Friederike Engehausen
email: friederike.engehausen@tuhh.de

Hermann Lödding
email: loedding@tuhh.de

Modeling Lateness for Workstations with Setup Cycles

Keywords: sequence-dependent setup times, manufacturing control, production planning and control, schedule reliability

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

Introduction

An important objective of manufacturing companies is the reduction of setup efforts since high setup times negatively influence a workstation's productivity and flexibility. Two scenarios of setup times can be distinguished. In the first one, the setup time of the next order is independent from the currently processed order. In the second case, setup times depend on the predecessor of the currently processed job and are hence called sequence-dependent setup times [START_REF] Conway | Theory of Scheduling[END_REF]. Independent from the industry, companies often face the challenge of sequencing with sequence-dependent setup times at some of the workstations. Bundling similar orders in setup families and defining a repetitive pattern for a cyclic production of these families has become common practice. A changeover between orders within a setup family requires only minor setup efforts, while the changeover between setup families causes major setup efforts. Whereas the bundling of orders increases the output rate of the workstation, the production schedule is mixed up, as orders are either accelerated or delayed. Delayed orders negatively influence the delivery reliability which is the logistic objective mainly perceived by the customer. Orders which are finished too early increase inventory costs of the company [START_REF] Lödding | Handbook of Manufacturing Control[END_REF].

Fig. 1 shows exemplarily the principle of building setup cycles. A FIFO (First-In-First-Out) processing sequence would require five major setups while the bundling reduces the amount to two major setup efforts. The potential for building setup families is also influenced by the WIP (work in process) level at the workstation: The higher the WIP level, the higher is the potential for bundling but also the resulting turbulences in the production schedule. Thus, companies with sequence-dependent setup times are in a trade-off between a high output rate, low WIP levels and a low variance of output lateness at the same time.

Fig. 1. Principle of building setup cycles

This paper is structured in five sections. After an introduction, we present the current state of research, on which the model presented in section three is based. Section four gives recommendations for a logistic positioning in the before mentioned trade-off. The last section gives a brief summary of the paper and an outlook on planned research.

Current State of Research

Modeling Output Lateness

Output lateness is the deviation of the actual and the planned end of order processing [START_REF] Baker | Principles of Sequencing and Scheduling[END_REF]:

L out =EDO act -EDO plan ( 1
)
where Lout is the output lateness (shop calendar days (SCD)), EDOact the actual end of order processing (SCD) and EDOplan the planned end of order processing (SCD). Hence, a positive output lateness indicates a late completion of an order and a negative lateness an early completion. Schedule reliability is defined as the percentage of orders that is manufactured within a defined lateness tolerance [START_REF] Yu | Terminkennlinie. Eine Beschreibungsmethodik für die Terminabweichung im Produktionsbereich[END_REF]:

SR= NO with L out,ll ≤ L out ≤ L out,ul NO •100 ( 2
)
where SR is the schedule reliability (%), NO the number of orders (-), Lout,ll the lower limit for permissible output lateness (SCD), Lout the output lateness (SCD) and Lout,ul the upper limit for permissible output lateness (SCD).

There are mainly two factors influencing schedule reliability: backlog and sequence deviation [START_REF] Lödding | Handbook of Manufacturing Control[END_REF]. Backlog influences the mean value of output lateness while sequence deviation determines its standard deviation [START_REF] Kuyumcu | Modellierung der Termintreue in der Produktion[END_REF][START_REF] Lödding | Modelling lateness and schedule reliability: how companies can produce on time[END_REF]. The mean lateness is the ratio between the mean backlog and the mean output rate [START_REF] Yu | Terminkennlinie. Eine Beschreibungsmethodik für die Terminabweichung im Produktionsbereich[END_REF]:

L out,m = BLO m ROUTO m (3)
where Lout,m is the mean output lateness (SCD), BLOm the mean backlog (-) and ROU-TOm the mean output rate (orders/SCD).

By definition, the mean lateness does not reflect the effect of sequence deviations of single orders on output lateness. Sequence deviations are defined as [START_REF] Meißner | Logistische Stabilität in der automobilen Variantenfließfertigung[END_REF]:

SDO i =rankO act,i -rankO plan,i (4)
where SDOi is the sequence deviation of order i (-), rankOact,i the actual rank of order i (-) and rankOplan,i the planned rank of order i (-). The rank of an order is determined by sorting the orders by their completion date and ranking them with consecutive numbers. The orders are sorted by their planned completion date to define the planned rank and by their actual completion date for the actual rank. Sequence-dependent lateness is then calculated by dividing the sequence deviation of an order by the planned output rate [START_REF] Kuyumcu | Modellierung der Termintreue in der Produktion[END_REF]:

L out,SD = SDO i ROUTO plan (5)
where Lout,SD is the sequence-dependent output lateness (SCD), SDOi the sequence deviation of order i (-) and ROUTOplan the planned output rate (orders/SCD).

Modeling Output Lateness due to Setup-Optimized Sequencing

As mentioned in section 2.1, lateness can be partitioned in the two parts lateness due to sequence deviations and lateness due to backlog. In preliminary studies we focused on the forecast of the output rate resulting from setup-optimized sequencing [START_REF] Engehausen | Produktionskennlinien für Arbeitssysteme mit Rüstzyklen[END_REF]. Thus, we assume that output rate is forecast reliably. The only remaining influence on lateness is the sequence deviation and hence the standard deviation of lateness. The mean value of output lateness is zero. Literature basically differentiates between class exhaustion and truncation rules for sequencing. Class exhaustion means the workstation will not change to another setup family as long as there are orders of the currently processed setup family in the queue. Truncation rules permit the changeover to another family while there are still orders of the currently produced family in the queue [START_REF] Russell | Sequencing Rules and Due Date Setting Procedures in Flow Line Cells with Family Setups[END_REF].

Eilmann et al. investigate different setup-optimized sequencing rules and their influence on productivity and standard deviation of throughput times. Scheduling with constant throughput times means the standard deviation of throughput times equals the standard deviation of output lateness. A FIFO sequencing results in the lowest productivity but also the lowest standard deviation of throughput times. Class exhaustion leads to the highest productivity but also increases standard deviation of throughput times. The authors also investigate the productivity of a truncation rule but do not show its effect on standard deviation of throughput times [START_REF] Eilmann | Optimierung der Rüstreihenfolge. Auswirkungen auf Produktivität und Durchlaufzeit[END_REF].

Bertsch investigates the effect of setup-optimized sequencing on output lateness. His strategy is to prioritize the setup family with the lowest ratio of setup effort and number of orders in the queue. To forecast the standard deviation of lateness due to setup-optimized he uses the same model as for a random sequence. The standard deviation increases with a higher WIP level and a higher number of setup families. The applied model works well as an approximation but the lower the number of setup families and the higher the WIP level at the workstation is, the higher is the difference between the forecast and simulated standard deviation [START_REF] Bertsch | Modellbasierte Berechnung der Termintreue[END_REF].

Sawicki compares different class exhaustion rules with truncation rules in terms of tardiness. He also investigates the application of different setup family sequences. His results suggest that usually class exhaustion performs best while specific values for parameters of truncation rules outperform class exhaustion [START_REF] Sawicki | The Problems of Tardiness and Saturation in a Multi-Class Queue with Sequence-Dependent Setups[END_REF].

All above mentioned authors explain the effects of certain setup-optimized sequencing rules on lateness mostly based on simulation experiments. The authors neither model the influence of sequence-dependent setup times nor do they give recommendations for a positioning in the trade-off. Also most authors did not analyze the interdependency between the WIP level and configuration of the sequencing rules.

3

Sequence Deviation due to Setup Cycles

Strategies for Setup-Optimized Sequencing

The heuristic of class exhaustion is often suggested by literature for sequencing at workstations with sequence-dependent setup times. Its application is easy and for the sake of a higher output rate scattering throughput times are usually accepted. Class exhaustion, as it is referred to in this paper, means that a setup family will be produced until there is no order belonging to this family left in the waiting queue of the workstation.

Another strategy for setup-optimized sequencing is a truncation rule that fixes a maximum number of bundled orders without preventing the workstation setting up for the next setup family. A cyclic order is fixed for both strategies (e. g. A-B-C) to decide which setup family is produced next without taking due dates into account.

Sequence Deviations

Figure 2 left shows how the maximum sequence deviation is caused when applying class exhaustion with the help of an example. In the worst case, order A arrives when the workstation has just begun the setup for family B. Hence, the order has to wait until all the other setup families have been produced. Due to a random distribution of setup families in the WIP, the time for the production of all families, which is denoted as the setup cycle time in the following, varies for each setup cycle. As a consequence, also the waiting time varies for each setup cycle. The figure shows a situation with 6 orders in the waiting queue of the workstation. A new order will arrive at the workstation each time the processing of an order is completed. Figure 2 right shows the effect of the truncation rule on sequence deviations. If an order of setup family A arrives when the workstation just started producing setup family B the order will wait at least until all other setup families have been produced. The upper limit causes the workstation to stop producing a setup family although further orders of the currently produced setup family could be present in the waiting queue (shaded order A in Fig. 2). In this case the waiting time for this order increases steeply by at least one setup cycle while the throughput times of orders of the other setup families are slightly reduced. With an upper limit maximum sequence deviations are not only defined by the length of one setup cycle but also by the amount of orders present in the waiting queue.

Fig. 2. Sequence deviation caused by setup cycles

The assumption is that in a job shop production the production schedule is made with constant planned throughput times which are independent from a setup-optimized sequence. This FIFO scheduling considers the WIP level and thus the planned rank of an order is increased by orders in the waiting queue which, according to the plan, would be processed before. If an order is within one setup cycle the maximum sequence deviation is only determined by the amount of orders processed until its respective setup family is produced. This equals the time of the current setup cycle less the size of the order's setup family in the current cycle. The setup cycle time is counted from the last production of the respective setup family. Thus, the maximum sequence deviation is:

SDO max,i =TSC-SSF i -WIPO (6)
where SDOmax,i is the maximum sequence deviation of an order belonging to setup family i (-), TSC the setup cycle time (-), SSFi the setup family size of setup family i in current setup cycle (-) and WIPO the work in process in number of orders (-).

Fig. 3. Sequence deviation for setup cycles with class exhaustion

Since the setup cycle length increases with a higher WIP level, the maximum sequence deviation increases simultaneously. In contrast, the minimum sequence deviation is only determined by the WIP level at the workstation. An order is able to overtake maximally the whole WIP: SDO min,i =1-WIPO [START_REF] Meißner | Logistische Stabilität in der automobilen Variantenfließfertigung[END_REF] where SDOmin,i is the minimum sequence deviation of an order belonging to setup family i (-) and WIPO the work in process in number of orders (-).

The absolute value of the minimum sequence deviation linearly increases with the WIP level. As arriving orders are randomly distributed beneath the setup families, the setup cycle time highly varies depending on the amount of bundled orders. As a consequence, the maximum possible delay of an order scatters. Simulation experiments with class exhaustion have been conducted to evaluate the relationship between setup cycle time and sequence deviation. Fig. 3 shows the results of a simulation run with a WIPO of 20 orders. All measured sequence deviations are located within the limits determined by equations 6 and 7.

4

Recommendations for a Logistic Positioning

Output Lateness due to Setup Cycles

Assuming a random input sequence of orders, the setup family size depends on the WIP level at the workstation. The higher the WIP level the higher is the mean setup family size and thus, the higher the output rate of the workstation [START_REF] Engehausen | Produktionskennlinien für Arbeitssysteme mit Rüstzyklen[END_REF]. With class exhaustion, the output rate constantly increases with a higher WIP level while the truncation rule cuts the output rate increase as soon as the maximum setup family size is reached. The standard deviation of output lateness linearly increases with the WIP level. The reason is that the absolute value of both the maximum and the minimum sequence deviation increase with a higher WIP level (equation 7). Class exhaustion results in a slightly lower standard deviation than truncation rules since orders are never delayed beyond one setup cycle. The truncation rule leads to the effect that an order is occasionally delayed beyond one setup cycle.

Fig. 4 right shows the influence of the number of setup families on the standard deviation of output lateness when the truncation rule is applied. With the same setup cycle time a higher number of setup families causes a higher standard deviation of output lateness. With 12 families the sizes of the single families are lower than with 6 families so the production is cancelled earlier and orders have to wait while 11 other families are processed. Depending on the WIP level, the maximum setup cycle time converges towards a certain value which equals the setup cycle time reached by class exhaustion. With a WIP level of 20 orders the setup cycle time of approx. 46 orders cannot be exceeded.

Recommendations for a Logistic Positioning

Results show that an active planning is required to limit the standard deviation of sequence deviation and thereby avoiding the delay of orders beyond one setup cycle. A higher WIP level results in a higher standard deviation of sequence deviation for both investigated strategies for setup-optimized sequencing. Although results imply that class exhaustion leads both to a higher output rate and a lower standard deviation, the sequence deviation cause by class exhaustion is often still too high to declare it a systematic sequencing rule. Applying class exhaustion means that mean setup cycle times only depend on the prevalent WIP level at the workstation. The higher the WIP level, the higher is the reached output rate but also the resulting standard deviation of sequence deviation (see Fig. 4 left). In contrast, the truncation rule has two control parameters: the WIP level and the upper limit for the setup family size. In general, increasing the WIP level offers the potential for a longer setup cycle time. However, if low values for the maximum setup family sizes are fixed, the production of a setup family is cancelled early. This leads to high waiting times for some orders of one or even two setup cycles.

Setup-optimized sequencing has the advantage of increasing a workstation's output rate. Nevertheless, sequencing heuristics not considering the orders' due dates increase the standard deviation of output lateness and thereby deteriorate schedule reliability. Applying simple sequencing rules as presented in this paper and investigated by above mentioned authors [START_REF] Eilmann | Optimierung der Rüstreihenfolge. Auswirkungen auf Produktivität und Durchlaufzeit[END_REF][START_REF] Bertsch | Modellbasierte Berechnung der Termintreue[END_REF][START_REF] Sawicki | The Problems of Tardiness and Saturation in a Multi-Class Queue with Sequence-Dependent Setups[END_REF] has a rather negative influence on schedule reliability.

However, if one of the two investigated sequencing rules should be applied, class exhaustion is the more preferable rule as it reaches a higher output rate with a lower standard deviation of output lateness (Fig. 4 left).

Fig. 4 .

 4 Fig. 4. Standard deviation of output lateness with setup cycles as a function of WIPO (left) and as a function of setup cycle time and number of setup families (right)

Summary and Outlook

This paper explains the influence of two different setup-optimized sequencing rules on output lateness: class exhaustion and sequencing with a truncation of setup family sizes.

These rules affect the standard deviation of sequence deviation or output lateness respectively.

Standard deviation caused by class exhaustion linearly increases with the WIP level because a higher number of orders is bundled and thus the potential of mixing up the schedule increases. Sequencing with truncation leads to a disproportionately high increase of the standard deviation with the WIP level. The reason is that the production of a setup family is stopped although orders of the respective family are waiting in the queue. These orders will thus wait at least one whole setup cycle until being processed.

In summary, the application of sequencing heuristics to increase output rate or productivity of a workstation with sequence-dependent setup times without taking due dates into consideration crucially worsens schedule reliability.

Currently, it is planned to develop an easily applicable sequencing rule which takes not only the increase of output rate but also the orders' due dates into account. Thereby, it is guaranteed that setup-optimized sequencing has a positive influence on the output rate while only insignificantly worsening the schedule reliability.

The authors would like to thank Deutsche Forschungsgemeinschaft (DFG) for funding the project "Modeling the influence of set-up cycles on the logistic objectives" (LO 858/9-1).


Jack P C Verhoosel
email: jack.verhoosel@tno.nl

Michael A Van Bekkum
email: michael.vanbekkum@tno.nl

Recipe-Based Engineering and Operator Support for Flexible Configuration of High-Mix Assembly

Keywords: Recipe-based manufacturing, operator support, modular and adaptive instructions

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

Introduction

In the manufacturing industry, there is currently a trend towards personalized manufacturing to meet specific customer needs. Instead of mass-production of a single product type, manufacturers must be able to quickly produce on-demand, customerspecific, slightly different types of the same product. To avoid maintaining a large stock of each of these product types, these manufacturers must be able to handle a high mix of low volume product types. This requires flexible assembly lines with operators that are well-supported in their constantly changing assembly task. Flexibility in executing these assembly activities should increasingly lead to high-quality, first-time-right, zero-defect products.

In the context of this trend towards flexible manufacturing, currently available assembly manufacturing systems cannot cope well with the resulting requirements. These systems are usually manually configured for a new product order, which is increasingly inefficient in a flexible manufacturing environment that combines manual assembly cells, collaborative robots (cobots) and automated test stations. Information coming from various supporting systems, such as ERP, MES and operator support systems, needs to be combined by the operator that configures the assembly line with materials, instructions and machine initialization settings.

The IT support in current MES solutions is very solution-specific and does not make use of standardized, modular components to build recipes for production processes. Furthermore, state-of-the art operator support systems do not consider adaptability based on the actual skill level and instantaneous operator capacity. Feedback from real-time measurements of performance, workload and motivational aspects and assembly instructions can provide customized instructions to the human operator and thereby improve motivation, workload and productivity/product quality.

In this paper, we present a data-driven approach to deal with these challenges that results from the cooperation within the Dutch FlexMan project between three industry partners in the manufacturing area, Bronkhorst, Omron and TE Electronics, and TNO as knowledge institute. The FlexMan project is one of the fieldlabs in the Smart Industry Initiative, that aims to accelerate development, test and implementation of smart industry technologies in collaborating networks of companies 1 . We define a knowledge model that captures the main concepts and their relations in flexible manufacturing. This model can be used as the basis for information collection, exchange and analysis in information systems used in flexible manufacturing. The model supports (1) easy definition of recipe-based manufacturing instructions for engineers and operators, and (2) flexible, modular and adaptive support for human/cobot instructions.

In the next section, we discuss the concept of flexible, recipe-based assembly and describe in more detail the environment in which the engineer and the operator need to deal with a high mix of low-volume product types. Then, we introduce our knowledge model and describe the various views on products, processes, resources and recipes. Successively, we describe a set-up in which the recipe concept of this knowledge model is used in the engineering process to easily reuse existing modular components for assembling different product types. In addition, we describe how the operator is supported with generic instructions defined in the knowledge model. Finally, we present our conclusions and future work in this field.

Flexible, recipe-based assembly

Manufacturers offer flexibility in products by giving customers the option to select for instance different colors, sizes, materials and other variables of the product in low volumes. This creates a few process engineering and operator support challenges.

Before introducing these flexibility options to the customer, the process engineer needs to design manufacturing processes for multiple types of the same product with only small differences. These manufacturing processes consist of a combination of assembly, configuration and testing steps including operator instructions that are mostly generic and thus reusable for each product type. They only differ in the specific materials and resources to be used which can vary in number, size, colour etcetera as well as in the order in which the process steps need to be executed.

Consequently, the process engineer needs a design environment that gives support to the task of easily developing a manufacturing process for a new product type. The (re)use of recipes with basic configurations of process steps and their operator instructions is a good approach to this engineer task. From these recipes, flexible instruction sets can be automatically generated to guide the division of the tasks of the operator possibly in combination with a collaborative robot (cobot) during the manufacturing process.

In a flexible manufacturing environment, the assembly line operator is confronted with a high mix of assembly tasks resulting from customer-specific low-volume product orders. In principle, any next assembly task differs from the previous one as a different product or product type needs to be assembled.

In such a rapidly changing environment, the operator support should be maximally suited towards the operator by giving him/her simple and clear instructions on what to do. An example of such a support system can be found in Fig. 1 in which instructions and assembly locations are projected onto the working space of the operator. Apart from the exact technology of the front-end operator support system (projection, presentation, hololens), the main challenge is to switch rapidly between the instructions of different product types. A recipe-based IT system that supports the operator is well-suited for this challenge. Consequently, a recipe-based knowledge model is needed that is used to make the various IT systems involved in manufacturing interoperable, such as the ERP system with product orders, the engineer system that produces the recipes, the MES system that guides the operational manufacturing and the operator support system that instructs the operator. In the next section, we describe this knowledge model in more detail.

Related work

Ontologies are explicit, formal specifications of terms in the domain and of the relations among them [START_REF] Gruber | A translation approach to portable ontology specifications[END_REF]; the fact that formal logic underlies the model makes the ontology machine-readable and -interpretable. Our knowledge model is an ontology of concepts and relations relevant to the processes of flexible manufacturing and recipebased assembly.

Existing assembly design and manufacturing models have been studied to define our knowledge model. These ontology models for assembly processes focus among others on unified approaches towards reconfigurable assembly system design [START_REF] Lohse | Towards a unified assembly system design ontology using Prot{é}g{é}[END_REF], geometry information or variability in an extensive assembly design ontology (AsD) [START_REF] Kim | Ontology-based assembly design and information sharing for collaborative product development[END_REF] and assembly process planning, defining process operations and required resources [START_REF] Huang | Ontology Model for Assembly Process Planning Knowledge[END_REF]. MASON [START_REF] Lemaignan | MASON: A proposal for an ontology of manufacturing domain[END_REF], is a well-known model that presents generic core ontologies for knowledge sharing and interoperability in the manufacturing domain, specifying concepts for products, operations and manufacturing resources. . An MDA approach to define concepts in the product lifecycle is presented in [START_REF] Chungoora | A model-driven ontology approach for manufacturing system interoperability and knowledge sharing[END_REF]. The ontology we present here builds on these existing domain models by reusing concepts relevant to the context and needs of our model.

A recipe-based semantic approach to assembly has been presented in [START_REF] Agyapong-Kodua | Recipe-based integrated semantic product, process, resource (ppr) digital modelling methodology[END_REF] as a methodology for product-resource requirement integration, without taking the concept of a recipe explicitly into account. International standardization for process control ANS/ISA-88-2010 [8] defines a procedural control models for batch process control. The ontology discussed in this paper, adapts some of the ideas behind this standard and defines the concept of recipe as well as recipe procedures explicitly in order to support recipe-based manufacturing in an assembly cell.

Knowledge model for flexible manufacturing

We discern four distinct models to formally define the domain: (1) a product model that defines building blocks for products and how they are related, (2) a process model that describes both logical and temporal structures for activities, (3) a resource model that expresses a functional and logical structure for manufacturing facilities and resources and finally (4) a recipe model that defines recipes as a set of instructions for manufacturing products. The model has been designed with input from all stakeholders in the FlexMan project to assure its compliance with current practices in manufacturing.

Product model

The product model (Fig. 2) consists of products (Product) as things that have been manufactured to be sold to a customer (third party). Products are manufactured in a facility (ManufacturingFacility) and are either components (Component) provided by third parties or complex subassemblies (Assembly) of their own that come from a source outside the assembling system in question. Every product is an assembly object (AssemblyObject) that has a certain state (State). That state (ready, nearly-ready, in-progress, etc.) changes due to activities in the manufacturing process (cf. process model). Manufacturing resources such as parts (Part) and raw material (RawMaterial) are input to the assembly process.

Process model

The process model (Fig. 3) describes both logical and temporal structures for activities (ActivityType) on four different levels:

• Processes (ProcessType), are collections of lower level activities with the purpose of facilitating assembling of an assembly or subassembly. They are described by means of recipes (e.g. assemble PC) and are composed of tasks. • Tasks (TaskType) are processes which facilitate a clearly definable portion of work towards the completion of a product (e.g. assemble parts A and B). A task is composed of a set of operations. • Operations (OperationType) are activities which facilitate a state change of entities that are part of a product within the scope of a specific Task. An Operation is composed of a set of Actions. Operations are normally carried out by Equipment Units. • Actions (ActionType) are the most fundamental activities (hold part A with tool 1), defined by use of equipment (EquipmentResourceType) and requiring a certain capability (Capability) from a physical agent (human, robot/cobot). Every activity has a description (ActivityDescription) and several time parameters (TimePoint), such as start-/stopping time.

Recipe model

In the context of assembly, we consider a recipe to be a set of instructions on manufacturing a product. Rather than viewing it as an object with properties we consider it to be a process, which has a series of steps or activities required for its execution and the materials and utensils needed. In our model (Fig. 3), a recipe (Recipe) holds the necessary set of information that uniquely defines the production requirements for a specific product, in accordance with [8]. It consists of a set of instructions (Instruction) that detail a manufacturing process by describing:

• What equipment is required (EquipmentResourceType).

• How equipment must be configured (ConfigurationInstruction).

• What ingredients are required (ManufacturingBillofMaterials).

• The procedure to execute the recipe in several steps (ProcedureInstruction)

Resource model

Our resource model expresses both a functional and logical structure for manufacturing facilities and resources (Fig. 4). A manufacturing resource (Manufactur-ingResourceType) is (1) a piece of equipment (EquipmentResource) such as a tool or a machine, (2) an agent (PhysicalAgent), being either a human (Human) or a robot (Robot) that has the capability (Capability) to perform an activity or (3) a certain material (MaterialResource). Manufacturing resources are present in a production facility (ManufacturingFacility), in a toplogy that may contain cells (Cell) with optionally one or more work stations (WorkStation). The current knowledge model provides a vocabulary and model for reasoning about recipe-based operator support in flexible manufacturing. By adhering to Gruber's principle of minimal ontological commitment and extendibility [START_REF] Gruber | Toward principles for the design of ontologies used for knowledge sharing[END_REF], we allow freedom to specialize and instantiate the model in a variety of manufacturing environments and provide support for a variety of assembly activities. The minimal manufacturing facility topology or equipment resource type specification e.g. both provide a basis for future extensions.

Demonstration set-up

To validate the usefulness of our knowledge model, we have created a demonstration environment (Fig. 5). The environment contains a process engineering system with a knowledge modelling tool, Topbraid Composer, that is used by the process engineer to instantiate basic, reusable process steps and their operator instructions based on our knowledge model. The process engineer can then use these reusable process steps and their operator instructions to define a master recipe for the assembly of a certain product type. These reusable process steps in the master recipe are independent of the actual OPS system that is used to support the operator in the assembly task.

The master recipe is being exported as an XML-file from the knowledge modelling tool and imported into the OPS MES system. This system has a specific editing tool that is used to extend the master recipe with OPS-specific configuration actions, such as the exact position on the assembly canvas. This results in an OPS-specific control recipe to be used by the operator. In our demonstration set-up, we use the OPS Light Guide system as the operator support system that instructs the operator. For demonstration purposes, we have used our knowledge modelling tool to define modular reusable process steps for three different types of the same flowsensor product manufactured by one of our project partners in the FlexMan project. A master recipe was generated for each of the three flowsensor types using these reusable process steps. The master recipes were only slightly different for each type in terms of size and color of material to be used. The master recipes could then be easily used to define three control recipes for the three types of flowsensors. When generating the master recipes, we encountered the following limitations and challenges:

• Generating a master recipe was done by instantiation of the knowledge model for a flowsensor type. Although the master recipe can be reused, adapting it to get a new master recipe for another flowsensor needs to be done carefully. Some of the instances can be copy-pasted and adapted, while others need to be reused as they are. For instance, ProcessType and TaskType instances need to be copy-pasted and slightly changed, while OperationType, ActionType and OperatorInstructions can be reused as they are or extended with new ones. This needs to be well-thought of by the process engineer. • Although the master recipe is meant to be independent of OPS-specific configurations, most of the instructions in these configurations are the same. Therefore, the wish emerged to add these reusable configuration instructions to the knowledge model. The challenge is then to not mix up the master recipe and the control recipe in the knowledge model and its instantiations.

Finally, we emulated an ERP system with product orders for the three types of flowsensors. These sequence of product orders was such that the operator was presented with a mix of the three types and thus with a flexible and changing assembly task. When using the knowledge model in the process engineering environment and the operator support system, we learned some lessons:

• Usage of modular reusable process steps simplifies the task of the process engineering for quickly designing a manufacturing process for a new product type.

• Designing an OPS-independent master recipe for a product type is an advantage for the process engineer as he/she does not have to deal with all the configuration details of a specific OPS-system. • The assembly line can easily switch between different product types as the control recipes are available for the operator with a specific set of assembly instructions to be executed.

Conclusions and future work

This paper discusses the development of a knowledge model for (1) recipe-based product design and configuration of assembly cells and (2) automated generation of operator instructions. The first version of the knowledge model specifies a model that focuses on composite assembly activities and provides recipe-based support for instruction sets that can be generated specifically for the product type that is to be assembled. This ontology is then used in a scenario to provide the master recipe with accompanying instruction set to an operator support system. Currently, the application scenario focuses on flexibility in providing operator support in a high-mix product variety. In more advanced scenarios, we can further improve flexibility by considering specific human/cobot capabilities, manufacturing facility dynamics and operator context, operator behavior and/or experience. As a future enhancement to the knowledge model, we are also considering product feature variability scenarios, that introduce more complex configurations of the same product type in manufacturing.

Fig. 1 .

 1 Fig. 1. Flexible operator support system with instruction projection.

Fig. 2 .

 2 Fig. 2. Product modelling in the knowledge model.

Fig. 3 .

 3 Fig. 3. Recipe and process modelling in the knowledge model.

Fig. 4 .

 4 Fig. 4. Resource modelling in the knowledge model.

Fig. 5 .

 5 Fig. 5. Demonstration set-up of flexible manufacturing based on the knowledge model.

Smart Industry initiative, https://www.smartindustry.nl/en/


Chiara Cimini
email: chiara.cimini@unibg.it

Roberto Pinto
email: roberto.pinto@unibg.it

Giuditta Pezzotta
email: giuditta.pezzotta@unibg.it

Paolo Gaiardelli
email: paolo.gaiardelli@unibg.it

The transition towards Industry 4.0: business opportunities and expected impacts for suppliers and manufacturers

Keywords: Industry 4.0, Smart manufacturing, Business transformation

Industry 4.0 is today one of the major opportunities for companies competing in the market. In last few years, more and more companies have started to define the path to move from their traditional production systems towards the Industry 4.0 paradigm; accordingly, different models have been proposed in literature to support the business transformation. This paper reviews the technological improvements proposed by Industry 4.0 to understand what are the main processes involved in the transformation and what are the suitable strategies to face the business and operational changes that are required. In particular, we identify and discuss two main perspectives: the suppliers and the customers. For both of them, different business opportunities are presented, and the expected performance improvements discussed.

Introduction

The Industry 4.0 paradigm has opened high potentialities to implement new business and operational models. Many manufacturing companies around the world have been encouraged to invest in research and industrial projects to enable the realization of smart factories [START_REF] Thoben | Industrie 4.0" and Smart Manufacturing -A Review of Research Issues and Application Examples[END_REF][START_REF] Kang | Smart manufacturing: Past research, present findings, and future directions[END_REF][START_REF] Qin | Categorical Framework of Manufacturing for Industry 4.0 and Beyond[END_REF]. In this context, several technology providers have started offering products and product-service solutions (PSS) based on key enabling technologies that have achieved a stable and mature phase of their lifecycle, i.e. IoT for data acquisition and transfer, autonomous and collaborative robots, IT platforms for big data [START_REF] Geissbauer | Industry 4.0: Building the digital enterprise[END_REF]. Despite this, many customers (i.e. manufacturing companies) are still restive to change their production processes to be Industry 4.0-compliant. There are several implementation barriers for smart manufacturing, not exclusively technology-related. The main issues refer to the business process transformation of manufacturers, and include, among others, organizational impacts and employees skills [START_REF] Prifti | A Competency Model for "Industrie 4.0" Employees[END_REF]. Further, some barriers depends on companies' business models and on their positioning in the supply chain.

The aim of this paper is to clarify what are the potentialities of implementing Industry 4.0 solutions in different contexts, discussing the potential business opportunities and benefits, and to provide a theoretical and practical contribution to the companies that are defining their path towards Smart Manufacturing.

2

The vision of Industry 4.0

The key enabling technologies

Different technologies have been classified as pillars for Industry 4.0 [START_REF] Mittal | Product Lifecycle Management for Digital Transformation of Industries[END_REF]. For example, a report from the Boston Consulting Group [START_REF] Rüssmann | Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries[END_REF] identified nine key technologies that can support the smart transformation of an industrial production system.

A first class of technologies refers to autonomous robots and additive manufacturing, advanced "hardware" technologies based on innovative devices and equipment that support the automation of a production process. One of the main aspects regarding automation solutions is the shift towards collaboration, adaptability, and autonomy to react to external changes [START_REF] Chaplin | Evolvable assembly systems: a distributed architecture for intelligent manufacturing[END_REF] [START_REF] Park | From automation to autonomy-a new trend for smart manufacturing[END_REF], resulting in the implementation of Cyber-Physical Systems (CPS) that provide cognitive capabilities to physical assets [START_REF] Lee | Industrial big data analytics and cyberphysical systems for future maintenance & service innovation[END_REF].

A second class of technologies encompasses solutions for data management, acquisition, transformation, visualization, and integration [START_REF] Khan | A Survey of Current Challenges in Manufacturing Industry and Preparation for Industry 4.0[END_REF]. In this class, we can identify technologies such as the (Industrial) Internet of Things (IoT) (the core technology for connecting objects and devices in manufacturing systems, allowing communication and cooperation [START_REF] Da Xu | Internet of things in industries: A survey[END_REF]), big data analytics, cloud technology (along with cybersecurity solutions) [START_REF] Brodsky | Toward smart manufacturing using decision analytics[END_REF], simulation and augmented reality that can be used in product and process design, production control, and optimization [START_REF] Tolio | Virtual factory: An integrated framework for manufacturing systems design and analysis[END_REF] using data coming from different sources.

These two classes of technologies allow the realization of horizontal and vertical integration [START_REF] Kagermann | Recommendations for implementing the strategic initiative Industrie 4[END_REF]. Horizontal integration refers to the creation of a network enabling realtime communication among suppliers, manufacturers and final customers. Vertical integration involves the IT structure inside an enterprise enabling data exchange from the sensors/actuators level to the ERP level, in order to provide real-time decision-making and corrective actions from the business to the shopfloor.

Although technology represents one of the main aspects driving the transformation of traditional manufacturing companies, the introduction of innovative solutions has to be guided by strategic considerations about business opportunities from the introduction of Industry 4.0 principles. In the next paragraphs, a better explanation of these business transformation opportunities is provided.

2.2

The design principles of Industry 4.0

Smart factories are characterized by the following features, or design principles, conceived to develop production processes aligned with the Industry 4.0 vision [START_REF] Hermann | Design principles for industrie 4.0 scenarios[END_REF]:

 Interoperability. Horizontal and vertical integration among different systems require the creation of specific standards and protocols. Although some attempts of standardization have been made by the German Plattform Industrie 4.0 with the Reference Architecture Model for Industry 4.0 [START_REF] Vdi | ZVEI: Reference Architecture Model Industrie 4.0[END_REF], the definition of interoperability standards still represents an open research issue.  Virtualization. In CPSs, each physical asset is characterized by a virtual counterpart that is used to perform simulation and optimization in a virtual environment [START_REF] Lee | Recent advances and trends of cyber-physical systems and big data analytics in industrial informatics[END_REF].  Decentralization. If changes occur along processes, distributed intelligence embedded in physical assets allows faster and more responsive corrective actions, facilitating decision-making processes, which can be managed at local level [START_REF] Schoenthaler | Design and Governance of Collaborative Business Processes in Industry 4.0[END_REF].  Real-time capability. The IoT enables real-time information flows, with the possibility to achieve real-time optimization inside the processes [START_REF] Hu | Toward scalable systems for big data analytics: A technology tutorial[END_REF].  Service orientation. CPSs, together with cloud technologies, enable the implementation of service-oriented architectures, where computation capabilities can be accessed via the web, and process operations can be performed only when required by products through sensing technologies.  Modularity. Advanced manufacturing solutions are conceived as modular, in order to be adapted to the different needs of the production. Reconfigurable Manufacturing Systems provide an example of a modular structure [START_REF] Koren | Design of reconfigurable manufacturing systems[END_REF].

The abovementioned features suggest that Industry 4.0 does not influence production processes exclusively from a technological point of view. Business processes require also a change of perspective to fulfil horizontal and vertical integration. For this reason, a deeper insight about business transformation proposed by Industry 4.0 emerges as essential.

3

The business transformation of Industry 4.0

The new paradigm of collaborative business processes

To understand the business transformation that will involve the factories of the future, it is necessary to study in depth the changes in business processes, as proposed by Industry 4.0 paradigm. The Industry 4.0 is a wide and global vision, involving the whole manufacturing environment, proposing new perspectives for business processes too. Thanks to the connectivity offered by IoT, a manufacturing process can be seen as one of the main elements of a more complex system (Figure 1). Here, the factory is one of the crucial points for data generation, while data exchange is always bidirectional, making suppliers, customers, transportation systems and employees fully integrated and relevant for the optimization of the whole system. In such a context, the network of stakeholders, products and equipment, allows an end-to-end engineering integration along the value chain [START_REF] Stock | Opportunities of sustainable manufacturing in Industry 4[END_REF], while five value creation factors -products, processes, equipment, humans and organization -can be identified, and thus properly managed by companies to undertake the Industry 4.0 transformation. Products become smarter, because they have embedded sensors and identification technologies [START_REF] Qin | Categorical Framework of Manufacturing for Industry 4.0 and Beyond[END_REF]. They can do computations and communicate with the environment, providing their identity, state and properties [START_REF] Schmidt | Industry 4.0-potentials for creating smart products: empirical research results[END_REF]. To communicate with these smarter products, the equipment and the processes have to change. Indeed, the factory becomes conscious and intelligent enough to make decisions at decentralized level and in real time about machines maintenance and production control [START_REF] Qin | Categorical Framework of Manufacturing for Industry 4.0 and Beyond[END_REF]. Also the work organisation changes, moving to "human cyber-physical system", defined by [START_REF] Romero | The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation towards Human-Automation Symbiosis Work Systems[END_REF] as a "system engineered to: (a) improve human abilities to dynamically interact with machines in the cyber-and physical-worlds by means of 'intelligent' human-machine interfaces, using human-computer interaction techniques designed to fit the operators' cognitive and physical needs, and (b) improve human physical-, sensing-and cognitive capabilities, by means of various enriched and enhanced technologies (e.g. using wearable devices)". Finally, relevant impacts at organizational level emerge. In particular, among the 68 competences identified as relevant for Industry 4.0 [START_REF] Prifti | A Competency Model for "Industrie 4.0" Employees[END_REF], technological skills, mainly related to the use of data analysis and IT, as well as behavioural competences (i.e. teamwork, collaboration, negotiation), have cross importance to succeed in an Industry 4.0 context. Further, an increasing importance is given to long-life learning and knowledge management, also regarding legislation and safety awareness.

Industry 4.0: a model for business transformation

The main challenge faced by companies embarking upon an Industry 4.0 transformation refers to the alignment of their business strategy with the vision proposed by the smart manufacturing paradigm. This point, referred to as "Envision" stage in [START_REF] Erol | Strategic guidance towards Industry 4.0-a threestage process model[END_REF], represents the first out of three steps characterising an Industry 4.0 transformation. It requires a deep understanding of the concepts at the basis of the Industry 4.0 paradigm, as well as a clear idea of the aims each company wants to achieve exploiting new technologies. For this reason, it mainly involves the management. Also customers, suppliers and technology partners can take part in this stage due to their influence on business model configuration. This phase, which ends with an investment estimation, is followed by a second stage concerning the formulation of specific roadmaps to translate longterm strategies into practical areas of development. Readiness assessment models and maturity models [START_REF] Schumacher | A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises[END_REF][START_REF]The Connected Enterprise Maturity Model[END_REF] play an important role in identifying companies' strength and weaknesses, and to understand the best development actions, according to the main organisational and business drivers characterising the Industry 4.0 environment where companies operate. Finally, during the "Enact" phase, a pilot project can be implemented in the company. Different opportunities can be envisioned, including either technological or organizational and infrastructure projects. A clear objective and a wide collaboration of all the involved stakeholders, with the composition of cross-functional teams, is needed to succeed. Only by identifying improvements and real advantages, it can be possible to extend pilot projects at large scale, transforming them into operational standards.

Different perspectives on business transformation

The optimal path of business transformation towards Industry 4.0 is highly affected by different company's features and position in the supply chain. Suppliers, technology providers, and machines / equipment manufacturers (suppliers for short) can be characterised by sophisticated PSS business models, where the product offering is bundled with services, support, self-services and knowledge [START_REF] Baines | The servitization of manufacturing: A review of literature and reflection on future challenges[END_REF]. Customers and users, instead, are usually manufacturing organisations using the technologies in their processes. Thus, suppliers embed the technology in their products-services, whereas the customers use the technology to make their processes more efficient and high-performing. Being the technology one of the main drivers for Industry 4.0 transformation, this differentiation strongly affects the opportunities that smart manufacturing introduction can bring to companies.

Also the other drivers of the transformation (e.g. product, processes, organization, governance) [START_REF] Schumacher | A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises[END_REF] can be related differently to the two perspectives of suppliers and customers. In addition, the expected impacts of Industry 4.0 in terms of measurable key performance indicators (KPIs) are not the same. While suppliers can benefit of major revenues for the increasing offering of digital solutions (made of services and products), customers can gain efficiency from a real-time controlled production, able to manage changes and predict failures and issues potentially generating downtimes or quality problems. All the aspects discussed above are presented in Table 1.

However, for the suppliers the transition towards Industry 4.0 is mainly supported by technological improvements, allowing them to develop their strategies through offering advanced PSSs. Here, the main challenges concern the acquisition of new technical competences, mainly to be used in R&D and IT departments. On the contrary, the most relevant business opportunities for customers involve operations management, for instance maintenance and quality practices or logistics issues. Also the workforce at the lowest level is involved, making the path to smart manufacturing more difficult [START_REF] Bauer | Industry 4.0 after the initial hype-Where manufacturers are finding value and how they can best capture it[END_REF], due to the complexity in acquiring technical competences about IT infrastructure and data management, coordinating actions among different departments, defining new practices in operations management, and estimating the return on investments.

Conclusions

This paper presented an overview of the vision of Industry 4.0 in terms of key technologies and design principles. We illustrated the new paradigm of collaborative business processes to highlight its main impacts on traditional manufacturing. Discussing the point of view of suppliers and customers, it emerged that the former have to face more issues related to the transformation of operational processes, whereas the latter can benefit of the maturity of many key technologies that can be embedded in their products-services.

Such considerations call for the necessity to deeper investigate the different business opportunities of Industry 4.0 for suppliers and manufacturers, in order to provide proper advices about the design of path towards Industry 4.0, taking in consideration companyspecific characteristics and requirements.

Fig. 1 .

 1 Fig. 1. Business scenario of Industry 4.0 [17]

Table 1 .

 1 Suppliers and Customers perspectives

		Business	KPI	Challenges
		opportunities
		New digital business models	 Increasing of sales revenues from added services on products  New sales revenues from platforms and cloud-	 Privacy issues in sharing data with external stake-
			based services	holders
	Suppliers	Digital engineering	 Reduced time to market, thanks to availability of integrated digital tools for product development  High level information from field for R&D im-provements	 Investment in integrated digital tools  Business partners not
		Digital sales	 Better customer relationship with the possibility to	able	to	collaborate
		and	offer personalized products/services	around digital solutions
		marketing		 Lack of digital skills in
				the workforce
	Manufacturers	Vertical operations integration Horizontal integration Smart maintenance and service	 Faster decision-making  Improved quality, with real-time process control  Increasing of energy efficiency  Lower transport and logistics costs, due to real time supply chain optimization  Lower warehousing costs and spare parts manage-ment costs  Preventive maintenance, thanks to real-time infor-mation from field  Increasing of asset utilization and availability	 Investment in infrastruc-tures for machines' con-nections  Cultural transformation to manage operations us-ing digital technologies  Economic benefits and return on investment
				more difficult to estimate
		Digital	 Better and faster training for operators
		workplace	 Increasing of productivity


Omid Maghazei
email: omaghazei@ethz.ch

Torbjörn Netland

Implementation of Industry 4.0 technologies: what can we learn from the past?

Keywords: Industry 4.0, Advanced Manufacturing Technology (AMT), Literature Review

.

Introduction

The fourth industrial revolution ("Industry 4.0") promises a multifaceted paradigm shift in manufacturing [START_REF] Schwab | The fourth industrial revolution[END_REF]. Industry 4.0 deals with the applications of intelligent products and production processes [START_REF] Brettel | How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective[END_REF]. To date, studies of Industry 4.0 have encompassed three interrelated trends. The first aspect is related to the advanced and expanding technologies in manufacturing industries, which allow for the digital transformation of products and production systems into fully integrated and automated settings. Examples are the Internet-of-Things, big data, cloud computing (and cyber security), virtual reality and augmented reality, robotics and artificial intelligence, and additive manufacturing (3D printing) [START_REF] Schlaepfer | Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies[END_REF]. The second aspect is concerned with the implementation process and use of such technologies in manufacturing industries. In this regard, Industry 4.0 is defined as an enabler for communications in the form of Cyber-Physical-Systems [START_REF] Brettel | How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective[END_REF][START_REF] Jazdi | Cyber physical systems in the context of Industry 4.0[END_REF]. The third aspect deals with the impact of creating these digital ecosystems on organizations, industries, environment, and societies [START_REF] Geissbaur | Industry 4.0: Building the digital enterprise[END_REF]. For instance, it is argued that implementation of Industry 4.0 can yield productivity gains, as well as other benefits, namely, quality improvement and greater flexibility of manufacturing industries [START_REF] Rüssmann | Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries[END_REF].

In retrospect, it is clear that manufacturing underwent a similar shift in the second half of the 20th century, the third industrial revolution. More specifically, a generation of computing and automation opened up a new source of competitive advantage for manufacturing settings to move toward automated factories [START_REF] Meredith | Implementing the automated factory[END_REF]. In this regard, Advanced Manufacturing Technologies (AMTs) were arguably central in the shift from the second to the third revolution.

An AMT can be broadly defined as "an automated production system of people, machines, and tools for the planning and control of the production process, including the procurement of raw materials, parts, and components, and the shipment and service of finished products" [START_REF] Pennings | New technology as organizational innovation: The development and diffusion of microelectronics[END_REF]. AMTs encompass a group of technologies that are hardwarebased (e.g., computer numerical control, flexible manufacturing systems, and industrial robots) and software-based (e.g., computer-aided design, material requirements planning, and manufacturing resource planning), linked through advanced computing technology called computer-integrated manufacturing (CIM) [START_REF] Udo | Advanced manufacturing technologies: Determinants of implementation success[END_REF][START_REF] Dangayach | Advanced manufacturing technology implementation: evidence from Indian small and medium enterprises (SMEs)[END_REF].

On the one hand, AMTs represent a number of advanced computer-based technological innovations, consolidated as a system, which can mainly enhance design, manufacturing, and administrative processes in production systems [START_REF] Boyer | Approaches to the factory of the future. An empirical taxonomy[END_REF]. Such an integrated system of physical assets, software packages, and planning methods fits perfectly with the attributes of Industry 4.0.

On the other hand, organizations aim at creating competitive capabilities, namely price, quality of products, product line breadth, delivery capabilities, and flexibility, by implementing AMTs, ultimately resulting in high levels of performance [START_REF] Tracey | Manufacturing technology and strategy formulation: keys to enhancing competitiveness and improving performance[END_REF][START_REF] Chung | Patterns of advanced manufacturing technology utilization and manufacturing capabilities[END_REF]. Several studies investigate the implementation aspects of AMT and what contributes to its successes and failures [START_REF] Udo | Advanced manufacturing technologies: Determinants of implementation success[END_REF][START_REF] Small | Developing a framework for the effective planning and implementation of advanced manufacturing technology[END_REF][START_REF] Sambasivarao | Selection and implementation of advanced manufacturing technologies: classification and literature review of issues[END_REF][START_REF] Zammuto | Gaining advanced manufacturing technologies' benefits: The roles of organization design and culture[END_REF][START_REF] Voss | Implementation: A key issue in manufacturing technology: The need for a field of study[END_REF]. Other studies deal with the effect of AMT on performance [START_REF] Jonsson | An empirical taxonomy of advanced manufacturing technology[END_REF][START_REF] Kotha | Strategy, advanced manufacturing technology and performance: empirical evidence from US manufacturing firms[END_REF][START_REF] Small | Advanced manufacturing technology: implementation policy and performance[END_REF][START_REF] Zhang | Achieving flexible manufacturing competence: the roles of advanced manufacturing technology and operations improvement practices[END_REF]. Implementation of AMT and its resulting effects have been widely investigated in the extant literature.

Thus, examining the literature of AMT and existing theories provides a reliable basis for gaining a deeper understanding of the dynamics of Industry 4.0. More importantly, this allows us to infer relevant observations, theories, and findings for the emerging shift into Industry 4.0. In other words, we learn from the past to prepare for the future of manufacturing [START_REF] Webster | Analyzing the past to prepare for the future: Writing a literature review[END_REF]. Therefore, the objective of this study is twofold: 1. To summarize the body of research on AMT; 2. To discuss what can be learned from the AMT research for Industry 4.0.

Methodology

This research is based on a systematic review of the AMT literature. A systematic literature review is defined as "a written document that presents a logical argued case founded on a comprehensive understanding of the current state of knowledge about a topic of study" [START_REF] Machi | The literature review: Six steps to success[END_REF]. In conducting this review, we employ the review process defined by Tranfield, Denyer [START_REF] Tranfield | Towards a methodology for developing evidenceinformed management knowledge by means of systematic review[END_REF].

Identification of keywords and search terms

This review is based on a journal search. Conducting a review based on a journal search ensures a certain level of quality [START_REF] Rowley | Conducting a literature review[END_REF]. In doing so, we develop a list of journals based on our preliminary research and journal rankings (see [START_REF] Petersen | Journal ranking analyses of operations management research[END_REF][START_REF] Prasad | International operations management research[END_REF]). Conference proceedings are not included in this review. The search keyword is "advanced manufacturing technolog*", and "AMT." The search field is mostly constructed from a combination of abstract (AB), title (TI), and keywords (KW). The primary result is 285 papers. In the following, we employ a round of selection criteria based on checking the scope relevance by title and abstract review, as well as evaluating access to the full manuscript. In that selection process, we do not apply any criteria for the number of pages, year of publication, or number of citations. The secondary result that is the focus of this review comprises 208 papers (see Table 1). As recommended by Tranfield, Denyer [START_REF] Tranfield | Towards a methodology for developing evidenceinformed management knowledge by means of systematic review[END_REF], we employ data-extraction forms that include general information (e.g., title, author, and publication details), study attributes (e.g., objectives or research questions, context of the study, methods), and core contributions (findings, links to other concepts, information on growing themes, key results, and additional information). This process requires a clear documentation of all steps.

In doing so, we develop a simple coding system to classify the areas of prior research on AMT. Consequently, we classify 208 papers into four main clusters of AMT evaluation, AMT implementation, AMT results, and AMT contingency. Moreover, we have some papers that combine these classes. In the following, we assign each paper with two codes based on its core contributions. The first code relates to the overall contribution of the paper, which allows us to adopt a descriptive analysis, whereas the second code reflects other details about the papers, allowing us to portray the overall scheme of the AMT literature.

Findings

Descriptive analysis

We first report on the frequency of the 208 publications from 1986 to 2016. Figure 1 shows that the publications on AMT are not evenly distributed, indicating a plethora of research during the late 1990s. In order to gain a detailed understanding of the trends and the focus of publications in the past, we classify our descriptive analysis into three decades: 1986-1995, 1996-2005, and 2006-2016.

Figure 1. Frequency of the publications in each year

The first decade: 1986-1995. This decade shows that most of the publications were focused on AMT evaluation and AMT implementation (see Fig. 2). For instance, the results show high focus on the evaluation of investment, justification, and decision making, as well as elaboration on implementation issues.

Thematic analysis

Our literature review allows us to summarize the wide-spanning areas of prior research on AMT (see Fig. 5). This framework illustrates a brief summary of the results of the second code in our review. It represents an overview of the key parameters during the evaluation and implementation phases. Moreover, it classifies possible outcomes of implementing new manufacturing technologies and shows the context variables that may influence this process.

Conclusion

The fourth industrial revolution is evolving, yet it has many similarities to its antecedent phenomenon, AMT. An examination of three decades of publications on AMT shows that the evaluation of new technologies, regardless of their maturity, has always been central to industries that plan to implement them. More specifically, analyzing the first decade implies that industries need to justify their decisions and carefully evaluate their investment in implementing new technologies. The second decade illustrates growing concerns about AMT results, mainly performance improvement, measurement issues, competitive advantage, and success factors. The third decade of AMT literature shows that some outcomes are likely to be influenced by contingent factors, such as organizational and national characteristics. Therefore, it is worthwhile for industries that are planning to implement new Industry 4.0 technologies to examine similar attributes in terms of AMT evaluation, implementation, outcomes, and contextual variables. To the best of our knowledge, the areas of investigations identified are potentially viable in the context of Industry 4.0 and to a certain extent can set the scene for the emerging industrial revolution.

Figure 2 .

 2 Figure 2. The frequency of the clusters in AMT publications: 1986-1995

Figure 3 .

 3 Figure 3. The frequency of the clusters in AMT publications: 1996-2005

Figure 4 .

 4 Figure 4. The frequency of the clusters in AMT publications: 2006-2016

Figure 5 .

 5 Figure 5. Summary of the areas in the AMT body of literature

Table 1 .

 1 List of journals, results, and fields of search

	Title	Final Selec-tion	Field of Search
	International Journal of Operations & Production Management (IJOPM)	34	AB
	Journal of Manufacturing Technology Management 1	28	AB
	International Journal of Production Economics (IJPE)	17	AB, TI, KW
	International Journal of Production Research (IJPR)	16	AB, TI, KW
	Journal of Operations Management (JOM)	16	AB, TI, KW
	International Journal of Advanced Manufacturing Technology	13	AB, TI, KW
	Technovation	11	AB, TI, KW
	Industrial Management and Data Systems	10	AB
	Journal of Engineering and Technology Management	8	AB, TI, KW
	Computers and Industrial Engineering (CIE)	8	AB, TI, KW
	Management Decision	7	AB
	International Journal of Industrial Ergonomics	6	AB, TI, KW
	International Journal of High Technology Management Re-search	6	AB, TI, KW
	Journal of Manufacturing Systems (JMS)	5	AB, TI, KW
	Decision Sciences	4	AB
	Production Planning and Control	4	AB, TI, KW
	Production and Operations Management (POM)	4	AB, TI, KW
	Strategic Management Journal (SMJ)	3	AB
	Harvard Business Review (HBR)	2	AB
	Research Policy	2	AB, TI, KW
	Academy of Management Journal (AMJ)	1	AB, TI
	Academy of Management Review (AMR)	1	AB or TI
	European Journal of Purchasing & Supply Management	1	AB, TI, KW
	Organization Science	1	TI or KW

Previously known as Integrated Manufacturing Systems (IMS) and World Class Design to Manufacture (WCDM) [http://www.emeraldinsight.com/loi/wcdm].


Henrik Schroeder
email: henrik.schroeder@tuhh.de

Axel Friedewald

Chris Kahlefendt

Hermann Loedding

Virtual Reality for the Training of Operators in Industry 4.0

Keywords: Virtual Reality, Virtual Technologies, Training, Education, Industry 4.0, Qualification, Flexibility, Individual Production, Smart Factory, On-the-Job Training

The time for maintenance operations is often restricted due to external circumstances. When conducted with inexperienced operators, additional uncertainties can arise and in case of a delay, high follow-up costs emerge. By using Virtual Reality (VR), operators may practice their skills in advance. This paper analyzes the training content and describes which parts of a generic operating cycle have the potential to be supported by VR as training technology. Furthermore, a training procedure and a resulting system architecture that allows to work efficiently with VR as a training technology are presented.

Introduction

As timeslots for complex maintenance tasks are often restricted, companies need experienced operators to fulfill these essential tasks in a short time. This is among others a characteristic of the machine industry, where operators may service an engine only during the planned maintenance time and in case of a delay, further loss-of-production costs or overtime payments can emerge. To avoid this, service operators usually rely on their experience or use paper based instructions for preparation. A problem with these utilities is that users cannot easily identify complex, three-dimensional subassemblies [START_REF] Halata | Augmented_Reality-gestützte Arbeitsunterlagen für die Unikatfertigung[END_REF]. Neither can users train their motor skills. To gain experience when executing those operations for the first time or after a long period, a training method is needed, that enhances cognitive skills as well as motor skillse.g. for movements in cramped areas. The training method shall enable the operator to interact with the prospective work object even without the training object being present, giving him the possibility to train at any place desired. Virtual Reality (VR) imitates a fictional situation for the user who can manipulate any object with no restriction to weight, size, time, location or with any danger to his well-being. It was already applied in several training showcases, e.g. [START_REF] Vaughan | An overview of self-adaptive technologies within virtual reality training[END_REF]. Still, up-todate literature does not sufficiently describe the parts of a task that companies may train with VR. Therefore, the goal of this paper is to identify the benefits VR provides as a training technology and to reduce the effort of the trainer to prepare a training. Furthermore, this article shall propose an adequate information flow to increase the training outcome of complex manufacturing tasks.

2

Current State of Research

Training Content of Complex Manufacturing Tasks

To define suitable training courses, the training content and the target competence levels have to be defined. The training content is mainly determined by the intended outcome [START_REF] Biggs | Teaching for quality learning at university: What the student does[END_REF] in this case a complex manufacturing task. These tasks may be described by the dimensions complexity, connectivity, non-transparency, dynamics and multiple targets [4]. To define which parts of a task may profit from VR based training, this paper uses a generic operating cycle developed by Tietze et al. It consists of the five generic steps information gathering and processing, material and tool allocation, component and site preparation, execution, feedback and post-processing [START_REF] Tietze | Analyse der Arbeitsproduktivität in der Unikatfertigung: Eine Grundlage für zielorientierte Verbesserungsprozesse in der Unikatfertigung[END_REF]. These steps may be split into sub-processes focusing on different capability factors.

As the pure content of a task is not sufficient to clarify the outcome, the target competences have to be specified in a proper way [START_REF] Biggs | Teaching for quality learning at university: What the student does[END_REF]. In the context of maintenance tasks, especially cognitive and motor skills are relevant. Referring to the oxford dictionary, a cognitive skill is the ability to acquire and understand knowledge [START_REF]Definition of cognition in English[END_REF] and a motor skill is the ability to produce the required muscular movements [START_REF]Definition of motor in English[END_REF]. Anderson et al. [START_REF] Anderson | A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives[END_REF] present a revised taxonomy of Bloom for cognitive skillsfrom remembering to creating. Klemme et al. [START_REF]Lehren und Lernen in der Physiotherapie: 41 Tabellen[END_REF] present the taxonomy of Dave for psychomotor skillsfrom imitating to naturalization. The different levels of the taxonomy can be measured by specific indicators concerning potential, activity and output [START_REF] Nedeß | Reporting Strategies for a Competence-oriented Management Information System[END_REF].

Training Technology

Many complex manufacturing tasks are usually required to meet certain standards of time and quality. As described in the introduction, today's training methods prepare the operator with written instructions and technical drafts. The preparation of these instructions is often time consuming and it usually takes a long time for the operator to find and understand the necessary information. Furthermore, written instructions provide no possibility to train motions for training purposesnot to mention necessary repetitions as crucial elements of successful trainings [START_REF] Green | Kooperatives Lernen im Klassenraum und im Kollegium: Das Trainingsbuch[END_REF].

VR is a combination of a computer platform, scene data, an input and an output device and a VR software [12]. The input device enables the communication between the user and the software. It may work by a tracking of the user's motions. This combination gives the user the impression to be physically present in the virtual environment [12]. It can be realized among others in form of a VR Cave or with VR glasses. As it is a promising technology to solve the problems mentioned above, several authors describe VR based training scenariose.g. [13,[START_REF] Ordaz | Serious Games and Virtual Simulator for Automotive Manufacturing Education & Training[END_REF]. Vaughan et al. [START_REF] Vaughan | An overview of self-adaptive technologies within virtual reality training[END_REF] even present existing VR training systems used in five different sectorsmedicine, industry and commerce, collaboration, serious games and rehabilitation. Their paper stresses the positive effects and the practicability of each concept. Still, none of the papers considers the implicit knowledge of experienced operators in the training. Rather, the authors focus on explicit knowledgee.g. written instructions. Furthermore, the concepts do not consider the potential to partially compensate physical training with a tracking of motions in VRonly cognitive skills are under examination.

Training Procedure

Simplified, training processes can be divided into preparation, execution and evaluation (post-processing) [START_REF] Merkens | Unterricht: Eine Einführung[END_REF]. In this context, the preparation mainly consists of the definition of the target competences, the definition of the resulting training technology and the authoring of a proper training scenario. For the execution and the evaluation, Haase et al. [13] propose four different strategies for VR-training: The discovery mode (no guidance, no evaluation), the presentation mode (full guidance, full evaluation), the guided mode (defined guidance and evaluation) and the free mode (no guidance, final evaluation). Even though Haase et al. explicitly exclude the training of motor skills, no restrictions disable the use of their training modes for this concept.

Combining Work Tasks and Training Aspects

This chapter connects a complex work tasks with the relevant aspects of a VR-based training. In this paper, a maintenance scenario of an engine is selected. Particularly, the operator shall clean the charge air cooler with a diameter of approx. 2 meters weighing about 450 kg. To handle the components the operator has to apply additional structures and a crane in non-ergonomically positions.

1. Information Gathering and Processing: The operator reads paper instructions for preparation. Thereby, he pictures the correct motions. Once on site, the operator compares the instructions, his perception and reality. He may repeat this, whenever a new part comes into sight during the disassembly process. This step contains only cognitive skills as knowledge and imagination. No motions are yet required. As the instructions provide most of the information necessary, the operator has to reach the level of applying for cognitive skills in terms of Blooms taxonomy. 2. Material and Tool Allocation: Prior to the planned maintenance stop, the operator chooses the required material and his tools and places them in a suitable manner. Therefore, this step requires cognitive skills in form of knowledge of the correct tools up to the level of applying. This is not necessarily described by the instructions. The motor skills of gathering the material can be neglected as this paper assumes that operators already have the motor skills for any sub-task of this step. 3. Component and Site Preparation: Again, prior to the planned maintenance stop, the operator gathers the necessary components and places them in a suitable manner. This step includes the correct use of the crane in a cramped area. Therefore, this step mainly consists of cognitive skills of knowing where to find the components up to the level of applying and motor skills for the correct use of the crane up to the level of manipulation.

4. Execution: The operator fulfills the tasks in accordance to his perception of the instructions. On demand, the operator goes back to the paper instructions and gathers further information. Usually, the operator is under time pressure during this step. At large, this step requires cognitive skills for applying the gathered information and motor skills for the correct execution up to the level of manipulation. Generally, this concept shall provide benefits in terms of a higher profitability for the training as well as a higher quality and a lower execution time for the actual task. Thereby, the concept has to fulfill certain requirements. Contrary to the preparation of a training scenario, different operators with diverse skills perform the training. While the degree of difficulty is already defined by the task, the training software has to include different levels of assistance to meet the diverse requirements of the operators. Furthermore, as the time for the execution of the training is limited, the operator needs his feedback as soon as possible. When conducted with human aid, additional waiting times would have to be accepted. Therefore, at best the software shall conduct a fully automated evaluation based on the individual competence level immediately.

Implementing Training Procedures in Virtual Reality

Fundamental Concept

As three persons participate in the training processthe trainer as the editor of the training scenario, the student as the executor and the trainer as the final evaluatorthe software shall provide the necessary views for each role. These views also refer to the simplified training processone to support the preparation, one the execution and one the evaluation (Fehler! Verweisquelle konnte nicht gefunden werden.). The first part is the preparation of the training. Therefore, the trainer uses the editor view and the function Editing to issue the Template. It includes the written instructions in form of steps in the VR-environment as the explicit knowledge. Then, a skilled person executes the steps in the VR-environment using the function Recording. This may be the trainer or an experienced operator. The function records and saves the actions in the Solutionin an unpretentious manner, this represents the implicit knowledge.

Second part is the execution of the training by the student. On the one-hand-side, he has to follow the instructions of the maintenance plan, which are generally not highly detailed and on the other hand has to perform a specific motion sequence. While the student follows the same instructions as the experienced operator before, the software records and saves the students actions. Thus, he creates the Result.

Finally, the function Evaluating derives any discrepancy between the experienced operator and the student. The trainer may define minimum requirements that the student has to accomplish in order to pass. The software then creates the Evaluation containing the evaluated results of the student and the grade. Fig. 1 summarizes this information flow and the according system architecture.

Fig. 1. Information Flow and System Architecture

As the implementation of a trainingespecially with VRinvolves additional costs for hard-and software as well as the additional time for the training, the following describe the setup and the expected benefits of each phase individually.

Preparation of the Training Content

Purpose of this phase is to enable the trainer to include both explicit and implicit knowledge and at the same time keep the necessary effort to a minimum. As stated in the previous chapter, the trainer includes the written instruction in a suitable format for the software. Then, he executes these instructions in VR. The software shall include the motions of the operator and thus allow the combination of explicit knowledge of the instructions and the implicit knowledge of the trainer in an unpretentious manner. There are two options to record the motions: (a) to develop standardized templates, adapt them to any new situation and compare their counterparts for evaluation (b) to divide any task into the smallest steps possible and compare them for evaluation. Option (a) results in a high amount of work, as the templates have to cover any of the existing situations.

In addition, a VR software specialist must carry out this work instructed by the trainer. Option (b) results in less work, as the specialist must not create a new template for any given situation. It is therefore the favored option.

Current instructions require the trainer to transfer the 3D-file to a technical 2D-draft. A positive consequence of the concept is that this part vanishes as the transmission from a 3D-file to VR is completely automated. On the negative side is the trainer who spends additional time as he carries out the instructions for the Solution. Trainings are more likely remembered when the student is involved rather than when he only observes a visual presentation, e.g. written instructions or 3D-PDFs [16]. Thus, using this concept has the potential to improve trainings and therefore reduces mistakes and working time of the work task. In addition, VR has the potential to partially compensate physical training by tracking the user's motions and therefore support the training of the required motor skills. This potential is not yet quantified.

Execution of the Training

Fig. 2. Remote Control and Virtual Reality

For a first proof of concept, the developed prototype uses the application programming interface of the software VDP by the ESI group. The developed module receives and saves the virtual coordinates of any object from the VDP to create the Solution and the Result. Next, the module compares the student's coordinates with the ones of the Solution and derives consequences. For a better comfort, the user may control the situation via smartphone or tablet within the immersive area of the Cave (Fig. 2).

Evaluation of the Training Results

The last phase is the measurement of the acquired competences. Therefore, the software describes and detects errors, rates solutions and determines the new competence level of the student.

In order to describe and detect errors, general criteria for the evaluation are defined. These are the time and the number of mistakes [START_REF] Jv | Presence Measurement Compendium[END_REF]. For an adequate feedback, it is necessary to further define the mistakes. For that, Berndt et al. propose the presence, the position and orientation of each part and the completeness of each working step [18]. These measurable criteria can then be connected to the acquired competences [START_REF] Nedeß | Reporting Strategies for a Competence-oriented Management Information System[END_REF]. The software has knowledge of the coordinates of any object in each step. Thus, it can calculate the differences of the Result and the Solution for each criterion and therefore detect errors.

The function Evaluating rates the differences of each step. This is based on the calculated differences and on the defined tolerances that the trainer may define for each criterion, each step and for each operator individually-e.g. an object shall be placed within a diameter of 5cm. In practice, the developed prototype generates the Evaluation automatically. As no human actions are required, no additional waiting time or effort has to be considered. The student can repeat the training with a full understanding of his previous mistakes and therefore improve his capabilities.

Finally, the software derives the acquired competence level based on the defined tolerances and on the errors of the studente.g. the student is capable to perform a task in a given time. This is based on rules defined by the trainer for passing a new competence levele.g. only a certain kind of error is detected in the training procedure.

Conclusion and Future Research

This paper outlines a concept for a VR-based training of a complex maintenance task. Therefore, it analyzes the generic operating cycle and determines the trainable parts for VR. These are especially cognitive and motor skills up to the level of applying respectively manipulation. Subsequently, the paper derives a concept with the three training phases preparation, execution and evaluation. It describes the resulting information flow and the expected benefits of each phase. At last, it proves the feasibility of the concept by presenting a basic prototype. As this is only an early approach towards a comprehensive training concept, future research will cover a more detailed view on the generic operating cycle, the critical parts of the work task and the compensation of physical training with VR. In addition, it will extend the current results towards VR glasses and Augmented Reality.

  Purpose of the execution is to teach the student all necessary skills defined by the trainer. He therefore executes the written instructions the same way the trainer did in the phase of preparation. As more or less experienced operators may participate in the training during this phase, the execution needs to provide different levels of assistance. The trainer may realize this either via (a) an adaption of the task or (b) an adaption of the aids. Option (a) results in a new training scenario as described in the previous chapter. When choosing option (b), the trainer may disable hints or instructions for the steps. The more experienced an operator becomes, the fewer hints are included by the trainer and thus, the more difficult a training becomes until the operator has gained the desired competences.

  [START_REF] Tietze | Analyse der Arbeitsproduktivität in der Unikatfertigung: Eine Grundlage für zielorientierte Verbesserungsprozesse in der Unikatfertigung[END_REF]. Feedback and Post-Processing: When production is re-initiated, the operator gathers the tools and components, cleans the workspace and documents his actions. Hence, only cognitive skills up to the level of applying are required.At large, the training content requires the training technology to train cognitive skills up to the level of applying and motor skills up to the level of manipulation. While the cognitive skills of course can be taught in principle in advance via paper instructions, videos, 3D-PDFs and VR, the motor skills need physical practice. For evaluating to what degree VR can compensate the physical training, this paper provides the setup of a general training concept.