Engineering Apps for Advanced Industrial Engineering - Core

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ScienceDirect Procedia CIRP 41 (2016) 632 – 637

48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015

Engineering apps for advanced industrial engineering Johannes W. Volkmanna*, Martin Landherra, Dominik Luckea, Marco Saccob, Michael Lickefetta, Engelbert Westkämpera a

Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany b Institute of Industrial Technologies and Automation ITIA, 20133 Milan, Italy

* Corresponding author. Tel.: +49-711-970-1943; fax: +49-711-970-1009. E-mail address: [email protected]

Abstract Today, manufacturing is being shaped by the paradigm shift from mass production to on demand dictated, personalized, customer-driven and knowledge-based proactive production. Thus, shorter product life cycles, an increased number of product varieties, high performance processes, flexible machines and production systems result in an increased complexity in all factory level domains from product design, process development, factory and production planning to factory operation. To handle this complexity, new knowledge-based methods, technologies and tools to model, simulate, optimize and monitor planned and existing manufacturing systems are required. This paper presents the challenges, the approach and an overview of the results of the EU-FP7 funded project Apps4aME (GA N° 314156) and provides a concise overview over the Engineering Apps (eApps) approach that the project is based on. The project aims at the comprehensive consideration of ICT-based support of Manufacturing Engineering in all the above mentioned domains, called advanced Manufacturing Engineering (aME). The different life cycles are aligned by the development of a Reference Data Model that provides a detailed overview of all relevant domain-specific and inter-domain interdependencies. This life cycle-oriented model enables an integrated product design, process development, factory planning as well as production planning and factory operation. All stakeholders in these activities are supported by eApps that are conceived, developed and validated with the help of four industrial use cases spanning very diverse industrial branches. © 2015 2015 Published The Authors. Published by This Elsevier © by Elsevier B.V. is anB.V. open access article under the CC BY-NC-ND license Peer-review under responsibility of the Scientific Committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS (http://creativecommons.org/licenses/by-nc-nd/4.0/). 2015. Peer-review under responsibility of the scientific committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015 Keywords: Digital factory; Engineering apps; Industry 4.0; Factory life cycle

1. Introduction Today, manufacturing is being shaped by the paradigm shift from mass production to on demand dictated, personalised, customer-driven and knowledge-based proactive production. Thus, shorter product life cycles, an increased number of product varieties, high performance processes and flexible machines and production systems result in an increased complexity in all domains from product design, process development, factory and production planning to factory operation. To handle this complexity, new knowledgebased methods, technologies and tools to model, simulate, optimise and monitor planned and existing manufacturing systems are required. Such new tools should allow changes to be made at early design phases to the product and the corresponding manufacturing processes in all factory

structures (from production network to site, area, segment, production system, cells, machines, sensors / actuators) in order to maximise the system efficiency. These tools must be smooth (smart and fault tolerant) in their interaction with human workers as well as working in an integrated way on different shop floor levels along the whole engineering life cycle. With this contribution, the authors present the promising results in studying and developing new solutions of supporting engineers in the holistic product and factory life cycle using mobile and light weight applications. 2. State of the art and challenges Several engineering methodologies exist in the different domains, such as the guidelines [1] for product design or references models for process development and factory

2212-8271 © 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015 doi:10.1016/j.procir.2015.12.031

Johannes W. Volkmann et al. / Procedia CIRP 41 (2016) 632 – 637

plann ning [2, 3]. T They enable a systematicc engineeringg of produ ucts, processees and factoriees. Manufactu uring Engineerring (ME)) addresses alll interrelated aspects of pro oducts, processses, factory and prooduction life cycles, fro om design and neering to facctory operationn, and facility y managementt up engin to reccycling / dispoosal and re-usse. Manufactu uring Engineerring dealss with the mainn challenges of o aligning theese Factory Leevel Dom mains from prooduct design, process deveelopment, facttory and production p plaanning to factoory operation. Haaving a holistic forward-loooking view on ME, a strrong consiideration of the employm ment of ICT technologiess is mand datory in orrder for the costs to bee reasonablee in comp parison to the effect [4]. Defined d as advannced Manu ufacturing Engineering (aM ME), the domaains from prodduct desig gn, process development, faactory and pro oduction plannning to facctory operatioon supported by b ICT techno ologies enablee the modeelling, simuulation, optiimisation, monitoring m and visuaalisation of products, technnical processes and factorries. The aME a is characcterised by thee following ch hallenges. Th he Factory Leevel comprisees the followin ng core domaains: produ uct design, prrocess developpment, factory y and producction plann ning and facttory operatioon, as illustraated in Figurre 1. Thereeby the chhallenge is the synch hronisation and simultaneous geneeration of all models m within n the domainss by grating and ussing or re-usinng Manufactu uring Engineerring integ know wledge in the early stagess of the desig gn, planning and moniitoring activvities [5, 6]. The major challenge is repreesented by thee serial naturre of the prod duct, process and factory decision m making alongg their life cycles. c From the produ uct requiremeents, the prodduct design is i generated and stored within a pproduct modeel. Once defin ned, the proccess devellopment geneerates the prrocess requirrements (proccess plan)) and the process model froom the design features [7]. The factory and prodduction plannning domain consists of the selection of the reequired resourrces for the faactory model and e programs. After this creation of the maanufacturing execution stagee, all the reelevant data and knowleedge has to be comm municated andd used for the factory operation. Th he life cycle alignment of all Factory y Level Domaains inducces such a com mplexity that can hardly bee handled withhout the su upport of ICT T technologiess. However, th he employmennt of a sing gle holistic m manufacturing engineering system s seems not to bee feasible, withh cost and impplementation efforts creatinng a pleth hora of implicaations [8].

Fig. 1. C Challenge in Mannufacturing Engin neering

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ut only the ho olistic and com mprehensive understandingg of Bu the Factory F Level Domains witth all its interrrelations enabbles the task-oriented t modularisatioon of compleex planning and optim mization activiities. Based oon this, the sin ngle tasks cann be suppo orted by appliication-based and solution--based ICT toools. The ICT I tools add dressing “misssion critical” activities have to focuss on the req quired data, information and knowleedge suppo orting processses that aree essential fo or designing the produ ucts, developiing the proceesses, plannin ng and operatting the production p and d the factoriess. Therefore application-ba a ased ICT tools t and systems, fully enrriched with kn nowledge havve to be developed to support thesee domain speecific tasks with w he Factory Leevel respeect to the inteerdependenciees between th Dom mains. To deveelop these ICT T tools, comp plementary to the life cycle orienttation, there is a need d for a prooper operability faacilitated by standardised d interfaces and intero exchaange formats. 3. Go oals, approacch and structuure Th he approach of o the project that is described in this paaper aims to suppo ort Manufaccturing Eng gineering (M ME) g product ddesigners, faactory plannners, stakeeholders (e. g. produ uction plannerrs, factory opeerators, …) with w “Engineerring Appss” (called eA Apps further on) to enab ble an integraated produ uct design, prrocess developpment, factory y and producttion plann ning and factory operationn. This integrration is flexxible and is i achieved through t the ddeployment, based b on a core c Referrence Model for f the holisticc planning an nd optimisationn of produ ucts, processees and factoories along th heir aligned life cyclees. Engineeriing Apps ccan be defin ned as a high h perfo ormance piece of softwarre or a digital tool, whhich suppo orts engineers (i. e. produuct designer, process plannner, factory and produ uction planneer, factory op perator and shop t daily acttivities with a specific focuss on floor manager) in their orting collabo oration [9]. eeApps are staandard-based and suppo intuittive to use. Additionally A tthey can be characterisedd by being g adaptable, context-aware and, if requirred, easily croosslinkable. The main purpose oof the eAppss can be furtther enriched by integ grating manuufacturing kn nowledge. Thhese ps allow the capturing, m modelling, rep presentation and eApp sharin ng of know wledge in alll phases off Manufacturring Engin neering. They support th the re-use of manufacturring know wledge in early e phases and the sharing of the manu ufacturing kno owledge betw ween these acttivities and coover a com mplexity rangee from very siimple to fully y integrated. They T can be b deployed (online ( or offfline) on multtiple devices like tabletts, smart pho ones, PCs ussing Cloud systems s or High H Perfo ormance Com mputer Clust sters. An ap pproach to the impleementation is shown in Figuure 2. Cu urrently avaiilable applicaations and systems suppport differrent phases of all these life fe cycles mosttly in an isolaated mann ner, they do not have the he capability to be fully and continuously integ grated, based oon standardiseed interfaces, in a onsequence, thhere heterrogeneous facttory IT landsccape. As a co are communication c n walls betweeen product designers, d facttory plann ners, productio on planners annd factory opeerators.

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Figg. 2. Apps4aME implementation i concept

On the one hhand, these eA Apps can be deployed andd used w devices likee smart phonees or tablets, w nott only on new where wo orkers can usee mobile soluution for decission making oon the sho op floor in thee production and a operation n phase. This iis one key y element to support workkers directly and a to increasse the effficiency in thee production planning p and factory f operatiion by inh herent collaboration enabledd at the usagee time. On thee other han nd they can bbe deployed on conventionaal devices likke PCs or High Perform mance Computters to reach a maximum im mpact t Manufactuuring Engineeering world. Thereby, T the eeApps in the sho ould not only fulfil their purpose p in thee production pphase, butt also activelyy support the propagation p off information in the pro oduct, processs and factory planning phaases. In this pproject thee flexible inteegration of thhe envisioned d eApps is reealised through modernn integration technologies, t such as life cycle oriented platform ms or using cuurrent cloud ap pproaches. 4. Results R The project tthat implemennts the given approach houuses a wid de variety off partners, with w its demo onstration acti tivities theerefore spannning a wide variety of in ndustrial brannches. Speecifically, thhere are fouur demonstraators: one inn the auttomotive inddustry, one in i the manu ufacturing syystems ind dustry, one in the food industry and d additionallyy, one gen neral logisticss demonstratoor that is indu ustry agnosticc with thee implementattion by a largge ICT provid der and Fraunnhofer IPA A as an appliied research organization. o Because all oof the dem monstrators m make use of thhe eApps apprroach, the projject is ablle to provide three basic reesults on very different scieentific lev vels. king down thee walls First of all, ass the approachh states, break bettween life-cyccles or even organizational o mises a entities prom deeeper and impproved use off the existing knowledge oon the sho op floor. Thiis usage cann be extendeed to the adj djacent eng gineering parrts of any prroduction com mpany. In thee real app plication, the effects of this t improvem ment can currrently often only be shoown for parts of the business processes, ddue to tecchnical or eeffort restricttions. New functions beecome avaailable, that w would’ve beenn exorbitantly y complex to create witthout the eAppps approach.

Secondly, witth some of thhe use cases, the employment of mantic ontolo ogies much clloser to the shop floor can be sem testted. Currently, most com mpanies are hesitant withh this com mplex technollogy. With the he solutions en nvisioned heree, one usee case fully addresses eenabling engineers withouut IT exp perience to haandle such addvanced appro oaches, while other usee cases try thee employmentt of this technology very close to a running r produ uction. This is one exam mple, as otherr new technologies aree also under consideration n for tests inn shop nts within thee project. floor environmen Last but not least, the projeect promises a verification of the currrent hype on eApps and thherefore in a small part also for thee Industry 4.0 0 approach. The hype is currently coooling dow wn, with indu ustrial users w waiting for som meone to provve the app plicability and d value of th the new apprroaches. Usinng the bro oad spectrum of industrial bbranches with hin this projecct, it is onee of the first real and grasspable statem ments of the effects e thaat can be achieved in realitty. These effeects can be veerified witthout creating g special labooratory enviro onments or with w a sin ngular consideration of speccially chosen environments. e y to industriall endAll three poiints are of vaalue not only useers, but also to t the scientiffic community y. This verificcation sho ould lead to an a adaption aand therefore sharpening of o the oveerall strategiees in this fielld, even thou ugh this discuussion maay take time to o be properly interpreted ev ven after the results r aree created. Wh hile the resul ults in detail will be published sep parately, this paper presentts an overvieew and puts it i into con ntext, as well as show the further way the project and a its parrtners will hav ve to go to furt rther realise th he eApps approoach. 4.1 1. Basic researrch results While the in nitial approacch description n already conntains diffferent categorries of eApps,, it became very clear that for f the app proach to be graspable g for external partn ners and to bee used in other o projects, a clearer andd more detaileed categorizattion is req quired. Based d on existingg classifications derived from currrent app maarketplaces oor from software developpment pro ocesses, a neew classificattion matrix was w derived. This enaables to sort the eApps affter a multitu ude of criteriaa. The claassification is clustered c in thhe following groups: g x Operational area a x Function x Complexity ż Computatiional power ż Autonomyy ż Integrationn x eApps platforrm ż Operating software ż Hardware x End device e to describe Each group contains severaal items, for example mputational poower. The classsification therefore thee required com hellps having critical informattion available before introdducing new w eApps into o a running eengineering environment full f of


legaccy systems. This informaation describes the technnical impliications (e. g. interfaces annd hardware requirements)) as well as provides ssorting possibbilities to help p find the eA Apps with the right function thaat was creaated having the u in mind. impliications from the specific user In n business scennarios, data can c come from m several sourrces and can be expreessed in a vaariety of form ms. Under thhese d informationn is conditions, as thhe use of thhe exchanged plicated, it iss necessary that t enterprises adopt a nnew comp parad digm of collaaboration bettween the various sourcess of heterrogonous dataa, thus overcoming the prroblems derivving from their lack off integration. This T challeng ge has been faaced Apps4aME prooject, where many m efforts hhave in thee context of A been guided towards the stanndardization of the manaaged mation, in parrticular withinn the manufactturing domainn. In inform this regard, as a means to haarmonize datta from diffeerent Core sourcces an approach based onn the definition of the C Facto ory Data Moodel (CFDM)) is used. Itt unambiguouusly repreesents recurrring core concepts c and d their relaated relatiionships throuugh a formalissm shared witthin the industtrial comm munity [10]. The main expected ad dvantage of this appro oach consistss in the possibility of aggregating and unify ying all the innformation, thhus significanttly enhancing the semaantic interopeerability betw ween differen nt heterogeneeous systems under the form of agentts, services or applications. Fo ollowing the approach of recent researches within the factory domain thhe CFDM is represented through t a sett of S Weeb Technologgies, ontollogies by addopting the Semantic which h offer key aadvantages to the whole Ap pps4aME prooject becau use they enablle to: x x x x

mal semanticss, Reepresent a form effficiently model and managee distributed data, d eaase the interopperability of diifferent appliccations, prrocess data ouutside the partiicular environment in whichh they were createed and x au utomatically innfer new know wledge about the t concepts aand their relationshiips, starting frrom the expliccitly asserted facts. he building oof a conceptuual model fro om scratch iss an Th extremely expensive activity if the appliccation domainn is comp plex, as it is the case in thhe given project. Nowadayys a large number of very compreehensive refeerence modelss is available, coveringg a wide rangge of domain ns. Reusing thhem g reducee the costs of a new impllementation [ 11]. can greatly This is the reasson why, att the stage of the CFD DM devellopment, it hhas been refferred to thee state-of-thee-art techn nical standardds covering diifferent domaains, for exam mple the Industry Founndation Classees (IFC) and the Standardd for E of Product model data (STEP). In the belief that the Exchange their reuse has sseveral advanntages, a kn nowledge reeuse frameework has beeen defined and formalized, that aimss at identtifying relevvant data models fo or the forrmal conceeptualization of the four inndustrial casess, while usingg the one of the dem monstration sccenario as a reference ccase y [12]. study

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Fig. 3.. The overlapping g knowledge dom mains for the four industrial scenariios

Th he results of the t frameworkk application showed that one of th he valid startiing point forr the conceptu ualization of the indusstrial cases is representedd by VFDM,, which aimss at formaalizing and in ntegrating the concepts of building, b prodduct, proceess and produ uction resourcce handled by y the digital toools suppo orting the facctory life-cyclle phases; thu us also enhanccing the semantic in nteroperabilityy of these tools [13, 14]. FDM implem mentation hass been extennded Moreeover, the CF accorrding to the requirements oof the four in ndustrial casess, in orderr to represent detailed infoormation conccerning the Foood, CRM M and Key Peerformance Inndicators dom mains that are not yet in ncluded in VFDM. Also, from the anaalysis of the four f indusstrial cases kn nowledge dom mains, variou us overlaps have h emerged (Figure 3). Laast challenge faced durinng the stage of the CFD DM devellopment is derrived from thee need to reviise some classsical data management problems, inncluding efficcient storage and y optimization n for semanticc data. For th his reason a sttudy query has identified a vaalid semantic repository, capable c to hanndle r on hug ge amount of Semantic datta and capablee to and reason realizze the analysees of intensivee data in real-ttime. This alloows colleccting and gath hering billionss of real-timee bytes of dataa on the organization resources, which are then processsed ntaneously to optimize theirr utilization [1 15]. instan 4.2. Specific S applieed research reesults Th he four dem monstrators off the projectt are locatedd in differrent industrial branches. A Additionally, they target very v differrent engineering tasks: evel internation nal project x Prroject manageement: high lev management, co onnecting sevveral stakehold ders spread ovver dge to issue a new n the world, gatheering and creaating knowled vel of control lev x Qu uoting and production monnitoring: execu ution and monitoring with hin the producction with very y little t inttroduction efffort as well as increasing the abilities of the sy ystems through h semantic ont ntologies

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x Logistics planning: logistics planning, including systems, knowledge and information, including several sites x Product monitoring: in line and interlinked product monitoring from the production line to the usage at the client. Looking at these four very different engineering tasks, it becomes obvious, that the demonstrators not only address different activities, but also address engineering activities on different levels. While some demonstrators mainly go deep into detail for a very specific task (logistics planning), others span multiple life cycles and across very different knowledge domains (project management). In sum, about 22 eApps are being created, depending on the way groups of them are counted. They also range broadly in complexity of their functions, interconnectivity and the other criteria that were described in the classification of eApps given above. This gives the project the ability to verify the effects of very different types of eApps in real production environments. In the following sections, one example of an eApp is explained in more detail, including a description of the effects and the KPIs that are expected to change. The example is derived from the last bullet in the list of engineering activities above, targeting product monitoring inside of complex production environments. In the case of the Apps4aME project, the partner is from the food processing industry. Food as a product group implies not only a complexity considering its storage, transport and delivery but also is hardly in the main focus of current production ICT providers. This leads to a lack of specifically targeted solutions with the result that existing solutions are adapted only slightly. One of the eApp derived for this case essentially enables a temperature logging, capable to measure the temperature of the product within the required frame. The temperature interval depends on the product group, e. g. for fresh meat between 0 and 2 °C. Products are especially prone to temperature changes during their transport to the client. If the product is not inside of the temperature interval, the whole lot is considered spoiled and needs to be disposed of. This is not only an ecological but also an economical problem, as the responsibility for the product lies with the supplier until it is unpacked from the storage facility of the client and put into their store. Even if it seems illogical at first, it is the simple reason of the requirement to prove that the product was always inside of the temperature interval when it reached the client facility. As it is only unpacked, sometimes up to one week after delivery, this cannot be done without fully monitoring the temperature with an according timestamp. There are temperature logging devices available, but they are expensive and cannot be connected to the ERP system. An ERP connection is required in order to derive the exact composition of the delivery and therefore deriving the applicable temperature limits. Within the Apps4aME project, an eApp was developed to address this issue. The eApp shows the upper and lower temperature limit, as well as the temperature curve that was read out from the data logger. The data logger is an iButton device, which provides

the capability to measure up to 8 days of delivery time in configurable intervals. The eApp is fully connected to the ERP and can add its information to the order information already available. Using this solution it is easy to prove, when and where in the delivery process, the temperature limit was exceeded. This enables the food industry to shift the responsibility of spoiled food to the actual party that is responsible. As a handheld device, common Sony mobile phones can be used to read out the data loggers with a USB device, which keeps the full price low. This also helps to show the client the full temperature progress directly at the delivery. Currently additional and more advanced functionalities are in preparation: Especially helpful can be the integration with the reasoning engine also derived in this project, in order to for example suggest the addition of a temperature data logger to a delivery to a client that was prone to problems in the past. The comparative price (using street prices) of the system is currently about 1/18th of the price of a temperature logger as they are available on the market. As the mobile device is a standard device available on the market, the complete solution is very applicable and supports the existing processes, whilst streamlining them. Next to the lower price, the identification of clients prone to temperature issues is expected to lead to a significant rise in the customers’ acceptance rate and therefore to a significant rise in customer satisfaction. Additionally, lowering the number of spoiled food has an ecological as well as a sizeable economic impact. Over all the resulting eApps, significant impacts are expected and in parts already shown in very different phases of the engineering processes, depending on the demonstrator. Some examples, sorted according to the four demonstrators: On its highest level, the first demonstrator “project management” addresses the reaction time, when the project lead is asked for the status of the project. This is supported by significantly decreasing the time that is required to access key data, as the systems automatically aggregate information and derive a status on different levels, depending on the type of user. The second demonstrator “quoting and production monitoring” significantly drops the required effort to introduce an IT based monitoring of the production. It also enables the creation of quoting based on new approaches to similarity matches that make a lot better use of the gathered historical data. This helps to get the accuracy of quotes for clients up and with its shop floor management integration also optimizes the lead times in the production. The “logistics planning” demonstrator drastically reduces the time and the complexity for logistics planning, especially for the data retrieval, when two or more production sites need to be tightly planned together. The fourth demonstrator with the “product monitoring” optimizes the delivery process and reduces delivery losses as shown above and also addresses the complex picking processes, as they are required in the food industry. Altogether, engineering tasks along the whole chain of life cycle are affected and overall, most impacts are either on time efforts that are required for difficult tasks or on the high costs


that usually come with complex and integrated software solutions. 5. Conclusion and outlook With the project being only a few months before its end, first conclusions can be drawn for the Engineering Apps approach. In sum, the approach works better than expected, keeping implementation efforts low and the solutions having the impacts that are expected. The integration of other modern approaches like semantic ontologies is very promising and are strongly on the way into production environments. Even with the solutions not yet being finished, it is already very clear to see that the impacts that are expected will be achieved in almost all cases with a significant lessening of the required learning curve of the employed eApps in comparison to current engineering software solutions. An unexpected side effect is that most shop floor personnel liked having eApps on their mobile devices which helped with the acceptance. With the eased integration of other legacy systems, the barriers between the life cycles – or on an organizational level, between departments – become lessened, if not broken down completely. While the quantification of the effects the singular eApps are having when being introduced into the real production environment is a task where work has currently just begun, it is already safe to say that the effects are drastic and the introduction efforts are significantly less than with current large scale engineering software solutions. With the demonstrators spanning very differing industrial branches, the effects of the eApps approach that are shown cannot be attributed to specificities of a single industrial branch but are likely generally applicable. Especially with industrial branches that are not the main focus of current ICT providers, the engineering app approach shows better fitting solutions for considerable lesser prices and will have an impact to widen the scope of the digitalization outside of the already well known big players. Nevertheless, it can be shown, that the big players benefit as well from the engineering app approach by reducing the necessity for unprofessionally created and maintained software “island solutions” and therefore promising more control over the complex software landscape. With the implementation nearing completion, the eApps are currently being deployed at the industrial partners. During this deployment, further and more advanced functions are being evaluated for their worth together with the according end users. Especially additional functions can now be addressed using the semantic ontology and the data warehouse with the integrated reasoning engine. Using the deployment at the end users, the previsioned KPIs will be measured before and after the introduction. This will lead to a quantification of the positive effects that are already visible. As a longer term outlook, the approach is entering a multitude of projects, both scientific and industrial and is on a good way to become a standard approach to introduce innovative software solutions and to address knowledge challenges in several life cycles.

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Acknowledgement This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 314156. The authors are pleased to acknowledge the former research team members of the Digital Factory, Fraunhofer IPA, Stuttgart under the management of Dr. Carmen Constantinescu for the inspiration and the foundation for this project.

References [1] Verein Deutscher Ingenieure e.V. Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte. Berlin: Beuth Verlag GmbH; 1993. [2] Verein Deutscher Ingenieure e.V. Fabrikplanung - Planungsvorgehen (Blatt 1). Berlin: Beuth Verlag GmbH; 2011. [3] Constantinescu C, Westkämper E. Grid engineering for networked and multi-scale manufacturing, In: Mitsuishi M, Ueda K, Kimura F, editors. Manufacturing Systems and Technologies for the New Frontier. London: Springer; 2008. p. 111–114. [4] Volkmann JW. Methode zur Konzeption und zur Bestimmung der Wirtschaftlichkeit des Einsatzes von Digital Engineering-Systemen. Stuttgart: Fraunhofer-Verlag; 2014 [5] Constantinescu C, Westkämper E. A reference model for factory engineering and design. In: Huang GQ, Mak KL, Maropoulos PG, editors. 6th CIRP-sponsored International Conference on Digital Enterprise Technology, DET 2009. Proceedings. Berlin: Springer; 2010. p. 1551–1564. [6] Landherr M, Neumann M, Volkmann JW, Westkämper E, Bauernhansl T. Individuelle Softwareunterstützung für jeden Ingenieur: Advanced Engineering Platform for Production. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 2012;107 (9). p. 628–631 [7] Landherr M. Integrierte Produkt- und Montagekonfiguration für die variantenreiche Serienfertigung. Stuttgart: Fraunhofer-Verlag; 2014. [8] Volkmann JW, Westkämper E. Cost Model for Digital Engineering Tools. In: Cunha PF. Proceedings of the 46th CIRP Conference on Manufacturing Systems CMS. Amsterdam: Elsevier; 2013. p. 676–681. [9] Westkämper E. Engineering Apps: Eine Plattform für das Engineering in der Produktionstechnik. wt Werkstattstechnik online 2012;102 (10). p. 718–722. [10] Tolio T, Sacco M, Terkaj W, Urgo M. Virtual Factory: An Integrated Framework for Manufacturing Systems Design and Analysis, Procedia CIRP 2013;7:25-30. [11] Efthymiou K, Sipsas K, Mourtzis D,Chryssolouris G. On knowledge reuse for manufacturing systems design and planning: A semantic technology approach, CIRP Journal of Manufacturing Science and Technology 2015;8. p. 1-11. [12] Modoni GE, Caldarola EG, Terkaj W, Sacco M. The Knowledge Reuse in an Industrial Scenario: A Case Study. In: eKNOW 2015. The Seventh International Conference on Information, Process, and Knowledge Management. Lisbon; 2015. p. 66-71. [13] Terkaj W, Urgo M. Ontology-based modeling of production systems for design and performance evaluation. In: Proceedings of 12th IEEE International Conference on Industrial Informatics (INDIN). Porto Alegre; 2014. p. 748-753. [14] Terkaj W, Urgo M. Virtual Factory Data Model to support Performance Evaluation of Production Systems. CEUR Workshop Proceedings 2012;886:44-58. [15] Modoni GE, Sacco M, Terkaj W. A survey of RDF store solutions. In: Proceedings of 2014 International Conference on Engineering, Technology and Innovation (ICE). Bergamo; 2014. p. 23-25