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Digital Shopfloor: Industrial Automation in the Industry 4.0 Era: Performance Analysis and Applications [Kõva köide]

Edited by (Athens Information Technology, Greece), Edited by (Innovalia Association, Spain), Edited by (Synesis-Consortium, Italy)
In todays competitive global environment, manufacturers are offered with unprecedented opportunities to build hyper-efficient and highly flexible plants, towards meeting variable market demand, while at the same time supporting new production models such as make-to-order (MTO), configure-to-order (CTO) and engineer-to-order (ETO). During the last couple of years, the digital transformation of industrial processes is propelled by the emergence and rise of the fourth industrial revolution (Industry 4.0). The latter is based on the extensive deployment of Cyber-Physical Production Systems (CPPS) and Industrial Internet of Things (IIoT) technologies in the manufacturing shopfloor, as well as on the seamless and timely exchange of digital information across supply chain participants. The benefits of Industry 4.0 have been already proven in the scope of pilot and production deployments in a number of different use cases including flexibility in automation, predictive maintenance, zero defect manufacturing and more. Despite early implementations and proof-of-concepts, CPPS/IIoT deployments are still in their infancy for a number of reasons, including:

Manufacturers poor awareness about digital manufacturing solutions and their business value potential, as well as the lack of relevant internal CPPS/IIoT knowledge. The high costs that are associated with the deployment, maintenance and operation of CPPS systems in the manufacturing shopfloors, which are particularly challenging in the case of SME (Small Medium Enterprises) manufacturers that lack the equity capital needed to invest in Industry 4.0. The time needed to implement CPPS/IIoT and the lack of a smooth and proven migration path from existing OT solutions. The uncertainty over the business benefits and impacts of IIoT and CPPS technologies, including the lack of proven methods for the techno-economic evaluation of Industry 4.0 systems. Manufacturers increased reliance on external integrators, consultants and vendors. The absence of a well-developed value chain needed to sustain the acceptance of these new technologies for digital automation.

In order to alleviate these challenges, three European Commission funded projects (namely H2020 FAR-EDGE (http://www.far-edge.eu/), H2020 DAEDALUS (http://daedalus.iec61499.eu) and H2020 AUTOWARE (http://www.autoware-eu.org/)) have recently joined forces towards a Digital Shopfloor Alliance. The Alliance aims at providing leading edge and standards based digital automation solutions, along with guidelines and blueprints for their effective deployment, validation and evaluation.

The present book provides a comprehensive description of some of the most representative solutions that offered by these three projects, along with the ways these solutions can be combined in order to achieve multiplier effects and maximize the benefits of their use. The presented solutions include standards-based digital automation solutions, following different deployment paradigms, such as cloud and edge computing systems. Moreover, they also comprise a rich set of digital simulation solutions, which are explored in conjunction with the H2020 MAYA project (http://www.maya-euproject.com/). The latter facilitate the testing and evaluation of what-if scenarios at low risk and cost, but also without disrupting shopfloor operations. As already outlined, beyond leading edge scientific and technological development solutions, the book comprises a rich set of complementary assets that are indispensable to the successful adoption of IIoT/CPPS in the shopfloor.

The book is structured in three parts as follows: The first part of the book is devoted to digital automation platforms. Following an introduction to Industry 4.0 in general and digital automation platforms in particular, this part presents the digital automation platforms of the FAR-EDGE, AUTOWARE and DAEDALUS projects. The second part of the book focuses on the presentation of digital simulation and digital twins functionalities. These include information about the models that underpin digital twins, as well as the simulators that enable experimentation with these processes over these digital models. The third part of the book provides information about complementary assets and supporting services that boost the adoption of digital automation functionalities in the Industry 4.0 era. Training services, migration services and ecosystem building services are discussed based on the results of the three projects of the Digital Shopfloor Alliance.

The target audience of the book includes: Researchers in the areas of Digital Manufacturing and more specifically in the areas of digital automation and simulation, who wish to be updated about latest Industry 4.0 developments in these areas. Manufacturers, with an interest in the next generation of digital automation solutions based on Cyber-Physical systems. Practitioners and providers of Industrial IoT solutions, which are interested in the implementation of use cases in automation, simulation and supply chain management. Managers wishing to understand technologies and solutions that underpin Industry 4.0, along with representative applications in the shopfloor and across the supply chain.
Foreword xix
Preface xxiii
List of Contributors xxvii
List of Figures xxxi
List of Tables xxxvii
List of Abbreviations xxxix
1 Introduction to Industry 4.0 and the Digital Shopfloor Vision 1(26)
John Soldatos
1.1 Introduction
1(3)
1.2 Drivers and Main Use Cases
4(5)
1.3 The Digital Technologies Behind Industry 4.0
9(5)
1.4 Digital Automation Platforms and the Vision of the Digital Shopfloor
14(7)
1.4.1 Overview of Digital Automation Platforms
14(2)
1.4.2 Outlook Towards a Fully Digital Shopfloor
16(5)
1.5 Conclusion
21(1)
References
22(5)
Part I
2 Open Automation Framework for Cognitive Manufacturing
27(44)
Oscar Lazaro
Martijn Rooker
Begona Laibarra
Anton Ruzic
Bojan Nemec
Aitor Gonzalez
2.1 Introduction
28(2)
2.2 State of the Play: Digital Manufacturing Platforms
30(11)
2.2.1 RAMI 4.0 (Reference Architecture Model Industry 4.0)
32(2)
2.2.2 Data-driven Digital Manufacturing Platforms for Industry 4.0
34(3)
2.2.3 International Data Spaces
37(4)
2.3 Autoware Framework for Digital Shopfloor Automation
41(16)
2.3.1 Digital Shopfloor Evolution: Trends & Challenges
41(1)
2.3.1.1 Pillar 1: AUTOWARE open reference architecture for autonomous digital shopfloor
46(1)
2.3.1.2 Pillar 2: AUTOWARE digital abilities for automatic awareness in the autonomous digital shopfloor
48(1)
2.3.1.3 Pillar 3: AUTOWARE business value
51(2)
2.3.2 AUTOWARE Software-Defined Autonomous Service Platform
53(1)
2.3.2.1 Cloud & Fog computing services enablers and context management
53(4)
2.3.3 AUTOWARE Framework and RAMI 4.0 Compliance
57(1)
2.4 Autoware Framework for Predictive Maintenance Platform Implementation
57(11)
2.4.1 Z-BRE4K: Zero-Unexpected-Breakdowns and Increased Operating Life of Factories
59(1)
2.4.2 Z-Bre4k Architecture Methodology
60(1)
2.4.3 Z-BRE4K General Architecture Structure
61(1)
2.4.4 Z-BRE4K General Architecture Information Workflow
61(3)
2.4.5 Z-BRE4K General Architecture Component Distribution
64(4)
2.5 Conclusions
68(1)
References
69(2)
3 Reference Architecture for Factory Automation using Edge Computing and Blockchain Technologies
71(32)
Mauro Isaja
3.1 FAR-EDGE Project Background
71(2)
3.2 FAR-EDGE Vision and Positioning
73(1)
3.3 State of the Art in Reference Architectures
74(7)
3.3.1 Generic Reference Architectures
74(1)
3.3.2 RAMI 4.0
75(1)
3.3.3 IIRA
76(3)
3.3.4 OpenFog RA
79(2)
3.4 FAR-EDGE Reference Architecture
81(10)
3.4.1 Functional Viewpoint
81(1)
3.4.1.1 Automation domain
83(1)
3.4.1.2 Analytics domain
83(1)
3.4.1.3 Simulation domain
84(1)
3.4.1.4 Crosscutting functions
84(1)
3.4.2 Structural Viewpoint
85(1)
3.4.2.1 Field Tier
86(1)
3.4.2.2 Gateway Tier
87(1)
3.4.2.3 Ledger Tier
88(1)
3.4.2.4 Cloud Tier
90(1)
3.5 Key Enabling Technologies for Decentralization
91(9)
3.5.1 Blockchain Issues
92(2)
3.5.2 Permissioned Blockchains
94(1)
3.5.3 The FAR-EDGE Ledger Tier
94(1)
3.5.4 Validation use Cases
95(5)
3.6 Conclusions
100(1)
References
101(2)
4 IEC-61499 Distributed Automation for the Next Generation of Manufacturing Systems
103(26)
Franco A. Cavadini
Giuseppe Montalbano
Gernot Kollegger
Horst Mayer
Valeriy Vytakin
4.1 Introduction
104(2)
4.2 Transition towards the Digital Manufacturing Paradigm: A Need of the Market
106(3)
4.3 Reasons for a New Engineering Paradigm in Automation
109(9)
4.3.1 Distribution of Intelligence is Useless without Appropriate Orchestration Mechanisms
113(3)
4.3.2 Defiance of Rigid Hierarchical Levels towards the Full Virtualization of the Automation Pyramid
116(2)
4.4 IEC-61499 Approach to Cyber-Physical Systems
118(5)
4.4.1 IEC-61499 runtime
118(2)
4.4.2 Functional Interfaces
120(1)
4.4.2.1 IEC-61499 interface
120(1)
4.4.2.2 Wireless interface
121(1)
4.4.2.3 Wrapping interface
121(1)
4.4.2.4 Service-oriented interface
122(1)
4.4.2.5 Fieldbus interface(s)
123(1)
4.4.2.6 Local I/O interface
123(1)
4.5 The "CPS-izer", a Transitional Path towards Full Adoption of IEC-61499
123(3)
4.6 Conclusions
126(1)
References
127(2)
5 Communication and Data Management in Industry 4.0
129(40)
Maria del Carmen Lucas-Estan
Theofanis P. Raptis
Miguel Sepulcre
Andrea Passarella
Javier Gozalvez
Marco Conti
5.1 Introduction
130(4)
5.2 Industry 4.0 Communication and Data Requirements
134(4)
5.3 Industrial Wireless Network Architectures
138(6)
5.4 Data Management in Industrial Environments
144(1)
5.5 Hierarchical Communication and Data Management Architecture for Industry 4.0
144(11)
5.5.1 Heterogeneous Industrial Wireless Network
145(1)
5.5.2 Hierarchical Management
146(1)
5.5.2.1 Hierarchical communications
147(1)
5.5.2.2 Data management
149(1)
5.5.3 Multi-tier Organization
150(1)
5.5.4 Architectural Enablers: Virtualization and Softwarization
151(1)
5.5.4.1 RAN slicing
151(1)
5.5.4.2 Cloudification of the RAN
153(2)
5.6 Hybrid Communication Management
155(3)
5.7 Decentralized Data Distribution
158(2)
5.7.1 Average Data Access Latency Guarantees
159(1)
5.7.2 Maximum Data Access Latency Guarantees
159(1)
5.7.3 Dynamic Path Reconfigurations
160(1)
5.8 Communications and Data Management within the AUTOWARE Framework
160(2)
5.9 Conclusions
162(1)
References
163(6)
6 A Framework for Flexible and Programmable Data Analytics in Industrial Environments
169(30)
Nikos Kefalakis
Aikaterini Roukounaki
John Soldatos
Mauro Isaja
6.1 Introduction
169(3)
6.2 Requirements for Industrial-scale Data Analytics
172(3)
6.3 Distributed Data Analytics Architecture
175(3)
6.3.1 Data Routing and Preprocessing
175(1)
6.3.2 Edge Analytics Engine
176(1)
6.3.3 Distributed Ledger
177(1)
6.3.4 Distributed Analytics Engine (DA-Engine)
177(1)
6.3.5 Open API for Analytics
177(1)
6.4 Edge Analytics Engine
178(8)
6.4.1 EA-Engine Processors and Programmability
178(1)
6.4.2 EA-Engine Operation
179(2)
6.4.3 Configuring Analytics Workflows
181(1)
6.4.4 Extending the Processing Capabilities of the EA-Engine
182(1)
6.4.5 EA-Engine Configuration and Runtime Example
182(4)
6.5 Distributed Ledger and Data Analytics Engine
186(5)
6.5.1 Global Factory-wide Analytics and the DA-Engine
186(1)
6.5.2 Distributed Ledger Services in the FAR-EDGE Platform
187(3)
6.5.3 Distributed Ledger Services and DA-Engine
190(1)
6.6 Practical Validation and Implementation
191(4)
6.6.1 Open-source Implementation
191(1)
6.6.2 Practical Validation
192(1)
6.6.2.1 Validation environment
192(1)
6.6.2.2 Edge analytics validation scenarios
193(1)
6.6.2.3 (Global) distributed analytics validation scenarios
194(1)
6.7 Conclusions
195(1)
References
196(3)
7 Model Predictive Control in Discrete Manufacturing Shopfloors
199(44)
Alessandro Brusaferri
Giacomo Pallucca
Franco A. Cavadini
Giuseppe Montalbano
Dario Piga
7.1 Introduction
200(8)
7.1.1 Hybrid Model Predictive Control SDK
202(1)
7.1.2 Requirements
202(2)
7.1.3 Hybrid System
204(1)
7.1.4 Model Predictive Control
205(3)
7.2 Hybrid System Representation
208(5)
7.2.1 Piece-Wise Affine (PWA) System
210(1)
7.2.2 Mixed Logical Dynamical (MLD) System
211(2)
7.2.3 Equivalence of Hybrid Dynamical Models
213(1)
7.3 Hybrid Model Predictive Control
213(3)
7.3.1 State of the Art
213(2)
7.3.2 Key Factors
215(1)
7.3.3 Key Issues
216(1)
7.4 Identification of Hybrid Systems
216(10)
7.4.1 Problem Setting
219(2)
7.4.2 State-of-the-Art Analysis
221(1)
7.4.3 Recursive Two-Stage Clustering Approach
222(1)
7.4.4 Computation of the State Partition
223(3)
7.5 Integration of Additional Functionalities to the IEC 61499 Platform
226(11)
7.5.1 A Brief Introduction to the Basic Function Block
226(3)
7.5.2 A Brief Introduction to the Composite Function Block
229(1)
7.5.3 A Brief Introduction to the Service Interface Function Block
230(1)
7.5.4 The Generic DLL Function Block of nxtControl
231(2)
7.5.5 Exploiting the FB_DLL Function Block as Interfacing Mechanism between IEC 61499 and External Custom Code
233(4)
7.6 Conclusions
237(2)
References
239(4)
8 Modular Human-Robot Applications in the Digital Shopfloor Based on IEC-61499
243(24)
Franco A. Cavadini
Paolo Pedrazzoli
8.1 Introduction
243(2)
8.2 Human and Robots in Manufacturing: Shifting the Paradigm from Co-Existence to Mutualism
245(2)
8.3 The "Mutualism Framework" Based on IEC-61499
247(4)
8.3.1 "Orchestrated Lean Automation": Merging IEC-61499 with the Toyota Philosophy
248(1)
8.3.2 A Hybrid Team of Symbionts for Bidirectional Mutualistic Compensation
249(1)
8.3.3 Three-Dimensional Characterization of Symbionts' Capabilities
250(1)
8.3.4 Machine Learning Applied to Guarantee Dynamic Adherence of Models to Reality
251(1)
8.4 Technological Approach to the Implementation of Mutualism
251(6)
8.4.1 "Mutualism Framework" to Sustain Implementation of Symbionts-Enhanced Manufacturing Processes
252(1)
8.4.2 IEC-61499 Engineering Tool-Chain for the Design and Deployment of Real-Time Orchestrated Symbionts
253(1)
8.4.3 AI-Based Semantic Planning and Scheduling of Orchestrated Symbionts' Tasks
254(2)
8.4.4 Modular Platform for Perceptual Learning and Augmentation of Human Symbionts
256(1)
8.4.5 Training Gymnasium for Progressive Adaptation and Performance Improvement of Symbionts' Mutualistic Behaviours
257(1)
8.5 The Potential to Improve Productivity and the Impact
257(4)
8.6 Conclusions
261(1)
References
262(5)
Part II
9 Digital Models for Industrial Automation Platforms
267(18)
Nikos Kefalakis
Aikaterini Roukounaki
John Soldatos
9.1 Introduction
267(3)
9.2 Scope and Use of Digital Models for Automation
270(3)
9.2.1 Scope of Digital Models
270(1)
9.2.2 Factory and Plant Information Modelling
270(1)
9.2.3 Automation and Analytics Processes Modelling
271(1)
9.2.4 Automation and Analytics Platforms Configuration
271(1)
9.2.5 Cyber and Physical Worlds Synchronization
271(1)
9.2.6 Dynamic Access to Plant Information
272(1)
9.3 Review of Standards Based Digital Models
273(4)
9.3.1 Overview
273(1)
9.3.2 IEC 62264
273(1)
9.3.3 IEC 62769 (FDI)
274(1)
9.3.4 IEC 62453 (FDT)
274(1)
9.3.5 IEC 61512 (Batch Control)
274(1)
9.3.6 IEC 61424 (CAEX)
275(1)
9.3.7 Business to Manufacturing Markup Language (B2MML)
275(1)
9.3.8 AutomationML
276(1)
9.4 FAR-EDGE Digital Models Outline
277(4)
9.4.1 Scope of Digital Modelling in FAR-EDGE
277(1)
9.4.2 Main Entities of Digital Models for Data Analytics
278(2)
9.4.3 Hierarchical Structure
280(1)
9.4.4 Model Repository Open Source Implementation
281(1)
9.5 Simulation and Analytics Models Linking and Interoperability
281(2)
9.6 Conclusions
283(1)
References
284(1)
10 Open Semantic Meta-model as a Cornerstone for the Design and Simulation of CPS-based Factories
285(32)
Jan Wehrstedt
Diego Rovere
Paolo Pedrazzoli
Giovanni dal Maso
Torben Meyer
Veronika Brandstetter
Michele Ciavotta
Marco Macchi
Elisa Negri
10.1 Introduction
286(1)
10.2 Adoption of AutomationML Standard
287(1)
10.3 Meta Data Model Reference
288(26)
10.3.1 Base Model
289(1)
10.3.1.1 Property
289(1)
10.3.1.2 CompositeProperty
289(1)
10.3.2 Assets and Behaviours
289(1)
10.3.2.1 ExternalReference
290(1)
10.3.2.2 Asset
291(1)
10.3.2.3 Behaviour
291(1)
10.3.3 Prototypes Model
292(1)
10.3.3.1 Prototypes and instances
292(1)
10.3.3.2 Prototypes and instances aggregation patterns
293(1)
10.3.3.3 AbstractResourcePrototype
295(1)
10.3.3.4 ResourcePrototype
296(1)
10.3.3.5 CPSPrototype
296(1)
10.3.4 Resources Model
296(1)
10.3.4.1 AbstractResource
297(1)
10.3.4.2 CPS
299(1)
10.3.5 Device Model
299(1)
10.3.5.1 Device
300(1)
10.3.5.2 DeviceIO
301(1)
10.3.6 Project Model
302(1)
10.3.6.1 Project
302(1)
10.3.6.2 Plant
303(1)
10.3.6.3 SimulationScenario
303(1)
10.3.6.4 SimModel
303(1)
10.3.7 Product Routing Model
304(1)
10.3.7.1 Relationship between product routing model and ISO 14649-10 standard
305(1)
10.3.7.2 Workpiece
306(1)
10.3.7.3 ProgramStructure
306(1)
10.3.7.4 ProgramStructureType
307(1)
10.3.7.5 MachiningExecutable
307(1)
10.3.7.6 AssemblyExecutable
308(1)
10.3.7.7 DisassemblyExecutable
308(1)
10.3.7.8 MachiningNcFunction
308(1)
10.3.7.9 MachiningWorkingStep
308(1)
10.3.7.10 MachiningWorkpieceSetup
310(1)
10.3.7.11 MachiningSetupInstructions
310(1)
10.3.7.12 ManufacturingFeature
310(1)
10.3.7.13 MachiningOperation
310(1)
10.3.7.14 MachiningTechnology
310(1)
10.3.7.15 FixtureFixture
310(1)
10.3.7.16 Assembly and disassembly
311(1)
10.3.8 Security Model
312(2)
10.4 Conclusions
314(1)
References
315(2)
11 A Centralized Support Infrastructure (CSI) to Manage CPS Digital Twin, towards the Synchronization between CPS Deployed on the Shopfloor and Their Digital Representation
317(22)
Diego Rovere
Paolo Pedrazzoli
Giovanni dal Maso
Marino Alge
Michele Ciavotta
11.1 Introduction
318(1)
11.2 Terminology
318(1)
11.3 CSI Architecture
319(8)
11.3.1 Microservice Platform
319(1)
11.3.1.1 Front-end services
320(1)
11.3.1.2 Security and privacy
321(1)
11.3.1.3 SOA enabling services
321(1)
11.3.1.4 Backend services
322(1)
11.3.2 Big Data Sub-Architecture
323(1)
11.3.2.1 Batch layer
324(1)
11.3.2.2 Stream processing engine
325(1)
11.3.2.3 All data store
325(1)
11.3.2.4 Message queueing system
325(1)
11.3.2.5 Serving layer
326(1)
11.3.3 Integration Services
326(1)
11.4 Real-to-Digital Synchronization Scenario
327(3)
11.5 Enabling Technologies
330(3)
11.5.1 Microservices
330(1)
11.5.2 Cloud Ready Architecture: The Choice of Docker
331(1)
11.5.3 Lambda Architecture
332(1)
11.5.4 Security and Privacy
333(1)
11.6 Conclusions
333(1)
References
334(5)
Part III
12 Building an Automation Software Ecosystem on the Top of IEC 61499
339(26)
Andrea Barni
Elias Montini
Giuseppe Landolfi
Marzio Sorlini
Silvia Menato
12.1 Introduction
340(1)
12.2 An Outlook of the Automation Value Network
341(7)
12.2.1 Characteristics of the Automation Ecosystem Stakeholders
342(1)
12.2.1.1 Automation solution providers
343(1)
12.2.1.2 Components suppliers
344(1)
12.2.1.3 Equipment and machines builders
345(1)
12.2.1.4 System integrators
346(1)
12.2.2 Beyond Business Interactions: Limitations and Complexities of the Existing Automation Market
347(1)
12.3 A Digital Marketplace to Support Value Networks Reconfiguration in the Automation Domain
348(13)
12.3.1 Architectural Characteristics of the Digital Marketplace
350(4)
12.3.2 Value Exchange between the Digital Platform and Its Complementors
354(1)
12.3.2.1 Customers
355(1)
12.3.2.2 Hardware developers
357(1)
12.3.2.3 Application developers
357(1)
12.3.2.4 Service providers
358(1)
12.3.3 Opportunities of Exploitation of an Automation Platform
358(1)
12.3.3.1 Opportunities for system integrators
358(1)
12.3.3.2 Opportunities for equipment and machines builders
360(1)
12.3.3.3 Opportunities for components suppliers
360(1)
12.3.3.4 Opportunities for automation solutions providers
360(1)
12.3.3.5 Opportunities for new players
361(1)
12.3.3.6 Service providers
361(1)
12.4 Conclusions
361(2)
References
363(2)
13 Migration Strategies towards the Digital Manufacturing Automation
365(28)
Ambra Cala
Filippo Boschi
Paola Maria Fantini
Arndt Luder
Marco Taisch
Jurgen Elger
13.1 Introduction
366(2)
13.2 Review of the State-of-the Art Approaches
368(6)
13.2.1 Migration Processes to Distributed Architectures
368(1)
13.2.2 Organizational Change Management
369(2)
13.2.3 Maturity Models
371(3)
13.3 The FAR-EDGE Approach
374(4)
13.4 Use Case Scenario
378(1)
13.5 Application of the Migration Approach
379(8)
13.5.1 Assessment
379(2)
13.5.2 Gap Analysis
381(1)
13.5.2.1 Technical aspects
381(1)
13.5.2.2 Operational aspects
382(1)
13.5.2.3 Human aspects
383(1)
13.5.3 Migration Path Alternatives
384(3)
13.6 Conclusion
387(1)
References
388(5)
14 Tools and Techniques for Digital Automation Solutions Certification
393(32)
Batzi Uribarri
Lara Gonzalez
Begona Laibarra
Oscar Lazaro
14.1 Introduction
394(1)
14.2 Digital Automation Safety Challenges
395(5)
14.2.1 Workplace Safety and Certification According to the DGUV
398(1)
14.2.2 Industrial Robots Safety According to ISO 10218-1:2011 & ISO 10218-2:2011
398(1)
14.2.3 Collaborative Robots Safety According to ISO/TS 15066:2016
399(1)
14.3 DSA Ecosystem Vision
400(5)
14.4 DSA Reference Architecture
405(2)
14.5 AUTOWARE Certification Usability Enabler
407(8)
14.5.1 AUTOWARE Certification Techniques
410(3)
14.5.2 N-axis Certification Schema
413(1)
14.5.2.1 Data collection
413(1)
14.5.2.2 Strategy
414(1)
14.5.2.3 Test execution
414(1)
14.5.2.4 Analysis & reports
414(1)
14.6 DSA Certification Framework
415(4)
14.7 DSA Certification Methodology
419(3)
14.8 Conclusion
422(1)
References
423(2)
15 Ecosystems for Digital Automation Solutions an Overview and the Edge4lndustry Approach
425
John Soldatos
John Kaldis
Tiago Teixeira
Volkan Gezer
Pedro Malo
15.1 Introduction
425(2)
15.2 Ecosystem Platforms and Services for Industry 4.0 and the Industrial Internet-of-Things
427(11)
15.2.1 ThingWorx Foundation (Platform and Ecosystem)
427(2)
15.2.2 Commercial Cloud-Based HOT Platforms
429(1)
15.2.3 Testbeds of the Industrial Internet Consortium
430(1)
15.2.4 Factory Automation Testbed and Technical Aspects
431(1)
15.2.5 Industry 4.0 Testbeds
432(1)
15.2.5.1 SmartFactory pilot production lines - testbeds
432(1)
15.2.5.2 Industry 4.0 production line
433(1)
15.2.5.3 SkaLa (scalable automation with Industry 4.0 technologies)
433(1)
15.2.5.4 Key finder (The keyfinder production line from SmartFactoryKL)
434(1)
15.2.5.5 SME 4.0 competence center kaiserslautern
435(1)
15.2.6 EFFRA Innovation Portal
435(1)
15.2.7 FIWARE Project and Foundation
435(2)
15.2.8 ARROWHEAD ARTEMIS JU Project and ARROWHEAD Community
437(1)
15.3 Consolidated Analysis of Ecosystems - Multi-sided Platforms Specifications
438(2)
15.3.1 Consolidated Analysis
438(1)
15.3.2 Multi-sided Platforms
439(1)
15.4 The Edge4lndustry Ecosystem Portal
440(5)
15.4.1 Services
442(1)
15.4.2 Solutions
443(2)
15.4.3 Knowledge Base
445(1)
15.4.4 Blog
445(1)
15.4.5 Sign-in and Registration
445(1)
15.5 Conclusions
445(1)
References
446(1)
16 Epilogue 447(4)
Index 451(2)
About the Editors 453
9781634258944
Foreword xiii
Ted Claypoole
Preface xv
Roland L. Trope
Thomas J. Smedinghoff
Acknowledgments xxiii
About the Authors xxv
About the Editors xxxi
Part I. Introduction
Chapter 1 The Challenge
3(6)
Roland L. Trope
Thomas J. Smedinghoff
Chapter 2 The Importance of Cybersecurity Due Diligence for an M&A Deal
9(34)
Roland L. Trope
1 Cybersecurity Challenges
9(6)
2 Vulnerability of Target's Digital Assets
15(1)
3 Vulnerability of Target's Operations and Businesses
16(2)
4 Vulnerability of Target's Dependency on Critical Infrastructure
18(1)
5 Contamination of the Acquirer's Networks and Data
19(1)
6 Lessons from Recent Cyber Incidents
20(23)
6.1 Neiman Marcus
22(3)
6.2 Yahoo!
25(9)
6.3 Target Corporation
34(1)
6.4 Sony Pictures
35(1)
6.5 Volkswagen
36(7)
Chapter 3 Cybersecurity Risks to an M&A Deal's Objectives
43(12)
Roland L. Trope
1 Key Cybersecurity Risks to an M&A Transaction
43(9)
2 Premises for Planning Cybersecurity Due Diligence
52(3)
Chapter 4 Basic Cybersecurity Concepts
55(18)
Thomas J. Smedinghoff
1 Cybersecurity
55(3)
2 Digital Assets to Be Protected
58(1)
3 Goal of Cybersecurity
58(4)
3.1 Confidentiality
59(1)
3.1.1 Authentication
60(1)
3.1.2 Authorization
61(1)
3.2 Integrity
61(1)
3.3 Availability
62(1)
4 Threats Addressed by Cybersecurity
62(5)
4.1 Physical and Environmental Threats
63(1)
4.2 Technical Threats
63(1)
4.3 People Threats
64(1)
4.4 Examples of Threats
64(3)
5 Security Controls
67(6)
5.1 Categorization Based on Timing of Security Controls
68(1)
5.2 Categorization Based on Nature of Security Controls
68(5)
Part II. Due Diligence: What The Acquirer Should Know
Chapter 5 Identification of Target's High-Value Digital Assets
73(16)
Jonathan P. Adams
Matthew Staples
1 Introduction
73(2)
1.1 Subject Matter and Goals
73(2)
1.2 Background
75(1)
2 Due Diligence Issues
75(13)
2.1 Identify Digital Assets
76(1)
2.2 Identify Storage Used
76(1)
2.3 Identify Control of Digital Assets
77(1)
2.4 Have Vulnerabilities Been Identified and Addressed?
78(1)
2.5 Separation of Business Versus Operational Digital Assets
79(1)
2.6 Reliance on Internet for Communication
80(1)
2.7 Risk Profile of Target Business Sector
80(1)
2.8 Supply Chain Dependencies
81(1)
2.9 Information Sharing Activities
82(1)
2.9.1 Receipt of Intelligence-Sharing Reports
82(1)
2.9.2 Receipt of Classified Cyberintelligence Information
83(1)
2.9.3 Recipient of Industrial Control Systems Cyber Emergency Response Team Alerts
84(1)
2.9.4 Recipient of DHS Notices
85(1)
2.9.5 Participation in an ISAC
85(1)
2.9.6 ISAC Information Coordination
86(1)
2.9.7 DHS Technical Assistance Agreements
87(1)
2.9.8 Information Sharing Agreements
88(1)
3 Assessment and Analysis of Results
88(1)
Chapter 6 Evaluation of Internal Cybersecurity Program
89(18)
Stuart Levi
1 Introduction
89(2)
1.1 Subject Matter and Goals
89(1)
1.2 Background
89(2)
2 Due Diligence Issues
91(13)
2.1 Senior Management and Board Involvement
91(2)
2.2 Reviewing Security Programs
93(1)
2.2.1 Identifying the Program That Is in Place
94(1)
2.2.2 Program Responsibility
95(1)
2.2.3 Program Compliance with Legal Requirements
95(1)
2.2.4 Is the Program Risk-Based and Tailored to the Target's Business?
96(1)
2.2.5 Cybersecurity Program Resilience
96(1)
2.2.6 Cybersecurity Program Implementation
97(1)
2.2.7 Cybersecurity Program Updates
97(1)
2.2.8 Third-Party Cybersecurity Assessments
98(1)
2.2.9 Cybersecurity Statements
99(1)
2.2.10 Vendor Management
100(1)
2.2.11 Incident Response Plan
101(1)
2.2.12 Impact of Acquisitions
103(1)
2.3 Role of Standards
103(1)
2.4 Budget for Cybersecurity
103(1)
3 Assessment's Impact on the Proposed Transaction
104(3)
Chapter 7 Assessment of External Dependency Cybersecurity Program
107(18)
Candace Jones
1 Introduction
107(3)
1.1 Subject Matter and Goals
107(2)
1.2 Background
109(1)
2 Due Diligence Issues
110(13)
2.1 Inventory Third-Party Relationships
110(2)
2.2 Vendor Governance and Management Program
112(1)
2.3 Integration of Cyber Risk into the Vendor Governance and Management Program
113(2)
2.4 Vendor Cybersecurity Assessments
115(2)
2.5 Onboarding and Offboarding
117(1)
2.6 Vulnerability and Acceptance Testing
118(1)
2.7 Continuous Monitoring of Vendor Relationships
118(1)
2.8 Cyber-Risk Monitoring Should Account for Risk Inherited from Vendors
119(1)
2.9 Incident Response Procedures
120(1)
2.10 Target's Obligations as Vendor-Flowdown Requirements
121(1)
2.11 Separating the Target from Its Affiliates
121(1)
2.12 Change Management
122(1)
3 Assessment and Analysis of Results
123(2)
Chapter 8 Identifying Breaches and Assessing Incident Response Capabilities
125(8)
David Flint
Robert Bond
1 Introduction
125(1)
1.1 Subject Matter and Goals
125(1)
1.2 Background
125(1)
2 Due Diligence Issues
126(7)
2.1 Breach History
126(1)
2.1.1 Prior Breaches
126(1)
2.1.2 Breach Response
127(1)
2.1.3 Ongoing and Collateral Issues
128(1)
2.2 Preparedness for Future Breaches
128(1)
2.2.1 Existence of an Incident Response Plan
129(1)
2.2.2 Verification and Testing of the Plan
129(1)
2.2.3 Expertise of Personnel
130(1)
2.2.4 Training and Education
131(1)
2.3 Third-Party Risk
131(2)
Chapter 9 Evaluation of Cybersecurity Regulatory Compliance
133(18)
Thomas J. Smedinghoff
1 Introduction
133(3)
1.1 Subject Matter and Goals
133(1)
1.2 Background
133(3)
2 Identifying Legal Obligations
136(5)
2.1 Statutes and Regulations
137(1)
2.2 Common Law Obligations
138(1)
2.3 Contractual Obligations
138(1)
2.4 Industry Standards
139(1)
2.5 Self-Imposed Obligations
139(1)
2.6 Cross-Border Issues
140(1)
2.7 New Standards
140(1)
3 Identifying Status of Compliance
141(11)
3.1 Assessing Compliance with Laws Requiring Process- Oriented Approach
141(1)
3.1.1 Written Security Program
142(1)
3.1.2 Identification of High-Value Digital Assets
143(1)
3.1.3 Periodic Risk Assessment
143(1)
3.1.4 Implementation of Security Controls Responsive to Risks
144(1)
3.1.4.A Are the Security Controls Responsive to the Risks the Target Faces?
144(1)
3.1.4.B Do the Security Measures Address the Required Security Controls?
144(3)
3.1.5 Regular Monitoring and Testing of Target's Security Controls
147(1)
3.1.6 Regular Review and Adjustment of Target's Security Program
147(1)
3.1.7 Oversight of Third-Party Service Provider Arrangements
148(1)
3.2 Assessing Compliance with Laws Requiring Specific Security Controls
149(1)
3.3 Successor Liability
150(1)
Chapter 10 Special Issues in Cybersecurity Due Diligence: Resilience and Reviews by CFIUS
151(26)
Roland L. Trope
1 Resilience
152(15)
1.1 Subject Matter and Background
152(1)
1.1.1 Example: Resilience of Bulk Power System Enterprises
155(1)
1.1.2 Example: Resilience of Financial Services Enterprises
157(4)
1.2 Due Diligence Issues
161(6)
2 Reviews of "Covered Transactions" by CFIUS
167(10)
2.1 Subject Matter and Background
167(3)
2.2 Due Diligence Issues
170(7)
Part III. Impact Of Due Diligence On The Proposed Transaction
Chapter 11 Addressing Risks Identified in Due Diligence
177(24)
Jonathan P. Adams
Matthew Staples
1 Subject Matter and Goals
177(1)
2 Problems That Could Emerge in Diligence
178(13)
2.1 Insufficient Ability to Assess Cybersecurity Risk
178(1)
2.1.1 Desired Documentation or Information Is Missing
179(1)
2.1.2 Desired Documentation Does Not Exist
180(1)
2.1.3 Shifting Regulatory and Legal Environments
181(1)
2.2 Serious Shortcomings in Target's Practices or Legal Risks
181(1)
2.2.1 Poor Practices or Weak Policies on Information Security
182(1)
2.2.2 Compliance Failures
182(1)
2.2.3 Contractual Breaches
183(1)
2.2.4 Data Breaches and Cyber Attacks
184(1)
2.2.5 Government Investigations
186(1)
2.2.6 Litigation and Prelitigation Activity
187(1)
2.3 "What You See Versus What You Hear": Discrepancies between Documentation and Target Narratives
188(1)
2.4 Assessing Target's Resilience to Cyber Threats and Attacks
188(2)
2.5 Review and Assessment of Findings and Report by Cybersecurity Specialist
190(1)
3 Recourse Available to Acquirer
191(11)
3.1 Limiting Exposure in the Underlying Agreement
191(1)
3.1.1 Indemnification
192(1)
3.1.2 Covenants
193(1)
3.1.3 Closing Conditions
194(1)
3.1.4 Purchase-Price Adjustment
195(1)
3.2 Limiting Exposure in the Disclosure Schedules
196(5)
Chapter 12 Representations and Warranties in M&A Agreements
201(12)
William R. Denny
1 Goals of Cybersecurity Representations and Warranties
202(1)
2 Representation and Warranty Issues
202(2)
3 Common Cybersecurity Representations and Warranties
204(5)
3.1 Compliance with Data Security Policies
204(1)
3.2 Compliance with Laws and Regulations
204(1)
3.3 Compliance with Contractual Obligations Regarding Data Security
205(1)
3.4 Contractual Restrictions on Third Parties to Protect Data
206(1)
3.5 Absence of Unauthorized Use or Access
207(1)
3.6 Security Measures to Protect Systems and Information
208(1)
3.7 Absence of Data Security Incidents
208(1)
4 Less Common Cybersecurity Representations and Warranties
209(2)
4.1 Disclosure of Data Security Plans to Acquirer
209(1)
4.2 Compliance with Self-Regulatory Principles
210(1)
4.3 Disclosure of Agreements
210(1)
4.4 Disclosure of Types of Proprietary Data Collected
210(1)
4.5 Limits on Cross-Border Processing and Transfers
211(1)
5 Coordination with Other Parts of Agreement
211(2)
Chapter 13 Concluding Observations: Emerging Challenges to Cybersecurity Due Diligence
213(8)
Roland L. Trope
Appendix: List of Common U.S. Data Security Laws and Regulations 221(12)
Index 233
John Soldatos, Oscar Lazaro, Franco Cavadini