Edge Computing and Distributed Ledger Technologies for Flexible Production Lines: A White-Appliances Industry Case

Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Available online at www.sciencedirect.com Information Control Problems in Manufacturing Information Control Problems in Manufacturing Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Bergamo, Italy, June 11-13, 2018 Bergamo, Italy, June 11-13, 2018 Information Control in Manufacturing Bergamo, Italy, JuneProblems 11-13, 2018 Bergamo, Italy, June 11-13, 2018

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IFAC PapersOnLine 51-11 (2018) 388–392

Edge Computing and Distributed Ledger Technologies for Flexible Production Edge Edge Computing Computing and and Distributed Distributed Ledger Ledger Technologies Technologies for for Flexible Flexible Production Production Lines: A White-Appliances Industry Case Edge Computing and Distributed Ledger Technologies for Flexible Production Lines: A White-Appliances Industry Case Lines: A White-Appliances Industry Case Lines: A White-Appliances Industry Case

Pierluigi Petrali*, Mauro Isaja. **, John K. Soldatos *** Pierluigi Pierluigi Petrali*, Petrali*, Mauro Mauro Isaja. Isaja. **, **, John John K. K. Soldatos Soldatos *** *** Pierluigi Petrali*, Mauro Isaja. **, John K. Soldatos *** Whirlpool EMEA EMEA S.p.A., S.p.A., Via Via Aldo Aldo Moro, Moro, 6, 6, Biandronno VA, VA, 21024, Italy Italy *** Whirlpool Whirlpool EMEA S.p.A., Via [email protected]) Moro, 6, Biandronno Biandronno VA, 21024, 21024, Italy (Tel: +39 0332 759111; e-mail: (Tel: 0332 e-mail: * Whirlpool EMEA S.p.A., Via [email protected]) Moro, 6, Biandronno VA, 21024, Italy (Tel: +39 +39 0332 759111; 759111; e-mail: [email protected]) ** Engineering Engineering Ingegneria Informatica SpA, Via Via Ferrini, 47 47 -- Loc. Loc. San Martino Martino ** Ingegneria Informatica SpA, (Tel: +39 0332 759111; e-mail: [email protected]) ** Engineering Ingegneria Informatica SpA, Via Ferrini, Ferrini, [email protected]) - Loc. San San Martino 53035 Monteriggioni, Italy (Tel: +39-0577.1887100, e-mail: 53035 Monteriggioni, Italy (Tel: +39-0577.1887100, e-mail: ** Engineering Ingegneria SpA, Via Ferrini, [email protected]) - Loc. San Martino 53035 Monteriggioni, Italy Informatica (Tel: +39-0577.1887100, e-mail: [email protected]) *** Athens Information Information Technology, Kifisias Ave. Ave. 44, Marousi, Marousi, 15125, Athens Technology, Kifisias 44, 53035*** Monteriggioni, Italy (Tel: +39-0577.1887100, e-mail: [email protected]) *** AthensGreece Information Technology, Kifisias Ave. 44, Marousi, 15125, 15125, (Tel: +302106682759; e-mail: [email protected]) *** AthensGreece Information Technology, Kifisias Ave. 44, Marousi, 15125, (Tel: e-mail: [email protected]) Greece (Tel: +302106682759; +302106682759; e-mail: [email protected]) Greece (Tel: +302106682759; e-mail: [email protected]) Abstract: Industry Industry 4.0 4.0 will enable enable the development development of of hyper-efficient hyper-efficient plants, plants, which which facilitate the the Abstract: Abstract: Industry 4.0 will will production enable the themodels development of hyper-efficient plants, which facilitate facilitate the implementation of emerging such as Made-to-Order and Configure-to-Order. In this implementation of such Made-to-Order and Configure-to-Order. In Abstract: Industry 4.0 will production enable themodels development hyper-efficient which facilitate the implementation of emerging emerging production models such as as of Made-to-Order andplants, Configure-to-Order. In this this direction, the the H2020 H2020 FAR-EDGE projectmodels has introduced introduced a reference reference architecture architecture and an an accompanying accompanying direction, FAR-EDGE project has a and implementation of emerging production such as Made-to-Order and Configure-to-Order. In this direction, the facilitates H2020 FAR-EDGE project has introduced a reference architecture and an computing accompanying platform that that the implementation implementation of digital digital automation solutions based on onand edge and platform the of automation solutions based edge and direction, the facilitates H2020 FAR-EDGE project has introduced a reference architecture an computing accompanying platform that facilitates the implementation of digital automation solutions based on edge computing and distributed ledger technologies, which enable fast, reliable and responsive automation. In this paper, we distributed ledger technologies, which reliable and responsive automation. In this paper, we platform that facilitates the implementation of fast, digital automation solutions based on edge and distributed ledger technologies, which enable enable fast, reliable andand responsive automation. In computing thisuse paper, we illustrate the use of these technologies for the implementation deployment of a practical case in illustrate the use these technologies for implementation deployment of practical case in distributed ledger which enable fast, reliable andand responsive automation. In thisuse paper, illustrate the use of oftechnologies, these technologies for the thewe implementation and deployment of aacan practical use casewe in the white appliances industry. Specifically, present how a sorter component be automatically the white appliances industry. Specifically, present how sorter component be automatically illustrate use of these technologies for thewe implementation deployment of acan practical use case in the whitethe appliances industry. Specifically, we present how aaand sorter component can be automatically programmed in order order industry. to ensure ensureSpecifically, that items items arriving arriving at aa conveyor conveyor are optimally optimally placed in automatically various bays. bays. the white appliances we present how a sorter component can be programmed in to that at are placed in various programmed in order totheensure that items arriving atina order conveyor are optimally placed in item various bays. The use case leverages edge computing paradigm to ensure that each physical is able to programmed in order tothe ensure that items arriving atin conveyor are optimally placed in item various bays. The use edge computing paradigm order to that each physical is to The use case case leverages leverages the edge computing paradigm inatime, order to ensure ensure that eachtechnologies physical item is able able to communicate its status to all the others. At the same distributed ledger enable the communicate its status to all others. At the same ledger The use case leverages edge in time, order distributed to ensure that eachtechnologies physical itemenable is ablethe to communicate its sorting status the toprocess all the thecomputing others. Atparadigm the same time, distributed ledger technologies enable the modelling of of the the asothers. a reliable reliable smart contract among all physical physical entities. The The benefits benefits of modelling sorting process as a smart contract among all entities. of communicate its status to all the At the same time, distributed ledger technologies enable the modelling of theinclude sorting tangible process improvements as a reliable smart contract among allwith physical entities. reduction The benefits of the deployment deployment in productivity, productivity, alongall significant in the the the in along with aaa significant in modelling of theinclude sorting tangible process improvements as a reliable smart contract among physical entities. reduction The benefits of the deployment include tangible improvements in productivity, along with significant reduction in the effort and time needed needed for the reconfiguration of the the sorter. sorter. effort and the of the deployment includefor tangible improvements along with a significant reduction in the effort and time time needed for the reconfiguration reconfiguration ofin theproductivity, sorter. effort and time needed for the reconfiguration of the sorter. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Industrial Industrial Automation, Automation, Simulation Simulation Edge Edge Computing, Computing, Distributed Distributed Ledger Technologies, White Keywords: Ledger Technologies, White Keywords: Industrial Automation, Simulation Edge Computing, Distributed Ledger Technologies, White Appliances, Bays, Conveyor, Appliances, Bays, Keywords: Automation, Simulation Edge Computing, Distributed Ledger Technologies, White Appliances,Industrial Bays, Conveyor, Conveyor, Appliances, Bays, Conveyor,    infrastructures, towards more automated, intelligent and infrastructures, towards automated, intelligent and  1. INTRODUCTION infrastructures, towards more more automated, intelligent and 1. INTRODUCTION streamlined manufacturing processes. To this end, the 1. INTRODUCTION streamlined manufacturing processes. To this end, the infrastructures, towards more automated, intelligent and streamlined manufacturing processes. To this end, the 1. INTRODUCTION deployment of CPS-based manufacturing systems can In today’s competitive global environment, manufacturers are deployment of CPS-based manufacturing systems can In today’s competitive global environment, manufacturers are streamlined manufacturing processes. To this end, the deployment of CPS-based manufacturing systems can In today’s competitive global environment, manufacturers are virtualize the conventional centralized automation pyramid striving to build hyper-efficient and highly flexible plants, virtualize the conventional centralized automation pyramid striving to build hyper-efficient and highly flexible plants, deployment of CPS-based manufacturing systems can In today’s competitive global environment, manufacturers are virtualize the conventional centralized automation pyramid striving meeting to build variable hyper-efficient and highly flexible plants, (i.e. SCADA (Supervisory Control and Data Acquisition), towards market demand, while at the same (i.e. SCADA (Supervisory Control and Data Acquisition), towards market demand, while at the same virtualize the conventional centralized automation pyramid striving meeting to build variable hyper-efficient and highly flexible plants, (i.e. SCADA (Supervisory Control and Data Acquisition), towards meeting variable market demand, while at the same MES (Manufacturing Execution System), ERP (Enterprise time supporting new production models such as make-to(i.e. SCADA (Supervisory Control and Data Acquisition), MES (Manufacturing Execution System), ERP (Enterprise time supporting new production models such as make-totowards meeting variable market demand, while at the same MES (Manufacturing Execution System), ERP (Enterprise time supporting new production models such as make-to- Resource Planning) deployed on powerful central servers), order (MTO), configure-to-order (CTO) and engineer-toResource Planning) deployed on powerful central servers), time new production models such as make-to- MES order (MTO), configure-to-order (CTO) and engineer-to(Manufacturing Execution ERP (Enterprise Resource Planning) deployed on System), powerful central servers), order supporting (MTO), configure-to-order (CTO) and engineer-towhich has proclaimed limitations, when it comes to order (ETO). Such models are at the heart of mass which has proclaimed limitations, when it comes to order (ETO). Such models are at the heart of mass Resource Planning) deployed on powerful central servers), (MTO), configure-to-order (CTO) and engineer-towhich has proclaimed limitations, when it comes to order (ETO). Such models are at the heart of mass supporting the integration of new technologies and devices at customization trend, which increases variety with only supporting the integration of new technologies and devices at customization trend, which increases variety with only which has proclaimed limitations, when it comes to order (ETO). Such models are at the heart of mass supporting the integration of new technologies and devices at customization trend, which increases variety with only a large scale. minimal increase in production costs [Pine97]. Massa large scale. minimal increase in production costs [Pine97]. Masssupporting the integration of new technologies and devices at customization trend, which increases variety with only a large scale. minimal increase in production costs [Pine97]. Masscustomisation requires scalable and advanced manufacturing customisation requires scalable and advanced manufacturing a large scale. minimal increase in production costs [Pine97]. Masscustomisation requires scalable and production advanced manufacturing systems (e.g., highly configurable lines), along systems (e.g., highly configurable lines), along customisation requires scalable and production advanced manufacturing systems (e.g., highly configurable production lines), along 1.1 H2020 FAR-EDGE: Edge Computing and Blockchains with a build-to-order approach, which push the limits of 1.1 H2020 FAR-EDGE: Edge Computing and Blockchains with a build-to-order approach, which push the limits of systems (e.g., highly configurable production lines), along 1.1 H2020 FAR-EDGE: Edge Computing and Blockchains with a build-to-order approach, which push the limits of for Industry factory automation systems beyond their state-of-the-art for Industry 1.1 H2020 FAR-EDGE: Edge Computing and Blockchains factory automation systems beyond their state-of-the-art with a build-to-order approach, which push the limits of for Industry factory automation systems beyond their techniques state-of-the-art capabilities and require novel automation that for Industry factory automation systems beyond their state-of-the-art capabilities and require novel automation techniques that capabilities and require automation novel automation techniques that Earlier efforts towards the decentralization of the factory deploy and reconfigure systems and production efforts towards the decentralization of deploy and reconfigure systems and capabilities and require automation novel automation techniques that Earlier Earlier efforts towards the focused decentralization of the the factory factory deploy and(e.g., reconfigure automation systems and production production automation systems have on the adaptation and resources workstations, robots) at the lowest possible automation systems have focused on the adaptation and Earlier efforts towards the decentralization of the factory resources (e.g., workstations, robots) at the lowest possible deploy and reconfigure automation systems and production automation systems have focused on the adaptation and resources (e.g., workstations, robots) at the lowest possible deployment of Multi-Agent Systems and SOA (Service cost. deployment of Multi-Agent Systems and SOA (Service automation systems have focused on the adaptation and cost. resources (e.g., workstations, robots) at the lowest possible deployment of Multi-Agent Systems and SOA (Service cost. Oriented Architecture) architectures for CPS and IoT devices, Oriented Architecture) architectures for CPS and IoT devices, deployment of Multi-Agent Systems and SOA (Service cost. Oriented Architecture) architectures for CPS (PLC) and IoT(Jammes. devices, including Programmable Logic Controllers Future Internet technologies (such as cloud computing, IoT Oriented Architecture) architectures for CPS (PLC) and IoT(Jammes. devices, including Programmable Logic Controllers Future Internet technologies (such as cloud computing, IoT including Programmable Logic Controllers (PLC) (Jammes. Future Internet technologies (such as cloud computing, IoT 2005). However, these architectures tend to be heavyweight (Internet-of-Things) and CPS (Cyber-Physical Systems)) including Programmable Logic Controllers (PLC) (Jammes. 2005). However, these architectures tend to be heavyweight (Internet-of-Things) and (Cyber-Physical Systems)) Future Internet technologies (such as cloud computing, IoT 2005). However, these architectures tend to be heavyweight (Internet-of-Things) and CPS CPS (Cyber-Physical Systems)) and rather inefficient real-time problems, therefore facilitate the deployment of advanced technologies and and rather inefficient for real-time problems, and therefore 2005). However, thesefor architectures tend to beand heavyweight facilitate the deployment of advanced technologies and (Internet-of-Things) and CPS (Cyber-Physical Systems)) and rather inefficient for real-time problems, and therefore facilitate theresources deployment of shopfloor, advanced thus technologies and cannot be deployed in the shopfloor without adaptations and production in the holding the cannot be deployed in the shopfloor without adaptations and and rather inefficient for real-time problems, and therefore facilitate the deployment of advanced technologies and production resources in the shopfloor, thus holding the cannot be deployed in the shopfloor without adaptations and production resourcesthein efficiency the shopfloor, thus holding the enhancements (Kothmayr 2015). In recent years, the advent promise to enhance and the performance of enhancements (Kothmayr 2015). In recent years, the advent cannot be deployed in the shopfloor without adaptations and promise to enhance the efficiency and the performance of production resources in the shopfloor, thus holding the enhancements (Kothmayr 2015). In recent years, the advent promise to enhance the efficiency and the performance of edge computing architectures provides aa years, compelling value production processes. These technologies are at the heart of of of edge computing architectures provides compelling value enhancements (Kothmayr 2015). In recent the advent production processes. These technologies are at the heart promise to enhance the efficiency and the performance of edge computing architecturesand provides a compellingfactory value production processes.revolution These technologies are atand the enable heart ofa of proposition for distributing decentralizing the fourth industrial (Industrie 4.0) proposition for distributing decentralizing edge computing architecturesand provides a compellingfactory value the fourth industrial (Industrie 4.0) production processes.revolution These technologies are atand the enable heart ofaa of proposition for distributing and decentralizing factory the fourth industrial revolution (Industrie 4.0) and enable automation systems, through placing data processing and deeper meshing of virtual and physical machines, along with automation systems, through placing data processing and proposition for distributing and decentralizing factory deeper meshing of virtual and physical machines, along with the fourth industrial revolution (Industrie 4.0) and enable a automation systems, through placing datatheprocessing and deeper meshing of virtual and physical machines, along with control functions at the very edge of network and the inter-connection of products, people, processes and automation systems, through placing data processing control functions at the very edge of the network and the inter-connection of products, people, processes and deeper meshing of virtual and physical machines, along with the inter-connection of products, people, processes and control functions at the very edge of the network and the inter-connection of products, people, processes and control functions at the very edge of the network and

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Figure 2 depicts the integration of blockchains for industry in FAR-EDGE. This concept is based on the introduction of a new logical tier, namely the Ledger Tier. The Ledger Tier complements the conventional field, edge and cloud tiers, which are integral components of any edge computing system in an industrial context. The Ledger Tier is a complete abstraction: it does not correspond to any physical deployment environment, and even the entities that it “contains” are abstract. These entities (called Ledger Services in FAR-EDGE), implement decentralized business logic as smart contracts on top of a distributed ledger. Hence, Ledger Services are, as the distributed ledger underneath, transaction-oriented: each service call that needs to modify the shared state of a system must be evaluated and approved by Peer Nodes before taking effect. Similarly to “regular” services, Ledger Services are implemented as executable code. However, they are not actually executed on any specific computing node: each service call is executed in parallel by all Peer Nodes that happen to be online at the moment, which then need to reach a consensus on its validity. Most importantly, even the executable code of Ledger Services can be deployed and updated online by means of a distributed ledger transaction, just like any other state change. Hence, in the context of FAR-EDGE, Ledger Services are used for the synchronization of distributed processes and the secure exchange of state between them.

facilitating distributed real-time control and scalable data processing. Therefore, edge computing is one of the most prominent options architecting industrial automation and real-time control systems. During the last two years, there are several initiatives that implement and validate edge computing platforms for industrial automation (Stanciu 2017). Prominent examples include: (i) the edge intelligence testbed which has been established by the Industrial Internet Consortium (IIC) in order to provide a proof-of-concept implementation of edge computing functionalities on the plant floor; (ii) Dell-EMC’s EdgeX Foundry framework, which is an open source project hosted by the Linux Foundation. The FAR-EDGE project, which is co-funded by the European Commission under contract No. 723096 of the H2020 programme, is researching, implementing and validating a novel edge computing platform for factory automation. The project’s platform is in-line with the state of the art edge computing platforms outlined above. As depicted in Figure 1, it aims at decentralizing automation through moving realtime functionalities at edge servers, while still leveraging the benefits of the cloud for functionalities with less stringent performance constraints.

Fig. 1. High-Level Computing Concept

Overview

of

FAR-EDGE

Overall, the Ledger Tier and Ledger Services provide the means for implementing smart contracts that reflect edge nodes’ consensus about the status of the physical world, which provides the means for synchronizing the various nodes and keeping the state of the plant complete and up to date. While edge computing operations can be performed at individual edge nodes, plant wide operations that require consistency across edge nodes are implemented through Ledger Services. Typical examples of such plant wide operations, include the calculation of plant wide KPIs (Key Performance Indicators), as well as the execution of plantwide automation functions that span multiple stations.

Edge

However, FAR-EDGE researches also a ground-breaking concept in industrial automation, which involves the introduction and use of blockchains for industry (Christidis 2016). Open API for Automation Open API for Virtualization

1.2 FAR-EDGE Functional Domains FAR-EDGE specifies a reference architecture and an accompanying platform design, which drives the use of edge computing and blockchain technologies in factory automation. A detailed presentation of this reference architecture is beyond the scope of this paper and presented in (Isaja 2017). Overall, the FAR-EDGE platform enables functionalities across three different domains (automation, analytics, simulation), which are described in following paragraphs.

Cloud Tier

Open API for Analytics

Orchestration

Ledger Tier

Configuration Data Publishing

Distributed Ledger

Synchronization

Edge Automation Services

Edge Automation Services

Edge Analytics Engine

Edge Analytics Engine

Edge Analytics Engine

Edge Tier

Edge Automation Services

389

Field Tier

Automation Domain: The FAR-EDGE Automation domain includes functionalities supporting automated control and automated configuration of physical production processes. While the meaning of “control” in this context is straightforward, “configuration” is worth a few additional words. Automated configuration is the enabler of plug-andplay factory equipment (known as plug-and-produce), which

Fig. 2. Tiers of the FAR-EDGE Compliant Systems 389

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in turn is a key technology for mass-customization, as it allows a faster and less expensive adjustments of the production process to cope with a very dynamic market demand. The Automation domain requires a bidirectional monitoring/control communication channel with the Field, typically with low bandwidth but very strict timing requirements. In some advanced scenarios, Automation is controlled – to some extent – by the results of Analytics and/or Simulation.

benefits of the migration to digital automation. Therefore, the merit of the present paper is not limited to illustrating the FAR-EDGE innovations, but also to boosting an understanding of Industry4.0 value potential.

Analytics Domain: The FAR-EDGE Analytics domain includes functionalities for gathering and processing Field data for a better understanding of production processes – i.e., a factory-focused business intelligence. This typically requires a high-bandwidth field communication channel, as the volume of information that needs to be transferred in a given time unit may be substantial. On the other hand, channel latency tends to be less critical than in the Automation scenario. The Analytics domain provides intelligence to its users, but these are not necessarily limited to humans or vertical applications (e.g., a predictive maintenance solution): the Automation and Simulation domains, if properly configured, can both make direct use of the outcome of data analysis algorithms. In the case of Automation, the behaviour of a workflow might change in response to changes detected in the controlled process – e.g., a process drift caused by the progressive wear of machinery or by the quality of assembly components being lower than usual. In the case of Simulation, data analysis can be used to update the parameters of a digital model.

The target use case concerns factories of Whirlpool Corporation, which is the world’s top white appliances manufacturer. In particular, it focuses on the configuration, operation and optimization of a sorter at Melano’s factories. In these factories, after a production is finished for a product, it is picked from the production line and delivered to the shipping bay with the help of a forklift. In the current status, there are 14 production lines and 19+1 shipping bays, LANEs.

2. BUSINESS CASE DECRIPTION AND SPECIFICATION 2.1 Overview

The use case focuses on improving the reliability and the resiliency of the Melano Sorter based on FAR-EDGE architecture and technologies. It should perform all operations within the cycle-time constraint. 2.2 Sorter’s Operation and Challenges The Sorter’s typical operation for Whirlpool’s HOB products, is based on the following workflow and algorithm (Figure 3):  The single HOB arrives in front of the palletizer.  If stack is not complete, the HOB is added to the stack.  If a stack is complete, the pallet is released.  The pallet is placed in front of the equipment (i.e. as part of a packaging process).  The film is stretched around the pallet.  A barcode reader reads all the serial numbers.  A 2D barcode is printed and applied on the pallet.  A 2D barcode reader reads the data matrix.  The serial number is checked and can be found ok or not.  If not ok it should be re-routed to exit the bay.  On the other hand, if the serial number if ok, the final bay is selected depending on a plan.  As a last step, the planner decides what the production plan will be for the next day. The challenges faced by the manufacturer with this process are twofold: System Unreliability: Sudden changes in production plan are not managed correctly and can often cause cease of production. Furthermore, there is not tolerance on hardware problems (e.g. barcode reader fault), which can cause production stoppage. System Rigidity: Every possible change (e.g., increase of capacity, speed change etc.) requires the system to be reprogrammed. This is mainly due to that the software is strictly bound to the physical reality, which makes the system inflexible.

Simulation Domain: The FAR-EDGE Simulation domain includes functionalities for simulating the behaviour of physical production processes for the purpose of optimization or of testing what/if scenarios at minimal cost and risk and without any impact of regular shop activities. Simulation requires digital models of plants and processes to be in-sync with the real world objects they represent. As the real world is subject to change, models should reflect those changes. For instance, the model of a machine assumes a given value of electric power / energy consumption, but the actual values will diverge as the real machine wears down. To detect this gap and correct the model accordingly, raw data from the field (direct) or complex analysis algorithms (from Analytics) can be used. These functionalities are available to implementers of FAREDGE compliant solutions. In order to understand and assess the potential benefits of such solutions, following section present a practical use cases that is being implemented and deployed in-line with the FAR-EDGE architecture and technologies. In particular, Section 2 following this introductory section illustrates the business issues that the edge computing solution comes to address, while Section 3 presents the solution itself. Section 4 is the final and concluding section of the paper. Note that the illustration of practical Indutry4.0 use cases is a key for understanding the inner workings and potential 390

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practice, edge nodes are physically realized based on lightweight computing platforms, such as the popular Arduino and Raspberry platforms. In this way, the implementation architecture leverages the benefits of the edge computing approach in terms of fast and timely access to information about the sorter process, which is available within the physical items. Fig. 3. Configuration of conveyer from line to lane According to the implementation architecture, the sorter can be implemented as a blockchain smart contract, which supports the optimized operation of the sorter. The latter optimization is based on the implementation of sorting algorithms that are derived following simulation of the sorter’s operation. To this end, the FAR-EDGE digital simulation component is used.

FAR-EDGE technologies can be used to increase the flexibility and reliability of the sorter. They can enable change of state between 2.3 Simulating the Operation of the Bays As part of the use case, the operation of the sorter can be simulated in order to optimize sorter schedule. This can be based on the collection of information for the available (18) shipping bays, based on the information retrieved from (7) palletizers and one 2D barcode reader, which is important for real-time data tracking. Hence, as part of this Industry 4.0 application, real time simulation of the bays can take place by listing all the events coming from the palletizers and 2D barcode reader in a weekly basis. Note that digital simulation falls within the FAR-EDGE platform’s functionalities and provides a means for testing and evaluating algorithms and decisions without disrupting the operations at the shopfloor.

The edge computing paradigm facilitates the digital simulation activities, which is the cornerstone of the sorter’s optimized operation. In particular, it enables each physical item to communicate its status to all the others. In this way, real-time information is made available to the simulator, which leads to optimized decisions. Most important the edge deployment makes it very easy to add or remove physical items as part of different automation configurations, which is the chief advantage of digital automation when compared to conventional operational technology. On the other hand, the implementation of the sorter’s logic as a smart contract enables the sharing of state between lines and lanes, along with the subsequent synchronization of their operation with the actual status of the physical elements. The smart contract provides strong reliability regarding the status of the various physical elements (e.g., the status of the bays), as it relies on consensus across the participating entities.

, 3. SORTER IMPLEMENTATION COMPUTING AND BLOCKCHAINS

USING

EDGE

In this section, we present the optimized and flexibly configurable implementation of the Melano automation system based on the FAR-EDGE architecture and technology. We start with a presentation of the implementation architecture of the system.

3.2 Edge Nodes Logic As already outlined, a dedicated edge node will be instantiated in order to control fast one of the physical items entailed in the solution, including both bays and the conveyor. Sorter Conveyor

3

The Sorter decides:

2

The conveyor provides its status:

•

Receive status from bays and conveyor

•

Based on algorithm sends message with product

•

Number and type of products in queue

to catch to selected bay

Each bay provides its status:

The bay act:

Fig. 4. Implementation Architecture Overview

Bay #n

Bay #2

Bay #1

4

•

Receive message OK to catch

•

Actuate

•

Availability to receive products

•

Number and type of products in queue

•

Recent unload performances

1

3.1 Implementation Architecture

Fig. 5. Edge Nodes Logic

The implementation architecture of the solution is depicted in Figure 4. Each physical item (bays, conveyor) is managed by an independent edge node, which is modelled as an agent. Likewise, each agent provides its status to all others. In

As part of the edge nodes each bay provides its status in terms of its availability to receive products, the number and type of products residing in the queue and its recent unload performances. Moreover, the conveyor provides its status as 391

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well, in term of the number and type of products that are available in its queue.

Overall, at the dawn of the Industry 4.0 era, we have presented a tangible and pragmatic use case, which leverages leading edge digital technologies. This use case facilitates the understanding of Industry4.0 technologies and their benefits in terms of productivity and flexibility in industrial automation. We expect this use case to serve as a reference blueprint for the definition, design and implementation of a broader range of edge computing use cases that will have a significant impact on manufacturer’s productivity.

Once this information is available, the sorter receive status information from bays and conveyor and sends a message with the product to be caught by each selected bay. The decision is based on an algorithm for optimal sorting and placement. As a final set, the bay receive the message that signals whether it should catch the product. If yet, it performs an actuating function and catches the product. The process is presented in Figure 5.

5. ACKNOWLEDGEMENTS This work has been carried out in the scope of the FAREDGE project (H2020-703094). The authors acknowledge help and contributions from all partners of the project.

4. CONCLUSION AND OUTLOOK The presentation of practical use cases is a key to raising awareness about the capabilities and benefits of digital manufacturing. It is also important for understanding the future of industrial automation in the Industry4.0 era. In this paper we have illustrate how edge computing and blockchain technologies can be used in order to implement a practical automation use case, which involves optimized placement and sorting of products in different bays. The use case implementation complies with the FAR-EDGE platform design and the way the latter mandates the use of blockchains for industry as a means of sharing and synchronizing field status across different digital entities. Through the actual deployment of the use case, Whirlpool aspires to achieve tangible benefits, including: 

Improved system productivity: This will be a direct result of the more resilient structure of the automation systems, as well as of the tangible reduction of the number of wrong or even impossible allocations of products to the bays. Such productivity improvements are yet to be measured and evaluated. The target is a 3%5% productivity improvement.



Decreased reconfiguration costs: The sorter’s adaptation to reconfiguration events (e.g. new product introduction, change in volume and mix, etc.) is expected to be cheaper (e.g., up to 25% less) and faster (e.g., up to 50% of current time). The FAR-EDGE deployment is expected to improve the flexibility of the production line, as it will set a basis for the implementation of a “plug and produce” concept. Note that this concept is directly associated with some of the promises of the Industry4.0 for manufacturer.

REFERENCES Christidis K. and Devetsikiotis M. (2016) “Blockchains and Smart Contracts for the Internet of Things.” IEEE Access 4 (2016): 2292-2303. FAR-EDGE Project (2017), www.far-edge.eu Isaja M., Soldatos J., Gezer V. (2017) “Combining Edge Computing and Blockchains for Flexibility and Performance in Industrial Automation”, The Eleventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM), pp. 159-164, November 2017. Jammes F. and Smit H. (2005) “Service-Oriented Paradigms in Industrial Automation,” IEEE Transactions on Industrial Informatics., pp. 62 – 70, vol. 1, issue 1, February 2005. Kothmayr T., Kemper A., Scholz A. and Heuer J. (2015) “Schedule-based Service Choreographies for Real-Time Control Loops”, Proceedings of the 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), September 2015. Stanciu A. (2017), "Blockchain Based Distributed Control System for Edge Computing", 21st International Conference on Control Systems and Computer Science (CSCS), pp. 667-671, 2017, ISSN 2379-0482, 2017.

Note that the above listed improvements are still to be benchmarked, as the deployment in the Whirlpool factory is on-going. Moreover, the evaluation will provide some tangible insights on the applicability of blockchain technologies to the synchronization of distributed control processes, including processes that have timing and performance constraints. In this direction, FAR-EDGE deploys a permissioned blockchain infrastructure as a means of alleviating the performance limitations of public blockchains (such as the blockchain infrastructure that supports the popular Bitcoin cryptocurrency). 392