Deogratias Kibira
email: deogratias.kibira@morgan.edu

Guodong Shao
email: gshao@nist.gov

Virtual Factory Framework for Supporting Production Planning and Control

Keywords: Virtual factory, Simulation, Production planning and control

Developing optimal production plans for smart manufacturing systems is challenging because shop floor events change dynamically. A virtual factory incorporating engineering tools, simulation, and optimization generates and communicates performance data to guide wise decision making for different control levels. This paper describes such a platform specifically for production planning. We also discuss verification and validation of the constituent models. A case study of a machine shop is used to demonstrate data generation for production planning in a virtual factory.

Introduction

Conventional simulation tools are generally limited in their ability to capture and analyze multiple decision levels and system configurations [?]. A virtual factory, on the other hand, creates an integrated model that reproduces scenarios of information flow and capable of generating multi-level metrics to guide users in decision-making. These decisions can among others increase agility and productivity by reducing product realization time [?]. Virtual factories have been constructed to aid manufacturing system design, implementation, and modification [?].

Besides designing production systems and products, Choi et al.

[?] sees the potential of a virtual factory to predict, solve, and manage problems during production. It is our view that the virtual factory's ability to integrate engineering tools and models such as simulations, design data, and optimizations could improve production planning activities. As such, this paper focuses on operations and performance monitoring, particularly production planning.

The rest of the paper is organized as follows. Section 2 reviews literature on technology, application of virtual factories, and verification and validation (V&V) concepts for the virtual factory. Section 3 describes the roles of a virtual factory for production planning as per control levels defined in the ISA-95 standard. Section 4 presents a demonstration case of a virtual factory. Section 5 presents final discussion and conclusion.

Related Work and Virtual Factory Validation

A virtual factory is composed of multi-level, multi-resolution models that are typically developed by different tools. This section overviews technologies employed for developing a virtual factory, various applications, and verification and validation issues.

-Technology requirements for a virtual factory: When carrying out formal V&V, Uncertainty Quantification (UQ) needs to be considered for better correctness and appropriateness [?]. Uncertainties can be epistemic or aleatoric in nature. Epistemic uncertainties arise from ignorance of involved processes, such as invalid assumptions in modeling. Aleatory uncertainties arise from inherent variability in processes, such as physical properties of a system. Model fidelity and data availability typically vary greatly across different system levels of resolution. This issue complicates both the computation of metrics that describe process performance, and decision-making based upon those metrics. V&V of a virtual factory as well as UQ can be achieved through intermediation environment, such as one created by Hibino et al. [?] to synchronize collected data and virtual factory computed data.

A Virtual Factory Approach to Multi-Level Production Planning

The virtual factory concept uses the ISA-95 standard (ANSI 2013) to specify decision levels that define functions supporting multi-level production planning. This standard was developed for all types of industries that represent different manufacturing processes, such batch, continuous, discrete, and repetitive processes. As such, the description of the virtual factory herein should likewise be universally applied.

-Framework and role of models: Figure 1 shows the functional hierarchical levels of ISA-95 as well as virtual factory roles at each level. At level 4, an aggregate plan is developed over a long-term planning horizon after investigating for stability using system dynamics [?]. Level 3 covers short to mid-term plans to determine actual start and finish times of individual product batches. Level 2 covers decisions on activities such as resource allocations. Level 1 is the manipulation of production process (level 0) to achieve required output. Data is collected in real-time at level 0 to update various models. -Multi-level performance analysis and improvements using the virtual factory: The objectives of production planning include minimize late orders, minimize inventory, or maximize resource utilization. Such objectives are basis for defining Key Performance Indicators (KPIs) which, along with metrics and constituent measures, are communicated and monitored. Decisions are then made to maintain them within a target performance envelop. The relationship between data, metrics and KPIs at different levels can be numerical, analytical, or heuristic influence. With heuristic influence, a KPI is expressed in terms of supporting data, parameters, metrics or other KPIs. The direction of change (increase or decrease) in the dependent KPI is investigated through the relationship equation. The Supply Chain Operations Reference (SCOR) model (SCC 2012) adopts this approach by taking KPIs and performs a metrics decomposition, performance diagnosis, or metrics root-cause analysis. SCOR then constructs a metrics dependency tree of multiple measures that would generally be generated by different models within the virtual factory.

Metrics decomposition establishes a diagnostic relationship showing how metrics serve as diagnostics for dependent KPIs. For example, overall equipment effectiveness (OEE) index, as defined by ISO 22400-2 [?], depends on availability, effectiveness, and quality rate. OEE belongs to level 3 of ISA-95 while its constituent measures can be monitored at level 2. Availability is determined The availability model can be constructed with high resolution using a programming language. Effectiveness performance model may be of low resolution constituted of run time per unit produced, number of units made, and actual production time. The quality rate is products that meet specifications compared with total units made.

Once a diagnostic relationship has been established, attention may be directed to a higher resolution of the production line model or resource responsible for a measure needing improvement while other parts of the virtual factory may remain at a lower resolution. The data, resources, and workflow through this model may then be further analyzed to balance any competing objectives that may occur. The analyst may also validate diagnosis and decision made through high visualizations of the virtual factory.

Case Study

The study is a case of monitoring KPIs at multi-resolution levels in metal machining using a virtual factory concept. The enterprise level determines an aggregate plan of same size batches to be produced per period (week) over a 12 period timeline. Lower level management then distributes batches for production to each of the two available machine cells that are shown in model Figure 2(b). Each machine cell has two processes: turning and milling. Machines undergo different states during production. As prototypical virtual factory is developed using AnyLogic simulation for three levels of decision control. Table 1 shows the functions and type of models employed at each level. 2 (a) and is built using System Dynamics (SD). The product batches for each period are input into the model to determine the production start rate at the routed shop.

The production start rate is converted into inter-arrival times for the work cell model. In turn, the cycle time and work in progress levels are obtained from the machine cell model.

-Machine cell model: This is a model of the processing of a product on the shop floor. Discrete event simulation (DES) is employed, as shown in Figure 2(b). Entities enter the system from the source and routed to the first available machine for both turning and milling. The machines undergo periodic failure and repair cycles.

-Machine level model: This is a model of states of a machine during normal operation. Machine failure and repair cycle are indicated in the statechart shown in Figure 2 (c). When a machine is "Up", default substate is idling to which a machine reverts after repair or after ejection of the previous batch.

Other machine states are "Down" or "Under repair" and, in these states, incoming parts cannot be routed to them. The machines undergo this cycle independently.

-Model interactions: When these models are integrated, the SD model receives input data from DES for update to aggregate planning. In turn, DES is updated with agent based simulations of machine processes. Figure 3 shows the exchanged data. Figure 4 shows that there is enough visual concurrency in monitored generated data: work in progress levels and production quantity between models at different resolution levels.

Such data can be used, for example, to monitor and maintain planned throughput rate. According to ISO 22400-2 [?], throughput rate = quantity produced or order execution time. Maximizing throughput in a job-shop production environment requires deploying the "shortest remaining processing time" priority rule [?]. If throughput rate is reduced, the causes are investigated using the constituent measures monitored at level 2 of ISA-95. These are analyzed with the discrete event simulation model. The cause could be an increase in order execution time which in turn depends on manufacturing cycle time. The causes of increase in cycle time can further be analyzed using work cell model.

Discussion and Conclusion

A virtual environment can be developed for generating and communicating production planning decisions from floor and optimize production, inventory, and cost objectives. Communicating performance of production plans and schedules in a virtual environment is beneficial to achievement of the smart manufacturing objectives. The industrial internet is one technology for connecting, collecting and communicating data. This framework is a first step in describing how the virtual factory can be used for developing and integrating models at different hierarchical levels. The example in this paper used a multi-method simulation software. To take advantage of strengths of different tools, a virtual factory would be developed using heterogeneous tools. Description of needed interfaces and review of existing standards will be the subject of future research work.

Acknowledgement:

This effort has been sponsored in part under the cooperative agreement No. 70NANB13H153 between NIST and Morgan State University. The work described was funded by the United States Government and is not subject to copyright. The contribution of David Lechevalier of NIST in the simulation work is acknowledged.

Disclaimer

No approval or endorsement of any commercial product by the National Institute of Standards and Technology is intended or implied. Certain commercial software systems are identified in this paper to facilitate understanding. Such identification does not imply that these software systems are necessarily the best available for the pur-pose.

Fig. 1 .

 1 Fig. 1. Role of virtual factory models according to ISA-95 levels

Fig. 2 .

 2 Fig. 2. Multi-resolution models of the virtual factory

Fig. 4 .

 4 Fig. 4. Work in progress and cumulative production with time for system dynamics and discrete event simulations

Table 1 .

 1 Functions of multi-level models according to ISA-95 standard

	ISA-Level	Physical system	Function	Virtual modeling method
	4	Enterprise	Aggregate planning	System dynamics
	3	Machine cell	Production scheduling	Discrete event simulation
	2	Machine	Machine loading	Agent based modeling
	-Enterprise level model: This model is shown in Figure


João F Santanna-Filho

Ricardo J Rabelo

Peter Bernus

Alexandra A Pereira-Klen

Improving the Sustainability of SOA Providers' Networks Via a Collaborative Process Innovation Model

Keywords: Collaborative networks, Innovation, Software services

Companies from the software sector have been seeking new, sustainable business models. A key strategy to achieve is innovation. Being a sector largely formed by SMEs, a general problem is to keep innovation a sustainable practice. Many companies have been shifting their systems' architectures to SOA, but despite its potentials, SOA projects are often costly, complex and risky. One approach to mitigate this is endowing companies with means to innovate collaboratively. Current innovation models are mostly devoted to the manufacturing sector, without supporting the many software and SOA specificities. We present a collaborative innovation process model as a contribution to fill this gap. The model combines open and network innovation approaches and allowing the tailoring of process composition to accommodate the uniqueness of innovation projects. The proposed model was evaluated by industry.

Introduction

The software sector plays a significant role in the world economy [START_REF] Kramer | The Role of the Information and Communications Technology Sector in Expanding Economic Opportunity[END_REF]. Advances in Internet technologies and the rise of the services oriented economy challenge the industry to develop new, competitive and sustainable business models [START_REF] Nie | Credibility Evaluation of Saas Tenants[END_REF].

One key strategy to achieve this is innovation [START_REF] Li | Multi-model Driven Collaborative Development Platform for Service-oriented E-Business Systems[END_REF]. A general problem is that the industry mostly consists of SMEs, which have limited capacity to keep innovation as an ongoing sustainable practice, aligned to their strategies in an environment of permanent change [START_REF] Westphal | Managing Collaboration Performance to Govern Virtual Organizations[END_REF].

One of these technological changes is the gradual shift towards implementing systems using the SOA (Service Oriented Architecture) paradigm [5]. In SOA, a software system is formed by a composition of a set of modules -called software services -which are decoupled, distributed, heterogeneous and of several natures, forming a single logical unit to create products and processes [START_REF] Papazoglou | Web Services: Principles and Technology[END_REF].

Despite its technological and commercial potentials, SOA projects can be complex and costly, difficult to manage, and hence risky [START_REF] O'brien | Quality Attributes for Service-oriented Architectures[END_REF]. Existing innovation models are basically devoted to manufacturing, without considering the software specificities [START_REF] Santanna-Filho | An Innovation Model for Collaborative Networks of SOA-Based Software Providers[END_REF]. This paper exploits the premise that the innovation initiative's risk can be mitigated if the SMEs involved in a given SOA development can jointly innovate, within wider and open networks, sharing risks, investments, resources (human and software/services assets), knowledge and benefits, while keeping their autonomy and independence.

Within this general goal, this paper focuses on the process perspective. It presents the final results of an innovation process model -called SIGMAdirected to networks of SOA providers to innovate collaboratively and openly towards generating a valuable outcome in a flexible and non-linear flow considering the particular requirements of every SOA project and its network governance model. Such outcome can be a software prototype, a proof of concepts, a set of ideas for new business models and products, among others. As a value proposition, this model intends to be used as a "guide" that systematizes the innovation process itself. This enables better innovation management, provides a basis for the introduction of good innovation practices, and leverages the continuous maturity improvement of processes, people and the involved organizations.

Basic Concepts and State-of-the-art Reviews

Collaborative Networks are strategic alliances focused on intense and fluid collaboration among autonomous organizations [START_REF] Afsarmanesh | Methods and Tools for Collaborative Networked Organizations[END_REF]. In order to support the dynamics of an innovation network, the Virtual Organization (VO) concept is of particular importance. A VO is a temporary and dynamic entity formed out of select autonomous network members that join their complementary core-competencies and general resources to attend to a demand [START_REF] Afsarmanesh | Methods and Tools for Collaborative Networked Organizations[END_REF].

Innovation Models covering the phases and processes of innovation, via the socalled funnel, namely: selection and/or generation of ideas, concept development, concept evaluation/selection, concept design/specification, implementation and exploitation (adapted from [START_REF] Du Preez | A Framework for Managing the Innovation Process[END_REF]). Innovation models evolved from linear to open and network models [START_REF] Rycroft | Self-organizing Innovation Networks: Implications for Globalization[END_REF][START_REF] Chesbrough | Open innovation: The new imperative for creating and profiting from technology[END_REF]. Gates are added between steps to constrain the continuation of the process. Processes may be executed in sequence or in parallel, and can go back and forth during the innovation processes. Different types of actors and roles may be involved, including member companies and external supporting partners [START_REF] Tidd | Innovation Management[END_REF]. A collaborative innovation network is a temporary or long-term alliance of disparate autonomous organizations with the willingness to jointly develop or exploit an innovative business vision by sharing of ideas, knowledge, capacity, resources and services, costs, risks and benefits, supported by the intense use of ICT, and grounded by principles of trust, preparedness, governance and intellectual property rights [START_REF] Santanna-Filho | An Innovation Model for Collaborative Networks of SOA-Based Software Providers[END_REF].

A Systematic Literature Review was conducted to oversee the state-of-theart. Journal papers and conference proceedings from 2002 and 2015 were searched in IEE-Explore, ACM, Web of science and ScienceDirect, complemented by the search in the EU CORDIS projects database [14]. The essential search terms used were innovation, collaborative networks, SMEs, SOA & services. No work has been found that dealt with the envisaged open and flexible innovation model based on a network of autonomous SMEs that can participate in all phases of the innovation process, regarding the particular nature of software and SOA. On the other hand, 8 papers ( [START_REF] Li | Multi-model Driven Collaborative Development Platform for Service-oriented E-Business Systems[END_REF][START_REF] Du Preez | A Framework for Managing the Innovation Process[END_REF][START_REF] Rycroft | Self-organizing Innovation Networks: Implications for Globalization[END_REF][START_REF] Janner | Modelling Service Systems for Collaborative Innovation in the Enterprise Software Industry-the st[END_REF][START_REF] Berre | Service Innovation and Service Realisation with VDML and ServiceML[END_REF][START_REF] Kourtesis | Software Co-development in the era of Cloud Application Platforms and Ecosystems: the Case of CAST[END_REF][START_REF] Hoyer | Collaborative E-business Process Modelling: A Holistic Analysis Framework Focused on Small and Medium-sized Enterprises[END_REF][START_REF] Belussi | A Typology of Networks: Flexible and Evolutionary Firms[END_REF][START_REF] Camarinha-Matos | Cloud-based Collaborative Business Services Provision[END_REF]) and 7 projects (BIVEE, Com-Vantage, IMAGINE, CoVES, Laboranova, PLENT and GloNet [[14]]) provided some useful insights for our proposed model. In summary: i) most works are directed to manufacturing, with some of them also covering related services (but not software services); ii) most works presents linear models, where the innovation process seems to follow the same mandatory steps, starting with a business plan, and some aspects that do not fit at all the reality of the software/SOA development process; iii) many models are incomplete in the sense that they support only some phases of innovation; iv) none supports collaboration along the entire innovation process; v) few models cover open innovation; vi) no work gives methodological support to aid companies regarding methods, techniques, etc. recommended to be used in each process.

Proposed Innovation Model

The model is based on two premises: the innovation process may be serendipitous and unique [START_REF] Hwang | The Rainforest: The Secret to Building the Next Silicon Valley[END_REF], involves creativity and therefore some unpredictability [START_REF] Mootee | Design Thinking for Strategic Innovation: What They Can't Tteach you at Business or Design School[END_REF]. Considering this, the model was generally inspired by the Design Thinking method [START_REF] Mootee | Design Thinking for Strategic Innovation: What They Can't Tteach you at Business or Design School[END_REF] and in particular its notion of innovation "spaces" and evolution stages. This was complemented and adapted taking the six classical innovation processes proposed in [START_REF] Du Preez | A Framework for Managing the Innovation Process[END_REF] into account.

Other requirements are: i) companies may participate in the whole or part of the entire innovation process and related software development cycle, depending on their agreed roles, rights and duties; ii) companies and supporting organizations may join, operate in, and leave the innovation network at different points in time and possibly do so a number of times, during normal network operation or when problems or changes occur; iii) there is no single, predefined or linear progression for the innovation "path".

Our model, called 'SIGMA', supports collaborative innovation from initial exchange of ideas to final delivery/deployment (Fig. 1). The three model spaces represent different moments a typical innovation initiative may go through: (i) Ideas development space: includes processes supporting the management of ideas and their preliminary evaluation/selection. This space is open for all members of the long-term collaborative alliance (e.g. the federation); (ii) Solution development space: includes processes supporting the development of the selected ideas by a VO; i.e. the companies selected considering the required expertise and financial, commercial, technological and software skills; (iii) Solution delivery space: includes processes supporting the delivery of the innovation's outcomes in the case of its commercialization.

The innovation process sequence in these spaces is determined on-the-fly based on the needs of the innovation initiative, i.e. processes may be revisited and some of them (or even an entire space) may not be performed (e.g. the third Fig. 1. The SIGMA innovation model space in case the innovation is not yet ready to be transformed into products). Yet, an initiative does not necessarily have to start from the first space and the ideas analysis process. The innovation can progress in decoupled cycles of evolution between the spaces, where new ideas, prototype versions, goals, etc., can be generated and refined along the time. In order to support such flexibility, and inspired by the SOA paradigm, the model's processes are seen as a set of decoupled building blocks. They act as reference processes that should be selected based on needs and be further instantiated by the VO team.

An innovation initiative can be interrupted, radically changed or shelved (for later use) anytime. The model also uses the classical gate concept for that, although in a slightly different way: a formal evaluation of process outcomes may act as a filter to continue the initiative (decided by the VO) but this is not a mandatory decision.

-Ideas Development Space. Idea Analysis: a company from the alliance can propose a joint innovation to the federation's committee, which will evaluate the initial idea's potential; Briefing: the idea is presented in detail, describing the necessary technologies, potential partnerships, eventual estimated ROI, foreseen market, etc; Commitment: the company that had the original idea may want to protect it using confidentiality contracts before presenting the idea to the other VO members; VO configuration: formation of the VO that will carry out the innovation. This involves partner search and selection, negotiations, setting up VO governance and revenue mode, and contract signature, as well as the definition of performance indicators to be applied upon partners and the innovation process; Consulting federation board : triggered in the case the VO needs advisory about some issues.

Although it can depend on how the governance model had been set up, the board usually does not have the power to kill an idea. This board can be formed by some alliance members and may also involve external experts.

The main goal here is trying to anticipate problems and propose possible solutions. -Solution Development Space. Presentation: a more complete project plan and ICT analysis are produced and the eventual business model is refined. It also includes issues of IPR and ownership, accounting, and knowledge gaps in the VO and in the federation; Software-service conceptualization: concept development of the SOA solution, the software architecture, the required services to be developed and their functional and non-functional requirements, etc;SOA solution development: it is similar to the previous one, but at a very detailed level. It includes services coding and composition, their integration and final verification. It covers the SOA/services life cycle development [START_REF] O'brien | Quality Attributes for Service-oriented Architectures[END_REF][START_REF] Kontogiannis | A research agenda for service-oriented architecture[END_REF], but respecting the par ticularities when the project is to be performed by a group of companies [START_REF] Cancian | Supporting Processes for Collaborative SaaS[END_REF]; Consulting VO Board : process triggered in the case the innovation team needs advisory about some issues, for example, the need for new members; SOA solution commercial preparation: this process ends the SOA solution development space, making the planned outcomes available for whomever has the rights. VO members make agreements and sign contracts, comprising commercial support for the product/service, IPR, commercialization model, price policy, etc. -Solution Delivery Space. Local infrastructure provisioning: depending on the agreed business model, contracts and QoS requirements, it is necessary to prepare the required infrastructure for the SOA solution deployment at the customer's infrastructure, third party provider, a specific member's site, or the alliance's site; Local deployment: process triggered to make the physical deployment of the SOA solution / product once the infrastructure is ready; Deployment in the cloud : deployment of the SOA solution / product in a third party or alliance's cloud(s). -Functional Guidelines (FG). Network members are basically SMEs, and their managers may not be fully aware of the issues to consider in each of the processes. FGs correspond to supporting methods, techniques and tools that may be considered by the VO team during collaborative innovation. Ten FGs have been identified after a compilation of several papers on innovation (e.g. [START_REF] Munkongsujarit | Key Success Factors for Open Innovation Intermediaries for SMEs: A Case Study of ITAP in Thailand[END_REF][START_REF] Van Zyl | Process Innovation Imperative [Software Product Development Organisation[END_REF]). FGs are grouped into business, operational and policies levels:( i) Business level : FGs related to commercialization of innovation. Involves the issues of Business Model management and Legal aspects management;

(ii) Operational level : FGs to support the daily operation of the innovation development. Involves the issues of Actors' roles, Project/Resources management, VO Network operation, Incentive systems, and Performance indicators; (iii) Policies level : FGs related to relations within the VO, the VO with other actors (internal or external), and with customers. Involves the issues of Governance, Software process improvement, and Knowledge sharing and management. The proposed model does not offer such methods, tools, etc.. Instead, adopting the vision of reference models, they have to be instantiated regarding existing practices, available financial resources, prepared people, etc.

Model Validation

The SIGMA model was evaluated by a group of SOA experts from a cluster of ICT/SOA provider companies located in two cities in the South of Brazil. A total of 29 professionals from 17 companies were involved in three small workshops. The selected companies had some level of experience on innovation. The model was presented and doubts clarified. Hypothetical but representative business cases were proposed by them so as to simulate near-real innovation scenarios. The professionals were split into groups in order to also simulate autonomous companies. The authors of this research acted as "moderators", sometimes representing members of the Network or the VO Board.

A survey composed of 18 questions was prepared adopting the GQM method [START_REF] Basili | The Goal Question Metric Approach[END_REF] and applied to the companies after the group dynamics. Twelve objective questions were answered using the Likert scale and there were 6 open questions.

In summary: i) 90% agreed about the separation of the process model into 3 spaces; ii) 80% fully agreed that the process systematization has the potential to decrease costs, time and/or risks of SOA innovation projects; iii) 100% agreed that the model properly supports the required flexibility of innovation in software and SOA; iv) all devised processes and FGs were considered as pertinent, although with different levels of importance; v) 100% agreed that SMEs of software providers will need to adopt more formal innovation practices and will be involved in wider value chains in the next 5 years; vi) although more directed to SMEs, 50% pointed out a sort of technical and cultural obstacles as well as organization impacts to adopt the model. Some initiatives were mentioned by the companies as enablers for the model adoption, like the creation of common training courses on the several related issues; the strengthening of the collaboration culture and trust among companies; and adequate legal frameworks also considering intellectual property rights. On the other hand, some barriers to adopt the model were also identified, like the lack of governance culture at the network level; the lack of systematic collaboration culture by SMEs; and lack of ICT tools to support the distributed innovation and project/innovation management.

Conclusions

This paper presented the final results of a novel innovation process model devoted to support collaborative innovation among software services provider SMEs. The model has been developed with the view of Collaborative Networks providing the foundation for supporting SMEs to work as a network, sharing costs, risks and benefits, where an innovation initiative is operated as a Virtual Organization (VO).

The most relevant supporting constituents to be taken into account in the innovation process were identified and briefly described. They are called 'functional guidelines' and aim at helping companies to be aware of the issues to be managed in the collaborative innovation life cycle.

The model presents a number of new contributions compared to related works, namely: it is directed to software and SOA; it introduces the concept of 'functional guidelines'; the model allows flexibility, non-linearity and on-the-fly composability of the innovation process for every innovation initiative; it introduces the concept of 'building block' as a reference process to be instantiated according to existing cultural or other general conditions; it adapts the SOA development process to cope with the requirements of collaborative innovation among disparate companies.

From the business standpoint, the adoption of the proposed model has the potential to provide a number of benefits, such as: the sharing of risks among partners, which decreases their losses in the case if unsuccessful innovation initiatives; faster response to innovation demands; greater synergy and networking among companies so generating a wider scope of complementary skills that can be used in future businesses in other value chains; higher ROI as companies' services can be reused by other companies; newer business models derived from the networked business architecture; and the breeding of more intense culture of innovation in the companies, reinforcing the ecosystem's strength.

Working collaboratively is a learning process. Companies are heterogeneous (culture, working methods, human resources' qualification, core competences, etc.) and autonomous, meaning that their different strategies must be accommodated and interoperate regarding their different priorities, exploitation plans, and trade-offs in terms of acceptable risks, trust and benefits. Although presented in its final version, the model cannot be seen as fixed. New processes and FGs can be added and existing one may evolve.

The innovation process model is not an end in itself. It is part of a much wider framework instead. Complementary issues, like having an innovation model, fostering and reinforcing leadership and innovation culture, having appropriate legal and funding mechanisms, among many other conditions, should be developed and supported for a successful and healthy innovation environment. The next main step of this research refers to validate the model in real cases and to test it close to a greater number of companies.


Christina Reuter
email: c.reuter@wzl.rwth-aachen.de

Jan-Philipp Prote
email: j.prote@wzl.rwth-aachen.de

Torben Schmitz
email: t.schmitz@wzl.rwth-aachen.de

Cost Modelling Approach for the Source Specific Evaluation of Alternative Manufacturing Networks

Keywords: Global Supply Networks, Manufacturing Networks, Location Decisions, Cost Modelling, Source Specific Cost Allocation

In order to seize the full potential of production on a global scale, companies need to constantly evolve their production networks and reconsider the number of sites. Decision making in this context forces corporations to process a substantial amount of information and complexity. Prominent examples of German companies (e.g. Stihl or Steiff) prove the present struggle of decision makers. Existing approaches are either too complex and effortful or they do not consider all decision relevant cost factors in a cause-fair way. This paper presents a pragmatic cost modelling approach which focuses on the identification and projection of decision relevant cost factors as well as their source specific allocation in terms of the impact on existing sites in the network.

Challenges within the Evaluation of Alternative Global Manufacturing Networks

Producing companies nowadays mostly operate in global manufacturing networks consisting of globally spread production sites. These networks are mainly determined by the quantity and the geographical distribution of its production sites. Evaluating alternative network scenarios in terms of adjusted number of sites is therefore one important mean to elaborate a manufacturing network. However, as recent research studies of the Fraunhofer ISI show, companies struggle with designing global manufacturing networks and location decision making in particular: 25% of locations decisions made by German companies needed to be revoked [START_REF] Zanker | Globale Produktion von einer starken Heimatbasis aus[END_REF]. Four major challenges within location decisions can be identified: The first challenge is the missing transparency about decision relevant (cost) information. In a production network each site is under the influence of a variety of internal and external interdependencies (e.g. cross-linkages to other sites and suppliers as well as the development of markets and economical or political conditions). Many managers, especially in SME, are overwhelmed by the complexity of the decision situation [START_REF] Abele | How to Go Global-Designing and Implementing Global Production Networks[END_REF]. Therefore it is necessary to establish transparency by understanding the cause-effect relationship between costs and cost drivers. Another shortcoming is the availability and validity of decision relevant cost data. The required accounting information might not be available or only in the local location controlling which decision makers might not want to incorporate in the process for confidentiality reasons.

Moreover local controlling divisions are still given an ample scope so that it takes high efforts to ensure the comparability of cost information. Studies showed that misvalued cost advantages were the main reason for companies to revoke their location decision. On the one hand cost savings, flexibility and quality are overvalued while on the other hand, time and investment efforts are often underestimated [START_REF]Global Production[END_REF]. Especially during the startup phase a potential new location needs to be supported and coordinated. In many cases the resulting overhead costs are not allocated to the causing new location but to the existing locations. Moreover the impact on existing sites in the network is often neglected. A new site might cause a structural cost impact on existing locations if for example complete product lines or only production volumes are relocated. Fixed costs for staff, production area or equipment cannot fully be adapted to the declining production volumes. This phenomenon is known as cost stickiness [START_REF] Via | Sticky Cost Behaviour: Evidence from Small and Medium Sized Companies[END_REF]. It leads to a lower productivity and ability to bear overhead costs in the existing sites. In general, the shift of fixed costs across the network is not duly taken into account.

To contribute to solving these deficits this paper pursues the following objectives:

-Systematical and comprehensible cost modelling using cost drivers -Allocation of relevant costs in a source-specific way (in terms of the decision situation) to depict impact on existing sites -Pragmatical approach to ensure applicability in industrial practice

Deficiencies of Existing Approaches

In the following state of the art section a cross selection of contemporary research on cost modelling and decision support in the field of evaluating alternative global production networks is presented.

Vahdani and Mohammadi present a bi-objective optimization model for supply chains aiming at the minimization of total costs and waiting times in services. Their cost model includes fixed costs, transportation costs, manufacturing costs as well as distribution costs. The cost model is combined with a priority queuing system and enriched by a uncertainty approach. To verify the results of the model various theoretical experiments are performed, no actual cost data however is utilized. The cost model does not depict costs in detail which change within a location decision. The modeling effort for costs is high since a closed loop supply chain is considered [START_REF] Vahdani | A Bi-Objective Interval-Stochastic Robust Optimization Model for Designing Closed Loop Supply Chain Network with Multi-Priority Queuing System[END_REF].

Schuh et al. developed the software tool OptiWo for the optimization of manufacturing networks. By the use of an optimization algorithm and a total landed cost function complete networks are modeled and evaluated. As the method focuses on a balanced state of a production network not all relevant costs are presented detailed enough to support single location decisions [START_REF] Schuh | Global Footprint Design based on genetic algorithms-An "Industry 4.0[END_REF].

A mixed integer programming model for integrated production and distribution planning is introduced by Yuan et al.. They utilize a branch-and-bound algorithm to assign multiple products to a certain numbers of sites and warehouses which distribute to the customers. The approach implicates a low modelling effort, while the focus however is mostly on costs for opening up and operating warehouses [START_REF] Yuan | A Network Prototype For Integrated Production-Distribution Planning With Non-Multi-Functional Plants[END_REF].

Wagner and Nyhuis deliver an approach for a global manufacturing system for product variants. Especially the needs of SME which often produce a variety of products with high demands on quality are taken into account. Production systems are designed and evaluated by a cost based system for relocation decisions. As a first step, the actual state is analyzed based on the total cost of ownership. Afterwards various scenarios are created to enable an evaluation of different locations [START_REF] Wagner | A Systematic Approach To Analysis And Design Of Global Production Networks[END_REF].

Christodoulou et al. provide an approach with 4 phases for the continuous configuration and improvement of production networks. The first step is to understand the need for change and to define a network strategy. Afterwards a Make-or-Buy analysis is made to find the products that should be considered. A multi-stage approach is used to determine the suitable manufacturing location of each product. Finally the relocation process is planned. The decision making process is rather in focus than a profound cost modelling [START_REF] Christodoulou | Making the right things in the right places[END_REF].

To support location decisions Yang uses the AHP decision model which allows comparing decision-makers preferences with location characteristics. The cost modeling however is not very profound. By evaluating alternative sites under both quantitative and qualitative factors it is possible to take managerial experience and judgement into account [START_REF] Yang | An AHP Decision Model For Facility Location Selection[END_REF].

All in all it can be stated that several approaches in this field exist. Especially the cost modeling approaches are quite effortful and time-consuming. Most approaches do not consider all required cost elements, which are affected by a location decision, in a source specific way. Neither do they offer the possibility to calibrate the cost model using (aggregated) actual costs. Thereby an opportunity to improve the methods validity is missed.

Cost Modelling Approach for the Source Specific Evaluation

In this paper we present a new cost modelling approach which enables a pragmatic cost modelling of decision relevant cost types based on cost drivers and the consumption of resources. Special attention is paid to source specific evaluation of the impact on existing sites in the network. The model consists of four phases. In the preparation phase the decision situation is specified (e.g. concrete objectives, data availabilities and implications) and the level of aggregation as well as the decision relevant cost types are derived based on the specifics of the decision situation. In the second step the operational cost model is built in a combined top down/bottom up approach to use the modeled and calibrated actual costs as basis for prediction of costs in the decision scenarios. An extended evaluation model is used in the next step to evaluate the additional costs associated with the decision (e.g. investments, transaction costs) and to include the structural impact on existing sites (e.g. sticky costs) in the assessment. Lastly a decision support is built to efficiently evaluate alternative network scenarios by a profitability calculation and a source specific unit cost comparison. The overall approach is outlined in a comprehensive overview in Figure 1.

The operational cost model and the source specific evaluation model (steps two and three) as the core of this paper are depicted in more detail in the following. Further Fig. 1. Source specific cost modelling approach information on the preparation phase, especially the derivation of decision relevant cost types, can be found in Reuter et al. [START_REF] Reuter | Approach for the Cause-fair Allocation of Costs in Location Decisions in Global Production Networks[END_REF].

Operational Cost Model and Calibration

The objective of the operational cost model is to depict all decision relevant costs of one product group in a pragmatic way and allow decision makers to track the costs back to their causing source. For this purpose, the resource-based cost modeling approach for product variants of Schuh is adapted to the specifics of a location decision process [START_REF] Schuh | Produktkomplexität Managen: Strategien-Methoden-Tools[END_REF]. The detailed operational cost modeling approach is shown in Figure 2. The model consists of a twofold top down/bottom up approach to derive the required data and parameters. The bottom up stream defines the basic set of cost types, resources and cost drivers which represent the cost consumption. The top down part prepares and aggregates actual costs from standardized sources like the external accounting for example in order to assign these cost information to the defined cost types. Both streams are merged in the next step and the coherences between cost drivers, resources and resulting costs are analyzed and quantified. The approach is based on the idea, that a cost driver is responsible for a certain resource consumption (e.g. produced quantity of a product group). This correlation can be formalized in a consumption function. The resource consumption (e.g. weight of raw materials) in turn causes costs (e.g. raw material costs). The correlation might be formalized in a cost function. The coherence of both functions can best be visualized by the use of a simplified nomogram shown in the following Figure 3.

Work Guideline

The effect of each cost driver on the consumption of its corresponding resource and ultimately on the related cost type is modeled individually but can be depicted by one mathematical equation that integrates both functions. In order to quantify the consumption function as well as the cost function, a regression analysis will be conducted. In a first step a standard ordinary least square (OLS) regression is used to predict the corresponding cost value (e.g. material costs) as the dependent variable. OLS is a standardized, easy to use statistical method for estimating the relationships among variables.

Stock & Watson provide detailed information on OLS [START_REF] Stock | Introduction to Econometrics[END_REF]. In order to conduct the OLS, actual data sets with several points in time (e.g. years 2013-2015) are needed which should provide information on the resource consumption, the value of the cost drivers and the corresponding actual costs. In order to ensure comparability, the numerical values of each set of data has to be prepared: inflation induced effects on costs as well as any one time special effects which do not correlate with the regular serial production need to be eliminated. Once the OLS delivers a first quantification of the coherences, the consumption and cost function can be validated by predicting the actual costs of the latest data set. Hereto only values for the cost drivers of the corresponding data set are needed. If the predicted costs are not sufficiently close to the actual costs (e.g. measured with the mean square error or root mean square error), the cost model can be calibrated iteratively by trying out different regression models. These procedures include generalized least squares analysis (GLS) or nonlinear regression.

The procedure of cost modelling is done for all cost types (per product group) and for all focused product groups. A complete overview of all relevant cost types, resources and cost drivers can be found in another paper presented by the authors [START_REF] Reuter | A Top-down/bottom-up Approach for Modeling Costs of a Manufacturing network[END_REF].

Extended Cost Evaluation Model

The extended evaluation model is used to evaluate the additional costs associated with the decision (e.g. investments, transaction costs) and to include the structural impact on existing sites through cost stickiness in the assessment. Investments as well as transaction costs (e.g. costs for coordination and control, travel expenses, trainings and educations, etc.) are well known concepts which do not need further detailing, since several approaches illustrate their consideration. [e.g. 3] Sticky costs however are not yet integrated in the location decision making process. They represent the potential negative impact a new site has on existing ones since their ability of bearing overhead costs is weakened. This is particularly the case for building costs, salaries and partly for machinery and equipment costs. If production volumes or complete products are relocated to a new location, idle capacities in terms of production and office area, management capacities and machinery and equipment are left at the original site. These idle capacities cannot be reduced immediately. A factory manager for example cannot be paid 80% of his salary if 20% of "his" products are relocated. The value of these idle capacities (fixed costs) can be interpreted as sticky costs.

The operational model (c.f. chapter 3.1) can be used to determine the level of cost stickiness per product group in the three cost types by using the corresponding cost drivers.

Decision support

The decision support incorporates the data from the cost models and combines a standard profitability calculation using the net present value (NPV) with a source specific unit cost comparison. The NPV focuses on the purely cash-based effects of the potential new site while the unit cost comparison includes implicit costs such as sticky costs in order to ensure a source-specific evaluation for the specific decision situation. A source-specific unit cost comparison provides transparency about the structural impact of the decision for the complete production network and helps to reduce the weight of allegedly short term cost advantages which lower wage levels might offer. Sticky costs however might easily overcompensate the wage level advantage. The following industrial case study illustrates the source specific unit cost comparison using a concrete example of one product group.

First Case Study and Basic Validation

The approach is provisionally validated with an industrial partner in the metal working industry. Objective of the bilateral project was the preparation of a decision paper for a potential new location in eastern europe. The management wanted to know whether the partly relocation of production capacities would make sense in order to utilize lower factor costs as a typical profitablity calculation (NPV based) indicated. The results of the source-specific cost modelling approach are shown in Figure 4 exemplarily for one product group as a unit cost comparison. By explicitly considering the sticky costs associated with the decision situation it becomes obvious that the savings in purchase and labour are overcompensated by the cost stickiness of building costs and salaries of the remaining management functions. All in all, this first case study proved the general applicability of the source specific cost modelling approach. Transparency regarding the decision specific cause-fair shift of fixed costs could be established.

Conclusions

The approach presented in this paper offers a pragmatic cost modelling method to improve location decisions by evaluating alternative manufacturing in a more source specific way through explicitly considering the impact on exiting sites. The cost model enables decision makers to gain more transparency on the cause and effect relationships between drivers, resource consumption and resulting costs. The impact on existing locations in terms of a weaker ability to bear fixed costs can be evaluated and integrated in the decision. The approach was validated with an SME. Further research needs to be done on how to handle special effects (e.g. productivity growth, periodic variations) and on how to include a degradation rate for cost stickiness. More validation needs to be done to further detail the approach and to ensure applicability in various fields/sectors.

Fig. 2 .

 2 Fig. 2. Operational cost modelling approach

Fig. 3 .

 3 Fig. 3. Example of a nomogram and equations

Fig. 4 .

 4 Fig. 4. Exemplary unit cost comparison for one product group

Acknowledgement The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence "Integrative Production Technology for High-Wage Countries".


Ana Milicic
email: ana.milicic@epfl.ch

Dimitris Kiritsis

Nesat Efendioglu

From English to RDF -A Meta-Modelling Approach for Predictive Maintenance Knowledge Base Design

Keywords: Meta-modelling, Ontology design, User story mapping

In recent years, ontology for the Product Lifecycle Management (PLM) domain has raised a lot of interest in research communities. In this paper we present the contribution in form of meta-model of the entire ontology design process. By implementing USM methodology as ADOxx model the entire procedure is enhanced though automatization of deterministic steps and strategic procedure conduction.

Introduction

In recent years, ontology for the Product Lifecycle Management (PLM) domain has raised a lot of interest in research communities, both academic and industrial. It has emerged as a convenient method for supporting the concept of closed lifecycle information loop [START_REF] Jun | Research Issues on Closed-Loop PLM[END_REF], which is one of the most important issues of PLM. By modeling relevant aspects collected from all lifecycle stages of a product, within one ontology, a common knowledge structure is created accessible to all actors. Information availability within the company might seem like a trivial aspect but it is actually one of the most challenging issues. Large companies are divided into number of departments which have only limited communication protocols, employees change projects or leave company and information is generated over a long span of time and locations. In the same time, having the right information at the right moment when decisions about design, production or maintenance are made, is crucial for business success. The complexity of the information source domain translates directly into complexity of design of the ontology which will be mapping all relevant knowledge and the information sources, present in the domain. There is number of different approaches and methodologies for ontology design addressing different problem setups and domain types, but that is out of scope for this work. In this paper, we address the User Story Mapping (USM )method [START_REF] Milicic | Towards the Definition of Domain Concepts and Knowledge Through the Application of the User Story Mapping Method[END_REF] which is designed specifically for creation of ontologies in PLM domain where most of the actors and information sources are not experts in semantic technology.

A USM is an approach that creates a backlog along scenarios and users. It answers the question on how an employee in the company would like to use knowledge base that is being designed, what are the activities that he performs and which resources he uses. Defining usage by all relevant users, thus defines the domain knowledge, the existing one, needed one and produced one . A backlog consists of several structure blocks as shown in Fig. 1.

1. Usage dimension -It describes how a user would use the knowledge base, for which activities and with what purpose. 2. User dimension -This dimension defines the types of users that will use the knowledge base 3. Backbone -This section describes the activities that a user performs within a usage step. This section is called backbone as it suits as a guideline for the definition of the user stories, which are actually a refinement of the backbone. 4. User stories as backlog items -This is the actual placeholder for the user stories. The user stories are ordered vertically under each activity and represent a refined ver-sion of an activity. It is recommended that user stories follow the pattern "As ¡user¿ I want to ¡feature¿ so that ¡value¿". USM is the first step of gathering information regarding the domain of interest and the following step can be defined as translating functional needs into list of concepts. This is done manually, by taking into account the whole grasp of the domain,existing templates, resources and standards and by identifying a list of relevant concepts. The process is itterative and done in collaboration between semantic experts and the domain experts until every user story can be expressed though ontology concepts and ontology properties between concepts.

In this paper we present the contribution in form of meta-model of the entire ontology design process. As previously explained, onotlogy design is itterative and can be time consuming esspecially when performed manually. ADOxx meta model enables faster, more controled design proces as well as collaborative environment for distributed teams. Further on, the entire procedure is shown for the use case of predictive maintenance tasks.

Meta-Modelling

Conceptual modelling is a technique to "study" the real world -the so-called system under study -and provides concepts to abstract the complexity of the real world and establish a model environment that enables to knowledge processing on models (e.g. simulation, analysis, transformation etc.) of complex systems thanks to the conceptual abstraction.

Meta-modelling is an established approach to specify conceptual modelling languages and a proven technology to enable conceptual integration of information coming from different enterprise levels as well as from different domains. Moreover, meta-modelling makes information available for both machine computation and human interpretation to create value out of that information [START_REF] Woitsch | Hybrid Modeling: An Instrument for Conceptual Interoperability[END_REF].

As above-mentioned the main objective of conceptual modelling is creation of value out of information stored in the models with applying appropriate knowledge processing algorithms. The ADOxx©is a meta-modelling platform, which enables realization of any kind of modelling method that consists of the conceptual modelling language, algorithms to process models of the modelling language and the procedure that defines the stepwise appropriate usage of the modelling language. Only prerequisite is, that the Generic Modelling Method Specification Framework -GMMSF (shown in Fig. 2) for conceptualization of the method is followed. Fig. 2: Generic modelling method specification framework (Source: [START_REF] Karagiannis | Metamodelling Platforms[END_REF][START_REF] K Ühn | Methodenintegration im Business Engineering[END_REF])

According to [START_REF] Karagiannis | Metamodelling Platforms[END_REF] [START_REF] K Ühn | Methodenintegration im Business Engineering[END_REF] the building blocks of a modelling method include: (1) the modelling language introducing modelling concepts pre-defined according their semantic, their syntax and their graphical notation, (2) the modelling procedure which defines the stepwise usage of the modelling language and may not be always available and (3) generic and domain specific mechanisms and algorithms enabling the computer-based processing of models. By applying this framework (and selecting ADOxx® as a realization platform) it can be guaranteed that defined modelling method can use all of the generic platform functionality (e.g. visualization, publishing, simulation, etc) and can be amalgamated with other modelling methods based on the requirements of the stakeholders (e.g. adding Ontology Modelling to extend User Story Mapping Method) or extended with new concepts.

User Story Mapping Modelling Method Realized with Using ADOxx©

The User Story Mapping Modelling Method and its corresponding modelling environment aim to offer a model-based approach supporting User Story Mapping Method (USM Method) during capturing domain specific knowledge, virtualizing and transforming knowledge understandable by humans and machines.

The modelling method shall support all steps of the USM method and it count all steps as challenge to be targeted. Hence, the challenges are (C1) to enable creation of user story map, (C2) to enable gathering other source of information, (C3) enabling creation of unique list of concepts that covers entire domain, (C4) definitions of relations and dependencies among new concepts and concepts in initial knowledge base and (C5) creation of dynamic knowledge base covering domain expressed in standard format, which is machine interpretable.

Modelling Language

The modelling language of the USM Method consists of two model types; (1) User Story Mapping Model and (2) OWL Diagram. The Fig. 3 depicts the metamodels of both model types and the inheritance from ADOxx©Meta-model.

User Story Mapping Model targets challenge (C1) to enable creation of user story map, hence it consists of the concepts; "User", "User Story", "Activity", "Backlog" and "Group", which have been introduced in the section Fig. 3, and identifies relations among them.

Addition to User Story Mapping Model, we decided to enable modelling ontologies, hence to add so-called the OWL Diagram. The OWL Diagram is sub-set of the OWL Web Ontology Language (W3C, 2004) and we utilized it as it is implemented in (ADOxx.org, 2016). The OWL Diagram targets challenges; (C2) to enable gathering other source of information, (C3) enabling creation of unique list of concepts that covers entire domain, (C4) definitions of relations and dependencies among new concepts and concepts in initial knowledge base and (C5) creation of dynamic knowledge base covering domain expressed in standard format, which is machine interpretable.

The formalized overview of the meta-models defined in FDMM [START_REF] Fill | FDMM: A Formalism for Describing ADOxx Meta Models and Models[END_REF], where;

-A meta-model is a tuple MM = MT, , domain, range, card where MT is the set of the defined model types, i.e. for i=1,. . . ,m we have:

MT={MT 1 , MT 2 , . . . , MT m }. -The MT i 's (i=1,..,m) are themselves tuples MT i = O T i , D T i , A i , where: -O T
i is the set of object types or classes, -D T i is the set of data types, and -A i is the set of the attributes.

-The domain is a function with domain: A→P(O T ) Fig. 3: Platform specific meta-model of USM modelling method -The range maps an attribute to the power set of all pairs of classes and model types, all data types, and all model types. range: A→P( j (O T j ×{MT j })∪D T ∪MT) -The cardinality function card :

O T ×A→P(N + 0 ×(N + 0 ∪{∞}))
The formalized overview of the meta-models defined in FDMM [START_REF] Fill | FDMM: A Formalism for Describing ADOxx Meta Models and Models[END_REF], where;

The Meta-model of USM Modelling Language is formalized in FDMM as following;

-User Story Map MT U SM -OWL Diagram MT OW L -MT USM = O T USM , D T USM , A USM -MT OWL = O T OWL , D T OWL , A OWL
Since we integrated already formalized and implemented OWL Diagram by [START_REF]ADOxx Realization Case: Web Ontology Language (OWL)[END_REF], we describe just the model type User Story Map in details;

-MT USM = O T USM , D T U SM , A USM -O T USM ={ USM Construct , Group,

Mechanisms and Algorithms

Using Mechanism & Algorithms capabilities of ADOxx modelling system, creation of list of ontological concepts is implemented by allowing ADOxx user to annotate Activities from Backbone with concepts from ontology. ADOxx user is enabled to define his own concepts and sub-concepts. For example, the Activity "Check Business Exploitation Results", needs to be annotated as "Data" → "Digital" → "Resource" → "User Feedback".

These new concepts Fig. 5 can be further used to annotate Activities. By annotating the entire Backbone of activities, list of concepts is created by merging all concepts that were used in annotation process, from those generic to more specific ones. ADOxx model will generate this list automatically and create a visualization. The next step in ontology design process is definition of relations between concepts. The challenge with definition of relations (or object properties, according to ontological terminology) is that some level of interaction can be usually found for every two concepts and only a small portion of them is relevant and useful for domain description. That is, it is not the issue of finding relation between two concepts, it is the issue of selecting the ones that are worth defining as object properties. Using ADOxx modelling tool, the recommendation system for this challenge was created, relaying again on Activity Backbone. The reasoning is that if one Activity, was annotated with two or more concepts, then it is reasonable to assume that those two concept should have object property connecting them. The tool is implemented as recommendation system, meaning that the ADOxx user is presented with an option to create object property which he can freely accept or decline Fig. 6.

Application Scenario

Predictive maintenance is a field with ever-growing popularity. Compared to a standard, fixed period maintenance, it saves resources, cost and environment.

The most relevant aspect of predictive maintenance is most certainly its reliability. Too wide confidence bounds will lead to non-optimal maintenance schedule while the too narrow confidence bounds might lead to costly donwtime and repairs. The key of the solution to this problem is having all relevant information about machine usage conditions, usage patterns as well as machine lifecycle over all. This is especially true for machines which are large investments or when a long down time is not acceptable.

In this paper, we examine the real-life example of company X, which is a producer of healthcare equipment. They specialize in high-end machines such as MRI scanners which are highly complex, sensitive for calibration and usage modes. In the same time, the long or frequent down time is not acceptable since the hospital's patient treatment depends highly on availability of those machines. Today, company has two sources of information about machine usage and calibration that is log-files (stored in the machine and shared periodically) and help desk files (where workers are taking notes of all reported problems with integration with the rest of equipment and usage problems). In the same time, these information are requested by number of actors and with different summarization level requirements. Designers of future products will want to understand the most frequent issues that certain existing line of products exhibited, while the service team will want to understand the usage patterns and previous maintenance activities.

In order to aggregate this complex and wide domain, USM method as ADOxx application was applied. In Fig. 7 we show a sample of user activities defined (details of the work had to be omitted due to privacy constraints ). Ontology generated based on given backlog is shown in Fig. 8 (again, only upper level concepts in parent-child hierarchy are displayed).

The onotlogy constitutes a knowledge base, where all the data are gathered and structured. It enables information retrieval using SPARQL semantic queries as well as integration to existing IT infrastructure of company X. It serves as ground layer for upper level applications preforming data analysis and serving as decision support system.

Conclusions and Outlook

By implementing USM methodology as ADOxx model the entire procedure is enhanced though automatization of deterministic steps and strategic procedure conduction. Each step can be performed independently and repeated if needed. Each step is documented and can be revisited or discussed in the future.

Having ADOxx model, opens a field of opportunities for future work that are out of scope of USM as ontology modelling methodology. ADOxx system allows export of models as RDF or XML structures making them available for other software tools. On the other hand, current export mechanism allow export of models while the procedures and algorithms defined on those models are not available outside ADOxx. One potential direction of future work is design of mechanism that will translate existing mechanisms into SWRL syntax.

Fig. 1 :

 1 Fig. 1: USM backlog (Source:[2])

Fig. 4 :

 4 Fig. 4: Weaving in order to use owl diagram into user story map.

Fig. 5 :

 5 Fig. 5: Addition of new sub-concepts in ADOxx.

Fig. 6 :

 6 Fig. 6: Object property definition in ADOxx.

Fig. 7 :Fig. 8 :

 78 Fig. 7: Sample of user activities

Using this approach, ontology in RDF could be enriched with ontological rules in SWRL giving more complete ADOxx model export and thus, interoperability.


Christina Reuter
email: c.reuter@wzl.rwth-aachen.de

Bartholomäus Wolff
email: b.wolff@wzl.rwth-aachen.de

Pia Walendzik
email: p.walendzik@wzl.rwth-aachen.de

Adaptive Configuration of the Organization in Manufacturing Startup Companies

Keywords: Process management, Organization, Organizational change

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Challenges in the Set-up of Manufacturing Startup Companies

In recent years companies face the challenge of designing more innovative products in less time to stay competitive. The number of product or business innovations has increased and companies are trying to provide an environment which is favorable for innovation and flexibility. Nevertheless some of the organizational structures of the companies don't fit with the new products or business innovations. Especially the organization's values and organization's processes need to fit with the innovation. As shown in Figure 1, there are different opportunities to handle those misfits. If a fit, both with the values and processes of an organization, isn't possible, the solution needs to be a separate team in a spin-off organization with new structures and processes [START_REF] Christensen | Meeting the Challenge of Disruptive Change[END_REF]. The example of Audi AG proves this idea. In 2013 the Audi Business Innovation GmbH was established as a spin-off of the Audi AG. The objective of this spin-off is the implementation of interdisciplinary work and flexible working methods to develop products in the field of future mobility and digitisation complements. The highly interdisciplinary work to enable innovations in shorter cycles was not possible within the structures of an established company [2].

Besides spin-off organizations there are more and more startup companies which focus on innovative product or business ideas. Spin-off and startup companies start without any given organizations. They need to build-up new organizational structures and processes. If the company focus is set on manufacturing processes compared to IT based innovation, the company needs more organizational structure directly from the beginning as there are usually more people involved. Manufacturing companies consist of different departments such as the production and manufacturing itself and several surrounding needed functions, e.g. purchasing, sales, material management, quality management, etc. Therefore structures and processes from startups developing software, where many examples can be found, can't be easily transferred. In 2011 IT based startup companies tried to focus on a new approach of developing software. Therefore software developers created a manifesto for agile software development. The Manifesto for Agile Software Development consists of four main principles [START_REF]Agile Manifesto[END_REF]:

-Individuals and interactions over processes and tools -Working software over comprehensive documentation -Customer collaboration over contract negotiation -Responding to change over following a plan Today besides software developing startup companies more and more manufacturing companies are trying to implement these principles within their product development processes to react more flexible towards changes. The challenge is to adapt the principles, such as interactions over processes and tools, as well in their organizations to be more agile within their organizational structures.

2 Deficiencies in the Set-up of Organizational Structure and Processes

In the startup phase manufacturing companies are identifying which organization and process suits. After the initial phase of orientation, there is a risk that the impression is created that more organizational structures and process standardizations are needed. Therefore companies try to implement structures comparable to those of large companies which don't fit their needs and will slow down their innovation process and loose agility. On the one hand the initial organizational design is important on the other hand the organizational change. Therefore the corporate structures and business processes are explained in the following as the main elements which changes the configuration of the organization. The organization development explains in general the growth of a company.

Business Process Management

Business Process Management's purpose is to enable strategic alignment with the business goals and processes [START_REF] Bergsmann | End-to-End-Geschäftsprozessmanagement[END_REF]. Within business process management there are several concepts and approaches towards that goal. The most relevant for this approach are the concepts of business process improvement, process innovation [START_REF] Vom Brocke | Handbook on Business Process Management[END_REF] or business process reengineering [START_REF] Harrington | Business Process Improvement: The Breakthrough Strategy for Total Quality, productivity, and competitiveness[END_REF]. Business process reengineering, introduced in the 1990s by Hammer and Champy focusses on a radical strategic change in the organization. In contrast to incremental improvement in the optimization of single processes, business process reengineering considers a change in the holistic structure of a company: the business strategy, organization structure, culture and its processes. The different approaches towards business process reengineering have in common that they try to achieve a more efficient organization by running through the steps of mobilization, continued with the diagnosis, the redesign and ends with the transition [START_REF] Harrington | Business Process Improvement: The Breakthrough Strategy for Total Quality, productivity, and competitiveness[END_REF]. The other named process management concepts are based on similar phases.

The concept of business process reengineering has been criticized as companies disregard the experience and expertise of their employees during the change process. Business process reengineering doesn't focus on people and their learning process but only on the process efficiency. In practical use many projects fail in the planning phase as the middle management is working against a structural change [START_REF] Davenport | Process Innovation: Reengineering Work Through Information Technology[END_REF]. The concept of business process reengineering is lacking flexibility for startup companies. To be more agile, startup companies need to run through the phases several time in shorter cycles.

Corporate Structure

The corporate structure consists of several elements, such as the organizational structure and procedures, resources, the information system and communication management as well as the corporate culture. The organizational structure and information and communication system will be explained in the following as they are most relevant for the approach [START_REF] Hammer | Reengineering the Corporation: Manifesto for Business Revolution[END_REF].

There are different types of organizational structures, such as functional, process or matrix organization. A functionally structured company is set-up with different department responsible for one task and competence each. Process oriented company focus on their business processes. A process organization focusses on the realization of process objectives and their optimization. The matrix organization tries to use economies of scale of the functional organization combined with the process focus on a special product or business unit. Employees in a matrix organization are working on two objectives from different leaders [START_REF] Chandler | The Dynamic Firm: The Role of Technology, Strategy, Organization, and Regions[END_REF].

The information system deals with the supply of all needed information for the employees to fulfill their tasks. Information is usually connected between each other and can be linked between different systems [START_REF] Vom Brocke | Handbook on Business Process Management[END_REF].

The communication system is closely linked to the information system. It covers the infrastructure to share the information. This can be technical equipment, reporting lines or different boards. New ways of combining information and communication are interactive management systems. Interactive management systems combine the process of creating and sharing information, as each employee can access those systems easily and renew information in a defined workflow [START_REF] Behrens | Interaktive Prozessorientierte Managementsysteme. Dynamische und Stabile Prozesse im Unternehmen mit nachhaltiger Nutzung des Organisatorischen Wissens der Mitarbeiter[END_REF].

Each of the presented categories of the organization structure needs to be considered when designing new organizations. Especially the different types of organization structures do not fit the needs of startup companies. During the growth the requirements towards the organization are changing so that neither one nor the other approach fits the whole time. The challenge for startup companies is the transformation from one organizational structure to another.

Organization Development

Organization Development is the reaction of an organization towards external changes. It comprises the strategic planning and implementation of organizational changes including the behavior modification to improve the organization. The objective of organization development is to increase employees' level of satisfaction and commitment, increase cooperation and collaboration abilities among the employees and to increase the organization's problem solving [START_REF] Lewin | Field Theory in Social Science: Selected Theoretical Papers[END_REF].

The change process in the organizational development can be described in three phases by Lewin [START_REF] Gross | Jobholder-Value und Portfolio-Work: die Neuerfindung der Arbeit[END_REF]. Organizational stability is maintained when there is a balance of two sets of forces acting upon the organization. The driving forces pushing on the organization to move in a new direction, whereas the restraining forces are hindering the movement. Therefore the first phase is about introducing a change by leaders to unfreeze the status quo. In the second phase leaders introduce the change by creating a possibility for movement towards the change vision. In the third phase the change gets stabilized by refreezing.

The approach by Lewin describes a process which should be done frequently in short cycles by startup companies to adapt to the given preconditions anytime.

In conclusion it can be said, that the business process management and organization development approaches explain how structures and processes can be adapted. The organization of manufacturing startup companies needs to be agile to adapt to the current situation on the one hand. On the other hand startup companies have different preconditions, such as limited resources. To handle the different preconditions the strategic organizational structures need to be changed. Therefore a guideline with the main levers for the organization configuration is needed.

Approach

The presented approach includes the main lever process management, the organization of corporate structures and the organizational development.

As shown in Figure 2, there is an appropriate balance for process management within the organization. The value proposition of process control depends on the scope and detailing of the process control. During the growth of the company the appropriate balance of process control can be different, so that a continuous observation and adaption of the organization is needed.

The presented approaches of business process management can be placed on the right side of the graph in the context of Startup companies. As described above they represent important methods to reorganize business processes.

On the other hand approaches such as the Lean Startup theory are usually based on low scope of process control which can be placed on the left side of the graph in the context of Startup companies. These are useful methods for the product development which reach their limits in terms of business process management. Turning points of the organization and its processes are so called business process instances [START_REF] Rosa | Business Process Management Workshops[END_REF]. In the original context business process instances are used to build business processes without any workflows. The aim of this paper is to put business process instances in a wider context to describe major changes in the organization, such as increase of employees or changing product requirements. At those turning points the existing organizational structures and the processes need to be reviewed if their value proposition is still at the appropriate balance. If a change is needed the three levers of process, organization and the strategy for growth should be taken into consideration to reconfigure the existing set-up.

The first lever represents the process standardization. Processes, which are predominantly focusing on the Process effectiveness, are reflected by flexible and creative processes, such as an agile product development. Process effectiveness is needed when starting the development of a new product. On the other hand predominantly process efficiency is shown through process standardization and itemization. Efficiency in the organization is needed to structure recurring processes. The trade-off is between process standardization or individual coordination.

The second lever is the form of organization between specialists and generalists. Specialization is given in a rigid organization when employees can focus on a special task. On the other hand generalists are employees working on different tasks which can be enabled through a flexible organization. The trade-off is given between differentiation and integration.

The third lever is the difference between fast growth and sustainability in the organization. Quick growth can be enabled through flat hierarchy as small teams stay agile and can take decisions quickly. Sustainability in the organization is the establishment of standards. The trade-off is between the a decentralized organization with decision-making authority in each department and a centralized organization.

Case Study

The approach will be investigated with an ongoing case study with a startup company. The method used to conduct the case study are semi-structured interviews to get information about the development of the startup company in regard to the changes within the processes and the organization.

The company is a startup company developing products for electromobility. The company was founded with around 30 people working in different departments such as the product development, prototyping production or even the purchasing. At the beginning each employee worked on a special task, mostly development tasks in agile development processes. There was only one hierarchy level to enable fast decision taking and each team could organize their work on their own. Information was mainly spread on inquiry via face-to-face communication as the employees were located close to each other. The communication system was based on short weekly meetings to keep each other informed about the current status of the projects. Processes were implemented step by step according to the needs of the company. At that time the appropriate balance of process control was found.

The startup company grew quickly within its first year by acquiring new employees. During the growth of this startup company the number of employees increased fast so that it wasn't possible to spread all information needed only on inquiry as important information got lost. This was the first business process instance. As shown in figure 3, the value of the existing process configuration was not the appropriate balance anymore but more detailing was needed. Therefore standard meetings and reporting structures for the different tasks in the organization were developed to keep each other informed. The processes were documented in a flexible interactive management system to be efficient in simple, reoccurring processes, such as the purchasing process. The second business instance has been the progress in the product development. As the product was specified more and more the development speed slowed down with the existing organization structures. The value of process control was not at the appropriate balance any more as the existing structure of information meetings for the whole team was too much. Therefore the organization was transformed to small autonomous teams by using the portfolio work [START_REF] Gross | Jobholder-Value und Portfolio-Work: die Neuerfindung der Arbeit[END_REF] approach. Each employee working in the purchase department was linked to one development team to do their purchasing tasks. Meetings were organized in smaller teams on special task and less meetings for all employees together. The investigation will be continued to identify more business process instances and the needed changes for the processes, the organization and the strategy for growth.

Conclusion

In summary, there is a lack of finding the appropriate balance for the process and organization management of startup companies. Therefore the challenge is to create adaptive processes and organization structures. The presented approach is based on business process instances to identify needed changes within the organization and its processes. Further work needs to be done to detail and validate the presented approach. The main levers for the configuration can be detailed. In a second step the identified specific business process instances need to be transformed into generic business process instances. The third step is the description of the needed change configuration according to the business process instances. Overall a validation of the concept needs to be done to prove the presented approach. Therefore the method of semi-structured interviews will be conducted with more startup companies during their growths.

Fig. 1 .

 1 Fig. 1. Integration of innovative ideas in organization (Source: Adapted from [1])

Fig. 2 .

 2 Fig. 2. Value proposition of process management (Source: Adapted from [4])

Fig. 3 .

 3 Fig. 3. Business process instances change value proposition of process management

Acknowledgement

The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence "Integrative Production Technology for High-Wage Countries".