Customer Benefits by Cyber-Physical Systems

Transcription

1 Customer Benefits by Cyber-Physical Systems Roland Rosen, CT RDA AUC January 30, 2017 Unrestricted Siemens AG 2017 Siemens

2 Digitalization changes everything Page 2

3 Megatrends Challenges that are transforming our world Digitalization In the future, we ll be living in a world that s increasingly interconnected by complex and heterogeneous systems; by 2020, the amount of data stored worldwide will have grown to 44 zettabytes. Around 50 billion devices will be linked online. Source: IDC, The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, April 2014; Dave Evans (Cisco): The Internet of Things, How the Next Evolution of the Internet Is Changing Everything, April 2011 Page 3

4 End-user behavior is radically changing based on new business models From Bookstore to ebook From Record Store to Streaming From Yellow Pages to Marketplaces From Taxi to Ride Sharing Page 4

5 Value creation processes are continuously changing based on new distribution of knowledge and information From Physical tovirtual Prototypes From Manual Installation to Online Update From Personal Contact to Electronic Mall From Traveling to Remote Service Page 5

6 Products are augmented digitally for different stakeholder along their entire lifecycle Online Documentation: User and Sales Connectivity: Monitoring and Access Digital Twin: Engineering and Operation Plug & Operate: Installation and Upgrade Page 6

7 Massive pervasion of technologies driven by exponential growth of computational power Communication and Connectivity Modeling and Simulation Autonomy and Intelligence Semantic Technologies and Big Data Page 7

8 Four Key Aspects of Automation of the Future: Modularity, Connectivity, Autonomy and Digital Twin Needs of manufacturing industry can be clustered into four core aspects Increased efficiency, shorter time-to-market, and enhanced flexibility required New production environment will be defined by Dynamic networks of local controllers Flexible production steps configured in response to rapidly changing situations Less detailed planning in advance Optimization of production, e.g. through Cyber-Physical Production Systems Self-organization, e.g. product steers its own way through the production process Digital Twins of the entire process and its constituent elements Modularity The manufacturing process is determined on a flexible basis, depending on the current situation Autonomy Intelligence that enables execution of high level tasks without detailed programming Connectivity Self-organization of networked manufacturing equipment, taking the entire value chain into account Digital Twin Synchronized digital and physical world. Digitization of the whole product life cycle via Cyber- Physical Production Systems Page 8

9 What is a possible way to address these challenges? Page 9

10 Industrie 4.0: Increasing complexity leads to new value systems and productivity, speed and flexibility remain the biggest challenges Product volume 1980 Customized mass production E.g. smartphone Quality 1955 Mass production 2000 Globalization Regionalization Complexity Productivity 1913 Manual production e.g. vehicle configurator Personalization Speed "People can have the Model T in any color so long as it's black." Henry Ford (1913) 1850 Product variety E.g. 3D printing Flexibility Based on: The Global Manufacturing Revolution; sources: Ford, beetleworld.net, bmw.de, dw.de. Page 10

11 Plattform Industrie 4.0 Overall guiding principles Customer benefits first Industrie 4.0 will be successful only if there is a market for Industrie 4.0 solutions. We need to create solutions that generate customer benefits. Build on own core competencies and strengths Use-case based approach We have profound knowledge of the physical world, e.g. machines, automation, mechatronics. We have a deep understanding of the core value creation processes in manufacturing industries. Digitalization offers the opportunity to bolster these strengths. Heterogeneity of manufacturing industries does not allow a one-size-fits-all approach. Elaboration of sets of problems and solution approaches. Not every elaborated problem may be of interest to a specific customer. A specific solution approach can address several problems. Page 11

12 Value Chains of Manufacturing Companies Source GMA 7.21 customer Marketing Sales Product Development Product Line Planning Product Line Maintenance Discontinuation Management Factory and Production Planning Production Engineering Maintenance and Disposal Planning After-Sales Services Recycling Production Erection Factory Disposal Operation Maintenance supplier manufacturing company Page 12

13 Value Chains of Manufacturing Companies Source GMA 7.21 customer Marketing Sales Product Lifecycle Management Product Development Product Line Planning Product Line Maintenance Discontinuation Management Production System Lifecycle Management Factory and Production Planning Production Engineering Maintenance and Disposal Planning Service After-Sales Services Recycling Production Production Erection System Lifecycle Management Factory Operation Maintenance Disposal Service supplier manufacturing company Page 13

14 Overview of Application Scenarios of Plattform Industrie 4.0 customer IPD Marketing Product Lifecycle Management Product Development Product Line Planning Sales SP2 Product Line Maintenance Discontinuation Management Factory and Production Planning SDP Production System Lifecycle Management Production Engineering AF Production Erection System Lifecycle Management Maintenance and Disposal Planning supplier HTI Factory Operation OCP Production SAL Maintenance Service After-Sales Services VBS Disposal TAP Recycling Service manufacturing company Page 14

15 Overview of Application Scenarios OCP Order-Controlled Production: describes dynamic composition of necessary production resources for an order AF Adaptable Factory: focuses on a production resource with respect to an adaptable design and addresses the consequences for supplier and system integrator SAL Self Organizing and Adaptive Logistics: considers entire inter- and intra-logistics The adaptable factory VBS Value Based Services: describes the design of service value networks if product- and/or process information is provided based on an IT-platform This application scenario focuses on a production resource and describes how this can be designed with respect to adaptability and TAP Transparency and Adaptability of Delivered Products: focuses on a product and describes design of transparency and adaptability of delivered products based on an IT-platform HTI Human-Technology-Interaction in the Production: describes future support of operator in the production based on how this impacts new technologies the supplier of production resource, SP2 Smart Product Development for Smart Production: describes collaborative product engineering, starting with product requirements and the designing system seamless integrator, engineering and workflows to deliver necessary information to production and service the operator of a plant. IPD Innovative Product Development: describes new methods and processes in product development with focus on early phases SDP Seamless and Dynamic Engineering of Plants: addresses increasing dynamics in plant engineering along entire lifecycle of a plant and importance of validation of engineering decisions Page 15

16 Implementation Example Page 16

17 Industrie 4.0 Demonstrator Flexible transportation system (Multi Carrier System) The Industrie 4.0 Demonstrator was implemented by the 4 companies in the management board of the Platform Industrie 4.0 service platform in cooperation with engineering workplace analysis workplace mechatronical system Page 17

18 Industrie 4.0 Demonstrator Flexible transportation system (Multi Carrier System) in cooperation with service platform engineering workplace analysis workplace Setup Manufacturing cell as mechatronical system with flexible physical transportation system and modular virtual processing units Engineering workplace for design of manufacturing cell Analysis workplace for visualization and optimization of operation of manufacturing cell Collection of energy consumption of carrier of transportation system in the cloud mechatronical system Addresses benefit based on selected application scenarios Enhance flexibility: Adaptable Factories (AF) Increase efficiency: Value-Based Services (VBS) Shorten time-to-market: Seamless and Dynamic Plant Engineering (SDP) Page 18

19 Enhance Flexibility: Reorganization of Manufacturing Cell Challenges Increasing market volatility and rising individualization of the products leads to smaller order sizes Consequences are frequent retooling of production including reorganization of production units Solution Modular design of the manufacturing cell from individually exchangeable processing steps Flexible transportation system, which intelligently adapts to the production cell Benefits for operator Reacting quickly to changing demands in the market Efficient production of individual products Easy and secured reorganization of a processing unit Benefit for machine supplier Offering modular and flexible machines Extension of portfolio by offering virtual representatives of machines reorganization Page 19

20 Conclusions Page 20

21 Plattform Industrie 4.0 Setup of a strong triangle for Recommendations, Testing and Standardization Recommended actions SME mobilization International cooperation Digital Transformation Initiation of cross-sector standards Coordination of national / international standards Strengthen the international collaborations Network of test centers Practical testing Validated input for standardization Page 21

22 Our vision for CPS: Autonomous systems know what they are doing Automated System Executes a carefully engineered sequence of actions (with variants) Does not understand the consequences of its actions Cannot change the sequence Autonomous Systems Execute high-level tasks without detailed programming. They understand their capabilities and decide how to apply them to a given task perceive their environment and dynamically modify the course of action to respond to changes are multi-purpose machines Task Skill set Sensor N Sensor 2 Sensor1 Execution Actuator N Actuator 2 Actuator 1 Sensor N Sensor 2 Sensor1 Execution Actuator N Actuator 2 Actuator 1 Page 22

23 Research questions are addressed in Siemens Core Technology Initiatives Deep Artificial Intelligence Digital Twin Unleashed Autonomous Systems Revolution Simulation Explore AI for industrial machine intelligence - Deep learning algorithms e.g. for prediction, knowledge creation, image recognition - Software base for deployment - Integration of quickly developing open source software and GPU computing Leverage the potential of today s distributed digital twin - Coupling of engineered models with operational data - Digital twin views + services - Real and digital world in sync - Plug & play of modular, IPmanaged digital twin units Enable operation and interaction of autonomous machines - Multi-purpose machines - Goal-oriented engineering - Robot assistance to human workers - Autonomy in cooperating systems Enhance simulation technology and push into new application domains - Management of complexity in seamless simulation environments - Rapid simulation in early phases - Novel simulation services during operation & service Page 23

24 Thanks for your attention Contact Roland Rosen Principal Key Expert Research Scientist CT RDA AUC Otto-Hahn-Ring Munich Phone: +49 (89) Mobile: +49 (1522) roland.rosen@siemens.com Internet siemens.com/corporate-technology Intranet intranet.ct.siemens.com Page 24