Irina-Emily Hansen
email: irina-emily.hansen@ntnu.no

Ola Jon Mork

Torgeir Welo

Managing Knowledge in Manufacturing Industry -University Innovation Projects

Keywords: Industry-University Collaboration, Knowledge Management, Innovation Project

Nowadays, manufacturing companies collaborate with universities in innovation projects to sustain or achieve competitive advantage. However, fundamental differences between the industrial and academic worlds hamper the utilization full innovation potential of such collaboration. As a countermeasure, industry stresses the need for the development of knowledge management tools that can increase the value of collaborative innovation projects. This paper covers a qualitative study of research-based innovation projects owned by manufacturing companies and partly funded by government, where the academia has the role as research provider. We seek to answer two research questions: (1) how can the strategies and objectives for collaboration to meet both partners' expectations be defined? (2) how to facilitate the projects to enhance the creation and exploiting of knowledge? The study identifies that a modified version of Nonaka's so-called five-phase model of organizational knowledge creation is applicable for the given context. Based on this, we propose a conceptual knowledge management model of university-industry collaboration in innovation projects. The proposed model provides (1) management initiatives that intensify knowledge creation and exploiting processes (2) ensures partners' commitment to collaboration along with the continuing improvement of university-industry collaborative concepts. It is proposes that the model will support knowledge managers of industry and university in conducting innovation projects more effectively and efficiently, as well as deliver even more innovation values to partners and society. The model can also assist national and federal research/innovation councils in decision-making when assessing industrial research project applications.

Introduction

Research-based innovation projects between industry and university leverage competitiveness in the global market, while providing scientific knowledge and value for society. However, substantial differences between manufacturing companies and universities hamper collaboration, often leaving innovation potential from projects unexploited [START_REF] Perkmann | Academic engagement and commercialisation: A review of the literature on university-industry relations[END_REF]. Industry stresses that the use of knowledge management tools will enable more and better quality results from innovation projects with universities [START_REF] Hansen | Towards a framework for managing knowledge integration in university-industry collaboration projects[END_REF]. This study aims to contribute to the understanding of this challenge by answering two research questions that target the main challenges in University-Industry Collaboration (UIC) projects: [START_REF] Perkmann | Academic engagement and commercialisation: A review of the literature on university-industry relations[END_REF] how can strategies and objectives to meet both partners' expectations be defined? [START_REF] Hansen | Towards a framework for managing knowledge integration in university-industry collaboration projects[END_REF] how to facilitate the projects to amplify the creation and exploiting of knowledge. This study encompasses university-industry (UI) innovation projects that are owned by industry and partly funded by government, where academia has the role as 'external' research provider. The companies studied herein are characterized by mechanical production in marine and maritime businesses; including producers of propulsion systems, shipbuilders, manufactures of equipment for fish factories and similar. Within this industrial context, innovation occurs typically due to solving specific industrial problem based on tacit knowledge acquired from work experience, often through learning-by-doing, using and interacting, i.e., a so-called DUI-mode of innovation [START_REF] Lundvall | Interactive Learning, Social Capital and Economic Performance[END_REF]. This is an important research topic since most existing UI studies are done on the scientific-technological type of innovation (STI) process in which innovation is invented by researchers for industry, and not the result of the joint activities between industry and university. The qualitative research conducted herein identifies that Nonaka & Takeuchi's model of organizational knowledge creation is applicable to the UI context [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF]. Based on that and a somewhat modified version of their model, we propose a conceptual model of knowledge management of university-industry collaboration in innovation projects. The model aims to intensify knowledge creation and improve exploiting processes. The authors believe that knowledge managers of industry and university can use this model as a practical guideline to execute innovation projects more effectively and efficiently, thus increasing innovation impact. Moreover, the model can support national and federal research/innovation councils in decision-making when assessing industrial research applications.

The reminder of the article is organized as follows: The next section (2) introduces the theoretical background. Section 3 presents the research methodology employed. The research findings are summarized in Section 4 and followed by a discussion in Section 5. Section 6 gives conclusions and further work.

Theoretical Background

The acknowledged organizational knowledge creation model is introduced by Nonaka & Takeuchi [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF]. It consists of five phases: sharing tacit knowledge, creating the concept, justifying the concept, building of an architype and cross-leveling knowledge.

The project team starts with sharing tacit knowledge. People share knowledge they acquired through personal experiences in the specific knowledge fields. For instance, the technology integrator can provide insights into feasibility of the technology integration in factories. Based on the ability to share tacit knowledge team members create a concept of a new product, process or service. The created concepts must be justified against criteria identified by knowledge goals and the needs of society. Justifying the concept often involves experts outside of the project group. Ones the concept is justified, it must be tested by an archetype. The last step, cross-leveling of knowledge, implies the sharing of knowledge derived from the project with the rest of the organization. Cross-leveling triggers new cycle of knowledge creation forming a spiral process accentuating that organizational knowledge creation is a continuous and process.

Methodology and Background for Study

The study covers the so-called 'user-driven research-based innovation' type of project. 'User' is an industrial company, which typically submits an application to the Research Council for financial support [5]. The Research Council (RCN) of Norway provides financial support for collaboration between industrial companies and research organizations to promote innovation and sustainable value creation through research-based innovation. RCN stimulates industry to innovate and mitigates the risk of innovation by covering around 40% of the cost of such projects. Typically, most cash funding for external academic research in such projects is funded by RCN. The company contributes in-kind (hours and equipment), some cash funding and acts as a contract partner with RCN for such projects and is therefore responsible for the project and its budget.

The context for this study are the manufacturing companies and a university campus located on the west coast of Norway. The size of the companies and their R&D capabilities vary significantly. There are some large companies with plans for research many years ahead, they are the ones that comprise their own basic research. However, the majority of companies are smaller with significantly shorter research horizons. This work focuses on two research questions: (1) how to define goals for collaboration to meet the expectations of industry and university, and (2) how to facilitate projects to amplify knowledge creation and exploiting. Fifteen individual semi-structured interviews were conducted. The respondents were six academic project managers, six industrial projects managers, two academic PhD candidates, and one PhD employed by one of the companies. Furthermore, the workshop with fourteen PhDs and two senior researchers was arranged to collect input to the study. The first and second authors of this paper facilitated the workshop. Moreover, seven observations of an ongoing project, including formal and informal meetings combined with nine semi-structured interviews were conducted as a part of this study.

Findings

The collected data was used to create a new knowledge-management model of UI collaboration in innovation. The proposed model reflects the necessity of several aspects to support the knowledge creation process in the UI context-ones that are not considered by Nonaka & Takeuchi's model: (1) commitment of resources, (2) managerial initiatives that support not only the creation of knowledge, but also its exploitation.

Commitment of dedicated resources to the project is one of the major tasks in managing UI projects [START_REF] Hansen | Towards a framework for managing knowledge integration in university-industry collaboration projects[END_REF]. Therefore, the proposed model is organized at three interdependent levels; i.e., each organization's strategic level, UI collaborative strategic level, and UI project level as shown in Fig. 1. Integration of the project level into UI collaborative level and integration of the latter into top-management level reflect the necessity of topmanagement to support and prioritize the projects by allocating them sufficient resources, even in competition with operational needs and daily business.

Fig. 1 Conceptual model of knowledge management of UI innovation projects

Exploiting knowledge in innovation projects is a major concern. Even when the projects generate new ideas, they are not always used in ongoing project. To accentuate the importance of this aspect, the last phase in UI collaboration model is formulated as 'knowledge exploiting'. Table 1 presents conceptual solutions that collectively support knowledge exploiting by answering the research questions on how to identify collaborative goals and facilitate knowledge processes. The collaborative concepts emerged from the data analysis. The scrutiny of the data from interviews and observations led to categorizing data into collaborative concepts on three levels: each organization's strategic level, UI strategic level, and UI project level.

The model incorporates continued improvement of collaborative concepts. Fig. 1 shows that each organization's grand concept for collaboration in innovation is verified at the UI collaborative level. Here, the step 'building architype' on level one triggers the knowledge creation process at UI collaborative strategic level two. Both concepts go through the test on project level: the knowledge-creation process on project level three evaluates the quality of the concepts from the levels above. Thus, the project functions as an archetype of level one and level two. For instance, a constant shortage of resources from university and industry in the project would imply a revision of the collaborative concepts at all levels.

Discussion

The study shows that the organizational five-step model of the knowledge-creation process introduced by Nonaka & Takeuchi can be applied to knowledge management in UI collaboration with some modifications. Modifications are consistent with the answers to the research questions: how to define collaborative goals and how to facilitate knowledge-creation and exploiting. The model contributes to UI collaboration by [START_REF] Perkmann | Academic engagement and commercialisation: A review of the literature on university-industry relations[END_REF] providing collaborative concepts that intensify knowledge creation and exploiting processes, (2) ensuring the commitment of partners in collaboration and providing for the continuing improvement of UI collaborative concepts. In the following, each of these contributions will be discussed separately.

Collaborative Concepts for Knowledge Creation and Exploitation

The concept at each level of collaboration contains specific initiatives that provide creating and application of new knowledge thereby ensuring success of UI innovation projects. The contribution to each phase of the knowledge creation process are considered individually.

The Sharing tacit knowledge phase includes sharing experiences to enable sharing mental models. The involvement of people with backgrounds from academia and in-dustry, such as senior researchers and highly educated industrial employees, helps partners to rapidly relate to each other. Integrating academic PhD candidates in a company's operational environment generates common experience with industry, making it easier to share tacit knowledge. University and industry knowledge strategies crystallize the multiplicity of shared mental models in one direction [START_REF] Nonaka | A dynamic theory of organizational knowledge creation[END_REF]. The managerial initiative at project level creating a momentum by delivering a value in the beginning of the project quickly, generates positive collaborative experience and accelerates sharing of tacit knowledge. For instance, researchers can use their methodological tools to solve a few small industrial problems at the very beginning of the project.

Concept creation is about how the partners are going to collaborate in innovation. Table 1 depicts the concepts guidelines how to define the goals for collaboration in innovation and the initiatives to achieve these goals. Dedication of the resources for innovation project is the core of the Concept creation. In practice, partners should assign which knowledge contributors are required for the project and the adequate quantity of time they will use on the project. For instance, the project requires that one engineer uses 40% of their time, two days a week, to design the prototype and participate in prototype building. The mechanic and electrician each will use 20% of their time to work on prototype. The same concerns the university. The project manager on behalf of the university and the researchers from the required knowledge fields, for example, expert in automation and software developer will be assigned time they are required to use on the project. Dedication of resources is crucial for project success. Otherwise, daily routines will take over and UI innovation project will be given less priority. Concept creation implies also initiatives that support 'shared language'. Avoiding academic terminology, use of industrial language, sketches, drawings, and mockups are the means that provide mutual understanding. The model emphasizes the role of the strategic UI project group that collaborates over time on many technological projects. Accumulating collaborative experience helps to execute the project more effectively.

Clarification of expectations in strategies and objectives supports Concept justification. This study suggests that the top management in university and industry has the main responsibility to incorporate justification criteria in organizational knowledge vision and strategy, which must be consistent with national and regional plans in research and development. The collaborative UI unit should establish a set of sub criteria in the form of a UI collaborative strategy for a long-term partnership, which is in line with the knowledge strategies of organizations. Consequently, the project objectives present the set of sub criteria that coincide with the justification criteria at the above levels. The research questions should be defined in line with the industrial needs and have some room for flexibility due to uncertainty in innovation projects. Involving stakeholders, people from different departments in companies and universities, end users of future innovative solutions, is vital for creation and justification of the concepts.

Building archetypes is a necessary part of creating a new process or new product. Rapid prototyping and frequent interactions with users are a prerogative for success. Additionally, a prototype is a great communication tool for people from different backgrounds. Using it frequently will improve communication between industry and academia and increase the quality of the knowledge creation processes.

Cross-leveling knowledge depends on university and industry committing resources to undertaking projects and implementing the results. The university should provide enough time for the researchers to be able to integrate new knowledge into educational programs and develop it further through other projects with industry. To ensure implementation of research findings in industry, one should actively involve company's customers and/or operational users of new knowledge in the project. That will ensure that project will meet industrial requirements and make the company commit the resources to execute the project and implement the results. Moreover, the engagement of operational users in the project will give them ownership of new knowledge, creating willingness to use it. The study also emphasizes that integration of technology experts in the project ensures the feasibility of applying project results.

Commitment and Continuous Improvement of Collaborative Concepts

Fig. 1 illustrates the embeddedness of the project and strategic UI collaborative levels in the main knowledge creation process of each organization. This means that creating and exploiting knowledge at the project level need support from a collaborative UI strategic unit and the decision-makers at the top-level of each organization. Therefore, the grand concept at each organization's strategic level emphasizes the necessity of topmanagement commitment to collaboration. Without this, the basis for initiating a new project is lacking. The model's dynamic provides continuous improvement of collaborative concepts. The collaborative concepts at three levels function interdependently. Universities and industrial companies define their own grand concept for collaboration with others in innovation. The grand concept must support the concepts for collaboration at the levels below: the common UI collaborative strategy level and the UI project level. Modifications on each level of the collaboration trigger the optimization processes on the other levels. Continuous improvement makes the model dynamic.

Verification of the proposed Knowledge Management Model

The proposed model is newly developed. The first assessment of the theoretical model was made in the workshop with the participants from one accomplished UI innovation project. The project leader from the university, the PhD candidate and industrial PhD candidate who, during the project had to take over the role of the project manager on behalf of the company, evaluated the collaborative concepts. The criteria for evaluation was the degree of impact form the concepts' substances on the projects: low, middle and high. The participants assessed the concepts individually. They also discussed the formulation and content of the statements.

The participants saw the model's potential to help university and industry deliver more innovation. They asserted that the model could also be a tool for setting up new projects for further exploration and exploitation of knowledge derived from the project. The workshop results help the researchers to develop a more practical version of the model for implementation. Conversion of the theoretical model into practical guidelines will make it easier for knowledge managers of university and industry to apply and validate the model. The plan is to use focus groups to test if practical guidelines give meaning and are suitable for practical application. The focus groups will involve experienced project managers from university, industry and representatives from RCN. The true contribution of this work can only be evaluated through the comparison before and after applying the proposed approach to the real innovation project. In this connection, it is worth noting that this is a typically three-years long project and it would take long time to get the final results. Moreover, one should develop the criteria for comparing the innovation impact of the projects with and without employing of the model. As many projects as possible is the best for validation. However, one should be aware of the amount of work and necessity to ensure the quality of validation.

Conclusion

This research contributes to knowledge management theory by adapting the organizational knowledge creation theory of Nonaka & Takeuchi to the context of UI collaboration projects. In practice the model has a potential to support university and industry in conducting innovation projects more effectively and efficiently. The model provides the collaborative concepts on the three levels: strategic organizational, UI strategic collaborative level and UI project level. The concepts are the knowledge management initiatives that support creation and application of knowledge in innovation project. They encompass the specific recommendations of how to define collaborative knowledge goals and the activities to achieve them. The model emphasizes the importance of continued knowledge exploitation that triggers constant improvement of the collaborative concepts on all levels. The future research shall validate the effect of application of the model to the projects. Although the study covers mechanical engineering companies in marine and maritime sectors on the west coast of Norway, the issues between university and industry are common in other industries and alike for any private-public collaboration.

Table 1 .

 1 University-industry innovation projects: collaborative concepts

	1	1	1
	Sharing	Concept	Justify
	Knowledge		Concept

 Clarification of partners' expectations  Anchoring the projects at top-management  Industry should navigate the project  Involve internal and external stakeholders  Provide common language  Create a momentum: keep enthusiasm


Hans Torvatn
email: hanstorvatn@sintef.no

Pål Kamsvåg

Birgit Kløve

Industry 4.0 Visions and reality-status in Norway

Keywords: Smart Manufacturing & Industry 4.0, Human-Machine Interaction (HMI) & Operator 4.0, Cyber-physical systems, Survey, Norway

The concept and vision of Industry 4.0 has been around for almost a decade and gain a lot of momentum and attraction globally. Central to the vision of Industry 4.0 is the concept of a "Cyber-Physical system", linking the IT elements of an enterprise (Cyber) with the physical system (man and machine) of an enterprise. This vision is well known and promoted as crucial in radically transforming todays manufacturing industry. While there is a plethora of papers and studies of the various "cyber" aspects, the concept, visions, benefits as well as the downsides of Industry 4.0, few papers have much to say about the actual implementation. Based on a digital maturity mapping of ten front line manufacturing enterprises in Norway this paper analyses implementation at shop floor level of both cyber and physical system and their interaction. From the survey data a clear picture emerges of the development of a cyber system, as well as worker usage and benefit of the system. However, the two systems don't interact very well, worker interaction is limited to plain old keyboard usage, instead of employing more mobile, handsfree, voice based or similar interaction methods. Currently there is no cyber-physical system, rather a burgeoning cyber system poorly linked to the physical world. If the cyber-physical system is to be realized there is a need for a rethinking and upgrading of man-machine interaction.

Introduction

Industry 4.0 (I4.0) as a concept and vision has been around since 2011. If not countless, at least thousands of academic papers have been written on it over the whole world. Most of these papers focus on the technological elements of I4.0, but there are enough papers outlining the concept and its merits. The human aspects of the concept, on the other hand, remain under researched.

While I4.0 is a German concept in its origin, it has become quite popular in Norway and other Nordic countries. Through whitepapers, workshops, networks, industry and government agencies, the idea has been promoted and encouraged in Norway. However, there has been few, if any, attempts to measure implementation rate or benefits for those implementing it. While some case studies exist, no surveys of status of I4.0 in Norwegian industry exists, and no one knows what the industry is struggling with in its implementation. This paper employs survey data to describe the situation in a group of frontrunning Norwegian manufacturing companies, participating in a national strategic research program called "Sustainable growth of Manufacturing".

2

Visions of Industry 4.0 I4.0 refers to the current trend of digitalization, automation and data exchange in manufacturing. According to the I4.0 Working Group, the German originators of the I4.0 initiative, progress in the field of information technology and concepts such as Internet of Things (IoT) and Cyber-physical systems (CPS) pave the way for a "fourth industrial revolution" [START_REF] Kagermann | Securing the future of German manufacturing industry[END_REF][START_REF] Hermann | Design principles for industrie 4.0 scenarios[END_REF]. Cyber-physical systems are merging the virtual and physical world through embedded networks which are capable of monitoring and controlling physical processes. These systems detect data from physical objects through sensors and is interacting with physical processes via machinery, actuators and human movements [START_REF] Wang | Current status and advancement of cyber-physical systems in manufacturing[END_REF][START_REF] Thoben | Industrie 4.0" and smart manufacturing-a review of research issues and application examples[END_REF].

The current digitalization of manufacturing challenges the traditional role of industrial workers [START_REF] Schneider | Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field[END_REF]. A shift from physical, repetitive and low skilled work to more complex and cognitive tasks is anticipated [START_REF] Kagermann | Securing the future of German manufacturing industry[END_REF][START_REF] Hecklau | Holistic approach for human resource management in Industry 4.0[END_REF][START_REF] Prinz | Learning factory modules for smart factories in industrie 4.0[END_REF] Operators at the shop floor will probably need to control more machines simultaneously and thereby know more about the production processes in the future [START_REF] Prinz | Learning factory modules for smart factories in industrie 4.0[END_REF]. In order to handle the increasing complexity of production and surged data flows from cyber-physical systems (CPS), the operators need to be supported by well-functioning assistance systems [START_REF] Prinz | Learning factory modules for smart factories in industrie 4.0[END_REF].

2.1

Operationalization of Industry 4.0

In a much cited paper by Hermann et al. [START_REF] Hermann | Design principles for industrie 4.0 scenarios[END_REF] four design criteria were outlined, these four criteria must be met for a system to be called an I4.0 system:

• Interconnection: This is the systems ability to communicate and collaborate internally and externally (human-human, human-machine and machine-machine). Wireless communication with sensors, IoT and IoE is a critical part of this. It also includes the security aspects of the systems.

• Technical assistance: This is the system's ability to offer assistance to humans in their work, both virtual assistance (information, cognitive support) and physical assistance by various tools.

• Decentralized decisions refer to the system's ability to delegate authority of decision making from managers through operators and ultimately to machines.

• Information transparency refers to the fusion of the physical and virtual world through the linking of sensor data with digitalized plant models, enabling the creation of a virtual copy of the physical world (Digital twin).

2.2

The human aspect of Industry 4.0 I4.0 is criticized for being just another "tech-concept". However the I4.0 literature, actually accentuate the human factor and consider the vision of a completely automated factory as neither desirable nor realistic [START_REF] Hirsch-Kreinsen | Industry 4.0" as Promising Technology: Emergence, Semantics and Ambivalent Character[END_REF]. The proponents of I4.0 expect digital assistance systems and a new generation of collaborative industrial robots to make work more exciting and rewarding across all hierarchical levels [START_REF] Kagermann | Securing the future of German manufacturing industry[END_REF][START_REF] Hermann | Design principles for industrie 4.0 scenarios[END_REF]. Hence, in order to create an optimal cyber-physical system, the human workers should be able to interact and use the cyber system as much as possible. As I4.0 is introduced to enterprises, this becomes even more prominent. With the advancement in technology "the number of computing devices that a person uses is increasing and there is a need of faster and non-intrusive methods of communicating with these devices" [START_REF] Fatima | Eye movement based human computer interaction[END_REF].

3

Survey method, questionnaire and sample

Design of survey

Sustainable growth of Manufacturing is a cross-disciplinary center for competitive high value manufacturing in Norway, established in 2015. Its vision is that with the right products, technologies and humans involved, sustainable and advanced manufacturing is possible in high cost countries such as Norway. I4.0 is a key element in this vision of preserving Norwegian manufacturing and keeping it competitive. In the spring of 2017, it was decided to carry out a survey for mapping the digital maturity of the participants. The participating companies were concerned about their ability to implement I4.0 and wanted an analysis of their performance.

It should be noted that the enterprises in question clearly belongs to the more advanced group of Norwegian manufacturers. While they vary in product, ownership, geographic region in Norway, they are all exporters with decades of operation at the location investigated. They are also strategically thinking regarding development, and they have all earlier experiences working with research milieus and use considerable internal efforts on this.

3.2

Designing and implementing the survey

A cross-sectional study was carried out in ten Norwegian manufacturing companies, covering all organizational levels and roles (N = 3188) in spring/summer 2017. The survey was constructed in dialogue with the enterprises building on the I4.0 design criteria. However, "interconnection" and "information transparency" were dropped from the survey, because the respondents (especially at shop floor level) were not expected to have knowledge of these issues.

The study was mostly conducted via email, but some paper copies were also distributed. After repeated follow-ups, the total sample consists of 1023 male and 160 females. With a total response rate of 37% the sample can be said to be representative of the participating enterprises, but as outlined the participating companies are not representative of the Norwegian industry. Within the enterprises not all respondents were relevant in the sample, given our focus on production and production workers. The sub sample (n = 305) of interest in this study are operators working in the production hall. When looking at the whole sample together, the most frequently age range are 41 to 60 years (59,67 %), and 9,8 % were women.

Results

Existing digital tools and systems at shop floor level

We surveyed a set of digital tools and systems at shop floor level. Figure 2 reports the usage of each digital tool. Note that this finding supports the prediction of Fatima et. al [START_REF] Fatima | Eye movement based human computer interaction[END_REF] on number of devices, numbers of devices per user are now clearly above 2 on average. A total of 98% of the respondents are using computers in their work. Computers are both a traditionally digital tool, but also an extremely powerful and versatile tool. It can be integrated with all kinds of systems and for almost all kinds of digital tasks, including of course all administrative tasks. However, as can be seen from Figure 2 the PC is not the only tool used. The typical operator uses several tools, new tools are added to the old, not supplanting them. As we can see, 61% are also using portable smartphones to carry out their work. Only 7% are using tablets or smart watch, and less than 3% voice control from portable equipment or smart glasses. In addition to PCs, Smartphones and Photo/video, we can see a fair use of "cybersystems". 58% use MES, all ten companies employ MES. Tracking systems (RFID and similar), robots and portable scanners are used by 39, 35 and 30% respectively.

For those workers that have administrative tasks such as record-and documentation of quality, ordering components and planning own production in their work description, a total of 67-77% have digital systems to perform these tasks. More specifically, 77% of the respondents uses digital systems for documenting product quality, compared to 20% that only have paper or oral documentation systems. Also, about 70% of the respondent group uses digital systems for orders, production planning and maintenance. Thus, it seems like I4.0 has gained some position on the shop-floor, as digital tools and systems is available, a least to some extent, at the shop floor level.

As outlined in the design principles of I4.0, supporting companies in identifying and implementing I4.0 scenarios, decentralized decisions is the ability of cyber-physical systems (machines) to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of interventions, exceptions or conflicting goals, are tasks delegated to a higher level (humans). However, only 9% of the respondents in the study report that ICT systems suggests or take decisions in normal operation, 29% report that people and ICT-systems cooperate to some degree in proposing and evaluating solutions, while 47% reports that all decisions are made by people in production. 16% reports they do not know. Thus, the I4.0 transformation towards decentralized decisions has started, as digital decision tools are to some degree available at the shopfloor level, but there is a lot of remaining work before reaching an I4.0-level.

For those respondents having access to digital systems in own production planning (N=156), only 6% have access to portable information (information available on the body through smart phones, tablets, smart watches etc.), and only 40% experiences that the information is updated instantly if any changes. Most workers have the information for use in production planning available at the workstation (82%), thus limiting their ability to move around at the shop-floor and at the same time have full control over production to continuously being able to optimize their production planning.

4.2

User, perceived usefulness and quality of digital technology 85% of the total sample size of N=305 uses digital tools to get information about the production in their work. However, only about half of the respondents that receive this type of information digitally, believe that the information they receive is sufficient (54%), updated (47%) and understandable (51%). Thus, the perceived quality of digital information about the production at the shop-floor level has room for improvement.

Still the operators see digitalization as useful for carrying out work tasks. More than 70% of the respondents believe that the quality of work gets better, and they get the work done faster, using digital tools. Also, about 60% believe that they get the work done safer, and by using digital tools gets work done which could not be carried out without such tools.

Analysis

State of the Cyber-systems

The survey clearly identifies the beginning of a cyber-system. We can see that the investigated companies have implemented manufacturing execution systems (MES), they use robotics and production planning systems, and have started to delegate authority to machines. The changes have reached the shop floor level, operators find the information useful albeit incomplete and rely on it in their work. Coordination, information, maintenance and order planning is being done digitally for two thirds of the operators. We have not discussed issues of interconnection (connectivity and security), but we know that the system is at least functioning in a daily work setting. While we clearly can see the beginning of a cyber-system, it is also clear that there is much potential for further development. Information is not perceived as trustworthy for half of the users, it is real-time for only 40%, and a third of the workers are not using digital coordination and order planning.

State of the Human computer interaction

"The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans" [START_REF] Rautaray | Vision based hand gesture recognition for human computer interaction: a survey[END_REF]. We can see that this is not the case in these companies. The most frequent form is still computer interaction, taking place via a keyboard and a mouse. Historically we interacted with computers as "key strokers" working on a keyboard. Over the years several new features and possibilities have been introduced. The first major update was the introduction of touch-screens, providing lightweight mobile and easy to use interfaces relying on wireless communication. From now on we had evolved into "screen pawers", and as can be seen from Fig 2 at total of 61 percent use their smartphone and 7 percent their tablet in work. The second upgrade is a set of technologies including virtual reality, augmented reality, various voice control and voice command systems, gestures/hand movements and eye movements. Even brain waves are now possible [START_REF]Brain-Computer Interfaces and Human-Computer Interaction[END_REF]. We can call that third generation "data whisperers", since several of these systems allow for speech commands. However, use is limited still and interaction between man and machine is happening through computers using keyboards or through smartphones/tablets.

To what degree are those various tools suitable at shop floor level? We will analyze this along two dimensions, mobility and the need to for the operator to employ his/her hands in interaction with computers. Starting with the latter we can see that an operator at shop floor level moves around and uses his/her hands a lot in the operation, it would seem obvious that an easy interaction should be mobile and allowing for hand-usage in work. PCs are not a good choice, relying on keyboards and hand usage. Neither are smartphones, because information must be retrieved by key-stroking or touching screens, and thus limits other hand-usage and ties the operator up in the task of commanding the smartphone instead of doing his physical work. Voice commands would be very appropriate if the noise level allows it, and eye movement would also be a good option for a worker.

Regarding mobility, PCs are not a good choice because they are stationary, which significantly reduces the operator's ability to move freely and still getting access to updated and important information when he needs it in his work. Smartphones are a powerful portable device that is easy to carry along and deploy, often has a very simple user interface and is user-friendly as most workers utilize this digital tool more or less all day outside of work. It can provide necessary real-time information and is very flexible in use as one can gain almost any information through the internet. Of course, as the smartphone is very flexible it can support voice-control or hands-free usage though customs applications delivering important information through speaker or headset. Digital tools such as smart glasses or voice control, though often not as flexible as smart phones, are completely hands-free, and thus support the operator in doing his/her tasks while at the same time receiving information.

However, so far it seems like operators' interaction towards digital systems in production planning is limited to plain old stationary keyboard usage, instead of using more mobile, handsfree, voice-based or eye-moving interaction systems. If I4.0-goals are to be achieved, operators at the shop floor will need to control more machines simultaneously, and therefore cannot be placed stationary in front of a workstation. As they most likely will need to know more about the production processes in the future to become strategic decision-makers rather than pure operators of machines, the operators will thus need to be supported by well-functioning portable assistance systems that provide the necessary real-time information which will contribute to letting them continuously optimize their own production planning.

Conclusion

Comparing the ideals of I4.0 to the reality of a group of frontrunning Norwegian Manufacturing enterprises we can clearly see the start of a cyber-physical system. There are digital tools and information in use at shop floor level, and decentralization of decisions have started. While we can see a start there is also a lot of room for improvement. This is especially true regarding the human parts of the system. Our findings indicate that the employees on the shop floor lack the necessary digital tools and assistance systems to form a truly interconnected cyber-physical system. Old (digitally speaking) human computer interactions like computers dominate. Some respondents use mobile digital tools, such as tablets and smartphones, but they are not nearly as common in manufacturing as in people's private lives (this is the case in Norway at least). Technologies like smart glasses, virtual reality and augmented reality are almost completely absent in the investigated companies. Human employees in all levels and departments of the organization need such tools to be part of the CPS and to improve their performance, and especially at the shop floor. For instance, smart glasses and voice-control could make it easier for operators to receive information and guidance while having their hands free and ready to handle their actual job. Mobile solutions, such as tablets or smartphones, could make operators more flexible and capable of controlling more machines simultaneously. In order to utilize these tools, the interfaces should be designed in a way which satisfies the demands of the operators.

Both hardware and software must be developed with respect to the workers at the shop floor. In order to improve the implementation of the cyber-physical systems we need to improve the collaboration between man and machine through better interfaces.

We consider this finding applicable also outside of Norway. As far as we know, no survey based studies outlining the situation in other countries in implementing I4.0 have been carried out. However, while the exact level of I 4.0 implementation is likely to vary, the challenges facing the operator at shop floor level is similar across nations, there is a need for mobile and hands free HCI in other countries as well. Therefore, we expect the general problem of poor human-machine interfaces to be relevant in settings outside of Norway.

Figure 1

 1 Figure 1 Digital tools employed at shop floor level. Percentage using this tool in their work.


Günther Schuh
email: g.schuh@wzl.rwth-aachen.de

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

Andreas Gützlaff
email: a.guetzlaff@wzl.rwth-aachen.de

Sebastian Henk
email: s.henk@wzl.rwth-aachen.de

Handling uncertainties in production network design

Keywords: Production networks, decision process, uncertainty

Decision making in production network design is complex due to a large number of influencing factors, options and uncertainties. Furthermore, the agility in production networks and therefore the decision demand increases while made decisions are often hard to revise. Hence, a fast yet holistic decision-making process is key for sustainable production network development. While many existing approaches target the overall network optimization, few of them include a systematic approach to cover uncertainty and barely any approaches cover the uncertainty of information and models used for the decision-making. In praxis, these approaches result in unsystematic and time-consuming decision-making processes. This paper presents an approach to take uncertainty systematically into consideration and splits it into internal uncertainty, which can be reduced by the decision maker, and external uncertainty, which has to be considered in the sensitivity analysis. The method was applied for the site selection of an automotive supplier.

Introduction and Motivation

As globalization progresses, companies are trying to grow by internationalizing their business activities and, at the same time, become less dependent on local economic fluctuations. This development is reflected in an increasing number of production locations for manufacturing companies [START_REF] Schuh | Konsortial-Benchmarking: Gestaltung von globalen Produktionsnetzwerken[END_REF]. Manufacturing companies are also increasing their market share across an increasing range of products. At the same time, product life cycles are shortening. An increasing number of variants and a decreasing number of identical parts are created in production [START_REF] Berger | Mastering product complexity[END_REF]. Due to the global distribution of added value and the increasing product complexity, production networks today are among the most complex and dynamic man-made systems, which often results in historically grown structures [START_REF] Váncza | Production Networks[END_REF]. At the same time, location decisions are difficult to revise, which is why decisions have to be made carefully [START_REF] Krystek | Internationalisierung: Eine Herausforderung für die Unternehmensführung[END_REF].

Existing approaches for production network design generate a large operational modelling effort in order to tackle the complexity and impact of decisions in the network (see 2.1). However, the approaches often lack a holistic view of uncertainty. Decisions are therefore often prolonged by unsystematic additional analyses [START_REF] Spetzler | Decision quality: Value creation from better business decisions[END_REF]. The aim of this paper is to present an approach for assessing uncertainty in the planning process of production networks. This enables a focus on the most important factors of the decision and the planning time can be significantly shortened by the absence of unnecessary iterations.

2

State of the Art

Existing Approaches for Production Network Design

Current approaches for production network design can be distinguished between models which describe how to proceed in production network design, optimization and simulation models andas a new trend within the last yearsbig data driven analysis approaches:

Procedure models describe consecutive steps to create and evaluate production network scenarios. LANZA et al. give an overview of recent approaches [START_REF] Lanza | Global Production Networks: Design and Operation[END_REF].

CHRISTODOULOU et al. for example describe a multi-stage approach for network design that proposes a complexity reduction by defining simplifying assumptions and splitting the network into subnetworks for each product group. The approach emphasizes the importance of creating reliable production cost models and finding a realistic demand forecast, but does not further detail how to achieve this. [START_REF] Christodoulou | Making the right things in the right places: A structured approach to developing and exploiting 'manufacturing footprint' strategy[END_REF] STOFFEL developed a V-Model to design large production networks that focuses on a strategy based reconfiguration of the whole network. It structures the information need and the level of granularity but also does not cover uncertainty in the decision making process systematically. [START_REF] Stoffel | V-Modell zur Auslegung großer Produktionsnetzwerke[END_REF] Optimization models are the most commonly described method to design production networks [START_REF] Lanza | Global Production Networks: Design and Operation[END_REF]. A detailed overview of optimization approaches is given by CHENG et al. [START_REF] Cheng | International manufacturing network: Past, present, and future[END_REF] and OLHAGER et al. [START_REF] Olhager | Design of global production and distribution networks[END_REF]. LANZA and MOSER for example provide a detailed approach that evaluates the impact of changing influencing factors and includes objectives such as delivery time, quality, flexibility and coordination effort [START_REF] Lanza | Multi-objective optimization of global manufacturing networks taking into account multi-dimensional uncertainty[END_REF]. Even though they give an optimal solution from a mathematical point of view, optimization models do not recognize wrong input data and have to quantify and rank qualitative network design objectives.

Simulation models support the decision making process by providing information about complex interactions, e.g. about the robustness of a network scenario regarding different production volumes as demonstrated by PUTNIK et al. [START_REF] Putnik | Simulation study of large production network robustness in uncertain environment[END_REF]. This models can be helpful to decrease uncertainty, but are not suitable to systematically create network alternatives.

Big data analyses like presented by GÖLZER et al. [START_REF] Gölzer | Designing Global Manufacturing Networks Using Big Data[END_REF] try to provide decision support systems based on the analysis of large data sets. An overview of recent approaches is given by TIWARI et al. [START_REF] Tiwari | Big data analytics in supply chain management between 2010 and 2016: Insights to industries[END_REF]. These approaches might help to gather information faster, but neither can they provide information about the reliability of the data sets nor can they consider qualitative aspects of production network design.

Handling Uncertainties

In general, proper decision making requires the consideration of environmental states [START_REF] Laux | Entscheidungstheorie[END_REF]. These conditions are often not known with certainty. Thus, uncertainty represents any deviation from the ideal of complete knowledge [START_REF] Walker | Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support[END_REF]. There are various ways to account for uncertainties in decision making. Those can be classified between the two extremes of totally neglecting uncertainties and solving complex stochastic problems.

The goal is to find a good compromise between planning effort and result [START_REF] Klein | Planung und Entscheidung: Konzepte, Modelle und Methoden einer modernen betriebswirtschaftlichen Entscheidungsanalyse[END_REF].

To identify and prioritize critical uncertainties, WALKER et al. distinguish three dimensions of uncertainty to enable an adequate consideration in the decision-making process [START_REF] Walker | Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support[END_REF]: Nature of uncertainty, Level of uncertainty and Location of uncertainty.

The nature of uncertainty focuses on the origin and describes whether the uncertainty is based on a lack of knowledge (epistemic uncertainty) or on inherent variability (variability uncertainty). While the former can be reduced by further research and empirical efforts, latter remains due to natural randomness as well as behavioral and societal variability [START_REF] Walker | Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support[END_REF]. The level of uncertainty describes how the uncertainty is ranked within a seven-level spectrum between deterministic knowledge and total ignorance. Depending on the level of uncertainty, a statistical or scenario consideration is required as shown in Fig. 1 [START_REF] Walker | Deep Uncertainty[END_REF]. The location of uncertainty characterizes where the uncertainty manifests itself within an existing or designed model complex. There are four different generic locations of uncertainty [START_REF] Kreye | Manifestation of uncertainty -A classification[END_REF]: Context uncertainty, Data uncertainty, Model uncertainty and Phenomenological uncertainty. While context uncertainty covers the endogenous and exogenous uncertainties which are caused by surrounding influences [START_REF] Weck | A classification of uncertainty for early product and system design[END_REF], data uncertainty describes uncertainties due to data incompleteness, data inaccuracy and variation in the input data [START_REF] Huijbregts | Framework for modelling data uncertainty in life cycle inventories[END_REF]. In addition, the inaccuracies of a simplifying model in compari-son to reality lead to model uncertainties containing conceptual, mathematical and computational model uncertainties. Phenomenological uncertainty affects the consequences of a decision [START_REF] Kreye | Manifestation of uncertainty -A classification[END_REF].

Approach

In order to systematically consider uncertainty in production network design, the presented approach follows the decision making process by SPETZLER [START_REF] Spetzler | Decision quality: Value creation from better business decisions[END_REF] and distinguishes the phases of initialization, information gathering, alternatives creation, evaluation and decision making (Fig. 2). Based on this steps, uncertainty factors are identified and prioritized to focus the analysis effort and the evaluation scope.

Fig. 2. Adaptation of the generic decision making process by SPETZLER [START_REF] Spetzler | Decision quality: Value creation from better business decisions[END_REF] for production network design

Initiation Phase

The focus within the initiation phase is to transfer the generous thoughts on an upcoming need for action into a structured decision problem. By determining the general scope and the focused objectives, the following selection of the evaluation method is enabled. A rough cost structure analysis helps to identify the cost critical aspects of the decision making, e.g. the importance of transport costs or wages as part of the total landed cost.

The literature contains a wide variety of evaluation methods (e.g. see SCHUH et al. [START_REF] Gausemeier | Szenario-Management: Planen und Führen mit Szenarien[END_REF]), which should be selected depending on the desired detail degree of the consideration and the importance regarding the cost structure. Based on the selected evaluation methods and the decision scope, the information need to apply the selected methods can be derived.

Modelling Phase

The modelling phase includes the steps of information gathering and alternatives creation. First, the elements and influencing factors of the evaluation method determined in the initialization phase are divided into internal and external factors on the basis of the uncertainty evaluation by WALKER et al. (Fig. 3). Internal uncertainty describes the uncertainties associated with the considerations within the decision-making process, which primarily depend on the level of detail selected. External uncertainties arise from external driven situations and developments. These are unknown to the decision maker and can only be anticipated to a limited extent by obtaining information. For the information gathering, a distinction is made between context and data. Context includes all internal and external developments that affect the decision-making process.

External to the company is for example the general market development, internal the planned introduction of new products. However, a distinction must be made here as to whether this is a factor that the decision-maker can influence or not. For external context uncertainty, only an assessment can be made. Complete certainty is not possible. Historical developments can be extrapolated and supplemented by expert assessments in order to limit development opportunities. The assessment of the level of uncertainty is based on the experience of the decision maker and the importance of the influencing factor in relation to the overall decision. Depending on the assessment of the uncertainty, a sensitivity analysis of the scenarios is conducted or new scenarios are created in order to quantify various development possibilities. Data uncertainty is broken down into flawed measurement, inconsistency and incompleteness. In contrast to external uncertainty, this raises the question of the extent to which uncertainty can be reduced by additional survey effort. This is usually possible by including additional data sources, new or additional data collection or expert surveys. The new data collection can be conducted iteratively until a sufficiently valid data basis for modelling has been created. While the final assessment of the sufficient validity depends on the risk affinity of the decision maker, it is necessary to consider the remaining uncertainty in the evaluation phase in order to achieve a high decision quality. It is important to achieve the required accuracy in this step before moving on to modelling, as otherwise the uncertainty of the data is transferred to the model uncertainty and the model cannot be calibrated.

For alternatives creation, a calibration of the as-is model with the existing network is necessary to determine the model uncertainty. The error deviation can be used for an iterative calibration of the model. The remaining uncertainty has to be mapped by sensitivity analyses after alternative scenarios have been created in the next step. If the uncertianty is very high, additional scenarios should be considered (Fig. 2). For the scenario generation, existing methods such as the previously introduced approach "Making the right things in the places" can be used [START_REF] Christodoulou | Making the right things in the right places: A structured approach to developing and exploiting 'manufacturing footprint' strategy[END_REF].

Evaluation and decision making

In the final step, the alternatives are converted into evaluation scenarios with sensitivities according to scenario techniques like the one developed by GAUSEMEIER et al.

[23]. All relevant influences are taken into account with their inherent uncertainty. The range of sensitivity per influencing factor depends on the uncertainty in the previous evaluation process. The final decision is based on the action alternatives and their sensitivities regarding the focused objectives. A holistic approach to scenario assessment based on Economic Value Added is presented by SCHUH et al., for example [START_REF] Gausemeier | Szenario-Management: Planen und Führen mit Szenarien[END_REF]. There can be two different results in the decision: Either there is a dominant scenario, or there are different scenarios that cannot be finally weighed against each other due to their sensitivity. In the latter case, it should be examined whether further sources are available to reduce the internal uncertainty. Otherwise, it is advisable to choose the scenario preferred from a strategic point of view.

Application

The expansion or opening of a press shop was planned for the production of body parts for a high-volume model of an OEM. During the initialization phase, the scope of consideration could be narrowed down to one existing or two potentially new plants: A low transport and a low cost site. As the sales price was fixed, the valuation was based on the cumulated cash out over the product lifetime of 14 years. A static cost model on an annual basis was chosen for modelling purposes and iteratively specified to reduce data and model uncertainty. A rough cost estimation in the initiation phase shows that transports and wages have a higher impact on the costs than the investment in machines and buildings. The information gathering therefore focused on those two factors, while investments and fixed costs were estimated with good accuracy on the basis of previous projects. While wage costs could be determined with good accuracy, transport costs are difficult to estimate, especially due to the long planning period and external factors. Therefore, a broad sensitivity corridor hat to be taken into account. The model results in a dominant scenario for the existing location, but due to the sensitivity of transport costs it is not absolutely dominant (Fig. 4). The management chose the dominant scenario due to lower investments and therefor lower risks of sunk cost effects. The entire decision making process took two weeks.

Conclusion and Future Research

A focused decision-making process can be derived from a preliminary analysis of the decision problem and an uncertainty-oriented procurement of information. By recording and evaluating the uncertainty in information acquisition and modelling, unnecessary detailing loops can be avoided by mapping the uncertainty in the sensitivity analysis or through additional scenarios. Detailed information is only obtained for the most relevant factors. Thus, even investment-intensive, far-reaching decisions can be made in a short time. The assessment of uncertainty, however, continues to depend on the experience and knowledge of those involved in the process and cannot be completely quantified. Further research is therefore needed to derive concrete specifications for the classification of uncertainty in relation to sensitivities and scenario alternatives.

Fig. 1 .

 1 Fig.1. Levels of uncertainty by WALKER et al.[START_REF] Walker | Deep Uncertainty[END_REF]

Fig. 3 .

 3 Fig. 3. Differentiation of uncertainty forms for further consideration

Fig. 4 .

 4 Fig. 4. Results of the method application

Acknowledgment

The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence "Internet of Production" -Project-ID: 390621612.


Chase Wentzky

Chelsea Spence

Apurva Patel

Nicole Zero

Adarsh Jeyes

Alexis Fiore

Dr Joshua D Summers

Dr Mary E Kurz

Kevin M Taaffe

Application of a Controlled Assembly Vocabulary: Modeling a Home Appliance Transfer Line

Keywords: transfer lines, work instructions, controlled vocabulary, line balancing, standard vocabulary

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

1

Why implement a controlled vocabulary?

Manufacturing and assembly of products is often a collaborative effort between human associates and machines. With an increasing demand in productivity and efficiency from human workers, it is especially important to ensure that the provided work instructions to human workers are accurate and descriptive. This includes both the content and delivery of work instructions. A controlled vocabulary is one such approach within manufacturing which focuses on the content of text-based instructions. It is meant to capture a particular implementation process, to provide clear instructions, and prevent error. In this context, a set of verbs describes the motions performed to do a task, which can then be used to create a standard design for dictating process descriptions [START_REF] Rychtyckyj | Standard Language at Ford Motor Company: A Case Study in Controlled Language Development and Deployment[END_REF][START_REF] Salmi | Standardized Vocabularies for Assembly Systems Modelling and Automation Alternatives Description[END_REF][START_REF] Peterson | Knowledge Management for Semi-Automated Automotive Assembly Instruction Authorship and Translation[END_REF]. Prior to this, written documents describing assembly tasks lacked uniformity and were ambiguous and open to interpretation. This led to a variety of problems ranging from manufacturing defects to safety concerns. To create consistency across technologies and products, controlled vocabulary has been developed, which can help mitigate the differences in interpretation. Ford Body and Assembly Operations was one of the first firms to create a language to standardize their work instructions protocol for assembly tasks [START_REF] Rychtyckyj | Standard Language at Ford Motor Company: A Case Study in Controlled Language Development and Deployment[END_REF]. From there, they were quickly able to see the benefits, especially with respect to the assembly planners. Not only was the writing of assembly instructions standardized, but ease of machine translation was also apparent. In combination, this helped the planners to predict assembly times based on the verbs used in the work instructions [START_REF] Peterson | Knowledge Management for Semi-Automated Automotive Assembly Instruction Authorship and Translation[END_REF]. A controlled vocabulary was also found to be helpful in creating decision support systems and to estimate assembly time [START_REF] Miller | Reasoning: Installation Process Step Instructions as an Automated Assembly Time Estimation Tool[END_REF]. In terms of the Toyota production system, standardization has been found to be one of the bedrocks of kaizen and the continuous improvement benefits of standardization have been well-documented [START_REF] Johansson | Current State of Standardized Work in Automotive Industry in Sweden[END_REF][START_REF] Pereira | Reconfigurable Standardized Work in a Lean Company--a Case Study[END_REF].

The standardization of vocabulary in instruction manuals or process sheets have shown to provide great value [START_REF] Rychtyckyj | Standard Language at Ford Motor Company: A Case Study in Controlled Language Development and Deployment[END_REF][START_REF] Peterson | Standardization of Process Sheet Information to Support Automated Translation of Assembly Instructions and Product-Process Coupling[END_REF]. As previously mentioned, companies like Ford and Toyota have implemented standard language and found an increase in comprehension by individuals throughout the whole organization. Upon evaluating the standardization, Ford found that clear vocabulary helped limit the number of errors that they encountered in their processes. Additionally, with technology on the rise within manufacturing systems, a standard language allows for not only human understanding, but machine and software understanding as well. In turn, this provides a more cohesive communicative understanding between human and machine. Implementing a standard language helps decrease the complexity of the instructions, which makes the manuals clearer and more concise. As a result, this provides more accurate data on the given tasks and a thorough understanding of the process [START_REF] Rychtyckyj | Standard Language at Ford Motor Company: A Case Study in Controlled Language Development and Deployment[END_REF][START_REF] Pereira | Reconfigurable Standardized Work in a Lean Company--a Case Study[END_REF].

Controlled Vocabulary Context

In previous work, a controlled vocabulary was proposed to analyze several aspects of an automotive assembly line [START_REF] Salmi | Standardized Vocabularies for Assembly Systems Modelling and Automation Alternatives Description[END_REF]. In this paper, this controlled vocabulary is applied to a home appliance transfer line to understand the general applicability of the proposed vocabulary and identify any potential improvements. This standard vocabulary provided in previous work was used, among other things, to better understand the appropriate level of automation for a given assembly task.

Although the aims of the previous study and this current research differ in some respects, the successes of the first study would suggest potential benefits in applying this vocabulary to a home appliance transfer line. This work explored what modifications, if any, would need to be made to apply the automotive standard vocabulary to a home appliance assembly line. Table 1 shows the standard vocabulary used for characterizing assembly tasks in the home appliance assembly line. It should be noted that not all of the standardized verbs specified in prior work are listed in Table 1, rather only the verbs used in this case are shown. Additionally, verbs associated with non-value-added tasks are shaded, while the remaining verbs are related to value-added tasks. In this study, value-added tasks are those that transform the product in a way that gives it some desirable feature or trait as defined by the end user.

The manufacturing system currently being evaluated is comprised of transfer lines for the assembly of home appliances. The appliances are assembled through a variety of stamping processes and automated assembly, with roughly 650 employees executing the process. The layout of the plant consists of two separate assembly lines for two different models (P1 and P2) of the appliance. Transfer lines were divided off in a similar fashion, with tasks for Assembly (A), Inspection (I), with several support lines. They will be described in this analysis as M1 and M2, and the three other miscellaneous support lines specified as X1, X2, and X3.

For the purposes of this analysis, the whole line was broken down into approximately 700 tasks, which were distributed throughout the plant between human assembly, machine assembly, and the infrastructure used to transfer products between workstations. Operators in this facility typically conduct their tasks either sitting or, more commonly, standing; most workers also have some sort of storage space that contain parts or assembly pieces. Some operators use basic hand tools or torque wrenches to aid in their assembly tasks and upon completion of their task, some operators use a foot pedal to advance the assembly to the next workstation.

3

Application of Controlled Vocabulary

Procedure

The standard vocabulary identified in prior work [START_REF] Salmi | Standardized Vocabularies for Assembly Systems Modelling and Automation Alternatives Description[END_REF] was applied to this new assembly process. The tasks used currently on the assembly line were reviewed to apply the controlled vocabulary such that the substantive information within the task list is not modified, but the actions verbs were changed to match the controlled vocabulary. The task list was divided into four equivalent sections which were then processed by four coders. These were then combined to create the complete list of assembly tasks.

The vocabulary used in this analysis is shown below in Table 2, where the first column shows the standard vocabulary from previous work, and the remaining columns show verbs used in task descriptions for the home appliance assembly line. As shown in the table, mapping of verbs in the current task description to controlled vocabulary resulted in a one-to-many mapping. This suggests that assembly instructions currently used in the home appliance assembly line may be using different terms to describe the same action. Additionally, the word "position" in the current task descriptions was found to be mapped to two verbs from the standard vocabulary: "align" and "place." This shows that there is ambiguity in the term "position", and it should possibly be avoided when describing assembly tasks in this setting. VA Tighten Tool 1 -followed by prepositional phrase 2 -without location restriction * -Identified as value-add in [START_REF] Peterson | Knowledge Management for Semi-Automated Automotive Assembly Instruction Authorship and Translation[END_REF] but modified as nonvalue-add in this study.

Applicability to Assembly Process

The process of verb standardization was found to be straightforward for the most part; however, not all of the assembly tasks were successfully converted into standardized verbs. Fig. 1 below shows the result of verb standardization. The graph is divided into seven sections, each of them being a segment or stage of the assembly line. The percentage of verbs that were compatible with the controlled vocabulary are shown by product model (P1 and P2) and by stage (A, I, M1, M2, X1, X2, and X3). It should be noted that the "X3" support line was only present for P1, and that there was no equivalent support line for P2.

Overall, 89% of the verbs in the original work instructions were addressed by the standardization. The "M2" stage, which corresponds to highly mechanized tasks, had the highest standardization across both product models. Many of the verbs that were not addressed occurred more frequently in stages "I" and "M1", which involved irregular tasks. For example, one of the tasks listed in the work instructions was vacuuming excess water from the appliance, with the assembly task described as "Wait time on vacuum." This task was not indicative of the assembly process at hand but was rather an intermediate "irregular" step that related to the assembly environment. When matching verbs to standardized language, it was important to have precise definitions. For example, place and align are standardized verbs that have similar meanings. To clarify, the group chose to use the verb "align" if the assembly instructions refer to an orientation, distance or angle with respect to another object, otherwise the verb "place" was used. The reason to differentiate between those verbs was to ensure when higher precision in assembly was required. This suggests that in addition to a standard list of verbs, each verb needs to be clearly defined to ensure accuracy and consistency of instructions. An example for each verb may also be beneficial to authors of assembly instructions.

Measures of Agreement

Once the final set of verbs was established, an intercoder reliability analysis was completed within and between coders. The four coders chosen for this test were all graduate students in either mechanical or industrial engineering and had work experience ranging from one to five years. The different coders each applied the standard vocabulary to a random sample of the larger set of work instructions. This sample was gathered by amassing every fifth line of the work instructions provided to assembly associates at their respective workstation. This sampling of the workstation instructions allowed for the entire length of the transfer line to be assessed. Each of the four coders assessed the same sampling of work instructions separately, and each coder's assignment of the standard vocabulary was compared to the other three sets. In order to properly understand the utility of the standard vocabulary amongst individuals with varying backgrounds, Cohen's Kappa (k) was calculated for each of the comparison cases between coders. For intercoder agreement, Kappa values greater than 0.600 are considered to indicate substantial agreement, while Kappa values above 0.800 indicate "almost perfect" agreement [START_REF] Mchugh | Interrater Reliability: The Kappa Statistic[END_REF][START_REF] Landis | The Measurement of Observer Agreement for Categorical Data[END_REF].The Kappa values found between each of the four coders is shown below in Kappa values indicated substantial agreement amongst the four coders, which is unsurprising given that all four had been given training with the standard vocabulary key in Table 2. To further analyze the utility of the new standard vocabulary, a similar analysis was completed using Cohen's Kappa, but in this instance, the analysis was within each of the four coders. After five weeks, the original four coders were given the same set of randomly selected tasks from the broader list of work instructions. Without consulting their previous responses, the coders were asked to apply the standard vocabulary once again, and agreement was assessed between this set of responses and their first set of earlier responses. This application of Cohen's Kappa helps to ensure that the vocabulary is a useful tool over time, and that the original intercoder agreement was not simply the result of recent training but was indicative of the vocabulary's usability. The Kappa values for each of the raters was greater than 0.731, suggesting a high-level of internal consistency amongst coders using the standard vocabulary [START_REF] Mchugh | Interrater Reliability: The Kappa Statistic[END_REF][START_REF] Landis | The Measurement of Observer Agreement for Categorical Data[END_REF]. In summary, the "rating" or application of controlled vocabulary in this case was found to be consistent between coders and within coders, suggesting that the underlying method of vocabulary application is robust.

Because each of the four coders was trained using the standard verb key, it may seem odd that there was not complete agreement (k = 1) amongst the coders. However, there were a few discrepancies present in the sample of work instructions that could explain this variation. Namely, there were discrepancies in the number of tasks that an individual work instruction represented. Some work instructions explicitly stated two distinct tasks (e.g. "Open door and insert weight.") or instructed the operator to interact with multiple parts (e.g. "Get bolts (3)."). In these cases, if coders split those tasks into several different tasks, the test was adjusted to only include verb assignments that each coder made. However, work instructions that included implied conditions may also affect how the coder assigned a standard verb based on prior knowledge of the process or the parts and tools being used. This ambiguity was also another possible explanation for some of the variation among coders in the sample tasks.

Summary

Due to the successes observed in the automotive industry from using standard vocabulary, an existing assembly vocabulary was modified to evaluate a home-appliance manufacturing line. Nearly 90% of the tasks listed in the original work instructions were addressed within the standardization. In conclusion, the use of standard vocabulary stemming from the automotive industry applied well to the home-appliance system with only slight variance. To test the agreement of the standard vocabulary list, an intercoder reliability test was conducted between and within the coders. This showed the strength of long-term agreement within the coders individually and the agreement between all the coders together. As a result, it holds true that controlled vocabulary is useful over time and the application was robust. Several additional benefits have been observed during the application of the standard vocabulary including:

• automation of assembly time estimation [START_REF] Miller | Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks[END_REF][START_REF] Cakmakci | Set-up Time Reduction Process and Integrated Predetermined Time System MTM-UAS: A Study of Application in a Large Size Company of Automobile Industry[END_REF] • benefits for system modelling using discrete event simulation [START_REF] Lee | Core Manufacturing Simulation Data--a Manufacturing Simulation Integration Standard: Overview and Case Studies[END_REF] • identifying the value stream of a product • the elimination of potential translation errors These additional benefits will be expounded upon in a later work, but also serve to demonstrate the optimized process flow that may accompany the implementation of a standard vocabulary in manufacturing and assembly.

Limitations

During the process of applying the standard vocabulary list to such a large number of assembly tasks, several limitations became apparent. Application of the standard vocabulary was done through document review only, meaning the actual work being done on the assembly was not always observed in order to apply the vocabulary onto existing instructions. While this is one of the biggest advantages of applying the vocabulary, this also presented some challenges. Even when certain verbs were straightforward to standardize, there were some exceptions when the task description was vague. The verb "obtain" has a specific meaning making it simple to assign a descriptive and accurate verb, while other verbs, like "secure", left more room for interpretation (i.e. a part could be secured by screwing, tightening, or clamping). When these verbs were encountered, more work was required to find the most accurate standardized language. By looking at previous tasks, more context could be gained. If an operator needs to get screws immediately before the part is secured, there is a high likelihood that the part is screwed in. Using the pictures that accompany the work instructions can also help clarify exactly what the operator is doing. If none of the verbs on the standardized list were applicable, new ones could be added.

Future Work

Future work would benefit from looking at the physical work done by the operators and seeing the value of applying the modified controlled vocabulary. Additionally, future work can also determine whether an appropriate level of specificity is provided within the standard vocabulary, and whether the level of specificity needed is different based on the products being assembled. Also, using the standard vocabulary for other manufacturing settings can help expand the applicability of the automotive assembly standard vocabulary from [START_REF] Salmi | Standardized Vocabularies for Assembly Systems Modelling and Automation Alternatives Description[END_REF]. Because this application was successful for the homeappliance manufacturing line, it is expected that applying the standard vocabulary in other manufacturing systems could also produce positive benefits.

Fig. 1 .

 1 Fig. 1. The results of the verb standardization by model and process stage.

Table 1 :

 1 List of Standard Verbs (adapted from[START_REF] Salmi | Standardized Vocabularies for Assembly Systems Modelling and Automation Alternatives Description[END_REF])

	Align	Disengage	Lay	Remove	Screw
	Clamp	Get	Move	Restock	Snap
	Clean	Insert	Open	Restrict	Tighten
	Connect	Inspect	Place	Scan

Table 2 . The final proposed standard vocabulary with key.

 2

	Standard Verb	Value-Add?		Verb Observed in Original Instruction
	Align	VA	Twist	Position 1	Turn	Adjust	Organize
	Clamp	VA	Attach	Clamp
	Clean	VA	Wipe
	Connect	VA	Attach	Install	Hook	Plug-in	Connect
	Disengage	VA	Unhook	Separate
	Get	NVA	Obtain	Pick up
	Insert	VA	Start	Insert	Install	Dip
	Inspect	NVA*	Inspect	Check
	Lay	NVA	Route	Pull
	Move	NVA	Move	Pull out	Close	Lift	Flip
	Open	NVA	Unwrap	Open
	Place	VA	Place	Position 2	Insert
	Remove	NVA*	Peel
	Restock	NVA*	Restock	Fill
	Restrict	VA	Secure	Tie
	Scan	VA	Scan
	Screw	VA	Screw	Secure
	Snap	VA	Snap
	Tighten

Table 3 .

 3 Cohen's Kappa (k) value for the agreement analysis between the four coders.

			100%
	PERCENT OF VERBS	ADDRESSED	20% 40% 60% 80%	98%	92%	61%	67%	70%	86%	100%	100%	88%	97%	93%	90%	72%
			0%
				P 1 P 2 P 1 P 2 P 1 P 2 P 1 P 2 P 1 P 2 P 1 P 2 P 1 P 2
				A		I		M 1		M 2		X 1		X 2		X 3
						Not Addressed	Addressed

Table 3 .

 3 Cohen's Kappa (k) value for the agreement analysis between the four coders.

		Coder 1	Coder 2	Coder 3	Coder 4
	Coder 1	-	0.671	0.761	0.775
	Coder 2	0.671	-	0.848	0.715
	Coder 3	0.761	0.848	-	0.811
	Coder 4	0.775	0.715	0.811	-