0 Fixed Capital $26.3 Total $353.3 Machines only cutting metal for 45% of an normal 8 hour day Source: ARC Advisory Group Assumptions Used to Calculate Operational Costs Operating Cost Industry Averages Operating Costs Per Unit of Production Output Machine Cutting $65/hour (1) Labor $25/hour (1) WACC 11% (1) Global Industry Averages 11

12 Possible Scenarios Affecting Utilization Variable Costs in Labor and Machine are only Indicators Operating Costs Per Unit of Production Output Specialized machines have limited use Machine Process setup and preparation limits utilization Excess machine tool capacity in the industry Undetected operational issues prevent machines from being utilized Process issues incurred as a result of tooling or material variations Inconsistency in the skilled labor pool operating machines Batch orders incur additional set up time 12

13 IoT is a Business Opportunity to Improve Machine Utilization Identifying Problems will Allow the Industry to do More with Less Machinery Reduce Operating time to produce work piece Improve Machine Production Output Improve Utilization of Labor Identify operational issues Identify poorly operating machines Increase the performance of the operator Close the loop with product design Close the loop with CAM systems 13

14 Operator Enablement Critical First Step

15 Increase Utility of Machine Tool by Enabling Operators Machine utilization continues to rely on operators, but skill level is declining Operators often first to identify processes need attention. Operators are an Intelligent Sensor Process Engineers Quality Skill Level Maintenance Technicians Operators Continued workforce reductions make it necessary to capture the knowledge of process engineers and maintenance workers to maintain quality. Time Today 15

16 Empower the Operator Low Skilled Operators Need to be Connected to an IoT Infrastructure to Improve Performance Workers senses Inspection target Skill Know-how Knowledge Operation by skilled workers Judgment based on implicit knowledge 16

17 Knowledge Discovery Through Analytics Embeds Rules in Machinery & Production Systems Consistent quality Non dependence on skilled workers Quantitative Characteristics Knowledge Know-how Data sensing From machinery Knowledge-info Control Technologies No skilled workers Extract Learn Infer 17

18 Knowledge Discovery & Analytics Analytical tools that rapidly recognize trends in production. Shorten the time from problem identification and corrective action. Rapidly identify startup problems to ensure faster time to market. Create an environment that is data driven instilling a consistency in corrective actions. Decouple operator ability of providing early detection of machine degradation from manufacturing performance. 18

19 Analytics Drives Knowledge Discovery and Creation Manufacturing Metrics Knowledge Creation with Analytics Manufacturing Intelligence Limit Performance Improvement Potential (using extraction from plant equipment) Analytics Gap Time Improving Manufacturing Metrics 19

20 Analytics Identifies Obscure Patterns in Data from the Production Systems Detection of Root causes of product quality Identification of critical and optimal manufacturing process parameters Prediction of effects of manufacturing process changes Identification of root causes and prediction of equipment breakdown 20

21 Analytics Closes the Loop on Design and Production Systems Machine Machine Machine Energy External Events Complex Reasoning with Analytics PLM CAM CAD Production Management Business Systems 21

22 Analytics and Fundamental Technology Regression Analysis Functional relationships between outputs and multiple inputs. Classification Given previously known input/output (process/product) classify with particular production conditions. Clustering Grouping products by characteristics where no previously know associations (i.e. operator at a machine or time of day, etc ) 22

23 Specific Technologies being Applied Neural nets Self organizing maps Genetic Algorithms Decision Trees Multivariate statistical projections Pattern Recognition 23

24 Next Generation Manufacturing extensive use of data, statistical, and quantitative analysis, explanatory and predictive models, and fact based management to drive decisions and actions throughout the business. Analytics may be input for human decisions or may drive fully automated decisions. 24

25 Will the Industrial IoT Change Factory Software and Automation? Maintenance Engineering Purchasing Corporate Machine Mfr. Service Provider Enterprise New IoT Analytics and Applications Private Secure Connection to 3 rd Parties (e.g. via protected Wi-Fi network) Production Management HMI / Workstations Smart Machine Wireless Infrastructure (Networks ) IoT Smart Module Local IoT Compute and Communicate module Fieldbus Process Control Safety Application Specific Appliances Plant Operations Logic & Motion Device buses Discrete Control Physical asset with sensors, actuators Emerging Option: Connect Assets Using New Technologies 25

26 The Connected Asset Value Chain Industrial Internet of Things Network of asset vendors Share ABC s asset data with vendors Monitor ABC s in-service products in plants Network of in-service products ABC Corp ABC Plant 1 ABC Plant 2 ABC Plant 2 ABC Plant n Multi-Cloud Connected Value Network Enterprise Model 26

27 ARC s Technology Forecast Chart Roadmap to Improve Machining Operations Help gauge strengths and weaknesses in using emerging technologies. Reflects the expected industry uptake of emerging technologies by leading, early-adopters 27

28 Thank You. For more information or a copy of the contact the author at [email protected]. Please visit our web pages at 28