Kay Burow

Marco Franke

Klaus-Dieter Thoben

5G-Ready in the Industrial IoT-Environment

Keywords: 5G-Ready, Flexible Production Systems, IoT

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

Introduction

5G is nowadays a well-known and common term, which has the potential to influence many parts of our lifestyle. Scenarios focus on better-customised solutions and services and will take care of new technical abilities in the industrial environment like high autonomous manufacturing or IoT-supporting applications for area-wide networks of facilities and devices supporting remote control. In addition, to enable these highly flexible systems, a seamless collaboration in communication is required with multiple heterogeneous participants [START_REF] Condoluci | 5G IoT Industry Verticals and Network Requirements[END_REF]. As 5G does not exist as a running system yet, literature research was done to figure out which aspects are of high relevance in a future application. The following section names the primary pillars of the actual development for commercial use. In the beginning, it will be more attractive for large enterprises (LE), later on, by using the given services, it shall be applied in small and medium-sized enterprises (SME) to improve existing solutions. Through guaranteed reliability of >99.999% and latencies below 5ms [START_REF] Manzalini | A 5G Infrastructure for "Anything-as-a-Service[END_REF], it will also have a huge impact on the further development of human-robot-collaboration (HRC)-manufacturing or even robot-supported surgery. For private use, autonomous driving, using the benefits of Smart Cities or Media on demand with ultra-HD resolution will improve the living standards enormously [START_REF] Osseiran | 5G Mobile and Wireless Communications Technology // 5G mobile and wireless communications technology[END_REF].

To achieve these objectives, different developments are in progress, namely [START_REF] Osseiran | 5G Mobile and Wireless Communications Technology // 5G mobile and wireless communications technology[END_REF]:

1. emBB (Enhanced Mobile Broadband), which will be used for large broadcast events, e.g. in places of high interest (stadium, festivals) where many participants use these services within a small area or big cities. Highest availability and multiple bandwidths are required to fulfil the needed capacities. 2. URLLC (Ultra Reliable and Low Latency Communications) which will make autonomous driving possible and is a key factor for HRC due to its high reliability and low latency. 3. mMTC (massive Machine Type Communications) we will be primarily used for machine to machine (m2m) communication, IoT-applications and its devices. These can be used within a Smart City or build the backbone of a Smart Factory.

All three objectives are shown in Fig. 1 aligned with possible use case scenarios by means of their requirements (e.g. enhanced capacity, massive connectivity). At the moment, the focus of 5G development mainly lies on emBB with its enhanced capacities for multimedia applications, described in the 3GPP Release 15 [START_REF]3GPP Release 15 Overview[END_REF]. A first trial network was already demonstrated in Seoul during the Winter Olympics in 2018 [START_REF]5G collaboration in lead up to 2018 winter sporting events[END_REF]. MTC and URLLC are mostly developed on the sketch yet, further developments and implementations will be described with the upcoming 3GPP Release 16 at the end of this year [7]. This paper focusses on the industrial environment, especially on mMTC with its IoT-devices and shows, which requirements are necessary to improve the production logistics environment for future connected factories.

Approach

As already mentioned in the section before, 5G is far from commercial use, especially using IoT. Therefore, the approach emphasises the provision of requirements for the mMTC of 5G to ensure its applicability for the integration of IoT in the scope of industry 4.0, which is motivated in the following.

To develop a proper setup, a very close collaboration between the telecommunication providers and the industrial stakeholder exists and is highly required. Here, the telecommunication providers set the framework, architecture and orchestration for the 5G network, whereas the industrial stakeholders define their needs and technical perspectives [START_REF]G ACIA. 5G for Connected Industries and Automation[END_REF]. One of these needs are connected, more flexible and decentralised networks for IoT-applications. These applications will normally use IoT related commercial-of-the-shelf (COTS) products such as temperature sensors or simple actuators, which do not need much data traffic; rather their amount of information is limited to a couple of digits.

Nevertheless, it is planned to connect hundreds of devices, their environment and production facilities, which will result in a high density of entities per area. Further, high availability and coverage are required to guarantee a seamless collaboration [START_REF] Andrews | What Will 5G Be?[END_REF]. This also means that different facilities can be connected to create a flexible cluster on a production site.

Another aspect is that these IoT related entities shall have an extended power duration of up to 10 years per device. To achieve this goal, it is only possible by lower the frequency and narrow bandwidth at a minimum, but still, fulfil a correct data transmission. Through the reduction, less power will be consumed, which results in a longer battery lifetime [START_REF]G ACIA. 5G for Connected Industries and Automation[END_REF]. For this, a new bandwidth is required and by now not developed. It means in theory, it is there, but the necessary infrastructure for this does not exist yet.

With the background from literature and the demands from the industry to further improvement, a specific research question was deployed, based on the specific background of the institute profile in production and logistics. Of high interest is, which opportunities offer 5G compared to existing, mostly wired, industrial systems, especially with the focus on SMEs.

To change the view to the industrial acceptance of 5G, new services or better service quality are required. Coming from the typical mass production, the modern customer demands specific and individual solutions. Having this in mind, it is clear that by orders on demand, much higher flexibility is required, which should not end up in a mess of heterogeneous product variant services. Cloud services are going to provide the needed transparency and accessibility for many. By putting the orders in a cloud and do the computing and data storage there, new services based on 5G capabilities have to be implemented. For SMEs, this is of high interest because missing services can be purchased. This kind of adaptability enables the idea of a flexible production system (on demand). Moreover, 5G enables the interconnection of different facilities (including the above mentioned IoT devices) in real-time. Thus, a supply chain, which is a compound of multiple SMEs, can be merged virtually. In this case, the compound could react immediately on changing events like changing customer demands, varying product quality or logistical issues. Considering the above-mentioned potential use cases, 5G is applied as an enabler for the corresponding services that have to be created upon it. To enable the inclusion of product usage data, the provision of predictive maintenance approaches, customizable production processes and the digitisation of supply chains into a future Industry 4.0-environment, the following key requirements, coming from literature and multiple stakeholders, for IoT-applications must be considered [START_REF] Osseiran | 5G Mobile and Wireless Communications Technology // 5G mobile and wireless communications technology[END_REF], [START_REF]G ACIA. 5G for Connected Industries and Automation[END_REF]:  R1: Support a high density of devices for mMTC-applications to guarantee its functionality  R2: Low power consumption to extend the lifetime of IoT-components  R3: High availability to support flexible and decentralised systems  R4: Cloud services for storing and handling data to support a high availability  R5: High reliability to guarantee the (and avoid failures) data transmission, which has an impact on the quality of services  R6: Flexible bandwidths, which will provide the required data transmission all the time  R7: Real-time monitoring for a transparent view on the production processes  R8: Tracking/tracing to fulfil the demands in logistic concepts, especially for realtime monitoring  R9: Security and safety: by means of industrial usage no external shall have access

Use Case

After listing the key requirements, a use case based on a production logistics scenario is set up on a low scale for demonstration purpose. With the research question in mind, a test scenario shall be developed with most of the above-mentioned requirements are therefore considered and will be implemented. With a running use case, it will be possible to compare this scenario with common concepts. The use case itself is based on a simple logistic scenario where a transport box can be tracked and traced, asked about its well-being and analysed delivering goods from one factory to another (Fig. 2) one and start from a theoretical perspective.

Fig. 2. Use Case depiction via an application graph

Given is a transport box with sensors (temperature, humidity, vibrations, GPS) (R1, R2, R3) in it to make the box "smart". The box is connected to a cloud (R4) via an LTEmodule (as a replacement for 5G) where the housekeeping (R5) will be sent continuously. The different distributed cloud service takes the tasks of the data aggregation and the monitoring to enable an automatic condition based monitoring functionality. Critical states or upcoming trends shall be immediately sent to a decision maker. A decision maker could be both a skilled employee and a tool like a PPS. Also, by using a mobile end device (e.g. tablet) (R6), a user can ask for specific data (e.g. temperature, position) directly to receive actual information in almost real-time (R7, R8). Furthermore, only the user will have access to the stored information and analysis results from the cloud services (R9).

Interesting herby is that the 5G framework will provide computing and orchestration services. The main goal in this use case is to transform the application in a way that it can be used and understood by the 5G framework. Therefore, it is depicted as an application graph so that the telecommunication providers can see all relevant services that are necessary for this setup.

The use case is still in the development phase, but the authors are eager to start with the testing end of the year. The evaluation will use KPIs to quantise the functional requirements.

Conclusion and Outlook

It should be clear by now that a running IoT-use case using 5G is not possible to be set up at the moment. Furthermore, without an existing 5G infrastructure, no real testing can be done. Nevertheless, the basic needs are described, and with these, the direction of development of the upcoming 5G standard is clear. Starting with a theoretical use case, a test scenario was developed, which uses mostly existing technology (e.g. LTE, Wi-Fi) and will be advanced to 5G when new components are available.

By showing the use case, the first steps in the Industrial 5G-environment are done. It is a major goal of the authors to continue with this work in the industrial environment as they see huge potentials by connecting independent entities and participants in a cluster for more flexible production on demand in the future. In a distant future, it hopefully will be possible to have a running industrial use case using 5G to balance its pros, cons and its limits to the existing models.

Fig. 1 .

 1 Fig. 1. Distribution of 5G use cases by means of their requirements[START_REF]High-Bands and Horizontal Slicing Challenge 5G[END_REF]

Acknowledgements

This work was supported by the European Union's Horizon 2020 research and innovation programme under the MATILDA project under grant agreement No. 761898.


Günther Schuh

Jan-Philipp Prote

Thomas Busam
email: thomas.busam@schuh-group.com

Rafael Lorenz

Torbjörn H Netland

Using Prescriptive Analytics to support the Continuous Improvement Process

Keywords: Continuous Improvement Process, Data-driven Production Management, Knowledge Management

The continuous improvement process (CIP) enables companies to increase productivity constantly by sourcing ideas from their employees on the shop floor. However, shorter production cycles require manufacturing companies to also adapt their production processes in a faster manner and reduce resources for CIP activities. Traditional CIP approaches fall short in such a fastpaced environment characterized by uncertainty. This study proposes a novel approach for increasing the efficiency and speed of the CIP by using data of previous improvements and predict current potentials. This results in a prescriptive model supporting the employees how to improve their processes.

Introduction

Manufacturing companies are required to constantly improve their capabilities to increase their productivity in the future. A main driver for increasing the productivity on a day-to-day basis is the continuous improvement process (CIP), which is at the heart of any manufacturing organization [START_REF] Anand | Dynamic capabilities through continuous improvement infrastructure[END_REF]. In the popular illustration of the Toyota Production System (TPS), for instance, the CIP stands in the center of the illustrated house and receives special focus [START_REF] Liker | The Toyota way: 14 management principles from the world's greatest manufacturer[END_REF]. The CIP helps to access and use the knowledge and suggestions of the employees to constantly question the current processes. However, not only the required capabilities itself change -also the rate at which this change is happening increased within the last years. Customers are asking for more individualized products for the same costs as mass-produced products. Digital technologies allow for needed productivity gains outside current limits. The usage of these technologies will significantly change the way manufacturing companies produce products. Such significant changes of the process, so called process innovations, will induce highly cyclical changes until new stable production conditions are achieved.

In order to ensure increasing productivity under such conditions, the CIP needs to be enhanced to overcome its time consuming latencies. Therefore, digitalization offers new opportunities for collecting and analyzing huge amount of data in a short time that a human could not handle. Hence, this paper proposes a model to enhance the CIP with digital capabilities to leverage its potential for future productivity gains.

Definition and Challenges of the Continuous Improvement Process

The CIP is rooted in the literature on lean management and total quality management (TQM) [START_REF] Lam | Achieving employee commitment for continuous improvement initiatives[END_REF]. It is a method of constantly developing processes further towards an overarching goal [START_REF] Rother | Toyota Kata: Managing people for improvement, adaptiveness, and superior results[END_REF]. This goal serves as a direction and guides the improvement actions. The employees perform these actions continuously along the way.

The CIP is often taken as a synonym for Japanese term kaizen. This term is first introduced by Imai [START_REF] Imai | Kaizen: The key to Japan's competitive success[END_REF] who further differentiates between three levels of kaizen: management, group, and individual. The focus of kaizen is the incremental, iterative, and continuous improvements of a system [START_REF] Brunet | Kaizen in Japan: an empirical study[END_REF]. In contrast, the literature defines the term kaikaku as step-wise improvements [START_REF] Bertagnolli | Lean Management: Einführung und Vertiefung in die japanische Managementphilosophie[END_REF]. These happen for instance with the introduction of a new manufacturing technology.

Whereas in earlier years companies tried to operationalize the CIP through tools and rigor meetings, companies recently acknowledge the importance of the softer aspects; For achieving sustainable results the CIP needs to be anchored in the company's culture [START_REF] Anand | Dynamic capabilities through continuous improvement infrastructure[END_REF]. Therefore, the key resource of the CIP are the employees [START_REF] Liker | The Toyota way: 14 management principles from the world's greatest manufacturer[END_REF]. Not only do they shape and influence the culture, but they also possess the specific knowledge about the process and are the problem solvers. This is critically as the CIP is not a top-down approach but lives throughout the organization.

With an increasing complexity of the processes due to quicker cycles of different products, traditional continuous improvement falls short on quickly identifying the root causes. Digitalization allows for the collection and analysis of a huge amount of data to manage such complex processes. However, incorporating information technology into the manufacturing process can detach the employee from the problem solving process: they might lack the expertise for analyzing the data needed to understand the process. Thus, data that can help in improving the processes remains far too often unexplored.

The performance and financial benefit of the improvement activities are not always clear. In a case study of two plants for instance the annual costs for the continuous improvement investment exceeded the annual cost savings from it for one plant [START_REF] Butler | A model of continuous improvement programme management[END_REF]. The financial pressure increases when island solutions are developed. If a similar problem has already been solved in another line or another plant, the company could take advantage of scaling the solution throughout the organization. However, if the knowledge about the presence of these improvements is not shared, the company will develop redundant improvements that create a non-standardized infrastructure [START_REF] Lorenz | Applying User Stories for a customerdriven Industry 4.0 Transformation[END_REF].

Following the literature, the ability to minimize the latency times regarding acquiring and analyzing data, as well as decision making towards the final implementation is key to efficient continuous improvement in a frequently changing production environment. To reach that goal, an approach is required that meets the requirements for the future continuous improvement developed above, namely:  Low individual effort during the process chain of analyzing data until decision making in a fast pace to preempt frequent changes of the production environment.  Real improvement of processes instead of simple failure prediction and handling.  Making use of the organization-wide knowledge.

State of the Art

Most of the different approaches for continuous improvement follow two different streams, namely management-or data-driven. The management-(or experiencedriven) CIP is the most researched one. The data-driven approach is growing fast due to new opportunities that come along with Industry 4.0. In the following, existing continuous improvement approaches for both streams are reviewed and evaluated.

Discussion of Existing Approach

Regarding management and experience driven CIP, Rother [START_REF] Rother | Toyota Kata: Managing people for improvement, adaptiveness, and superior results[END_REF] describes the "Toyota Kata" as the currently most prominent approach. Newer explanations of this approach like "Toyota Kata Culture" or "Leading improvements successfully" share the same concept, that, based on a vision, the current state has to be improved towards a target condition. Working one´s way up on that path will reveal many obstacles, which have to be addressed step by step in the problem solving cycle (respectively the PDCAcycle). This approach is executed by the employees, driven to learn how to improve their processes daily. In an addition to that, those problem solvers embody the first link of a hierarchy of coachees and coaches, way up to the plant manager. Rother [START_REF] Rother | Toyota Kata: Managing people for improvement, adaptiveness, and superior results[END_REF] recommends this "Coaching Kata" to further enhance the problem solving ability of the coachees as well as providing a quick escalation and plant overarching goal setting. Hence, those two aspects make this approach very time and resource intensive when applied thoroughly [START_REF] Rother | Toyota Kata: Managing people for improvement, adaptiveness, and superior results[END_REF][START_REF] Rother | Toyota kata culture: Building organizational capability and mindset through kata coaching[END_REF][START_REF] Kamberg | Die Verbesserungskata. Verbesserung erfolgreich führen, mit der Toyota-Kata und Lean Management Prozesse verbessern und Mitarbeiter entwickeln / von Marco Kamberg[END_REF].

In the field of data driven improvement, Groeger et al. [START_REF] Gröger | Prescriptive Analytics for Recommendation-Based Business Process Optimization[END_REF] develop prescriptive analytics for recommendation-based business process optimization at process runtime. Using data mining, the quality output of a product can be predicted via a decision tree for specific processes. The discovered rules of the decision tree are then translated into recommended actions. If during an executed process a metric deviation is predicted, these actions should be taken. This approach focuses on quality issues and how to avoid them, but does not address the challenge of how to improve a process regarding time nor cost. Kassner and Mitschang incorporate unstructured information like pictures or text descriptions into a decision support to handle exceptions on the shop floor. Their described architecture "MaXCept" does this in 5 steps: exception recognition, exception classification, exception escalation, solution recommendation and task-expert matching [START_REF] Kassner | MaXCept --Decision Support in Exception Handling through Unstructured Data Integration in the Production Context: An Integral Part of the Smart Factory[END_REF]. Although being intended to be automated and in real-time, their approach is still depending on experts and focuses on solving operational problems on the shop floor rather than addressing process improvement. Ringsquandl et al. [START_REF] Ringsquandl | Graph-based predictions and recommendations in flexible manufacturing systems[END_REF] introduce a graph-based analytics framework derived from a comprehensive requirement analysis in order to reduce the customization efforts which come along with most advanced manufacturing analytics. Being developed on an architectural level, this approach still requires adjustments to consider extensive domain knowledge like constraints or relations of a specific process into the algorithm [START_REF] Ringsquandl | Graph-based predictions and recommendations in flexible manufacturing systems[END_REF]. Wuest [START_REF] Wuest | Identifying product and process state drivers in manufacturing systems using supervised machine learning[END_REF] provides a method applying Supervised Machine Learning to the "product state concept" by incorporating product and process inter-and intra-relations rather than only focusing on individual processes. Using product states consisting of a set of state characteristics enables a product oriented view of the physical value stream. Those characteristics can be altered by process inter-and intra-relations, the so called state drivers. Wuest incorporated this element into the analysis through applying support vector machine-based feature ranking. The results of this method can be used for e.g. quality monitoring and advanced process control. However, this concept is still depending on expert knowledge when it comes to incorporating implicit process interand inter-relations.

Deficits of Existing Approaches and Summary

Management and experience driven approaches on the one hand take up too much resources of employees and time regarding the described challenges in the previous chapter. Data driven approaches on the other hand lag behind when the production environment is changing too quickly. The effort to design and implement a new improvement system or application exclusively for the changed process is surpassing the benefits of the achievable improvements, given the limited time until the same process changes once more [START_REF] Wuest | Identifying product and process state drivers in manufacturing systems using supervised machine learning[END_REF]. Moreover, the existing approaches focus on predicting quality instead of recommending real process improvement. To overcome the challenges of both streams, a new approach for an efficient continuous improvement process in an unstable and therefore frequently changing production environment is proposed in the following.

Approach for a data-driven continuous improvement process

The novel approach presented in this chapter combines the advantages of the previously described two directions to meet the requirements stated in the theoretical background. Fig. 1 illustrates the three phases of the approach. The aim is to provide tailored solution suggestions to a responsible employee who has chosen a specific process to focus on. Artificial intelligence is used to choose suggestions from the database of previous suggestions. The required input besides already conducted improvements are the organization-wide targets, respectively the broken down ones for the chosen process and its parameters. The following describes the approach of each of the three phases. The aim of this first phase is to determine the required data and format to prepare this information as an input for the subsequent phases. Therefore, the digital maturity is evaluated based on the index developed by Schuh et al. [16]. to detect the required changes on the quality and availability of the company's process data. Only when the company is ranked on level three or higher the procedure can deliver robust improvements. This level requires an already digitized production while collecting and visualizing data in a manufacturing execution system (MES) [16].

As illustrated in Fig. 2, this phase connects the available process data of the current state with situational data of the respective process environment (e.g., room temperahumidity, etc.). This information has to be stored together as an event log to be accessible by the system for later comparison with other event logs or choosing the next process to be improved. A complete event log consists of a process use-case, describing the current condition with parameters, KPI`s and the respective targets for a specific process, data of the environment, improvement idea and the corresponding results. These often separately stored information can be linked via timestamps and identification numbers. Whenever an improvement idea is performed, it will be saved as a new event log to be available in the constantly growing organization-wide database. Ideas of the past will also be imported likewise. One of the objectives of the model in the second phase is to discover unknown or implicit cause-and-effect relations of the production processes. To find these relations and thereby foster better improvement ideas, the second phase integrates the knowledge discovery in databases (KDD) process as described by Fayyad et al. [START_REF] Fayyad | From Data Mining to Knowledge Discovery in Databases[END_REF] to feed the model. Thereby, unknown relationships between different processes or parameters are added to a library of already known manufacturing-relevant causeand-effect relationships, for instance provided by Hopp and Spearman [START_REF] Hopp | The McGraw-Hill/Irwin series[END_REF]. With these discovered relationships new improvement ideas can already be developed at this stage. The results of past improvements will also be added in the suggested format described in chapter 4.1 to the collection of formulas. The formulas in the library are incorporated into a multi-variant model to work as a digital shadow, which can also be used to forecast situations that have never existed before. Instead of simply feeding and training the multi-variant model with process data, this approach suggests to explicitly train it with the extracted formulas of the library and past improvements in an artificial generated data-set upfront. Later on, the preconditioned model will continue learning currently unknown relations on its own, especially by taking environmental data into account. This way a neural network for instance can focus on new relations in the real data-set instead of having to learn already known relations or formulas from the start.

Explanation Model for Pattern Recognition of User Stories and Derivation of Improvement Activities

The model of the third phase serves as an indirect interface to the person in charge to improve a specific process. The aim is to suggest the most suitable improvement and its effects, which can be visualized via an individually designable app on a portable devise as the direct interface. The suggestion is based on a similarity analysis between the process to be improved and the event logs in the organization-wide database of already implemented improvements. This phase begins with the description of the actual problem of the production process. The KPIs and parameters of the current state in contrast to the targets reveal a delta and are consolidated in a set of numbers similar to an event log as shown in figure 4 on the right side of the input vectors.

Fig. 4. Logic of the model to suggest improvement actions

This vector is then compared with the vectors of all event logs in the database. This comparison is performed by calculating the distance based on the Jaccard Metric, followed by applying hierarchical clustering to identify clusters of similarities. These will select the most similar event log by comparing the vector of the selected process and the ones of all event logs. Thereby, similar processes with the smallest vectorvariations (e.g., parameters, KPIs, targets or environment data) are suggested. The effect of those variations on the future result are then predicted by making use of the second model. The final model might also suggest applying two different improvement ideas at the same time if the delta between the current and the target state is too big for only one existing improvement solution. The hierarchical clustering allows the person in charge to choose from a set of different options suggested by the model. After implementing one of the suggested improvement ideas, the idea and its outcome are integrated into the database and constitute to a new event log.

Conclusion

Given the opportunities of Industry 4.0, the continuous improvement process can hugely benefit by incorporating the right algorithms and an advancement of the methodology. Besides improving required efforts this approach also enables an easy way to harvest historical data for recommendations and to turn implicit or even unknown process knowledge systematically into explicit organizational knowledge.

If not yet documented, this approach helps to start filling up the organizations database with first improvement ideas in a structured format, which of course will take some time to reach a beneficial quantity. Further research should focus on the industrial application of this approach, which initially requires a detailed design of how to realize the three phases, and identify contingencies for different environments and industries.

Fig. 1 .

 1 Fig. 1. Three phases for identifying suggested improvement action

Fig. 2 .

 2 Fig. 2. Logic of the merged database of improvement data

Fig. 3 .

 3 Fig. 3. Structure of the preconditioned multi-variant model


Farhad Ameri
email: ameri@txstate.edu

Reid Yoder

A Thesaurus-guided Method for Smart Manufacturing Diagnostics

Keywords: smart maintenance, knowledge graph, thesaurus, Natural Language Processing

The unstructured historical data available in the databases of Computerized Maintenance Management Systems represents a wealth of diagnostic knowledge. In this paper, a methodology for converting the maintenance log data into formal knowledge graphs is presented. The methodology uses text analytics techniques, in combination with humanassisted thesaurus development methods, for generating a formal thesaurus, or knowledge graph, that encodes the semantic relationships between multiple maintenance entities. The knowledge graph proposed in this work uses Simple Knowledge Organization System (SKOS) standard. A java-based tool is developed that uses the generated knowledge graph as the input and conducts light-weight reasoning to support smart maintenance diagnosis

Introduction

Manufacturing companies often strive to adopt advanced maintenance and control tools and methods to minimize their machine downtime and maximize the availability of their critical assets. In particular, Computerized Maintenance Management Systems (CMMS) are widely used in most industries to manage, plan, and organize preventive and planned maintenance activities [START_REF] Wienker | The Computerized Maintenance Management System an Essential Tool for World Class Maintenance[END_REF]. The records of maintenance work orders and activities are often stored in the partially structured database of CMMS packages for archiving, reporting or analysis purposes [START_REF] Lopes | Requirements Specification of a Computerized Maintenance Management System -A Case Study[END_REF]. The data in CMMS databases can be potentially used as a source of diagnosis knowledge. However, the maintenance log data is often underused [START_REF] Melinda | Cleaning historical maintenance work order data for reliability analysis[END_REF]. Enormous collections of historical maintenance logs, representing a wealth of diagnostic knowledge, can be found in most industries. As the number of reports related to maintenance issues in the CMMS databases grows, manual search and analysis of the reports becomes more cumbersome and less efficient. Without proper tools and techniques for analyzing, mining, and contextualizing that knowledge, the usefulness of these maintenance logs is severely limited. The underlying research challenge that motivates this work is to generate more structured and formal knowledge models, based on the unstructured and informal data available in the maintenance logs that can support smart and automated diagnosis process.

Advanced techniques supported by Natural Language Processing (NLP) and Machine Learning (ML) can be applied to extract useful patterns and rules from the raw text that are otherwise hidden in the historical maintenance work order data. However, most often the intervention of human expert is needed for validating the generated models and taxonomies. In general, full automation of taxonomy and ontology creation process is not a feasible approach to follow based on the current state of Artificial Intelligence technology.

The objective of this work is to introduce a hybrid methodology for generating more structured knowledge models from the unstructured maintenance log data. The methodology uses text analytics techniques, in combination with a human-assisted thesaurus development method, for the purpose of generating a formal thesaurus (i.e., knowledge graph). The resulting knowledge graph is intended to encode the semantic and lexical relationships between various entities in the maintenance domain. The proposed methodology uses Simple Knowledge Organization system (SKOS) formalism for thesaurus modeling and representation [START_REF] Miles | SKOS simple knowledge organization system reference[END_REF].

A Java-based tool is developed that uses the generated SKOS thesaurus as the input and conducts root cause analysis and diagnosis based on the observed symptoms in a given maintenance artifact (i.e., part or equipment).

Related Works

The body of work related to analyzing the historical maintenance data is rather sparse. Winker [START_REF] Wienker | The Computerized Maintenance Management System an Essential Tool for World Class Maintenance[END_REF] developed a rule-based approach for cleaning the historical work order data available in CMMC databases. The objective was to improve the quality of the data in order to ensure that the data is fit and reliable for further analysis. Sharp et al. [START_REF] Sharp | Toward Semi-autonomous Information[END_REF] used multiple Machine Learning and Natural Language Processing techniques, including unsupervised classification method based on Support Vector Machine (SVM), for automated clustering and tagging of maintenance data. Tagging maintenance logs with appropriate label results in creation of more structured and clean data and reduces the ambiguity in the data. The generated tags can also be used as controlled vocabulary. These works address the necessary steps for pre-processing the data and giving it more structure and semantics. However, the final product cannot be regarded as a formal knowledge graph that can support smart maintenance diagnosis.

Maintenance Diagnosis Thesaurus

As mentioned before, the Maintenance Diagnosis (MD) Thesaurus that is developed in this work is based on Simple Knowledge Organization System. The MD thesaurus provides a formal vocabulary of maintenance terms and at the same time serves as a knowledge graph. In this section, a brief introduction to SKOS is provided and the motivations for using SKOS as the thesaurus representation formalism are discussed. Also, the definitions for some of the key concepts of the thesaurus are provided.

Simple Knowledge Organization System (SKOS)

SKOS is a standard data model, published by, World Wide Web Consortium (W3C), that provides a structured framework for creating different types of controlled vocabulary such as thesauri, concept schemes, and taxonomies to be consumed on the web. A SKOS concept is any unit of thought such as an idea, an object, or an event.

Each concept in SKOS has exactly one preferred label (skos:prefLabel) and can have multiple alternative labels (skos:altLabel). For example, Rusting is the alternative label for Oxidation as it is used frequently for referring to the same concept. The broader concept of the Oxidation is Chemical Reaction, while Homolytic Oxidation and Heterolytic Oxidation are the narrower concepts; meaning that they are more specialized forms of Oxidation. The concepts that are related to Oxidation include Galvanization, Coating, and Rust. The concepts are made related to one another based on some type of semantic connection between them. For example, the reason coating is a related concept to oxidation is that coating is the method that is typically used for preventing oxidation. The exact type of relation, however, is not specified in the thesaurus. Each SKOS concept can also have a definition provided in plain English or any other natural language. One major advantage of the SKOS thesauri is that they can be extended, enriched, and validated incrementally by community crowds and shared as linked open data due to their open and standard syntax and semantics. A SKOS thesaurus forms the nucleus of a knowledge graph that can be continuously enriched to support various datadriven and knowledge-intensive application such as semantic search and reasoning, text mining, data integration and alignment, and data analytic.

Corpus

The maintenance data provided by three manufacturing companies was used to build the text corpus associated with the thesaurus. The data across three companies contain a total of approximately 10,0000 Work Orders (WO) collected over a 10-year period. The logs are often organized into tabular format and include contextual data, such as the Affected Equipment, Description of Problem, Resolution of Problem, Time/Date Work order was issued, Time/Date Work order was completed, and Maintenance Technician Assigned.

Structure of the MD Thesaurus

Developing a SKOS thesaurus typically starts with creating the top-level categories of terms and further populating the lower-level categories by the terms extracted from the corpus. Currently the MD thesaurus has three main concept schemes (collections), namely, Artifact, Maintenance Problem, and Maintenance Treatment as defined in this section. The first level concepts under each category are called top concepts. Fig. 1 shows the partial view of the top concepts in the MD thesaurus. For example, leaking, as shown in Fig. 2, is considered to be a functional problem as it is considered to be an undesirable behavior of a system Non-Functional Maintenance Problem (Top Concept) Def.= Non-Functional Maintenance Problem occurs when a production equipment (artifact) or one of its parts have one or more undesirable qualities or attributes.

Fig. 2. Leaking as an example of a functional problem

Maintenance Treatment (Concept Scheme) Def.= A process in which the act is intended to modify or alter some other physical entity with the intention to resolve a maintenance problem. Different types of maintenance treatment include repair, adjustment, replacement, and rebuild.

Fig. 1. A partial view of the concept hierarchy in MD thesaurus

The top concepts provide the necessary sub-categories under which more specific concepts, extracted from the corpus, can be classified.

ATP Sub-graph

The ATP sub-graph is a sub-graph of the SKOS graph that only contains the skos:related relationship between the concepts. The nodes in the ATP sub-graph are of types Artifacts, Problems, or Treatments. The ATP sub-graph describes how artifacts, problems, and treatments are related to each other.

Thesaurus Formation and Extension Process

A commercial tool, called PoolParty Taxonomy&Thesaurus Management system [START_REF] Blumauer | PoolParty Technical White Paper[END_REF], was used for creating and extending the MD thesaurus. In PoolParty, each thesaurus can have a document corpus from which the terms can be extracted. In this work, each maintenance record is considered to be a document in the corpus. The MD corpus contained more than 10,000 documents at the time of preparation of this paper. There are two methods for extracting the terms form the corpus and converting them into thesaurus concepts. The first method is based on direct tagging of the relevant terms in the corpus documents. The tagged terms are later added to the collection of Candidate Concepts. A candidate concept is formally integrated with the thesaurus when its broader concept is specified by the thesaurus developer. For example, the phrase broken gear can be manually tagged in a document by the developer and then placed under broken part, as its broader concept. The second method is through automatically extracting a list of n-grams from the corpus and adding the relevant terms to the collection of candidate concepts. In this work, the first method was used.

Thesaurus Validation

The usefulness of the MD thesaurus highly depends on its level of completeness and accuracy. To validate and verify the thesaurus with respect to completeness and accuracy, it was used for tokenizing the maintenance records outside the corpus. The tokenization process entails splitting the text into individual concepts from the thesaurus. A maintenance record is considered to be adequately tagged if at least one artifact, one maintenance problem, and one maintenance treatment can be identified in the record. Table 1 shows an example of a maintenance records with complete tags.

Diagnosis Guided by ATP Sub-graph

The ATP sub-graph can be used for identifying the failures related to a given artifact such as a gear or a pump or a hydraulic system. For example, for artifact A shown in Fig. 4, the related problems are P1, P2, and P3. Since P1 is related to P2 and P2 is related to P3, a loose causality relationship can be inferred. Also, because the links connecting the vertices are bi-directional, it is not possible to determine which problem is the root cause and which problem is the observed effect. But as mentioned before, the goal is to provide a lightweight model with simple semantics to enable some basic reasoning and approximate root cause analysis. For more deterministic and complex reasoning, more expressive ontologies will be needed. If the user picks a specific problem as the probable root cause, then the ATP sub-graph can point to the potential treatments. Fig. 4. The active portion of the APT sub-graph when a specific artifact is selected.

Implementation

A Java-based tool, called Smart Maintenance Diagnosis System (SMDS) was developed based on the proposed methods. The SMDS tool uses the Apache Jena API for Java. The tool receives the thesaurus in RDF/JSON format as the input . There is a default thesaurus embedded in the tool. However, the user can always upload the most recent version of the thesaurus. Jena is used to parse the thesaurus and to create a map of systems, parts, symptoms, causes, treatments, and the relations between them. The map is then used to analyze and diagnose a selected system or part issue. In the first step, the user provides some contextual information regarding the diagnosis scenario. System type (e.g. landing gear) and part type (e.g. gear) comprise the contextual information in this implementation. Then the user selects the observed symptoms from the drop-down menu. The symptoms are filtered by the tool so that the user can only select from the symptoms (maintenance problems) related to the selected part.

After the symptoms (effects) are selected, then the tool provides the user with a list of potential causes. The potential causes are actually the maintenance problems that are related to the selected symptoms using skos:related relationship. Fig. 5 shows the screenshot of the final recommendations of the tool. In the provided example, corrosive wear is identified as the potential causes for the selected symptoms, namely, worn gear and gear noise.

Conclusion

In this paper, a thesaurus-guided maintenance diagnosis method is proposed. The thesaurus is linked to a text corpus extracted from the CMMS maintenances logs provided by three participating companies. A smart maintenance diagnosis tool was developed and tested based on the proposed method. The test results provided correct diagnosis based on the scope of the thesaurus. The proposed MD thesaurus uses SKOS standard. SKOS knowledge graph provides a solid basis for machine learning and cognitive computing efforts with an organization. SKOS is widely adopted and there exist hundreds of SKOS vocabularies on the web. Therefore, the proposed MD thesaurus can be linked and integrated with other vocabularies to enhance the semantic coverage of the knowledge graph.

One shortcoming of the proposed approach is that it ignores the type of the relations between two concepts and treats all relationships to be the same. This caveat can be countered by superimposing more expressive ontologies on top of the light-weight thesaurus to enable more advanced reasoning. However, developing and extending axiomatic and heavy-weight ontologies can be very costly and time-consuming. Lightweight SKOS thesaurus can be easily developed and extended to support a first-order reasoning process when diagnosing a maintenance problem. In the future, the APT subgraph will be augmented with probabilistic values to create Bayesian Networks.

Artifact

  (Concept Scheme) Def. = A physical entity such as individual parts, subsystems, or equipment can be the bearer of a failure Maintenance Problem -Failure (Concept Scheme) Def.= Event in which any part of an equipment or machine does not perform according to its operational specifications or does not possess its desirable qualities Functional Maintenance Problem (Top Concept) Def.= a state in which an asset or system fails to perform a specific function to the desired level of performance.

Fig. 3 .

 3 Fig. 3. The structure of the APT sub-graph

Fig. 5 .

 5 Fig. 5. SMDS tool screenshot (4): the tool suggests the treatments based on the selected root causes As mentioned before, since ATP sub-graph is built based on the observations about the past failures, the resulting conclusions are always approximative based upon the evidence given. The textual description of the recommended treatments are extracted from skos:definition property of the concept.

Table 1 .

 1 A maintenance record tagged with sufficient tokens

	Concept	Top Concept Concept	Freq.
	Schema		(preferred
			label)
	Artifact	Part	coil	1
	Artifact	Part	indexing valve	1
	Maintenance	Functional	not indexing	1
	Problem	Maintenance
		Problem
	Maintenance	Treatment	replace	1
	Treatment	Action
	Artifact	Part	valve	1
	Problem Description:

Table will not index. Resolution: Table indexing valve coil found bad and replaced


Rasmus Andersen
email: rasmus@mp.aau.dk

Thomas D Brunoe

Kjeld Nielsen

A Framework for Identification of Complexity Drivers in Manufacturing Companies

Keywords: Complexity management, complexity driver, identification, framework, manufacturing

The initial issue of complexity management in companies is the identification of the drivers of complexity. However, current literature lacks methods for assisting practitioners in the initial identification of such drivers. This paper, therefore, presents a novel framework for assisting practitioners with identifying complexity drivers in manufacturing companies. The framework uses a generic value chain and a generic product structure as its two dimensions. Multiple workshops are then conducted with company representatives across different value chain fields focusing on two main parts: First, surveys are used to assign complexity ratings to different generic product structure elements. Secondly, the complexity ratings are elaborated on by workshop participants. The process provides valuable insights into the perceived complexity drivers. The framework is then verified through a case study in the process industry. Based on the case study, multiple complexity drivers were identified across both value chain fields and product structure elements. The case study findings show that the framework facilitates practitioners in identifying organization-wide perceived complexity drivers. The framework contributes to both industry and research by addressing a neglected aspect of complexity management. It achieves this by providing a comprehensive and structured approach for the initial identification of complexity drivers across product elements and value chain fields.

Introduction

Globalized markets, increasingly heterogeneous customer requirements, and demands for lower costs are factors pressuring the competitiveness of traditional manufacturing companies [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF]. These factors have resulted in increased external complexity for manufacturers. In response, companies have increased the complexity of their own operations to accommodate these changed conditions [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF][START_REF] Seifert | Managing Complexity in Remanufacturing[END_REF], often resulting in increased costs and reduced performance [START_REF] Piya | Supply Chain Complexity Drivers and Solution Methods[END_REF]. Such costs are identified across organizations in supply chain management [START_REF] Piya | Supply Chain Complexity Drivers and Solution Methods[END_REF][START_REF] Sun | Best Practice Sharing for Complexity Management in Supply Chains of the Semiconductor Industry[END_REF], production systems [START_REF] Kohr | Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches[END_REF][START_REF] Asadi | Drivers of Complexity in a Flexible Assembly System-A Case Study[END_REF], and various internal projects [START_REF] Schuh | Conceptual framework for evaluation of complexity in new product development projects[END_REF][START_REF] Latos | Transformation of working environments through digitalization: Exploration and systematization of complexity drivers[END_REF]. However, complexity is not always undesirable, and may even be a source of competitive advantage [START_REF] Latos | Transformation of working environments through digitalization: Exploration and systematization of complexity drivers[END_REF], if managed properly. Most companies are aware of the adverse effects of complexity [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF]. Yet, despite attention from industry concrete tools to effectively handle complexity related issues in manufacturing companies are lacking [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF]. Due to the issues arising from increased complexity in companies complexity management has received increased attention in literature in the last decade [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF]. However, before complexity can be managed, it must be understood what causes it [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF]. These "causes" are referred to as complexity drivers in literature. Examples of complexity drivers in manufacturing companies include variety [START_REF] Piya | Supply Chain Complexity Drivers and Solution Methods[END_REF][START_REF] Kohr | Identifying Complexity Drivers in Discrete Manufacturing and Process Industry[END_REF] and novelty [START_REF] Schuh | Conceptual framework for evaluation of complexity in new product development projects[END_REF] of products, as well as variety of assembly equipment [START_REF] Asadi | Drivers of Complexity in a Flexible Assembly System-A Case Study[END_REF]. Identification of complexity drivers is, therefore, critical for the subsequent operationalization and quantification of the complexity costs incurred for the company [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF], and investigating this perspective therefore forms the motivation of this paper. The remainder of the paper is structured as follows: Section 2 reviews existing research on identification of complexity drivers in manufacturing industry. Based on these findings, Section 3 presents a novel framework aimed at assisting practitioners in this endeavor. Section 4 then introduces the methodology of the case study followed by Section 5, which presents the findings from the case study. Section 6 discusses the limitations of the framework after which Section 7 concludes on the major findings and suggests areas for further research.

Literature Review on Complexity Driver Identification

Literature has been reviewed for frameworks to assist in identification of complexity drivers in manufacturing companies. This review consisted of a two-step process. First, relevant papers referenced in Vogel and Lasch [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF] were reviewed resulting in 9 papers. Secondly, to ensure the inclusion of the most current research a block search was performed in Elsevier's Scopus and Clarivate's Web of Science covering the period from 2016 to 2019, using the search string: "complexity driver*" OR (driver* NEAR/3 complexity) AND (identi*). Upon screening the 19 papers retrieved, the literature search produced 11 relevant papers. Since this paper focuses on the identification of complexity drivers in industry, all identified papers have been analyzed as follows: First, the identified papers are reviewed concerning the repeatability of the methods presented, as this is considered an important factor for applicability by practitioners. This is evaluated according to the degree of detail with which the initial method of gathering complexity driver data is described. Secondly, the intended usage area of the frameworks is categorized. This is to determine whether the frameworks are intended for general use in manufacturing companies, or specific to a certain subject area such as flexible assembly systems. Due to space constraints, the general findings from the papers referenced by Vogel and Lasch [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF] are briefly summarized first. Hereafter, the overall findings from the database searches are presented.

Reviewing the papers cited by Vogel and Lasch [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF] found that these are concerned with diverse application areas including the remanufacturing industry, supply chains, and various organizational projects. Furthermore, most of these papers rely on qualitative methods for the identification of complexity drivers, such as literature reviews, interviews, and expert reviews. However, the description of the methods used is in most cases not sufficiently detailed to be replicated by practitioners. For the papers where the methods are described relatively detailed, the methodologies do not allow for adaptation to specific contexts or does not provide a company-wide perspective on complexity issues.

To provide an easier overview of the findings of the subsequent database searches and literature review, the overall results have been summarized in Table 1.

Table 1. Reviewed literature categorized according to methods used in identification of complexity drivers as well as the generalizability of the frameworks.

Paper Complexity driver data collection method

Complexity driver focus area Asadi et al. [START_REF] Asadi | Drivers of Complexity in a Flexible Assembly System-A Case Study[END_REF] Brainstorming, questionnaire Assembly system Kohr et al. [START_REF] Kohr | Identifying Complexity Drivers in Discrete Manufacturing and Process Industry[END_REF] Interview Manufacturing industry Kohr et al. [START_REF] Kohr | Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches[END_REF] Simulation study, interview, questionnaire, literature review

Manufacturing industry

Latos et al. [START_REF] Latos | Transformation of working environments through digitalization: Exploration and systematization of complexity drivers[END_REF] Interview Organizational projects Leang et al. [START_REF] Leang | The impact of manufacturing complexity drivers on performance-a preliminary study[END_REF] Not specified Manufacturing industry Piya et al. [START_REF] Piya | Supply Chain Complexity Drivers and Solution Methods[END_REF] Literature review, expert review Supply chain Schmid et al. [START_REF] Schmid | A Functional Platform Strategy for Integrated Machine Tools[END_REF] Not specified Production equipment Schuh et al. [START_REF] Schuh | Adaptive Design of Engineering Change Management in Highly Iterative Product Development[END_REF] Literature review, expert review Organizational projects Schuh et al. [START_REF] Schuh | Approach to evaluate complexity in new product development projects[END_REF] Literature review Organizational projects Schuh et al. [START_REF] Schuh | Conceptual framework for evaluation of complexity in new product development projects[END_REF] Not specified Organizational projects Sun et al. [START_REF] Sun | Best Practice Sharing for Complexity Management in Supply Chains of the Semiconductor Industry[END_REF] Questionnaire, expert review, workshop Supply chain Asadi et al. [START_REF] Asadi | Drivers of Complexity in a Flexible Assembly System-A Case Study[END_REF] acknowledges the need for adapting the complexity driver identification method to specific contexts by allowing survey respondents to add missing complexity drivers. Regardless of the fundamental method used for identifying complexity drivers, Kohr et al. [START_REF] Kohr | Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches[END_REF] notes that the analysts must determine the relevant sources to use, further implying a focus on context specificity. Most of the literature reviewed base their approach on qualitative methods. Specifically, only 1 out of 11 publications reviewed base their methodology on quantitative methods. Kohr et al. [START_REF] Kohr | Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches[END_REF] notes that given existing data models, a quantitative approach for identifying complexity drivers may be less resource demanding than a qualitative approach. They contribute this to the manual work involved in designing, performing, and analyzing surveys and interviews [START_REF] Kohr | Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches[END_REF]. However, for companies where the necessary quantitative data is unavailable, using a qualitative approach may be favorable from a resource perspective. Furthermore, although often considered as inherently objective, data models are still subject to the perspective chosen by the data analyst, thereby questioning the objectivity of the quantitative approach [START_REF] Kohr | Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches[END_REF].

The results of the literature review indicate that there is a gap in literature concerning detailed and concrete methods for the initial identification of complexity drivers. This presents an issue for both researchers and practitioners undertaking complexity reduction efforts and is therefore important to address.

Framework for Identification of Complexity Drivers

Based on the identified gap in the existing literature, this section presents a novel framework for the initial identification of perceived complexity drivers in a manufacturing company. The framework constitutes two parts: i) the structure of the framework and ii) the associated process to follow. These parts will be described in the following.

Framework Structure

The framework is structured as a multi-dimensional matrix and is illustrated in Fig. 1. The first outer dimension of the framework is a representation of a generic value chain using the seven fields summarized by Vogel and Lasch [START_REF] Vogel | Complexity drivers in manufacturing companies: a literature review[END_REF]. The value chain fields are product development (PD), procurement (PC), logistics (L), production (PR), order processing/distribution/sale (OPD), internal supply chain (SC), and remanufacturing (R). Taking outset in a generic value chain makes the framework generally applicable within the manufacturing industry. The framework can be adapted to individual companies by subtracting irrelevant value chain fields, such as the remanufacturing field for companies not engaged in this activity. The second outer dimension of the framework is the generic product structure, which has been implemented through the generic bill-of-materials (GBOM) introduced by Hegge and Wortmann [START_REF] Hegge | Generic bill-of-material: a new product model[END_REF]. Describing product families through a generic product structure enables coverage of the full breadth of product variants with only one representation [START_REF] Hegge | Generic bill-of-material: a new product model[END_REF]. Providing the intersection between the two dimensions of the framework is the complexity rating matrix. For each GBOM element and value chain field, two values must be provided. The first value denotes the perceived complexity of managing the existing portfolio of GBOM element variants. The second value denotes the perceived complexity associated with adding a new variant to the portfolio. By including complexity ratings for both existing and new GBOM element variants, a holistic perspective of complexity related issues is maintained.

Framework Process

The second part of the framework is the process which enables application of the framework in practice. To ensure inclusion of all value chain fields, workshops should be scheduled for each and include company representatives from corresponding departments. The process involves three overall phases. First, at the beginning of each workshop, a brief introduction to the purpose and objective of the workshop is given. Secondly, the workshop participants are then asked to collectively rate their perceived complexity of each GBOM element. This is done by using a five-point Likert scale, where 1 represents the lowest perceived complexity and 5 the highest perceived complexity. Lastly, based on the complexity ratings provided, the participants are asked to elaborate on each individual rating. This process provides the analysts with valuable insights into which aspects of each GBOM element that are considered complexity drivers by the workshop participants. Through this process, analysts can gather information about potential complexity drivers in their organizations as well as the perceived importance of these. This forms a foundation for further analysis of the costs incurred by each of these perceived drivers of complexity, which in turn guides managers to where complexity reduction initiatives are most needed in their organizations.

Methodology

To verify the framework and illustrate its applicability in industry it has been applied in a case company. This section first introduces the case company and then describes the approach followed for the workshops conducted.

Case Description

The case company is a mid-sized production company operating in the business-tobusiness market. The company's main product range is liquid household chemicals produced on either fully or semi-automated production lines. The company is experiencing challenges caused by market developments like those referenced in Section 1. This has resulted in a product portfolio which has grown steadily and includes several hundred product variants and a high rate of annual renewal of the portfolio. For these reasons, the company has recently initiated a complexity management program. The company, therefore, makes for an interesting case for studying methods for identification of complexity drivers. During this study, information was primarily obtained through active participation in workshops and interviews with company representatives. Additionally, company records were used in constructing the GBOM.

Case Study Procedure

In preparation for the workshops, the BOMs for the liquid chemical product group were analyzed to extract the GBOM. The GBOM was then verified by product developers from the company. The resulting product structure comprises the five generic elements: cap, bottle, formula, label, and packaging. The generic value chain fields were verified by managers resulting in exclusion of remanufacturing. Workshops were then scheduled with between two and four representatives from departments corresponding to the value chain fields in the framework. The participants included both managers and specialists, thereby providing insights from both an overall and detailed perspective. Initiating each workshop was a brief introduction to the overall complexity management program followed by an introduction to the framework and purpose of the workshop. Following the introduction, a printout of the framework was handed to the participants, whom were then asked to collectively rate their perceived complexity of each of the five generic subassemblies. To ensure that the participants were not influenced by the ratings made by other departments, a blank framework template was used for each workshop. Once all ratings had been submitted, the participants were asked to elaborate on their ratings by describing them using short keyword sentences on post-its. The postits were then grouped based on which of the five generic subassemblies they referenced.

Case Study Findings

Combining the scores from each individual workshop conducted in the case company resulted in the complexity ratings shown in Fig. 2. The ratings have been color-coded based on their severity. Based on the total ratings for each GBOM element, it is found that "Bottle" and "Formula" are the overall highest rated, each with a total score of 39. They can therefore be considered nearly identical in perceived complexity. The complexity ratings for these two subsystems are also close in a majority of the value chain fields. For example, their ratings differ by no more than one score in 9 out of the 12 value chain ratings. Examples of complexity drivers mentioned for the "Bottle" subsystem includes long changeovers when changing between bottle formats in production and high inventory space taken up by bottles when compared with their consumption rate in production. The complexity drivers mentioned for the "Formula" subsystem includes having to revise production schedules when new variants are added to the production mix, as well as getting changes to formulas approved by certification organizations and customers. On the other hand, labels are generally rated as resulting in least complexity for the company, with a total score of just 21. Complexity drivers mentioned for this subsystem are having to track a high number of variants in both physical inventory and keeping them up to date in the ERP system. In total, more than 53 different complexity drivers were mentioned for the five GBOM elements. Considering the value chain fields, it appears that complexity challenges are mostly affecting the logistics and production fields. In comparison, the procurement and supply chain fields appear less affected by complexity, especially when considering complexity related to new variants. In general, visualization of the perceived complexity through the complexity ratings in addition to the complexity drivers enables managers to make decisions concerning where to focus future analysis of complexity costs and subsequent complexity reduction efforts.

Discussion

The aim of the presented framework is to assist practitioners in the initial phase of complexity driver identification: exploring perceived complexity drivers. The case study showed that the framework succeeds in this regard. However, potential limitations of the framework have been noted. The GBOM representation is limited to products similar in structure. Complexity exploration may thus be limited in scope in companies, where the entire product portfolio cannot be represented by a single generic GBOM. Furthermore, while the products analyzed in the case study are simple, consisting of only a handful of elements, very complex products such as automobiles may pose a challenge to manage in the framework. For such products, a different unit of analysis may ensure a manageable level of detail. This may involve either using highlevel subsystems (e.g. drivetrain, body, chassis, etc.) or focusing on only a single highlevel subsystem (e.g. only drivetrain) and its constituent subsystems. While the framework relies on qualitative data to assess perceived complexity, using quantitative data and automated data processing may facilitate a dynamic framework implemented in e.g., a business intelligence solution. However, pursuing this direction would entail a different set of challenges concerning how complexity-related costs are measured accurately. Another aspect, which may question the validity of the framework is using combined scores for existing and new product variants. If the two aspects have different units of magnitude, weighing them equally risk skewing the results of the framework.

Conclusion

In this paper we have presented a framework to assist practitioners in the identification of context specific complexity drivers. The framework achieves this by means of the generic value chain and generic bill-of-materials, with the former ensuring a broad perspective on complexity drivers and the latter a detailed insight into these. The framework has been verified in a case company where it facilitated the identification of complexity drivers across company functions and product elements. The combination of the complexity ratings and the identified complexity drivers further assists practitioners in focusing their subsequent investigations of where complexity reduction initiatives in their organization should be directed.

Further research should be aimed at investigating both limitations and potentials of the framework. This involves investigating the applicability of the framework for more complex products as well as the applicability in organizations with dissimilar product structures. Given the framework's focus on application by practitioners, further research should center on prescriptive analytics and address how the results of the framework should be interpreted in addition to presenting guidelines on what actions to take.

Fig. 1 .

 1 Fig. 1. The generic complexity driver identification framework.

Fig. 2 .

 2 Fig. 2. Complexity rating results from applying the framework in the case company.


Joakim Wikner

Fredrik Tiedemann

Customization and variants in terms of form, place and time

Keywords: customization, product proliferation, flow thinking, decoupling point

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

Introduction

The increase in globalization and offshoring of production to low-cost countries have led to greater pressure for cost reduction, especially on standardized products. In this context, the ability to offer customization can help offset the disadvantages of producing products in high-cost countries, for example. Customization usually implies a higher degree of product proliferation (i.e., introduction of variants), and although these concepts are closely related, there are some differences between them. However, this distinction is not always straightforward. Customization is a concept that is frequently used to distinguish between standardized and customized items. The pure cases are usually obvious, such as when a company designs and then produces a product based on a specific customer order or when a commodity is produced based on speculation and is stocked and sold in a retail store. However, when a modular product is designed so that it can be configured based on a customer order, using a predefined solution space, it is not as evident in the literature whether the product is standardized or customized. This example is related to the question of what is to be produced, that is, form. However, according to Debreu [START_REF] Debreu | Theory of value: An axiomatic analysis of economic equilibrium[END_REF], an offering is characterized by not only its physical properties (form) but also where (place) and when (time) it will be available. Conse-quently, if place and time are included, more challenges arise in defining whether something is standardized or customized. A variant is also a more general term than customization since it is not bound to a direct (real) customer demand and may instead depend on the characteristics in the bill of materials (BOM). Therefore, the purpose of this study is to analyze customization in terms of form, place and time, as well as relate it to variants.

Physical flow structures and time-phased flow structures

Value is created in a network of activities, where input is transformed into output. This value creation is characterized by the resources used, flow control and flow structure, among others. The flow structure is of special interest in customization and variant creation. Nonetheless, as customer demand also covers when a demand arises in relation to the cumulative lead time, the time perspective is of interest as well.

Flow structures focusing on form and place are called physical flow structures. This type of structure involves different levels of abstraction; it is therefore necessary to decide on a specific level. The so-called AVX classification is often used in the material and capacity requirement literature [START_REF] Oden | Handbook of material and capacity requirements planning[END_REF]. This classification is based on the item level, where an item is perceived as indivisible, meaning that the activities (production or transportation) required to transform the item are abstracted and not considered. Instead, the focus is on the material structures (i.e., BOM), where the end product is at the top. The so-called VAT classification is similar, also includes resources and is found in the theory of constraints [START_REF] Umble | Synchronous management: Profit-based manufacturing for the 21st century[END_REF]. As such, VAT does not focus on items but on operations, resulting in a lower level of abstraction. The letters V and A in VAT correspond to the same letters in AVX, where T equals X, but the point of departure is the operation rather than the item. For both the AVX and the VAT structures, two flow structures (i.e., converging and diverging) make up the foundation, as illustrated in Table 1. For a converging flow, B1 and B2 are combined to obtain item C; for a diverging flow, item B can result in C1 and/or C2. A converging flow consists of two or more flows joining in a point to form one flow, meaning that from an end product perspective, it is possible to move upstream the flow in order to identify all parts and raw material input of the flow. For diverging flows, the opposite occurs, where one must start from the raw material moving downstream to identify all parts of the flow and the end products. This means that it is difficult to relate to diverging flows from an end product perspective; instead, one should take a raw material perspective. Diverging flows can further be divided into two main types, namely attribute based and decision based. The attributebased type is passive, meaning that the end result is already given at the point of divergence, such as when a chemical process splits a raw material into two or more materials. The decision-based type is active, meaning that a decision can be made regarding what mix to create, usually termed variants. These three types of flow structures are summarized in Table 1. The interpretation of flow structures in terms of form transformation has already been discussed, but the place transformation has not yet been explained to the same extent. Essentially, it is about relating identity not only to what (form) but also to where (place), which is the meaning of the term stock-keeping unit (SKU). Place is therefore related to having something at one location (central) or at several locations (local). The place dimension is related more to active decisions rather than attributes. 1), which is usually illustrated horizontally to clearly illuminate the time dimension of the activities performed (in contrast to a traditional BOM). Each segment of the structure shown in Fig. 1 represents a transformation in terms of form and place. The time phasing means that each transformation step is offset by the corresponding time for each segment, that is, its lead time.

Fig. 1.

Example of a time-phased flow structure (based on [START_REF] Wikner | On decoupling points and decoupling zones[END_REF]).

The time-phased flow structure depicted in Fig. 1 represents the conditions in a given situation for a given point in time, that is, one state. Since the flow structures can adopt many different forms and combinations, it is difficult to define a generic space of states. Nevertheless, to sum up, this means that all transformations in the three dimensions need to be completed to create customer value. In other words, the demanded combination of form, place and time needs to be attained for the flow state to fulfil the requested state.

Change of state through postponement and preponement

Flow structures can be changed in several ways, but two concepts explicitly associated with change in form, place and time are postponement [see, e.g., 5, 6] and preponement [START_REF] Blackburn | Reverse supply chains for commercial returns[END_REF]. The word 'postpone' means 'put off to a later time' [8]. Although 'prepone' is not found in the last cited reference [8], the prefix 'pre' means 'earlier than', 'prior to' or 'before'. To postpone can thus be understood as to reposition to a later point in time, whereas to prepone means the opposite, to reposition to an earlier point in time. Both postponement and preponement are thus related to changes in an existing state, that is, repositioning an already existing position. Furthermore, both have their basis in the time dimension, meaning that a time-phased flow structure is a suitable point of departure. This also basically means that both terms are based on time and the repositioning of a transformation in relation to a fixed point of reference, which in this case is the customer's requested state. In this paper, the repositioning in time is called 'displacement' and is thus a collective term for postponement and preponement.

X A1 A2 C B1 B2 A C1 C2 B X1 X2 L Z =2 L Y =3 L V = 2 L W = 2 L X =1 Z Y X W V Q U L U = 4 L Q = 3

Displacement of form, place and time

Displacement of form and place transformation can occur by either creating new flow structures or changing the time required to perform the transformation activities. Table 1 indicates five different types of form and place conversions that can be achieved by displacement. From a form perspective, this means that through postponement, the form is achieved later in the flow (e.g., by modularization), using standardized modules instead of building complete unique products from scratch. In contrast, preponement could be used to create variants of a previously standard product.

Postponement of place transformation usually refers to differentiation in location at a later point in time, for example, by keeping products in a centralized warehouse instead of distributing them to local warehouses. This can be formulated as creating a variant in terms of a product's place property at a later point in time. Correspondingly, preponement means that the variant in terms of the place property is created at an earlier point in time to ensure local availability, for example. ]. Displacement therefore conforms to the definition of the customer order decoupling point (CODP), based on how the delivery lead time relates to the system lead time [START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF]. The CODP is defined as the point that separates the forecastdriven part from the customer-order-driven part of the flow. To summarize, displacement, as well as form, place and time transformation, can be combined in six ways, as shown in Table 2. Basically, there are two types of changes in state. For form and place, the change is related to variants in flow, with an 'absolute' displacement since the displacement is made in time units. Time is then related to the flow driver and a relative displacement since the displacement occurs in relation to the delivery lead time. This also means that for form and place, the points of departure are the individual points of transformation in the flow. In contrast, for time, the point of departure is the CODP, which has its starting point in the delivery lead time.

Customization and displacement

Flow structures represent how the transformation of inputs to outputs is performed. The form and the place dimensions specify the type of transformation carried out, whereas the time dimension positions the different transformation activities in relation to each other. Together, these three dimensions can define a state of transformation. Displacement in terms of postponement and preponement then represents how a transformation can be changed, thus signifying a change of the pre-displacement state, which can have a different nature. Changes of form and place are structural in nature, but time is usually related to whether an exogenous customer demand drives the flow or if the flow is based on an anticipated future demand (i.e., a forecast). As such, displacement is intimately related to the CODP. This presents a general overview of how transformation can be described in terms of state and change. Even so, it does not include a distinction between standardized and customized activities. Different types of variants are included in the presented description, but it lacks a clear distinction of whether the variants are based on customer demand or something else.

Customization in form, place and time

Variants from the customer perspective tend to be associated with the distinction between standardized and customized products. Standardized refers to the customers' inability to influence the properties of a product that is offered to all customers (i.e., customer generic). In contrast, customization means that the product has some type of element adapted to a specific customer. From the form perspective, this means that a standard product is not based on an individual customer's requirements. Of course, this does not exclude the possibility of considering the market's expectations when designing the product. However, a customized product is based on individual customer requirements and is thus customer unique. Sometimes, the frequency of the demand is also considered, distinguishing between customer-unique and delivery-unique types.

For the latter, the ordered product is obviously unique for a specific customer order, but a customer-unique item could be ordered several times by the same customer.

A comparable reasoning can be stated in terms of the place perspective. A standardized place implies that customers can retrieve their products from a specific place, such as a central or a local warehouse. A customer-unique place should then correspond to a delivery made to a specific postal address, that is exclusively the customer's own location. For the form, the question of frequency is raised, but this does not have the same meaning for the place. The place is more of a grey scale between the completely standardized place, where the product receives its final form, and the customized place, where the product is used. In between them, a number of places are neither completely standardized nor completely customized but intermediate. The two perspectives can be summarized as shown in Table 3, where the columns indicate the degrees of customization in terms of form (F), and the rows present the degrees of customization in terms of place (P). To distinguish among different degrees of uniqueness, various types of strategic decoupling points have been introduced, one being the customer adaptation decoupling point (CADP) [see, e.g., [START_REF] Wikner | On decoupling points and decoupling zones[END_REF][START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF][START_REF] Wikner | Decoupling points and product uniqueness impact on supplier relations[END_REF]. This means that the top left combination is a complete standardized product, and the bottom right combination is a complete customized product in terms of both form and place. All other combinations are some forms of intermediate products. The CADP is positioned between customer-unique and customer-order-unique types, and as it relates to form, it can be denoted as CODPF. Similarly, a CADP can be defined for the place dimension, denoted as CODPP, which is correspondingly positioned in between intermediate and local types. Customization of form and place can be viewed as a scale ranging from standardized to customized, regardless of whether form or place is considered separately or in combination. This distinction between standardized and customized has a long history and is sometimes associated with the driver and the CODP. By combining the driver with uniqueness, as presented in Table 4, four possible combinations are obtained. Case I represents situations where standardized products are made based on forecasts (i.e., standardization is usually associated with activities upstream of the CODP). Case III is the other extreme case where customized products are made due to a commitment to a customer order (i.e., customization is usually associated with activities downstream of the CODP). When the CODP is used to define both uniqueness and flow driver, no more combinations can be found. If the typology is instead aligned with assumptions in [START_REF] Wikner | On decoupling points and decoupling zones[END_REF][START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF][START_REF] Wikner | Decoupling points and product uniqueness impact on supplier relations[END_REF], the CODP is only related to the flow driver, whereas the CADP is used for the flow differentiation. By using the CADP, one more relevant combination can be found, namely standard products made due to a commitment to customer orders, that is, Case II (see Table 4). However, since customization should not be carried out based on a forecast (speculation), the fourth logical combination is perceived as undesirable and thus considered not applicable (N/A). Changing the state in flow structures with a focus on customization brings several challenges. Instead of focusing on form, place and time, the points of departure here are the three types of decoupling points, see in Table 5. Note that the three decoupling points are not independent of each other in creating customization. Unlike the general concept of variants, customization in form, place and time has a point of reference in terms of the customer order. Rational customization requires that a customer order has been received; thus, the CODP constitutes a restriction of how far upstream the CADPF and the CADPP should be positioned in a time-phased flow structure. Displacement of 'form and time' and 'place and time', as shown in Table 5, therefore means that form and place are each shifted in time where time is included. Customization in terms of form has traditionally been created in a factory before the product is delivered to a customized place. This means that the CADPF traditionally is positioned before the CADPP in the flow. However, new technology, such as additive manufacturing using three-dimensional printing, has enabled customization in form to be created closer to the customers.

Conclusions

The purpose of this study is to analyze customization in terms of form, place and time, as well as relate it to variants. The main conclusion drawn from this study is that both customization and variants are related to flow states, represented by flow structures. A state can be defined in terms of the three dimensions: form, place and time. With their relations to different strategic decoupling points and through the three dimensions, strategies such as postponement and preponement can also be identified as forms of support for changes in a state. Using flow thinking [START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF] and the two strategic decoupling points (CODP and CADP) provides a better understanding for the questions of what, where and when customization can be applied. Hence, this study contributes to the existing literature by increasing the knowledge of what can be considered standard variants and customization, respectively. Further research could include theoretical development, for instance, where the relationship among postponement, preponement and flow thinking could be more clarified. Practical applications could also be further examined, where the understanding of displacement improves the capability to manage operational development, enabling more effective and efficient flow solutions.

  time Q-Z = Activities performed L i = The lead time of the activity i

Fig. 2 .

 2 Fig. 2. Customization and displacement (based on[START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF]).

Table 1 .

 1 Combination of flow structures and form/place.

		Converging	Diverging (attr.-based)	Diverging (dec.-based)
	Form	Assembling or mixing	Splitting in a process	Creating variants
	Place	Moving from local to cen-tral warehouse	-	Moving from central to lo-cal warehouse

Time (when) is another central property in transformation. Complementing form and place with time creates a representation of all three dimensions. One conventional way of doing this is through a time-phased BOM (see Fig.

Table 2 .

 2 Combinations of displacement and form, place and time properties.Displacement of time seems as if time is counted twice, but here, the term time refers to two different things. Displacement has traditionally pertained to the change of the point in time when a transformation is carried out in relation to the requested delivery lead time [see, e.g., 9

	Preponement	Postponement
	Form Form variant created earlier in the flow	Form variant created later in the flow
	structure	structure
	Place Place variant created earlier in the flow	Place variant created later in the flow
	structure	structure
	Time Customer order decoupling point (CODP)	CODP positioned later in the flow
	positioned earlier in the flow structure	structure

Table 3 .

 3 Customization of form (F) and place (P).

		F: Customer generic	F: Customer unique	F: Delivery unique
	P: Central	Standardized
	P: Intermediate
	P: Local			Customized

Table 4 .

 4 Combinations of driver and uniqueness (based on[START_REF] Wikner | On decoupling points and decoupling zones[END_REF][START_REF] Wikner | Decoupling points and product uniqueness impact on supplier relations[END_REF]). Customization can thus be described as a combination of three dimensions: (1) Regarding form, customization answers the question of what and is related to the CADPF.[START_REF] Oden | Handbook of material and capacity requirements planning[END_REF] As for place, customization answers the question of where, related to the CADPP. (3) Concerning time, the flow driver answers the question of when, related to the CODP. Customization can thus be described as a state with three dimensions. A product delivery is thereby a combination of the flow driver in time and the flow differentiation (customization) in form and place. Note that this state is only one subset of the possible states defined as variants, where only a portion is related to 'real' customer demand. Changes in the state of variants can then be made using displacement (i.e., postponement or preponement), meaning that one state (S1) transitions into another state (S2), as illustrated in Fig.2. Note that S1 and S2 could signify that the same customizations are offered in two different ways, as shown in Fig.2, or that two different types of customizations are offered. Two examples of states for a structure are illustrated in Fig.2. In the left part, S1 indicates that B can be used for creating either C1 or C2. A postponement of the variant proliferation point (CADP) would result in S2, where C is kept 'generic' and could be used for creating either X1 or X2. Both structures thus mean that X1 and X2 are created but in two different ways, where S2 means that the customization can be made at a later point in the structure, enabling a shorter delivery lead time.

		Forecast-driven Customer-order-driven
	Standardized	Case I	Case II
	Customized	N/A	Case III

Table 5 .

 5 Decoupling points and displacement.

	Decoupling point	Changing state (Displacement)
	CADPF	Displacement of form and time, CADPF should not be upstream of CODP
	CADPP	Displacement of place and time, CADPP should not be upstream of CODP
	CODP	Time postponement or time preponement

Acknowledgement

This research has been conducted under the KOPtimera project, funded by the Swedish Knowledge Foundation and Jönköping University.