E G Nabati
email: nabati@uni-bremen.de

M T Alvela Nieto

A Decker

K.-D Thoben
email: thoben@uni-bremen.de

Application of Virtual Reality Technologies for Achieving Energy Efficient Manufacturing: Literature Analysis and Findings

Keywords: Manufacturing, Energy efficiency, Virtual Reality

Improvement in current manufacturing settings for enabling energy efficiency is a challenge for many manufacturers. Although virtual reality has been so far applied in manufacturing for training, visualization and product development, the use of this technology in manufacturing for increasing energy efficiency has been less addressed. This paper investigates the potential of virtual reality for a better analysis of energy demands in manufacturing. By envisioning and illustrating energy flows and consumption, virtual reality can support energy efficiency. The paper provides a systematic review of the literature. The findings are analysed from the perspective of research gaps in making virtual-based technologies to enable energy-efficient manufacturing. Particularly, the elements and factors (opportunities) and methods that can be transmitted from current research to energy-efficient manufacturing, are identified and discussed.

Introduction

Today's manufacturing is one of the world's biggest consumer of energy. By 2030 a target of 32.5% energy savings has been endorsed through negotiations between the European Commission and Parliament [6]. Consequently, European manufacturers face today the challenge of reducing energy consumption while maintaining quality and productivity. Besides, advances of Industry 4.0 provide new opportunities to better measure energy, understand its patterns or mechanisms and improve manufacturing operations concerning energy. Determining and understanding energy consumption at each stage of manufacturing is crucial for enabling energy efficiency. Though improvements in energy efficiency (EE) have been made across all branches of manufacturing, significant potential still exists [5].

One technology of Industry 4.0 that can further contribute to the efficient consumption of energy is virtual reality (VR). In general, VR makes it possible for users to step into a three-dimensional (3D) illustration of a real-world environment, through a computer interface [START_REF] Mujber | Virtual reality applications in manufacturing process simulation[END_REF]. VR-solutions pose good potential for saving energy in manufacturing industries. Since they can add a layer of energy information to the machinery and equipment. Areas of research, such as smart buildings and facility management, have managed earlier than manufacturers to adapt VR to the energy analysis. For example, authors including [START_REF] Ham | 3D Visualization of thermal resistance and condensation problems using infrared thermography for building energy diagnostics[END_REF] provided a VR-tool for the modeling and analysis of energy as well as its 3D-visualization within buildings.

The findings on VR-solutions from the area of smart buildings can be transferred into the manufacturing industries, i.e., analyzing the higher energy consumers on the shop floor of a manufacturing plant. Inspired by this possibility, this research seeks to identify the components (elements) of manufacturing, such as machinery and processes, other than buildings, which affect the energy consumption in manufacturing. In addition, factors and opportunities to include in the energy modeling with VR are noted. Knowing the available methods is also important for developing VR-solutions about energy. To the best authors' knowledge, engineering tools for the design and development of VR-applications in energy-efficient manufacturing (EEM) are not published or reported yet. Further, results of surveys as [START_REF] Choi | Virtual reality applications in manufacturing industries: Past research, present findings, and future directions[END_REF][START_REF] Berg | Industry use of virtual reality in product design and manufacturing: a survey[END_REF] suggest that only a few studies have tackled VR-technologies for achieving efficiency in terms of energy consumption. Consequently, practical studies related to EE on VR from the current research need to be identified; so that meaningful insight can be leveraged.

This paper provides an overall analysis of the potential of VR into EEM, whereby it answers the following research questions (RQ) through a review of state-of-the-art: RQ1. Which factors and elements should be considered for the transmission of currently available research to apply VR for increasing EE in manufacturing?

RQ2. What are the methods available in the literature to apply VR in EEM?

The paper is structured as follows: Section 2 presents the literature search methodology. Section 3 reveals and discusses the results of the literature review. Section 4 reports the findings in terms of research gaps as well as the knowledge acquired after the analysis of studies. Here limitations of VR are also presented. In section 5, conclusions are drawn.

Methodology based on Systematic Review of Literature

For the literature review, a methodology called PRISMA [START_REF]PRISMA: Transparent Reporting of Systematic Reviews and Meta-Analysis[END_REF] is adopted. Two leading online databases, Scopus and Google Scholar, are selected. While Scopus provided papers with a good scientific contribution, Google Scholar is used as a complementary search tool to find articles from a more practical point of view. As Fig. 1 shows, three sets of keywords are searched. The period considered is 2004-Fig. 1. Methodology for literature review 2020. The first search criteria lead to around 928 articles. The identified papers were screened through reading the title and 106 papers are selected. The excluded papers primarily addressed VR in, i.e., reconfigurable manufacturing systems and sustainable engineering education. These trends within manufacturing are not the focus of this paper. Afterward, abstracts and keywords of 106 papers were justified. The result was a selection of 38 eligible papers for this study. These papers were read in full-text and later, 11 papers were selected for answering the above research questions.

Results and Discussion

The results of the literature review are recapitulated in Table 1 andTable 2. The answers to RQ1 are provided in the Table 1. In order to understand the elements and factors, column "(Level) Elements" investigates to what extent state-of-art of VR is linked to which elements of the manufacturing level(s). The considered manufacturing levels in this paper are operational and tactical. Therefore, the first column of Table 1 assesses the area of application within manufacturing. Next, the column "Opportunities to EEM" shows the results of identified factors related to energy efficiency. This column also assesses the focus of the studies on different aspects of EEM.

In contrast, Table 2 assesses, which environment(s) the research has been validated on (case studies). The review of case studies reveals manufacturers the feasibility and potential of VR-applications in particular environments and it indicates similarities (or not) for possible transfers into new practical studies. For the RQ2, the last column of Table 2 presents methods and tools for implementing VR. Furthermore, the next two sub-sections analyse the contents of Table 1 and Table 2 concerning VR for EEM. In section 3.1, the analysis of Table 1 respecting to RQ1 is presented. Section 3.2 provides a discussion about Table 2 for RQ2.

Elements and Factors in Current Research to Apply VR for Increasing EE in Manufacturing

Table 1 shows that the gathered studies of VR mainly focus on the operational level. Among the results of Table 1, 8 of 10 selected papers address VR in EEM for the operational layer of manufacturing. The elements of the operational level are process, machine, line and facility. The classification of these elements can be considered valid, as many approaches within EEM, such as the study of [START_REF] Fysikopoulos | On a generalized approach to manufacturing energy efficiency[END_REF], divide the analysis of energy into the same set of elements. Also, the analysis of Table 1 shows that the current research has addressed these elements individually. That is, there is still no research, which examines the flows of energy between the elements of manufacturing, especially during operation. Therefore, developing a model of processes-machines-lines and their interdependencies in a manufacturing facility can better evaluate energy demands. Spatio-temporal energy model( X-time series, y-consumption devices, z-energy consumption) [START_REF] Mawson | The development of modelling tools to improve energy efficiency in manufacturing processes and systems[END_REF] Process, machine Machine and product design to EE (awareness, transparency) [START_REF] Juraschek | Mixed Reality in Learning Factories[END_REF] Process, facility Augmentation of energy flows demands (process states, maintenance, instructiveness) [START_REF] Schulze | Energy management in industry -a systematic review of previous findings and an integrative conceptual framework[END_REF] Management KPIs as integrative energy management [START_REF] Whyte | Viewing asset information: Future impact of augmented reality[END_REF] Line Visual interface for asset information [START_REF] Nugraha Bahar | CAD data workflow toward the thermal simulation and visualization in virtual reality[END_REF] Facility Immersive thermal simulation in virtual environment

Other findings from the Table 1 shows that the majority of the research concentrates on the transparency of energy performances. As energy is not visible to the human eye, which complicates the impairment of energy efficiency. Therefore, the availability and visualization of energy data and information of energy assets, for example, in the form of sustainable key performance indicators (KPIs) or flows increase awareness about demands and support its efficiency [START_REF] Assad | A framework to predict energy related key performance indicators of manufacturing systems at early design phase[END_REF][START_REF] Pelliccia | Energy Visualization Techniques for Machine Tools in Virtual Reality[END_REF][START_REF] Whyte | Viewing asset information: Future impact of augmented reality[END_REF][START_REF] Schulze | Energy management in industry -a systematic review of previous findings and an integrative conceptual framework[END_REF][START_REF] Juraschek | Mixed Reality in Learning Factories[END_REF][START_REF] Mawson | The development of modelling tools to improve energy efficiency in manufacturing processes and systems[END_REF]. Nevertheless, energy demands in real applications are influenced by complex settings, such as the effects of different process parameters, workflows and quality requirements. But energy flows and energy consumers considering multiple configurations of parameters are almost absent in the literature (Table 1).

In [START_REF] Nugraha Bahar | CAD data workflow toward the thermal simulation and visualization in virtual reality[END_REF][START_REF] Pelliccia | Energy Visualization Techniques for Machine Tools in Virtual Reality[END_REF][START_REF] Haefner | Interactive Visualization of Energy Efficiency Concepts Using Virtual Reality[END_REF][START_REF] Mawson | The development of modelling tools to improve energy efficiency in manufacturing processes and systems[END_REF][START_REF] Juraschek | Mixed Reality in Learning Factories[END_REF], computer-aided design (CAD) models are used for the interactive examination and manipulation of VR-applications. However, most geometries on VR are not reflecting the real dimensions of, i.e., a certain machine. So, it makes the energy model inaccurate. Moreover, visualizations of many energy flows in one component or many components in one visualization such as [START_REF] Nugraha Bahar | CAD data workflow toward the thermal simulation and visualization in virtual reality[END_REF][START_REF] Pelliccia | Energy Visualization Techniques for Machine Tools in Virtual Reality[END_REF][START_REF] Haefner | Interactive Visualization of Energy Efficiency Concepts Using Virtual Reality[END_REF] in Table 1 can become unclear to the observer.

Besides, energy demands in [START_REF] Pelliccia | Energy Visualization Techniques for Machine Tools in Virtual Reality[END_REF] were assumed to be constant, whereas the energy losses of, i.e., a cooling system, were neglected. Furthermore, the EE-KPIs defined in [START_REF] Assad | A framework to predict energy related key performance indicators of manufacturing systems at early design phase[END_REF][START_REF] Chaim | Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment[END_REF] do not account for the fluctuation of the process times and/or cycle times. Both process time and cycle time influence the energy consumption of a particular line at the production phase. Thus, energy performances can become misleading when time-series factors are not taken into account.

Lastly, from Table 1, an estimation about the potential of the energy savings through VR-solutions is neither visualized nor (at least) quantified. Despite the fact that evaluative information about the savings (actual and ideal energy demand) may raise awareness among manufacturing practitioners. In this context, a VR-based solution can assist in determining the energy costs of a process or machine with a particular set of parameters and predefined quality.

Methods of VR for Enhancing EEM

Firstly, simulation and CAD modeling are the most common methods listed in Table 2. The VR-tools in Table 2 were mainly selected concerning to the case studies. As an example, VueOne is an engineering software environment, which supports the assembly. It is selected by [START_REF] Assad | A framework to predict energy related key performance indicators of manufacturing systems at early design phase[END_REF], respecting the case study of the battery assembly.

About the methods that are specifically designed for the analysis of EE in manufacturing, there are only a few works available (Table 2). For machines, the technique of particle system on VR allowed the visualization of dynamic changes in energy flows over time [START_REF] Haefner | Interactive Visualization of Energy Efficiency Concepts Using Virtual Reality[END_REF]. In the particle system, the direction of energy flows is also highlighted. In [START_REF] Katchasuwanmanee | Simulation based energyresource efficient manufacturing integrated with in-process virtual management[END_REF], a 3D-regression method evaluated the correlations of workflow, consumption of energy and quality error. However, evaluation of other potential factors affecting energy demands as settings of process variables were not included.

Secondly, a tendency in Table 2 exists to base VR on point-cloud models and particle systems when it comes to virtual buildings [START_REF] Whyte | Viewing asset information: Future impact of augmented reality[END_REF][START_REF] Haefner | Interactive Visualization of Energy Efficiency Concepts Using Virtual Reality[END_REF]. These methods can be combined with the use of drones and photogrammetry for quicker creation of 3D-models on VR. This approach from smart buildings is also applicable and rentable in manufacturing when it comes to elements such as lines and building of a factory.

Thirdly, the literature analysis showed that the majority of the VR-methods in Table 2 are developed from a theoretical point of view. Then, their performances are validated in specific case studies and experiments [START_REF] Assad | A framework to predict energy related key performance indicators of manufacturing systems at early design phase[END_REF][START_REF] Chaim | Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment[END_REF][START_REF] Whyte | Viewing asset information: Future impact of augmented reality[END_REF]. Hence, there is a need for contributions from a more practical point of view, which addresses the real-world application of VR in EEM.

Lastly, in the context of manufacturing, VR-technologies constitute augmented reality (AR), mixed reality (MR or XR) and virtual manufacturing (VM) [START_REF] Mujber | Virtual reality applications in manufacturing process simulation[END_REF][START_REF] Mawson | The development of modelling tools to improve energy efficiency in manufacturing processes and systems[END_REF]. According to [START_REF] Mawson | The development of modelling tools to improve energy efficiency in manufacturing processes and systems[END_REF], AR is VR placed over the real world, but with the provision of additional information. Thus, the authors of [START_REF] Mawson | The development of modelling tools to improve energy efficiency in manufacturing processes and systems[END_REF] infer that AR can be seen as a subset of VR. MR encompasses both virtual and AR technologies.

Moreover, VM is a term represented as a virtual world for manufacturing. This paper addressed all these mentioned VR-technologies.

Findings

From the previous section, it can be drawn that VR in EEM has been mostly used to reduce the time required to collate and understand energy information. An energy model for the context of manufacturing should consist of many elements such as machinery, production line, facility, processes and interdependence of energy demand from them. More detail about the essential factors in the application of VR for EEM, namely, transparency of energy flows, assessing energy demands and energy losses, process parameters affecting energy demands are discussed in section 3.1. The highlighted opportunities to EEM on VR have a high degree of flexibility. They are quickly adaptable, as they are not bounded by physical hardware. This allows to experience different settings and/or scenarios of higher energy consumers that are, in reality, too costly or not available in hardware. Further, our observations show that a tendency exists in the literature to research VR-technologies related to time and costs, quality and reduction of design errors. However, the different settings of parameters influence the results in the form of energy but also in the form of emissions and wastes of processes, e.g., in a milling process. Wastes and emissions are difficult to integrate on VR and hence their embodied energy can not be quantified. To our understanding, it is also a matter of what designers (users) can do from a VR-technology standpoint but also how human actions in the virtual environment influence decision making.

Additionally, best practices and benchmarks of energy efficiency in manufacturing on VR-solutions are still not published. Probably, as a consequence of the ambiguity of the cause-effect relationships in the manufacturing processes.

Limitations of VR in EEM. Although the VR's advantage in reproducing a controllable environment is desirable for application in EEM, there are some limitations too. (1) Costs of establishing a VR-based system is rather high.

Before designing a VR-based system for EEM, the long-term benefits of this technology, in terms of savings, should be calculated against its costs. (2) Multisensor illustration of energy flows. It is challenging to split several energy flows and illustrate them in a virtual environment. There can be overlaps between the illustrations of energy. This point can confuse the observers or bring errors in energy analysis. [START_REF] Chaim | Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment[END_REF] The danger of continual use for the health of employees. On the one hand, the currently available head mount glasses for VR and MR are heavy. The weight is because of battery or processing appliances. Therefore, they might cause problems such as neck pain for the employee if they are used for a long time. On the other hand, they are placed over the eyes. Too much use can cause eye-sight problems.

Conclusions

Our research provides a literature review on 11 papers about VR-technologies for enhancing energy efficiency in the manufacturing domain. Opportunities and tools of VR for the context of EEM were presented and discussed extensively in sections 3 and 4. The results of the analysis admit that using VR on EEM can improve knowledge about energy consumption in manufacturing environments. Future studies may transfer, combine and adjust the analysed methods (Section 3.2) of VR for further exploring its opportunities in EEM (Section 3.1 and 4). As the deployment of VR-technologies and its usability demands considerable energy at a cost to manufacturing too.

Beyond the scope of this paper, several types of research tackle the connection of digital twins to VR for establishing digital factories of the future. However, they do not discuss VR in energy analysis about efficiency, considering a whole factory. Therefore, there is a need for future research of EEM on VR, not only as a stand-alone solution but also as a multi-level holistic approach.

It should be noted that in this paper, because of limitation in space, only the results of the analysis for 11 selected papers that has the most relevancy to the research questions, have been published. Analysis of all 38 eligible papers from the perspective of EEM will later be provided in the special issue.

Table 1 .

 1 Results of the literature review for RQ1

	Ref. (Level) Elements	Opportunities to EEM
	[1] Process, Machine, Line at de-	Integration of EE-KPIs into expert systems (transparency, decision-
	sign phase	making)
	[3] Management	Sustainable KPIs in game-fuction (adv./disadv., understanding, im-
		pacts)
	[15] Machine	Energy-flow techniques for visualization (causes, awareness)
	[11] Line	In-process virtual management of energy, workflow, and quality er-
		ror
	[8] Facility

Table 2 .

 2 Results of the literature review for RQ2

	Ref. Case Study	Method(s) : Tool(s) of VR
	[1] Battery module assembly	Discrete event simulation tools: VueOne, Witness DES
	[3] Serious-games learning env. Virtual twins
	[15] Turning, milling	3D-Sankey, Billboad and 3D-Particle methods: CAD
	[11] Shop-floor	3D-Virtual simulation
	[8] None	VR with immersive visual data mining: 3D-CAVE, CAD
	[12] None	Discrete event simulation and 3D-particle methods:CAD
	[10] Galvanic and oven process	Holographic augmentation, interactive visualization: CAD
	modules
	[18] Railway maintenance	4D reality, point-cloud methods
	[2] Human operators, packag-	3D-Visualisation suite: RTT DEltaGen, Siemens Jack, Autodesk
	ing, interior tractor	VRED, ESI ICIDO


Maria Linnartz
email: maria.linnartz@fir.rwth-aachen.de

Volker Stich

Kennzahlentyp

Software-based Assistance System for Decision Support on Supply Chain Level

Keywords: Supply Chain Management, Decision Support, Software Tool. Legende: X: Eigenschaf t erf üllt Kennzahlentyp Kennzahlentyp

In recent years, the complexity of the management of supply chains has increased significantly due to the growing individualization of products and dynamics of the market environment. To remain competitive, ensuring efficient and flexible processes and procedures along the entire supply chain are of particular importance for companies. Especially in the inter-company context, decisions must be made as quickly and correctly as possible. To enable good decision-making processes data must be processed and provided in a targeted manner. Currently, however, the necessary transparency is often lacking within the supply chains. In this article, a software-based assistance system for decision support on supply chain level is presented that aims to increase the transparency and efficiency of the decision-making process. A concept for decision support on supply chain level is presented. This paper focuses on the conceptual linkage of relevant decisions and data. Therefore, indicators are identified and linked with the relevant decisions. Moreover, a suitable way of visualizing the identified indicators for each decision in a user-friendly manner is defined. These results are then used to implement the software tool.

Introduction

Many companies are facing an increasingly dynamic and volatile market environment. Various factors like increasing customer expectations, demand for individual products, shorter product life cycles and intensified cost pressure have led to an increase in complexity in managing supply chains. In this environment, efficient and flexible processes along the whole supply chain are necessary. At the same time, more and more data is generated along the supply chain. Utilizing this data holds the potential to increase transparency and improve processes and decisions. [START_REF] Biswas | A Proposed Architecture for Big Data Driven Supply Chain Analytics[END_REF][START_REF] Kersten | Trends und Strategien in Logistik und Supply Chain Management. Chancen der digitalen Transformation[END_REF][START_REF] Dombrowski | Ganzheitliche Produktionssysteme und Industrie 4.0[END_REF] Currently, however, the necessary transparency is often lacking within the supply chain. A reliable and up-todate basis for decision-making is often not available. [START_REF] Brecher | Nutzen und Potenziale modellbasierter Datenanalyse[END_REF] The required data is often not available or not available in sufficient quality [START_REF] Kersten | Trends und Strategien in Logistik und Supply Chain Management. Chancen der digitalen Transformation[END_REF]. Companies need to be able to extract useful information from huge amounts of data and provide this information to decisionmakers to stay successful [START_REF] Biswas | A Proposed Architecture for Big Data Driven Supply Chain Analytics[END_REF].

To face these challenges, assistance systems for decision-makers in supply chain management and logistics are needed. Such systems can ensure a structured provision of required data and thereby increase the transparency. Therefore, assistance systems lead to faster decision making.

The aim of the software-based assistance system for decision support on supply chain level presented in this paper is to increase the transparency and efficiency of the decision-making processes on the strategic, tactical and operative level through a demand-oriented provision of data. The decision support is achieved through the analysis and linkage of data in indicators and the visualization of these indicators within a software-based assistance tool.

State of the Art

There is a wide variety of existing approaches used for decision support in different tasks within supply chain management. To provide decision support assistance systems need to be based on a sufficient database, process and utilize data and contain comprehensive technical solutions [START_REF] Kersten | Trends und Strategien in Logistik und Supply Chain Management. Chancen der digitalen Transformation[END_REF][START_REF] Klocke | Assistenzsysteme in der Produktionstechnik[END_REF]. To achieve the above-mentioned goal, the assistant system should address concrete decisions in the area of sourcing and delivering, contain a data model, provide the relevant evaluated data through a user interface and be implemented in a software tool.

A general framework for decision support along the whole product life cycle is the Internet of Production (see Fig. 1) which was introduced at the Aachen Machine Tool Colloquium in 2017. It covers the different phases of the product life cycle (development, production and use). The basis for analysis and decision support is raw data from different business application systems like Enterprise Resource Planning (ERP) systems. Raw data is processed using algorithms and business analytics to create smart data which contains a high information content. This information is then used to provide decision support for the decision-maker through assistance systems. [START_REF] Klocke | Vernetzte adaptive Produktion[END_REF] In the framework of the research project Cluster of Excellence "Internet of Production" several decision support apps within the different cycles are developed. So far, it does not contain a specific assistance system for decision support on supply chain level.

According to a systematic literature review by TENIWUT AND HASYIM [START_REF] Teniwut | Decision support system in supply chain: A systematic literature review[END_REF], decision support systems in the context of supply chain management often focus on specific tasks like determining delivery routes or choosing a supplier.

IVANOV [START_REF] Ivanov | An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations[END_REF] presents an adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations. While this framework identifies specific decisions and relevant data, it does not provide a data model or advise on the data provision for the decision-maker. BISWAS AND NARAHARI [START_REF] Biswas | Object oriented modeling and decision support for supply chains[END_REF] present generic supply chain objects to model a variety of supply chains. MUKKADES ET AL. [START_REF] Mukaddes | Developing an Information Model for Supply Chain Information Flow and its Management[END_REF] identify information categories for a demand-oriented provision of data on supply chain level. SCHUH AND BLUM [START_REF] Schuh | Design of a Data Structure for the Order Processing as a Basis for Data Analytics Methods[END_REF] develop a data structure for order processing as a basis for data analytics. These works offer data structures to model supply chains but they do not link these data to specific decisions. Moreover, they do not focus on the design of the user interface for data provision.

Existing software tools and business application software for different tasks along the order processing process contain user interfaces for decision support but often focus on a few specific decisions. As an example, anyLogistix uses optimization and simulation results to support decisions in the area of location and capacity planning, procurement and transport guidelines and order consolidation. Moreover, the tool uses key indicators to measure the quality and effects of the decisions. [START_REF] Ivanov | Decision-oriented teaching notes for model-based management decision making[END_REF] However, the tool does not cover all relevant decisions. SAP APO (Advanced Planning and Optimization) as an example for business application software in supply chain management contains different modules amongst others for demand or network planning. Moreover, it includes a Supply Chain Cockpit that offers an overview of the entire supply chain as well as a view of all products, resources, locations, production process models and transport relationships. [START_REF] Kurbel | Enterprise resource planning and supply chain management. Functions, business processes and software for manufacturing companies[END_REF] Even though SAP APO enables decision support, it is system-oriented and requires high implementation effort.

Existing conceptual contributions only cover some of the derived requirements. The approaches focus either on concrete decisions or on general data models. The provision of the relevant evaluated data through a user interface and implementation in a software tool is mostly missing. Existing software tools provide user interfaces but usually focus on individual and specialized tasks. Moreover, these systems are often not efficiently linked with each other.

Thus, a software tool that offers a comprehensive picture of the status of the sourcing and delivering processes on supply chain level by linking data from different sources is currently missing. To increase transparency on supply chain level, such a system should cover decisions on strategic, tactical and operative level and ensure the demand-oriented provision of data for decision-makers in a software tool.

3

Software-based Assistance System

The software-based assistance system presented in this paper systematically links data from different sources to generate information that supports the decision-maker in sourcing and delivering decisions on different decision levels. It has been developed as part of the research project Cluster of Excellence "Internet of Production" and thus uses the above-described framework of the Internet of Production which ensures systematic linkage and processing of data and user-friendly decision support.

The concept comprises five modules (see Fig. 2). To utilize the increasing amount of data to support decisions, a systematic linkage of decisions and data (Module 3) and a demand-oriented provision of evaluated data (Module 4) is needed. This is achieved through a systematic comparison of information needs and offers. Identifying relevant decisions (Module 1) and the development of a data model (Module 2) serve as the basis for modules 3 and 4. Module 1 uses a process reference model to identify relevant decisions. Module 2 applies a standardized notation to ensure a structured and formal data model. Module 5 combines the developed content of modules 1 to 4 and implements them within a software tool. This ensures the applicability of the results. The paper focuses on the linkage of decisions and data, the demand-oriented provision and the implemented software tool.

Fig. 2. Concept for decision support at supply chain level

A detailed description of the results of the first modules can be found in [START_REF] Pause | Assistenzsystem zur Entscheidungsunterstützung in der Supply Chain[END_REF] and thus will only be summarized shortly. The identification of relevant decision is based on planning, sourcing and delivering processes described in the SCOR model [START_REF]Supply Chain Council: Supply Chain Operations Reference Model[END_REF]. Strategic sourcing decisions concern the definition of the procurement strategy and long-term planning of the required articles and stock. In the tactical decisions, the material program is detailed, whereby, among other things, the required quantities are specified. The operational decisions concern specific procurement orders. Strategic delivery decisions include determining the distribution strategy, the distribution channels and the distribution concept. Within the tactical decisions, carriers are selected and the capacities of the vehicle fleet are determined. Decisions on concrete transport orders and route planning are made within the operational decisions. The result of the second module is a UML-based data model containing the data which is needed in order to provide decision support. It consists of data classes, attributes and links between different data classes.

To link decisions and data an impact matrix is developed to analyze which data classes are needed for which decisions. This impact matrix is iteratively developed further. On one side, the influence matrix serves as a basis for identifying relevant indicators for decision support and, on the other side, existing literature-based indicators enable the revision of the influence matrix. Thus relevant indicators in the context of sourcing and delivering are identified using a literature analysis. These indicators are then linked to different decisions. Indicators condense information and enable statements about quantifiable facts in the past or future [START_REF] Alpar | Anwendungsorientierte Wirtschaftsinformatik[END_REF]. To cover the different decision levels absolute and relative indicators, as well as financial and non-financial indicators, are used. While relative indicators represent a ratio of two variables, absolute indicators are absolute values that summarize data. Moreover, indicators can be evaluated by company-wide or periodically comparisons. Absolute indicators are mostly non-financial indicators that will be used for decisions on strategic level. Moreover, the absolute indicators can be used to analyze the development over defined time horizons and are thus used for periodical comparisons. Examples for absolute indicators for the sourcing area are number of suppliers, number of procurement orders and number of procurement order items. For decisions in the area of delivering examples for absolute indicators are the number of distribution points, the number of means of transport and the number of customer orders. Relative indicators serve as decision support on all decision levels. Moreover, they comprise several financial indicators. Examples for indicators in the area of sourcing are stock turnover capability, stock range and costs per stock movement. Examples for indicators that are relevant for delivering processes are means of transport utilization rate, delivery service level and average transport distance. The indicators are calculated using the data model.

To ensure a demand-oriented provision of data, a suitable way of visualizing the indicators to support the decision-maker is defined. Characteristics for sufficient data visualization are a target group-specific and context-related presentation in an easily understandable, quickly comprehensible and precise form [START_REF] Kahl | Kontextspezifische Visualisierung von Prozesskennzahlen[END_REF]. For the visualization of the indicators, different diagram types are used in the context of this work (bar chart, line chart and network diagram). The application of each diagram depends on the indicator type, the unit of the indicator and the type of indicator comparison. For each decision, the indicators that support the decision and a suitable way of visualization are identified. Moreover, the time horizons for the calculation of the indicators are determined.

Presentation of the Software Tool

To provide decision support, user-friendly and context-dependent information visualization and interaction formats are of particular importance [START_REF] Bauernhansl | [END_REF]. Thus, the above-described results are implemented in a software tool to ensure the practical use of the developed results. In doing so various software components are utilized. The most important components are a scripting language (Python), a web framework (Django) and a relational database management system (PostgreSQL).

The foundation for the implementation of the developed concept in a software-based solution is a relational database. This database is derived from the above-described data model. The database integrates data from different data sources like ERP systems and Supplier Relationship Management systems.

The software tool consists of various screens. Fig. 3 shows the welcome screen and a detailed screen for operative sourcing decisions of the software tool. The welcome screen of the software tool summarizes the main decision areas (sourcing and delivering) and the decision levels (strategic, tactical, operative). Choosing one of the decision levels for sourcing or delivering leads to the list of decisions that are covered within the tool. For each listed decision the tool then contains detailed screens that summarize the necessary information to support the decision-maker. These screens comprise visualized indicators that have been identified in Module 3 and Module 4.

The operative decision from which supplier a material should be ordered serves as an example of the visualization (see Fig. 4). This decision is strongly influenced by supplier evaluations. For the five best-rated possible suppliers their service level, the response time and the delay rate are visualized. Moreover, the average price and the average procurement time for these suppliers are compared.

Summary and Need for Further Research

To increase the transparency and efficiency of decision-making processes in the area of sourcing and delivering a software-based assistance system is developed. The decision support is reached through the visualization of data and indicators. Relevant decisions on strategic, tactical and operative level and a data model serve as the basis. The paper focused on the linkage of decisions and data through indicators and the definition of a suitable way of visualizing the selected indicators in the context of specific decisions. Moreover, it is demonstrated how these results are implemented in a software tool.

The concept systematically links data from different sources to provide a comprehensive overview of relevant sourcing and delivering decisions. The software tool ensures the demand-oriented provision of evaluated data and practical feasibility. In doing so, the presented concept and software tool bridge the described research gap.

The tool will be validated using real company data and conducting expert interviews with different user groups. In the developed software tool the different decisions are currently dealt with separately. Further research is thus needed to link the decisions. In doing so, interdependencies between the decisions need to be analyzed. These results can then be integrated into the software tool for instance through overview dashboards or push messages for the user. Another additional functionality for the software tool is the integration of analytical methods to gain even more information from existing data.

Fig. 1 .

 1 Fig. 1. Infrastructure of the Internet of Production [6]

Fig. 3 .

 3 Fig. 3. Structure of the software tool

Fig. 4 .

 4 Fig. 4. Screen for operative sourcing decision "Which supplier is ordered from?"

Eigenschaften absolut X X X relativ X X X X dimensionslos X X dimensionsbehaf tet X X X X X periodisch X X Xunternehmensw eit X

Acknowledgments: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC-2023 Internet of Production -390621612.


Darko Stefanovic

Dusanka Dakic
email: dakic.dusanka@uns.ac.rs

Branislav Ste

Teodora Lolic

Process Mining in Manufacturing: Goals, Techniques and Applications

Keywords: process mining, manufacturing, literature review

Process mining is a discipline positioned between business process management and data mining. It applies algorithms on real event data extracted from information systems that support business processes, to construct as-is process models, and improve them automatically. The benefits can be versatile, from gaining insight into the real execution of a process, to detecting process bottlenecks, activity loops, or social networks of process resources. Several literature reviews have focused on the application of process mining in the healthcare industry and on process mining discipline in general, without the reviews of other application domains. This paper presents the results of a systematic literature review on case studies of process mining projects applied in the manufacturing industry. Case studies are analyzed according to the following aspects: project goals, information systems or devices/equipment that generate event data, particular business processes, event log characteristics, different types and perspectives of process mining performed, tools and techniques used for preprocessing activities, discovery, conformance checking, process enhancement, and social network analysis. Finally, an attempt is made to discover the impact of goals, types of processes, and event log characteristics on the selection of process mining types, perspectives, tools, and techniques.

Introduction

With the increasing availability and lower cost of technology, most manufacturing organizations can manage their business processes with some information system (Enterprise Resource Planning -ERP or Manufacturing Execution System -MES) [START_REF] Beric | The implementation of ERP and MES Systems as a support to industrial management systems[END_REF][START_REF] Dakic | Production planning business process modelling using UML class diagram[END_REF][START_REF] Beric | Development of the MES software and Integration with an existing ERP Software in Industrial Enterprise[END_REF]. Furthermore, in the Era of Industry 4.0 and big data [START_REF] Crnjac | From Concept to the Introduction of Industry 4.0[END_REF], modern manufacturing systems generate a large amount of data that has the potential to become actionable information resources. Different data analysis techniques were used for the analysis of processes specific to the manufacturing industry, such as manufacturing and maintenance management processes [START_REF] Lopes | Criticality evaluation to support maintenance management of manufacturing systems[END_REF]. Business intelligence, knowledge discovery, and data mining are some of the tools that fill the need for automated data analysis. Pro-cess mining is a new research field that enables the automatic discovery of business processes and numerous additional process enhancement techniques, such as performance analysis. It is defined by the IEEE Task Force on Process Mining, as follows [START_REF] Van Der Aalst | Process Mining Manifesto[END_REF]: "The idea of process mining is to discover, monitor, and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today's (information) systems."

Process mining has been mostly applied in the healthcare industry, following with information technology, finance, and manufacturing industries [START_REF] Dakic | Business process mining application: a literature review[END_REF]. Literature reviews of case studies in the healthcare industry [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF], as well as several reviews of process mining applications [START_REF] Dakic | Business process mining application: a literature review[END_REF] and state of the art [START_REF] Tiwari | A review of business process mining: state-of-the-art and future trends[END_REF], proved the feasibility and applicability of process mining. However, no study focuses on the applicability and benefits of process mining in the manufacturing industry. This paper aims to discover specific information found in case studies in the manufacturing industry through a systematic literature review that is assumed to be relevant for the future conduction of process mining projects in the manufacturing industry.

Firstly, it is relevant to discover which information systems, manufacturing devices/equipment, and business processes are analyzed in these case studies, as well as project goals. Event log characteristics can also be meaningful, as they can impact the decision on tools and techniques. Preprocessing activities, found to be the most difficult to perform, are also analyzed based on utilized tools and techniques. Furthermore, there are three types of process mining that will be observed: discovery, conformance, and enhancement [START_REF] Van Der Aalst | Process Mining Manifesto[END_REF]. Process discovery techniques produce a process model from an event log without using any a priori information about the process. Conformance compares the discovered process model with an event log of the same process and is used to check if event data conforms to the model. Enhancement improves an existing process model by using information about the actual process recorded in the event log. There are also different process mining perspectives [START_REF] Van Der Aalst | Process Mining Manifesto[END_REF]. The control-flow or process perspective focuses on the control-flow, i.e., the ordering of activities, and it is equivalent to discovery. The case and time perspectives are usually performed together [START_REF] Dakic | Business process mining application: a literature review[END_REF], as they are comparable to process enhancement. The organizational perspective (social network analysis) focuses on information about originators in the log, i.e., which actors\performers are involved and how they are related. These different types and perspectives are used to solve particular problems that occur in real-life processes, and it is essential to outline their share in process mining applications. This paper will also describe different tools and techniques that were applied, grouped by types and perspectives of process mining. The results of this systematic literature review will tackle the benchmarking challenges of process mining and help process analysts in the manufacturing industry gain insight into the detailed possibilities and benefits of process mining applications.

The remainder of the paper is organized as follows. Section 2 describes the design of a systematic literature review. Section 3 presents the results of the review, and Section 4 concludes the paper and presents future work.

Research Design

A comprehensive and well-structured literature review firstly elaborates on the need for a systematic literature review, then formulates research questions, search strategy, study selection criteria, and performs study quality assessment [START_REF] Kitchenham | Procedures for Undertaking Systematic Reviews[END_REF]. Although there are papers that elaborate on the usefulness of process mining in the manufacturing industry, there are no literature reviews that offer summed information and conclusions on the topic, except [START_REF] Dakic | Business process mining application: a literature review[END_REF], where authors reviewed process mining in all industries. The following research questions are established: RQ1: What are the main goals of process mining projects in manufacturing? RQ2: Which information systems, devices, or equipment are the generators of the event data, and which company processes are being automated?

RQ3: What are the characteristics of analyzed event logs? RQ4: What preprocessing tools and techniques were used? RQ5: What types and perspectives were performed, and which tools and techniques were used? RQ6: Do goals, types of processes, or event log characteristics influence the selection of process mining types, perspectives, tools, and techniques?

The SCOPUS database and Google Scholar were searched with the following search terms: "Process mining" and "manufacturing" and "case studies". The search resulted in 256 papers.

Inclusion criteria applied to papers are: IC1: Papers have to be published full-text as articles or conference proceedings. IC2: Paper has to present a case study. IC3: The paper has to be written in English. Exclusion criteria applied to papers are: EC1: Paper presents a case study from other industries. EC2: Paper presents the same case study, written as a different publication. EC3: Paper is referencing process mining but is using other technologies in the case study.

After applying defined inclusion and exclusion criteria on available papers, papers were critically appraised based on their relevancy and type of information they contained. Finally, there were 14 primary studies available for data extraction.

Flow diagram of the systematic literature review process is presented in Fig. 1.

Data Extraction Results

Besides the process discovery, process mining was successfully applied to detect throughput and waiting times of a process and to discover bottlenecks and feedback loops [START_REF] Hidayat | Process model extension using heuristics miner: (Case study: Incident management of Volvo IT Belgium)[END_REF][START_REF] Rozinat | Process mining applied to the test process of wafer scanners in ASML[END_REF][START_REF] Er | Analysis of production planning in a global manufacturing company with process mining[END_REF][START_REF] Son | Process mining for manufacturing process analysis: a case study[END_REF][START_REF] Yahya | The development of manufacturing process analysis: lesson learned from process mining[END_REF][START_REF] Dakic | Process mining possibilities and challenges: A case study[END_REF][START_REF] Rbigui | Purchasing process analysis with process mining of a heavy manufacturing industry[END_REF][START_REF] Fleig | Process Mining for Business Process Standardization in ERP Implementation Projects-An SAP S/4 HANA Case Study from Manufacturing[END_REF][START_REF] Feau | A method to build a production process model prior to a process mining approach[END_REF]. Moreover, process mining SNA techniques were used for finding the roles and relationships of the resources (e.g., machines, employees) [START_REF] Hidayat | Process model extension using heuristics miner: (Case study: Incident management of Volvo IT Belgium)[END_REF][START_REF] Er | Analysis of production planning in a global manufacturing company with process mining[END_REF][START_REF] Yahya | The development of manufacturing process analysis: lesson learned from process mining[END_REF][START_REF] Dakic | Process mining possibilities and challenges: A case study[END_REF][START_REF] Farooqui | From factory floor to process models: A data gathering approach to generate, transform, and visualize manufacturing processes[END_REF], presented through resource networks. Other applications of process mining were focused on detecting compliance issues with the expected behavior of a process [START_REF] Bettacchi | Understanding Production Chain Business Process Using Process Mining: A Case Study in the Manufacturing Scenario[END_REF][START_REF] Hidayat | Process model extension using heuristics miner: (Case study: Incident management of Volvo IT Belgium)[END_REF][START_REF] Diba | Compliance and Performance Analysis of Procurement Processes Using Process Mining[END_REF][START_REF] Son | Process mining for manufacturing process analysis: a case study[END_REF], predicting manufacturing cost based on production volume and time [START_REF] Tu | Analysis and prediction cost of manufacturing process based on process mining[END_REF], estimating process cycle times [START_REF] Ruschel | Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing[END_REF], and discovering business essential process variants before undergoing an ERP implementation project [START_REF] Fleig | Process Mining for Business Process Standardization in ERP Implementation Projects-An SAP S/4 HANA Case Study from Manufacturing[END_REF]. Fig. 2 presents the types of publications by year.

Fig. 2. Types of publications by year

Fig. 3. presents the goals of the process mining projects, sorted descendingly by the number of case studies with the same goal. . presents processes from the manufacturing industry that were the subject of process mining. The manufacturing process [START_REF] Son | Process mining for manufacturing process analysis: a case study[END_REF][START_REF] Tu | Analysis and prediction cost of manufacturing process based on process mining[END_REF][START_REF] Yahya | The development of manufacturing process analysis: lesson learned from process mining[END_REF] and the procurement process [START_REF] Diba | Compliance and Performance Analysis of Procurement Processes Using Process Mining[END_REF][START_REF] Rbigui | Purchasing process analysis with process mining of a heavy manufacturing industry[END_REF][START_REF] Fleig | Process Mining for Business Process Standardization in ERP Implementation Projects-An SAP S/4 HANA Case Study from Manufacturing[END_REF] were analyzed the most, following with the production planning [START_REF] Er | Analysis of production planning in a global manufacturing company with process mining[END_REF][START_REF] Dakic | Process mining possibilities and challenges: A case study[END_REF], incident management [START_REF] Hidayat | Process model extension using heuristics miner: (Case study: Incident management of Volvo IT Belgium)[END_REF][START_REF] Feau | A method to build a production process model prior to a process mining approach[END_REF], maintenance management [START_REF] Farooqui | From factory floor to process models: A data gathering approach to generate, transform, and visualize manufacturing processes[END_REF]25], product assembling [START_REF] Bettacchi | Understanding Production Chain Business Process Using Process Mining: A Case Study in the Manufacturing Scenario[END_REF] and product testing process [START_REF] Tu | Analysis and prediction cost of manufacturing process based on process mining[END_REF]. Mostly used information system was the ERP system [START_REF] Diba | Compliance and Performance Analysis of Procurement Processes Using Process Mining[END_REF][START_REF] Er | Analysis of production planning in a global manufacturing company with process mining[END_REF][START_REF] Dakic | Process mining possibilities and challenges: A case study[END_REF][START_REF] Fleig | Process Mining for Business Process Standardization in ERP Implementation Projects-An SAP S/4 HANA Case Study from Manufacturing[END_REF], which supported all procurement and production planning processes. Other processes were supported by the Supply Chain Management system [START_REF] Bettacchi | Understanding Production Chain Business Process Using Process Mining: A Case Study in the Manufacturing Scenario[END_REF], the MES [START_REF] Tu | Analysis and prediction cost of manufacturing process based on process mining[END_REF], the Shipbuilding Processing Plan Management system [START_REF] Rbigui | Purchasing process analysis with process mining of a heavy manufacturing industry[END_REF], Supervisory Control And Data Acquisition (SCADA) system [START_REF] Feau | A method to build a production process model prior to a process mining approach[END_REF], and a problem handling system [START_REF] Hidayat | Process model extension using heuristics miner: (Case study: Incident management of Volvo IT Belgium)[END_REF]. As manufacturing devices and equipment that can generate event data are considered, CNC machine [START_REF] Ruschel | Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing[END_REF], programmable logic controller, and robot stations [START_REF] Farooqui | From factory floor to process models: A data gathering approach to generate, transform, and visualize manufacturing processes[END_REF] were analyzed.

Fig. 4. Analyzed processes in the manufacturing industry

Table 1. presents information about performed techniques, algorithms, and tools in all case studies grouped by different process mining types and perspectives. It can be concluded that all case studies performed process discovery and control-flow perspective. Enhancement was performed in 57% of the case studies [START_REF] Diba | Compliance and Performance Analysis of Procurement Processes Using Process Mining[END_REF][START_REF] Rozinat | Process mining applied to the test process of wafer scanners in ASML[END_REF][START_REF] Er | Analysis of production planning in a global manufacturing company with process mining[END_REF][START_REF] Son | Process mining for manufacturing process analysis: a case study[END_REF][START_REF] Tu | Analysis and prediction cost of manufacturing process based on process mining[END_REF][START_REF] Yahya | The development of manufacturing process analysis: lesson learned from process mining[END_REF][START_REF] Dakic | Process mining possibilities and challenges: A case study[END_REF][START_REF] Feau | A method to build a production process model prior to a process mining approach[END_REF]. Conformance checking was performed in 28% of the case studies [START_REF] Diba | Compliance and Performance Analysis of Procurement Processes Using Process Mining[END_REF][START_REF] Rozinat | Process mining applied to the test process of wafer scanners in ASML[END_REF][START_REF] Er | Analysis of production planning in a global manufacturing company with process mining[END_REF][START_REF] Son | Process mining for manufacturing process analysis: a case study[END_REF] and SNA in 21% [START_REF] Son | Process mining for manufacturing process analysis: a case study[END_REF][START_REF] Yahya | The development of manufacturing process analysis: lesson learned from process mining[END_REF][START_REF] Dakic | Process mining possibilities and challenges: A case study[END_REF]. To answer the RQ6, firstly, it was analyzed if the case studies with the same goal had performed the same process mining types and perspectives. It was concluded that there are patterns by which process mining was conducted based on the overall goal of the case study. Case studies that aimed to discover the process performed controlflow perspective (discovery type) in 100% of the cases and case/time perspective (enhancement) in 80%. The case studies with the goal to perform performance analysis used control-flow and case/time perspectives. The case study with the goal of performing conformance analysis conducted all three types of process mining. Case studies that aimed to develop a new approach for process mining in manufacturing performed discovery and enhancement. The case studies with the goal to improve the process conducted process discovery in 100%, process enhancement in 50%, conformance checking in 25%, and organizational mining in 25% of the cases. The case studies with the goal to predict the process flow or use process mining for decision making, performed only process discovery. Finally, case studies that aimed to discover the best algorithm for process mining in manufacturing performed only process discovery. Secondly, it was analyzed if a process type influences the process mining types and perspectives. Because most process types were discovered and enhanced, there was no significant correlation. However, all production planning processes and 60% of procurement processes were supported by an ERP system. It was discovered that event log characteristics such as event log format, size, and number of cases and activities do not influence types and perspectives of process mining nor chosen tools and techniques.

Conclusion

This paper aimed to gain insightful information about the application of process mining in the manufacturing industry. The data extracted from the case studies show an overview of the goals of process mining projects, information systems, analyzed processes, and event log characteristics. More significant is an overview of all techniques, algorithms, and tools, grouped by the process mining types and perspectives. Finally, the paper presented an analysis of the impact that goals, processes, and event log characteristics had on the selection of process mining types, perspectives, and techniques/algorithms. The discovered relationships between the key features of process mining could be tested as hypotheses, by including a higher number of case studies, even considering all industries in which process mining is applied.

Fig. 1 .

 1 Fig. 1. Systematic literature review flow diagram

Fig. 3 .

 3 Fig. 3. Number of papers with the same process mining goal

Table 1 .

 1 Algorithms/techniques and tools

	Tools


Marco Simonetto
email: marco.simonetto@ntnu.com

Fabio Sgarbossa

Introduction to Material Feeding 4.0: strategic, tactical, and operational impact

Keywords: Material Feeding, Industry 4.0, Assembly System, Strategic, Tactical, Operational

Mass customization, the process of producing a low-volume high-variety of products, is changing production environments. In Material Feeding (MF) this means a huge increment in the number of parts, and information, that need to be managed during the different MF activities. If companies want to get high performances from their MF activities, they need to be able to manage these changes in the best manner. Industry 4.0 technologies are introducing new opportunities to help companies in the execution and control of the MF activities. It is important for companies to be able to understand how to implement these technologies in their processes and how to take these opportunities. In order to facilitate this, in this paper the concept of Material Feeding 4.0 (MF 4.0) is presented for the first time, as Material Feeding where the Industry 4.0 technologies are introduced. The impact of the identified technologies is studied at a strategic, tactical and operational level.

Introduction

The demand for customized products is increasing. A high demand of customized products implies that the production and assembly lines require to be fed with an enormous amount of different parts. Material feeding (MF) describes all the activities that are responsible for the provision of components, parts, equipment, etc. to production and assembly systems when needed [START_REF] Battini | Design of the optimal feeding policy in an assembly system[END_REF]. If companies want to satisfy the demand of their customers these activities need to be continuously improved and optimized. In this paper the focus will be in the assembly system (AS). MF, in AS, can be performed in different ways, by storing all parts near the assembly line or pre-processing parts and delivering them when needed [START_REF] Sali | Line feeding optimization for Just in Time assembly lines: An application to the automotive industry[END_REF]. In the last years with the so-called Industry 4.0 revolution, both AS and MF are considering the introduction of the technologies that this revolution is offering in their planning and control processes. However, although other works already exist that try to study the effects of these technologies on the AS, [START_REF] Cohen | Assembly systems in Industry 4.0 era: a road map to understand Assembly 4.0[END_REF][START_REF] Bortolini | Assembly system design in the Industry 4.0 era: a general framework[END_REF], still no work exists about their effects in the planning and control processes of the MF systems. Although in literature the terms Supply Chain 4.0 and Logistics 4.0 have been already discussed with a broader perspective, [START_REF] Frederico | Supply Chain 4.0: concepts, maturity and research agenda[END_REF][START_REF] Winkelhaus | Logistics 4.0: a systematic review towards a new logistics system[END_REF], in this paper we focus on the specific process and set of activities related to MF. The idea is to understand which are the emerging technologies that can be implemented in the MF activities and how their implementation can impact at three levels: strategic, tactical, and operational. The remainder of this paper is structured as follows. Section 2 provides a review of the existing literature concentrating on the MF activities. Section 3 illustrates and discusses the results obtained from the review of the literature. Finally, in Section 4 the paper is shortly summarized.

Review of the existing literature

Examples of Industry 4.0 technologies in AS are: Internet of Things (IoT), Big data, Cloud Computing, Augmented Reality, Collaborative Robots, and Additive Manufacturing [START_REF] Cohen | Assembly systems in Industry 4.0 era: a road map to understand Assembly 4.0[END_REF]. These technologies affect the performance of the AS activities. In MF it is not yet defined which emerging technologies can be used. The main activities of MF are transportation, preparation, and material management [START_REF] Schmid | A classification of tactical assembly line feeding problems[END_REF]. Transportation implies the process of moving all the components, parts, from a point A where they are stocked to a point B where they are requested. The preparation implies the processes of handling and repacking parts into load carriers used for the corresponding line feeding policy [START_REF] Schmid | A classification of tactical assembly line feeding problems[END_REF]. Material management instead includes all the process related to the storage of components and products. For the storage of components, two of the possible solutions are the central receiving stores and the supermarkets [START_REF] Battini | Just-in-Time supermarkets for part supply in the automobile industry[END_REF]. An extensive review of the existing literature on the subject has been carried out to see what is possible to find about Industry 4.0 technologies that can have an impact on these three activities. We used Scopus database as search engine limiting the research from January 2006 to March 2020, English as language, and journal and conference papers as sources.

Transportation

In the planning and control of the transportation activity one of the most important decisions is the choice of the transportation devices. Examples of traditional transportation devices are the forklift, tow train, or feeder line [START_REF] Battini | Just-in-Time supermarkets for part supply in the automobile industry[END_REF]. Thanks to the technology development, new solutions have appeared in the last few years [START_REF] Lottermoser | Method of usability for mobile robotics in a manufacturing environment[END_REF]. First the Automatic Guided Vehicles (AGVs), driverless vehicles that follow a fixed path in order to move from a point to another [START_REF] Le-Anh | A review of design and control of automated guided vehicle systems[END_REF], and now Autonomous Mobile Robots (AMRs) are getting more popular due to their flexibility [START_REF] Wurman | Coordinating hundreds of cooperative, autonomous vehicles in warehouses[END_REF]. These mobile robots can move without following a fixed path and use cameras and sophisticated software to identify their surroundings and take the most efficient route to their destination [START_REF]MiR autonomous mobile robot[END_REF]. AGVs and AMRs are platforms in which you can implement different additional equipment/resources such as lifting systems and collaborative robot. They can be used as "workstations" to assemble the products [START_REF]Here's how Audi plans to scrap the assembly line[END_REF] or to move shelves from the warehouse to the AS [START_REF] Wurman | Coordinating hundreds of cooperative, autonomous vehicles in warehouses[END_REF].

Based on how companies decide to use them, they will face different problems in order to optimize the performances. For example, if the AGVs/AMRs are used to move the shelves for the picking operations, an example of a possible problem might be when non-completed shelves have to be moved using AGVs/AMRs that usually move complete shelves. A solution to this problem is presented in [START_REF] Yoshitake | New Automated Guided Vehicle System Using Real-Time Holonic Scheduling for Warehouse Picking[END_REF] where the authors proposed a scheduling method to move the shelves even if they are not complete. In [START_REF] Wan | Context-aware cloud robotics for material handling in cognitive industrial Internet of Things[END_REF] a cloud-based architecture that controls and improves the collaboration of the AMRs is presented. Simulations showed that using this cloud-based architecture gives the opportunity to improve energy efficiency and save costs. AMRs seem to be a very prominent technology in order to improve the transportation activity. This is also thanks to the development of more powerful Data Analytics techniques.

Preparation

In the preparation activity for AS we can find the picking and kitting process [START_REF] Battini | Design of the optimal feeding policy in an assembly system[END_REF]. The process of picking can be described as manually seeking a particular item in storage according to a list, taking that object and putting it into a bin/container the appropriate transport vehicle, and bringing the objects to the required location for processing [START_REF] Regenbrecht | Augmented reality projects in the automotive and aerospace industries[END_REF].

The kit preparation process instead supplies AS with kits of components. The kits are prepared at a kit preparation workspace apart from the AS [START_REF] Fager | Modelling time efficiency of cobot-supported kit preparation[END_REF]. Picking and kitting operation can be facilitated using a AMRs to transport the necessary components from the warehouse to the AS [START_REF] Wurman | Coordinating hundreds of cooperative, autonomous vehicles in warehouses[END_REF]. AMRs are not used only for the transportation. In the last few years many manufacturers have started to develop mobile robots with various functions [START_REF] Lottermoser | Method of usability for mobile robotics in a manufacturing environment[END_REF]. If a collaborative robot is integrated with a mobile robot this can be called collaborative mobile robots [START_REF] Andersen | Integration of a skill-based collaborative mobile robot in a smart cyber-physical environment[END_REF]. In [START_REF] Andersen | Integration of a skill-based collaborative mobile robot in a smart cyber-physical environment[END_REF] the collaborative mobile robot is used to perform a part-feeding task. The validity of the solution is granted by the successfully performed tests. Another example of a collaborative robot mounted in a mobile robot is presented in [START_REF] Fager | Modelling time efficiency of cobot-supported kit preparation[END_REF]. The collaborative robot sorts the components that have been picked by an operator into a batch of kits. The mobile robot gives the opportunity to the collaborative robot to do its operations moving together with the human operator. This is done safely and relieves the human operator of activities associated with sorting components into kits. Kitting preparation in the context of material supply to AS can be performed utilizing augmented reality (AR) [START_REF] Hanson | Augmented reality as a means of conveying picking information in kit preparation for mixed-model assembly[END_REF]. AR is used in single kit-preparation and in batch preparation. The experiment demonstrates how AR is competitive both in terms of time-efficiency and picking accuracy. AR is used also in picking activities.

The main improvements that AR can give to picking operations are the errors reduction and time efficiency [START_REF] Regenbrecht | Augmented reality projects in the automotive and aerospace industries[END_REF][START_REF] Krajcovic | Order picking using augmented reality[END_REF][START_REF] Schwerdtfeger | Pick-by-vision: there is something to pick at the end of the augmented tunnel[END_REF]. The preparation of the different components and parts to deliver at the AS is a very important activity. A mistake in this activity means a reduction in the performances of the AS. The introduction of AR in this activity is made in order to reduce the probability of making mistakes.

Material management

Data Analytics techniques, such as data mining algorithms and machine learning algorithm, are becoming more popular and powerful with the advent of Industry 4.0 [START_REF] Choi | Big data analytics in operations management[END_REF]. These techniques can be used to improve the material management activities of a company. For example, in [START_REF] Xin | An optimization algorithm based on text clustering for warehouse storage location allocation[END_REF] a machine learning algorithm is proposed to optimize the warehouse storage location allocation. The solution can improve the efficiency of warehouse operations in case of weak correlation between the stock keeping units. Whereas in [START_REF] Liu | Big Data Forecasting Model of Indoor Positions for Mobile Robot Navigation Based on Apache Spark Platform[END_REF] a positioning big data forecasting model is used in order to predict the trajectory of the mobile robots. This can improve the safety, reliability and stability of the mobile robot navigation. The introduction of mobile robots in the warehouses can mean a change in their design. Mobile robots allow spatial flexibility and expandability. If a warehouse needs more capacity, one simply adds more pods, drives, and stations [START_REF] Wurman | Coordinating hundreds of cooperative, autonomous vehicles in warehouses[END_REF].

In order to manage the inventories of warehouses and ASs it is possible to adopt new smart solutions. Internet of Things (IoT) and Cloud computing can be used to automate inventory systems [START_REF] Alwadi | Smart solutions for RFID based inventory management systems: A survey[END_REF]. An example of a solution that can improve this automation of inventory systems is presented in [START_REF] Buer | The Data-Driven Process Improvement Cycle: Using Digitalization for Continuous Improvement[END_REF] with the concept of self-optimizing Kanban. Selfoptimizing Kanban systems autonomously adjust their capacities as well as the quantity of cards in circulation according to predefined performances. In order to use these systems, it is important to collect all the data of the quantities inside the warehouses in real-time and to use data analytic algorithms to manage the collected data. The data are the most important resource in material management. It is important to know which data need to be collected and how. Once the data are collected, they need to be analyzed in order to obtain valuable information. IoT technologies and Data Analytics techniques will help companies in this MF activity.

Material Feeding 4.0

In Figure 1 we see the results obtained from the review of the literature. From the figure we can see that the most adopted technologies in MF activities are: mobile robots, augmented reality, IoT technologies, Cloud Computing and Data Analytics. These are the technologies that can be present in a MF 4.0 system. Table 1 summarizes where the technologies impact in the different activities at a strategic, tactical, and operational level. The table helps to think about new research challenges that can appear if some of the technologies identified are introduced in the MF system. In order to state the impact of the different technologies at a strategic, tactical, and operational levels in MF 4.0, the scope of every level is shortly explained in the following. Decisions of different levels affect each other and should be considered in an integrated way regarding the intra-organizational decision levels as well as the interorganizational hierarchy [START_REF] Bhatnagar | Models for multi-plant coordination[END_REF]. Due to page restriction, we will use the mobile robots as an example.

Table 1. Decision areas of Material Feeding 4.0. Impact of the different technologies in planning and control.

Strategic level in MF 4.0

Strategic decisions are those decisions that have an influence over the years and a longterm impact on the performance of the MF. Once a strategic decision is made, it is very unlikely to be altered in the short term. They are usually taken at the highest levels of management, include a wide range of uncertainties and carry higher levels of risk. In MF 4.0, the most important decision at this level for all the MF 4.0 activities, is the choice of the technologies to adopt. It is possible to decide gradually which are the technologies to implement in the different activities. Which type of mobile robots to buy will influence all the decisions that a company has to face after their purchase. A mobile robot can be used only as a transportation system or it can be integrated with collaborative robot for picking activities [START_REF] Lottermoser | Method of usability for mobile robotics in a manufacturing environment[END_REF][START_REF] Fager | Modelling time efficiency of cobot-supported kit preparation[END_REF]. This together with the flexibility of the guidance system of a mobile robot and its dimensions will influence the design of the possible paths and of the warehouses (central receiving stores or the supermarkets) where it will work [START_REF] Le-Anh | A review of design and control of automated guided vehicle systems[END_REF][START_REF]MiR autonomous mobile robot[END_REF]. The design of the warehouses can be also influenced by the different types of containers that the mobile robots can transport [START_REF] Wurman | Coordinating hundreds of cooperative, autonomous vehicles in warehouses[END_REF]. The investment that a company makes when it decides to buy the mobile robots is not related only to the purchase of the robots. Someone needs to know how to use them. If the mobile robots are introduced in one or more of the MF activities, it is important to know which skills are needed to use them and what is necessary to do in order to best implement them. This can influence the performance given by the mobile robots. For example, if the company decide to not hire new workers that already know how to use them, it is possible that during the first period, their performance will be lower than the expected. This because a certain amount of time is needed to learn how to use them.

3.2

Tactical level in MF 4.0

Tactical decisions are decisions and plans that concern the more detailed implementation of the strategic decisions, usually with a medium-term impact on a company. At the tactical level, the decisions about which are the technologies to implement are already made. For the mobile robots this means that the company has decided which type to buy. At this level, for all the MF 4.0 activities, it is important to prepare the different technologies to be used. The first thing to understand is which are the feeding policies to be adopted [START_REF] Battini | Design of the optimal feeding policy in an assembly system[END_REF]. The flexibility of mobile robots can change the application of the feeding policies making one policy more convenient than another. For example, it can be more convenient to prepare kits than move entire pallets. Mobile robots are also more flexible with respect to the transported volumes and they can adapt themselves with the variation of the material flow. The chosen policies can influence the number of mobile robots that the company needs and respect with traditional transportation devices it is possible that new algorithms are necessaries to calculate this number. In the scheduling phase of the mobile robots, the company decides when, where and how a mobile robot should act to perform tasks [START_REF] Le-Anh | A review of design and control of automated guided vehicle systems[END_REF]. A new problem to solve during this phase is which mobile robot to use based on the operations needed to perform [START_REF] Lottermoser | Method of usability for mobile robotics in a manufacturing environment[END_REF]. This because there are different types of mobile robots that are going to be implemented based on the activities that they must do. In this phase is important to consider also the ergonomic aspect of the system. In fact, the safety precautions that one has to implement can be different in order to use the different type of mobile robots. These can change from a mobile robot that is used only as transportation system from another that works together with human workers, for example when it is used as a workstation or when it helps the human operator in the preparation of the kits [START_REF]Here's how Audi plans to scrap the assembly line[END_REF][START_REF] Fager | Modelling time efficiency of cobot-supported kit preparation[END_REF].

Operational level in MF 4.0

Operational decisions are related to day-to-day operations of the companies. They have a short-term horizon as they are taken repetitively. At this level, for all the MF 4.0 activities, it is important to ensure that technology works as best as possible. The vehicle routing problem decides the route a mobile robot should take and the sequence of loads (or jobs) that this vehicle should visit [START_REF] Le-Anh | A review of design and control of automated guided vehicle systems[END_REF]. The routes that a mobile robot can travel are different from those of a traditional transportation devise [START_REF] Battini | Just-in-Time supermarkets for part supply in the automobile industry[END_REF]. The routes change also depending on whether the mobile robots must follow a fixed path or not. Regardless of the technology, at the operational level it is possible to find the control of the system. This will give the opportunity to continuously improve the MF 4.0 system and to avoid that it stops working. Checking the operation of a mobile robot is more difficult and requires new data to be collected and new knowledge. This is because they are not guided by human workers, and if something is not working properly, no one can be aware of it if a proper control system is not implemented. It is important to create new solutions that can control the mobile robots during the execution of their activities [START_REF] Wan | Context-aware cloud robotics for material handling in cognitive industrial Internet of Things[END_REF]. The data collected from the mobile robots need to be analyzed, and once these data become information these need to be understood before then take any decisions. Not understand the information generated by the execution of the different activities from the mobile robots means not being able to understand if the system is working properly. This means having a system that is not working with the desired performance and that is not possible to change it in order to improve them.

Conclusion

This paper focused on the individualization of the most common Industry 4.0 technologies that can be used during the execution of the MF activities. The considered MF activities are transportation, preparation, and material management. This gives us the opportunity to introduce the concept of MF 4.0. MF systems where the different activities are done with the help of the new technologies of Industry 4.0. Moreover, we give some suggestions about the decisions that need to be taken in MF 4.0 with respect to a strategic, tactical and operational impact level. This is only an introduction in the topic of MF 4.0. There seems to be a high potential for future works in this research stream. Future research for example should focus on understanding how to measure the performance of the different technologies implemented in the MF 4.0 activities. This is related to another possible work that is understanding which are the data that need to be collected from the MF 4.0 activities and how to collect them. The technologies are becoming always more powerful and user friendly and their introduction will increase in the next few years. It will be important to know in advance which will be possible issues that companies will face when they decide to implement one of these technologies.

Fig. 1 .

 1 Fig. 1. Industry 4.0 technologies for Material Feeding activities.