Sofie Bech

Thomas Ditlev Brunoe

Kjeld Nielsen

Process for enhancing the production system robustness with sensor dataa food manufacturer case study

Keywords: Food manufacturing, Sensor data, Production system robustness

Global markets for food products are changing towards accommodating larger product variety while experiencing shorter lifespans of products. These market trends impose pressure on the existing manufacturing infrastructure, however the food industry lacks investment in manufacturing technology. This research illustrates how the robustness of the existing production system can be enhanced using sensor data, including a case study of a Danish food company. The paper presents an outline of an iterative process performed in a case company to identify and carry out the potential of utilize sensor data from the existing production setup, and an example of the potential and return of investment is introduced. Several challenges are recognized when implementing sensor data to enhance the production system robustness. The in-house competencies are lacking in order to install the setup of sensors and operating in a database with a big amount of data. In addition, the challenge of continuously maintaining the data and the analyses is present. The research points to the importance of involving operators to better understand the context of the production. In conclusion, the case company has achieved information of the production based on data and not only running adjustments, based intuition of the operators and has thereby enhanced the production system robustness.

Introduction

Within the food industry, global markets are today driven by increasing global competition, and a demand for changes in product mix, and frequent introductions of new products [START_REF] Koren | Design of reconfigurable manufacturing systems[END_REF][START_REF] Wiendahl | Changeable manufacturing-classification, design and operation[END_REF]. In addition, the food and beverage markets has shifted towards a very dynamic market, with increasing competition, lower profit margins and extensive new regulations [START_REF]OpenText: Enterprise information management for the food and beverage industry[END_REF][START_REF] Gargouri | A study of scheduling problem in agro-food manufacturing systems[END_REF]. Nevertheless, in many companies, the food and beverage production setup has difficulties accommodating these new challenges partly due to the production setup being designed and operated with tacit knowledge and the production equipment has a long service life [START_REF] Mcintosh | Late customisation: Issues of mass customisation in the food industry[END_REF]. Another aspect is lower investments in manufacturing technology compared to other industries. As an indicator of this lacking investment, in 2016 the food industry, commissioned 8,200 industrial robots worldwide, while the automotive industry commissioned103,300 units in 2016 accounting for a share of 35% of the total supply of industrial robots [6]. Moreover, Kohr et al. [START_REF] Kohr | Identifying complexity drivers in discrete manufacturing and process industry[END_REF] state that in comparison, the automotive OEM industry has a relation of 30% of sites of R&D vs production sites, whereas the process industry has only a 11 % ratio. This difference in investments and R&D indicates that food and beverage companies are less likely to design and commission new manufacturing lines, when introducing new products and variants. Hence, new products need to be introduced in existing manufacturing systems, which yields a requirement for higher process robustness. The majority of research on designing robust processes focuses on establishing new manufacturing systems, i.e. green field operations. Green field research in production robustness thus cannot be applied directly due to the lack of willingness to invest in new equipment. Therefore, to accommodate the new market demand with existing equipment, this research addresses whether sensors can be utilized to collect data from the existing manufacturing equipment to increase process robustness. As the cost of sensors setup in manufacturing has decreased over the last few years, this has given way for this opportunity. The challenges and opportunities outlined in this section leads to the research question of this paper:

How can robustness of the existing production systems be enhanced, using sensor data?

Method

To address the research question, a case study is conducted within a Danish food manufacturing company. The methodology of case study is selected due to needed for analyzing the phenomenon of sensor data of the existing production system, in its specific context (the food manufacturing industry) [START_REF] Voss | Case research in operations management[END_REF][START_REF] Eisenhardt | Theory building from cases: Opportunities and challenges[END_REF]. The case company is introduced in section 2.1. The research takes an outset in a two-year project in the case company where the authors participated. A new process for establishing sensor data acquisition, forming and testing hypotheses and transforming those into actions to improve the manufacturing system is proposed by the authors and outlined in section 3. This process is combined with the case study to exemplify the process and determine the potential in the specific case. The contribution of the research is thus twofold: 1) a proposed iterative method for establishing sensor-based data collection in food manufacturing, and 2) a case study outlining the application of the method and examples of actual improvements in the manufacturing system suggested by the method.

Case company

The case study was conducted in a Danish manufacturer of Danish pastry. The company has two separate production lines with a total annual production of 12.700 tons. The products have different recipes, fillings, toppings, and shapes, implying thousands of possible combinations. Nevertheless, a the process flow is on an overall level generic, and is illustrated in Fig. 1, including the following activities: 1) mixing of the dough, 2) processing of dough and incorporating margarine, 3) shaping of products and adding filling and topping, 4) raising in a warm environment and 5) freezing. Fig. 2. The information flow at the case company Fig. 2. Illustrates a section of the previous information flow at the case company, structured according to the automation pyramid [START_REF] Schilberg | Virtual production Intelligence-Process analysis in the production planning phase[END_REF][START_REF]Isochronous wireless network for real-time communication in industrial automation[END_REF]. The boxes represent the systems that operate on different levels, and lines represent flow of information. There is a dedicated system controlling the mixing process of the dough which has a direct connection with the PLC on the field level, which is the general picture of most processes in the production lines. The factory level addresses the overall business processes and the supply management, and at this level the data is only required for production planning etc. Today, at the case company the information flow between the factory level and the cell and control level sparse and rarely utilized. Specifically, performance data which could be utilized for optimizing the production planning and control is not implemented. The communication between the factory level and cell and control level is primarily manual, and no direct integration is implemented. Hence, information primarily flows from factory level and down, whereas no information formally flows from the lower levels to factory level. This implies that decisions on factory level are not able to be supported by information from the lower levels. On cell level and control level, part of the lines are controlled by individual systems and limited to no communication is done between these systems. This sparse communication is illustrated by a dotted line in Fig. 1. Moreover, the systems on cell level and control level are mainly manually controlled by an operator. Since little information on machine settings is formalized, this results in a production system run on tacit knowledge of how the operator intuitively thinks the process should be controlled, and a system with little fact based justification of decisions on higher levels.

Identifying the Potential

As stated in the introduction, the food industry generally lacks behind other industries in terms investing in utilizing sensor data for improving operations. Although adding sensors to existing production systems is an inexpensive alternative to establishing new more digitized production systems, identifying how the sensor data can improve the processes is a major task in the case company. Moreover, determining the potential outcome on beforehand is a time-consuming task, and thus the proposed process is iterative, encouraging experimentation with data from inexpensive sensors. Figure 3 illustrates the process from initial hypotheses to conclusion and capitalization. In the following, each part of the process in fig. 3 is outlined. 1) The process starts with hypotheses, stated by experts in the company, of what the potentials might be of using sensor data in the existing production system, and what data is necessary to address these hypotheses. Establishing the hypotheses involves several parts of the company, including operators, technicians, engineers and data analysts. The initial hypotheses thus imply requirements for new sensors. 2) The data needed, and quality requirements for the data depends on the hypotheses. There are basically two solutions to obtain data of the existing production equipment: a) Adding new sensors or b) accessing existing sensors in existing equipment. When adding new sensors to existing equipment, the location of the sensors and type of sensors must be carefully considered. With a tough, and strongly regulated production environment as the food industry, all sensors must stand a cleaning. Next, the sampling frequency of data logging must be determined, so that the information is sufficient but not excessive. 3) Collect sensor data. Once sensors are installed, and integrated into computer network, all sensor data is stored in one common database, which is made available to different stakeholders within the company. The timespan needed is implied by the initial hypotheses. 4/5) Analyzing the data to address the hypotheses. If data is logged by using existing equipment PLCs, there is a task of translating the names in the PLC into terms that are familiar to stakeholders, since there may be a lack of documentation of the existing equipment. If the production line consists of both a continuous and a discrete flow, there is a task of relating the data to the same timeframe, especially when the production process is operator driven. Once this is completed the actual analyses can be performed which should be done by applying data analytics software, such as R Studio, Microsoft Power BI or a combination, depending on the actual analyses necessary to address the hypotheses. If the hypotheses can be addressed, the analyses can be concluded and results can be capitalized on, and the loop may be terminated. Alternatively, if the hypotheses are rejected, new hypotheses can be formulated and evaluated through a new loop. 6) As a side effect of this process, questions can be raised of new potentials. Moreover, as a spinoff from the analyses new hypotheses may be generated. If the data foundation is sufficient for the new analyses, this can lead to new findings through data analytics. If not, the process takes a new loop as illustrated in Fig 3 . by establishing sensor data collection, collect sensor data, and analyzing data. Chapter 4 presents an example application from the case company.

Example

This section presents an example from the case company applying the method illustrated in fig 3 . From the initial hypotheses to establish sensor data, collect sensor data for the analyses and conclude and capitalize. Furthermore, this example includes figures on cost and savings as a result of applying the method. 6.New hypotheses sensors were added to the system with the purpose of understanding the effect on the temperature. Three online sensors measuring the dough temperature were added, and two sensors were implemented in order to log the ambient temperature in the factory. The data collection had a 10 month time-span with a frequency of one set of data points per minute. The frequency of the data logging was determined by interviewing operators determining how often changes were made. The logged data was analyzed by loading the dataset into Microsoft Power BI, applying the R plugin for advanced statistical analyzes. Following this, a correlation analysis was made determining the correlation between the initial temperature of the dough, dough temperatures further along the process chain, the ambient temperature and the amount of cooling CO2 injected into the dough. The analysis indicated that the initial temperature of the dough in many cases had been lower than actually necessary, and determined a clear correlation between the ambient temperature and the increase in dough temperature. This correlation was translated into a table indicating how much CO2 should be injected at different combinations of ambient temperatures and initial dough temperatures in order to achieve the desired dough temperature along the production line. In conclusion, the hypothesis was confirmed, since using temperature sensor data from the production it was now possible to define the optimal start temperature of the dough and determine the right amount of CO2.

Cost of investment

The initial investment included new sensors, installation, documentation and connection to a database. The direct cost of that was 30.000 DKK (4,500 USD). The return of investment is directly related to a reduction of CO2 injected. Today the annual cost CO2 used for regulating dough temperature is approximately 1 million DKK. However, by introducing acceptance of a higher initial temperature of the dough, CO2 consumption can be reduced by 30-40 %. Therefore, solely based on the CO2 reduction, this investment has an approximated return on investment of roughly one month. As an additional benefit of higher initial dough temperature, other analysesa indicate a quality increase of the final product. Moreover, these analyses also indicated fewer production stops and less product waste due to the increase in quality.

As a side effect of this analysis, new potentials were identified. rather than controlling the initial dough temperature in the processing part of the production, it raises the question of controlling the temperature earlier in the process. In other words, going from reacting based on the temperature measured to predicting the amount of CO2. This prediction can be based on the ambient temperature and the temperature of the ingredients. Thereby, controlling the temperature based on the real-time data with a direct feedback loop. However, to make this analysis new sensors have to be added to the flour silo and the water dispenser. This is currently an ongoing project at the case company.

Conclusion

The increasingly dynamic markets impose pressure on food and beverage manufacturers but investment in new state of the art production lines are in some cases not possible, and the industry generally lacks investments in new technology. The aim of this research was to identify how sensor data from the existing production system can be utilized to achieve a more robust production system, and to address this, an iterative process for establishing sensor data collection and analyses was introduced, and a case study has been conducted within a Danish food manufacturing company. In addition, an example of applying the method in the case company was included. By introducing sensors in the production in the case company, the knowledge of how temperature affects the dough and case company can reduce the amount of CO2 used for cooling was established. This part of the project had a return of investment of one month and as a potential side effect a quality increase in the product. Moreover, further recommendations for applying sensors and data analyses, found from this research are outlined below:

Extend in house competences: Installing the setup of sensors, establishing a link to in a database with a big amount of data, and navigating this data requires special competences. This research revealed a lack in the in-house competences in the case company. Thus, the case company has outsourced all setup of data logging and database development. However, still it is a major learning curve in making value adding analyses with more than 10.000.000 data points.

Maintain the analyses: Using data as the foundation of decisions in the production, calls for continuous maintenance of the analyses / assumptions and formulation of new hypotheses

The importance of including operators: in order to involve the operators in linking the data into a complex production context in the case company, several interviews have been performed, thereby relating the data to specific challenges in the production. This proved to be a significant change management task.

Possibility for having a fact based discussion of the production, across management: Having production data changes the focus from operator intuition driven production to rather data driven production. Reducing the operator dependency introduces better quality of the products due to more stable process.

Applying the method proposed in this paper in the case company resulted in more robust manufacturing processes. Being in control of manufacturing processes and increasing robustness is expected to allow the company to increase product variety and reduce time to market, providing a more agile manufacturing setup fit for serving markets requiring increased variety and more frequent product introductions.

Fig. 1 .

 1 Fig.1. A generic process flow at the case company[START_REF] Bech | Changeability of the manufacturing systems in the food industry -A case study[END_REF]

Fig. 3 .

 3 Fig. 3. The process of establishing sensor data collection, analyzing and capitalizing on using sensor data


Susann Kärcher
email: susann.kaercher@ipa.fraunhofer.de

David Görzig

Thomas Bauernhansl

Modeling Manual Assembly System to Derive Best Practice from Actual Data

Keywords: Assembly, Model, Best Practice

In manual assembly systems, there is often little transparency and great potential for optimization, especially in assembly systems with small batch sizes. In this paper, a model is developed that supports an approach to automated assembly optimization. For this optimization, actual data is collected in manual assemblies. Based on the data, the optimized assembly sequence is derived by developing a best practice. Best practice describes a combination of assembly processes performed by the workers during the data collection. The model shows the relationships and the dependencies in the assembly systems and allows to improve it. First, the considered assembly system is defined as a socio-technical system and general modeling principles are prepared. After presenting the benchmark approach to derive the best practice, the requirements for the model are identified. Then, the model is developed in four steps: The system boundary is defined, the features are described, and the model is formalized. Finally, the model is applied and tested in an example project and its purposefulness is shown.

Introduction

Nowadays, the planning of assembly still needs high effort [START_REF] Lotter | Montage in der industriellen Produktion[END_REF]. Therefore, especially with small lot sizes, the assemblies are often only roughly planned and lack in transparency for planners and workers. There is still great potential for optimization. [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] In order to reduce the effort, the aim is to plan assembly automatically. One approach towards automated assembly planning is to collect actual data, to analyze it and then to derive an improved assembly sequence, based on best practice. Best practice describes a combination of assembly processes performed by the workers during the data collection. [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] The aim of this paper is to develop a model supporting such an approach. The model should represent the manual assembly system sufficiently well and should still be pragmatic. Furthermore, it should allow data from assembly processes to be recorded and analyzed. The model allows to improve manual assembly systems by deriving a best practice observed during the analysis.

2

Model of Assembly Systems

Manual Assembly System as a Socio-Technical System

A system is a set of interrelated objects that are seen as a whole in a certain context and as separate from their surroundings [START_REF]Technische Produktdokumentation -Vokabular -Begriffe für technische Zeichnungen[END_REF]. Beer [START_REF] Beer | Kybernetik und Management[END_REF] equates the term 'system' with the word connectivity to emphasize, that the parts of a system relate to each other. Hence, no part is independent of other parts and the behavior of the whole is influenced by the interaction of all parts. [START_REF] Ulrich | Anleitung zum ganzheitlichen Denken und Handeln[END_REF] Systems consist of individual elements that each have functions and properties. Relationships, for example information flows, material flows etc., connect the individual elements with each other. [START_REF] Haberfellner | Systems Engineering. Grundlagen und Anwendung[END_REF] A system is bordered against its environment (system boundary), depending on the purpose and the problem [START_REF] Patzak | Systemtechnik -Planung komplexer innovativer Systeme. Grundlagen, Methoden, Techniken[END_REF]. Socio-technical systems generally consist of the components, humans, resources as well as tasks and objectives. They are particularly complex, since these components can have various characteristics. [START_REF] Patzak | Systemtechnik -Planung komplexer innovativer Systeme. Grundlagen, Methoden, Techniken[END_REF] Social and technical systems constitute a unit in the form of a work or an action system [START_REF] Ropohl | Allgemeine Technologie. Eine Systemtheorie der Technik[END_REF]. Trist et al. accentuate that it is necessary to optimize the social and the technical system in combination [START_REF] Trist | Some social and psychological consequences of the Longwall Method of coal-getting[END_REF]. A company contains technical (e.g. tools, machines) and social systems (e.g. people) which generate value through the production of products. Work connects the social and the technical system.

[10] Thus, work systems are socio-technical systems [START_REF] Ulich | CIMeine integrative Gestaltungsaufgabe im Spannungsfeld von Mensch, Technik und[END_REF]. Assembly as a part of production can also be described and analyzed as a system [START_REF] Warnecke | Die Montage als Teil des Produktionssystems[END_REF]. As a working system a manual assembly system also describes a socio-technical system.

Model

A model is the image of a section of reality [START_REF] Dörner | Modellbildung und Simulation[END_REF]. Models represent a process or a system sufficiently accurately [START_REF]Internationales Elektrotechnisches Wörterbuch[END_REF] and always represent a 'constructed reality' [START_REF] Stachowiak | Modelle. Konstruktion der Wirklichkeit[END_REF]. A model is a simplification or abstraction of the reality and can never illustrate it in all its aspects. The purpose of the model is decisive. [START_REF] Haberfellner | Systems Engineering. Grundlagen und Anwendung[END_REF] There is always a necessity to use the model or the representative instead of the original [START_REF] Wüstneck | Zur philosophischen Verallgemeinerung und Bestimmung des Modellbegriffs[END_REF], for example if a reduction, enlargement and simplification is necessary for an illustration of the original or if the original is reduced to basic contexts which explain or predict the behavior [START_REF] Stachowiak | Allgemeine Modelltheorie[END_REF]. Reality thus becomes better understandable and manageable [START_REF] Bandow | Das ist gar kein Modell! Unterschiedliche Modelle und Modellierungen in Betriebswirtschaftslehre und Ingenieurwissenschaften[END_REF]. Stachowiak [START_REF] Stachowiak | Allgemeine Modelltheorie[END_REF] summarizes three general characteristics of the model concept: the mapping characteristic (the model corresponds to the representation of the original), the shortening characteristic (the model records only the characteristics relevant to the creator and user), and the pragmatic characteristic (the model represents the original only for certain subjects, within certain time intervals, limited to certain operations).

Modeling Manual Assembly System in Context of Deriving Best Practice from Actual Data

The model is intended to support an approach for optimizing manual assembly systems by deriving a best practice, based on recorded data. The idea is not to search for the optimum, but to derive the best solution from all the solution strategies to assemble the product executed by the workers. Thus, the optimization is based on actual data instead of plan data. [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] The presented approach benchmarks different solution strategies and adapts the four steps of Watson's benchmark approach [START_REF] Watson | Benchmarking. Vom Besten lernen[END_REF] to manual assembly, which are summarized in the following figure. In the 'planning' phase the goal is planned and the model is created or adapted [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF].

In the 'data gathering' phase actual data is collected by a system in which sensors are installed on components, tools and equipment to automatically identify process steps and record process times [START_REF] Kärcher | Sensor-driven Analysis of Manual Assembly Systems[END_REF]. In particular, acceleration sensors, magnetometers and gyroscopes are used for this purpose. Data from video and an app is recorded as an auxiliary until the system recognizes all processes reliably. [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] In the 'data analysis' phase the best practices has to be found. Therefore a digraph is created. Each solution strategy to assemble the product is represented as a path in the digraph. If the workers execute the processes in the same order faster, the required assembly times are updated. Finally, the shortest path has to be found. [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] The fourth step is to introduce the improvements [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF]. This paper focuses on the model development during the 'planning' phase.

Derivation of Formal and Content-Related Requirements of the Model

A model is always required to meet both, formal and content-related requirements. Patzak [START_REF] Patzak | Systemtechnik -Planung komplexer innovativer Systeme. Grundlagen, Methoden, Techniken[END_REF] derives formal characteristics of a good model: It should be empirical and formally correct, functional and manageable at low effort in creation and application.

Furthermore, the model should meet the following content requirements: As described in chapter 2.2, the purpose of a model is crucial.

The planning aim is to record the assembly processes of different workers and to derive the best solution strategy to assemble a product from the different solution strategies identified in the context of the analysis. The target value is to minimize time. Consequently, the best assembly sequence is the sequence that results in the shortest assembly time. [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] As derived in chapter 2.1, a manual assembly system is a complex socio-technical system. To analyze and optimize it (e.g. the assembly sequence) in an automated way, a model is needed that can handle this complex socio-technical system. Already existing models do not have the same purpose.

Modeling

Graphical modeling is selected for a transparent representation and easier understanding of the system parts and their relation [START_REF] Rumpe | Modellierung mit UML[END_REF]. Specker [START_REF] Specker | Modellierung von Informationssystemen. Ein methodischer Leitfaden zur Projektabwicklung[END_REF] distinguishes four design aspects in the analysis and the modeling of complex systems: the process view (logical and temporal sequence of operations), the function view (similarity of elementary functions), the object view (processing elements) and the task view (personal assignment of operations). The elements and their connections are in the focus of this model, which is why the object view is chosen.

Wiendahl [START_REF] Wiendahl | Stolpersteine der PPS. Ein sozio-technischer Ansatz für das industrielle Auftragsmanagement[END_REF] summarizes four steps for model creationsystem delimitation, feature description, model formalization, model validationon the basis of which the model is developed.

System Delimitation. In this step, the facts and the purpose are defined properly. According to systems engineering, the problem should be detailed from rough to fine. [START_REF] Haberfellner | Systems Engineering. Grundlagen und Anwendung[END_REF][START_REF] Wiendahl | Stolpersteine der PPS. Ein sozio-technischer Ansatz für das industrielle Auftragsmanagement[END_REF] The pragmatic and the shortening characteristic describe that a model represents the system for a certain purpose and records only elements relevant to the creator [START_REF] Stachowiak | Allgemeine Modelltheorie[END_REF] (see also chapter 2.2). The following figure shows the manual assembly system considered in this paper. The system consists of workers, work stations, products, assembly orders and assembly processes [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF]. Data is collected over time from different workers who assemble different products at different work stations. The assembly system is a subsystem of the production system. Input of the systems are information, assembly orders and parts. Output are the assembled products. Preliminary and subsequent areas such as parts production and logistics are outside the system boundaries.

Feature Description. In the following, the main features of the model are described:  Product: For optimization, it is important, to clearly identify the product in order to relate the assembly process steps and sequence to it. A product is a variant of a specific product type and may have options.  Work station: The work station influences the required assembly time, e.g. due to different material supply, which results in different gripping areas. When gathering data, it must therefore be recorded at which work station a product was assembled.

 Process: An assembly consists of individual assembly processes such as 'screwing base plate'. The assembly sequence is described by specifying the predecessor and successor for each assembly process.  Worker: The decisive factor in this socio-technical system is the worker. Depending on previous experience, competence, etc., she or he can execute assembly tasks in different ways and uses different tools and devices. For pragmatic reasons, other influencing factors of the socio-technical system (e.g. motivation and daily form of the worker, other environmental conditions) are not considered in this model.  Assembly order: The input of the assembly system and trigger of the assembly processes are the assembly orders.  Optimization: The optimization derives the best practice to assemble the product.

Model Formalization. Unified Modeling Language (UML) is selected as the modeling language because it is established as a standard. A class diagram provides an overview of the code structure and its internal relationships [START_REF] Rumpe | Modellierung mit UML[END_REF]. Fig. 3 shows the formalized model. The features described above are represented in classes which are specified in the following:

The class 'Worker' includes the attribute 'worker_ID'. The worker's name is not saved in this context. The method 'worker_experience()' counts, how often the worker has already assembled a product, separated in type, variant and options. The method 'worker_ performance()' calculates, how fast the worker carries out the processes compared to the average. The results are not used to evaluate the workers themselves, but to evaluate her or his solution strategies and to identify outliers in the data. An inexperienced worker usually cannot carry out an assembly significantly faster than an experienced worker. The faster assembly of the inexperienced worker may result in a loss of quality.

The class 'WorkStation' defines the work station via 'work_station_ID'. 'work_station_performance()' establishes connections between the work station and the process times (e.g. the material supply at work station 1 is better than the material supply at work station 3).

The class 'Product' describes the product uniquely by the attributes 'prod-uct_name', 'product_ID', 'product_type', 'product_variant' and 'product_options'. The method 'product_total_quantity()' sums up the number of assembled products per type, variant and option.

An object of the class 'AssemblyOrder' is clearly identified by 'assembly_or-der_ID' and refers to products and quantity. Furthermore, it is assigned to at least one worker and one possible work station.

The class 'Process' gets the actual data from the class 'ActualData' and is described by 'process_ID' and 'process_name'. Moreover, it belongs to a 'process_category' and has a predecessor and a successor. It is always assigned to a specific product, worker, work station and assembly order ('process_relations'). The process duration is calculated on the basis of the start and end dates ('process_duration()'). 'pro-cess_def()' identifies the processes and puts them into context. The class 'OptimizationBenchmark' runs the optimization methods and stores the results in the 'OptimizationResult' class. The method 'digraph' creates the digraph, checks it, e.g. if there are outliers or circles, and finds the shortest path (see [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] for further information). In a manual assembly, often several products are assembled. Sufficient data is not collected for all products during data collection. The 'times_other_products()' method can fill this gap by deriving times for other variants. 'same_process()' is looking for identical processes. Further optimizations are performed in 'other_optimization()'.

Model Validation.

In this step, it must be checked whether the model represents the system sufficiently well (mapping characteristic). In the literature, the iterative modeling is emphasized frequently. Dörner [START_REF] Dörner | Modellbildung und Simulation[END_REF], for example, recommends to start with a first draft of the model and to improve it step by step (successive approximation).

The model has already been used in a practical example and had already been improved further. The example project was the assembly of a rear axle. Three workers assembled the product three times each and the process times were recorded (see [START_REF] Kärcher | Approach to Generate Optimized Assembly Sequences from Sensor Data[END_REF] for further details concerning the project). All data was collected in a structured manner and initial optimization was made possible. Fig. 4 shows exemplary instances.

Conclusion and Outlook

In this paper, a model is presented that supports the optimization of manual assemblies. Actual data is collected and, based on the data, the optimized assembly sequence is derived by developing a best practice. The joint mapping of the technical and social systems enables their joint optimization in a socio-technical system. The model was developed in four steps: system delimitation, feature description, model formalization and model validation. The application in the example project shows that the model allows the data to be structured in a meaningful way and is limited to the most important elements.

In the following work, the model will be applied to several industry projects and iteratively further developed. Moreover, the optimization classes are worked on.

Fig. 1 .

 1 Fig. 1. Approach to apply benchmark in manual assembly (own figure based on [2]).

Fig. 2 .

 2 Fig. 2. Analysis of the manual assembly system considered in the model.

Fig. 3 .

 3 Fig. 3. Model.

Fig. 4 .

 4 Fig. 4. Exemplary instances.


Fredrik Tiedemann

Joakim Wikner

Postponement revisited -A typology for displacement

Keywords: postponement, preponement, displacement, flow thinking, decoupling point

Since its introduction, postponement as a supply chain strategy has received a lot of attention in the operations management and the supply chain management literature. Nevertheless, there are still mixed answers about the meaning of postponement and as such, about its operational benefits. For instance, while some scholars argue that postponement results in a shorter delivery lead time, others claim the contrary. To reconcile these apparently conflicting findings, the purpose of this study is to establish a typology that highlights the three key properties of displacement, which is a collective term for preponement and postponement. By breaking down postponement into the three dimensions of form, place, and time, as well as introducing its antithesis preponement, a typology for displacement is presented and illustrated using a well-known postponement case.

Introduction

Postponement, also known as delayed product differentiation [START_REF] Aviv | The benefits of design for postponement[END_REF][START_REF] Aviv | Design for postponement: a comprehensive characterization of its benefits under unknown demand distributions[END_REF][START_REF] Blackburn | Reverse supply chains for commercial returns[END_REF][START_REF] Garg | Managing product variety: an operations perspective[END_REF][START_REF] Swaminathan | Design for postponement[END_REF][START_REF] Lee | Modelling the costs and benefits of delayed product differentiation[END_REF], delayed differentiation [START_REF] Christopher | Logistics and supply chain management: strategies for reducing costs and improving services[END_REF], and late customization [START_REF] Garg | Managing product variety: an operations perspective[END_REF][START_REF] Swaminathan | Design for postponement[END_REF], was already put in practice in the 1920s [START_REF]World class logistics: the challenge of managing continuous change. 1st ed[END_REF][START_REF] Wang | Postponement in mass customization: a literature review[END_REF][START_REF] Yang | Implications of postponement for the supply chain[END_REF] but was initially introduced in the marketing literature by Alderson [START_REF] Alderson | Marketing efficiency and the principle of postponement[END_REF] as an approach to reducing or eliminating the risk and uncertainty costs associated with the differentiation of goods [e.g., [START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF][START_REF] Yang | Postponement in supply chain risk management: a complexity perspective[END_REF][START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF][START_REF] Van Hoek | The thesis of leagility revisited[END_REF]. Since then, many success stories owing to postponement have been reported in the literature [e.g., [START_REF] Aviv | Design for postponement: a comprehensive characterization of its benefits under unknown demand distributions[END_REF][START_REF] Li | Analysis of postponement strategy for perishable items by EOQ-based models[END_REF]. However, there are still mixed answers about the meaning of postponement. Starting with Alderson [START_REF] Alderson | Marketing efficiency and the principle of postponement[END_REF], several works [e.g., [START_REF] Aviv | Design for postponement: a comprehensive characterization of its benefits under unknown demand distributions[END_REF][START_REF] Lee | Modelling the costs and benefits of delayed product differentiation[END_REF][START_REF] Li | Analysis of postponement strategy for perishable items by EOQ-based models[END_REF][START_REF] Yang | Management of uncertainty through postponement[END_REF][START_REF] Swaminathan | Managing broader product lines through delayed differentiation using vanilla boxes[END_REF][START_REF] García-Dastugue | Interorganizational time-based postponement in the supply chain[END_REF] define postponement as an approach to reducing or eliminating the risk and the uncertainty associated with the differentiation of goods, where the differentiation processes in a supply chain should be delayed as long as possible.

The second group of works [e.g., [START_REF] Cooper | Logistics strategies for global businesses[END_REF][START_REF] Mikkola | Supply-chain integration: implications for mass customization, modularization and postponement strategies[END_REF] argues that the point of differentiation should be postponed as close as possible to the customer order entry (i.e., the customer order decoupling point [CODP]) but not necessarily at or downstream of the CODP. The third group [e.g., [START_REF] Wang | Postponement in mass customization: a literature review[END_REF][START_REF] Yang | Implications of postponement for the supply chain[END_REF][START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF][START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF][START_REF] Van Hoek | The thesis of leagility revisited[END_REF][START_REF] Zinn | Planning physical distribution with the principle of postponement[END_REF][START_REF] Yang | The application of postponement in industry[END_REF][START_REF] Van Hoek | Logistics and virtual integration: postponement, outsourcing and the flow of information[END_REF][START_REF] Bowersox | Logistical management: the integrated supply chain process[END_REF][START_REF] Waller | Postponement, product customization, and market-oriented supply chain management[END_REF] defines postponement as the delay of activities until customer orders are received, that is, at or downstream of the CODP. The mixed answers regarding the definition of postponement have obviously resulted in mixed answers about how postponement relates to operational performance and even why it is beneficial to pursue [START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF]. For instance, some authors argue that higher levels of postponement lead to longer delivery lead times [e.g., [START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF][START_REF] Waller | Postponement, product customization, and market-oriented supply chain management[END_REF], whereas others [e.g., 5, 28] claim the opposite. As such, what some researchers deem postponement might be considered preponement by others (preponement is a newer term, first mentioned by Blackburn et al. [START_REF] Blackburn | Reverse supply chains for commercial returns[END_REF] and is here considered the antithesis of postponement). Thus, to reconcile these apparently conflicting findings, the purpose of this study is to establish a typology that highlights the three key properties of displacement. Note that the movement of a transformation activity within a flow structure is here referred to as 'displacement' and is thus used as a collective term for postponement and preponement. In the next section, the idea behind postponement and its relation to the CODP is discussed. Thereafter, a typology for displacement is developed using the three generic dimensions of postponement, that is, form, place, and time [see, e.g., [START_REF] Christopher | Logistics and supply chain management: strategies for reducing costs and improving services[END_REF][START_REF] Alderson | Marketing efficiency and the principle of postponement[END_REF][START_REF] Aitken | Designing and managing multiple pipelines[END_REF]. The typology is then applied to one well-known postponement case. The paper ends with a discussion, conclusions, and further research.

2

Defining form and place postponement in terms of the CODP For many kinds of products, the individual customer demand is unique, especially when taking into consideration the basic use, special features, colors, sizes, and places of purchase [START_REF] Alderson | Marketing efficiency and the principle of postponement[END_REF]. However, products belonging to the same family usually share common components and/or processes, meaning that in their initial stages of production, these products are in a generic form and place [START_REF] Aviv | The benefits of design for postponement[END_REF][START_REF] Garg | Managing product variety: an operations perspective[END_REF][START_REF] Lee | Modelling the costs and benefits of delayed product differentiation[END_REF]. It is not until specialized components are inserted and/or special processes are performed that the items are progressively differentiated into specific end-products [START_REF] Lee | Modelling the costs and benefits of delayed product differentiation[END_REF][START_REF] Garg | On postponement strategies for product families with multiple points of differentiation[END_REF], referred to as the point of differentiation [e.g., [START_REF] Aviv | The benefits of design for postponement[END_REF][START_REF] Garg | On postponement strategies for product families with multiple points of differentiation[END_REF][START_REF] Tang | Perspectives in supply chain risk management[END_REF]. Each step taken to differentiate a product based on speculation involves a certain marketing risk [START_REF] Alderson | Marketing efficiency and the principle of postponement[END_REF], that is, risk and uncertainty costs tied to the differentiation of the good [START_REF] Yang | The application of postponement in industry[END_REF][START_REF] Bucklin | Postponement, speculation and the structure of distribution channels[END_REF]. The idea behind postponement is therefore to delay the points of differentiation, keeping products generic as long as possible. As such, despite the mixed answers about the definition of postponement, the general idea behind it is to reduce the risk of performing activities based on speculation (forecast), especially the activities related to variants and customization. Postponing such activities closer to or under a commitment from a customer order reduces the risk and uncertainty costs tied to the differentiation of goods [START_REF] Yang | The application of postponement in industry[END_REF][START_REF] Bucklin | Postponement, speculation and the structure of distribution channels[END_REF]. The concept of displacement (i.e., postponement and preponement) is thus tightly related to the flow-thinking ontology [see 33], specifically the flow driver, that is, the CODP, which is the point that separates the forecast-driven part of the flow from the customer-order-driven part of the flow [START_REF] Wikner | On decoupling points and decoupling zones[END_REF]. The position of the CODP then also coincides with the upstream part of the delivery lead time, which is the customer's requested delivery lead time [START_REF] Wikner | On decoupling points and decoupling zones[END_REF]. It is thus logical that many definitions of postponement use the concept of a customer order [e.g., [START_REF] Wang | Postponement in mass customization: a literature review[END_REF][START_REF] Yang | Implications of postponement for the supply chain[END_REF][START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF][START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF][START_REF] Van Hoek | The thesis of leagility revisited[END_REF][START_REF] Cooper | Logistics strategies for global businesses[END_REF][START_REF] Mikkola | Supply-chain integration: implications for mass customization, modularization and postponement strategies[END_REF][START_REF] Zinn | Planning physical distribution with the principle of postponement[END_REF][START_REF] Yang | The application of postponement in industry[END_REF][START_REF] Van Hoek | Logistics and virtual integration: postponement, outsourcing and the flow of information[END_REF][START_REF] Bowersox | Logistical management: the integrated supply chain process[END_REF][START_REF] Waller | Postponement, product customization, and market-oriented supply chain management[END_REF]. Pagh and Cooper [START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF] even state that the converse concept of postponement is speculation, that is, involving the activities carried out upstream of the CODP [START_REF] Wikner | On decoupling points and decoupling zones[END_REF]. Forza et al. [START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF] use the CODP as a point of departure for creating their form postponement typology, which offers a coherent picture of form postponement and its effects on operational performance. Forza et al. [START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF] also mention that if the point of differentiation would be moved upstream, rather than downstream, the logical opposite of form postponement would be achieved, called form preponement in this paper. Nevertheless, by defining form postponement or place postponement based on the customer order (i.e., the CODP), postponement can be achieved by simply extending the delivery lead time, that is, moving the CODP upstream in the flow. As such, several parts of the structure are added later and can therefore be considered postponed in relation to the CODP. This modification is illustrated in the time-phased bill of materials (BOM) in Fig. 1, where the left part of the figure is the state before the displacement and the right part is the displaced state (i.e., the postponed state). Before the postponement, the CODP is situated six time-units from the delivery of the product, that is, the delivery lead time consists of six time-units. In the postponed state, the delivery lead time is extended to ten time-units. As such, the transformation activities Q, U, V, and W are postponed in relation to customer requirements, where U, V, and W are even carried out after the receipt of a customer order. However, the word postpone means to put off to a later time, where pre means earlier than, prior to, or before. Postponement can thus be regarded as repositioning to a later point in time, with preponement being the opposite, repositioning to an earlier point in time. Both postponement and preponement are thus related to changes in an existing state, that is, repositioning an already existing position. For the example of Forza et al. [START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF], this means that the activities of form transformation are carried out closer to or upon request from a customer order (i.e., customer-order-driven). However, the form is still achieved at the same point within the BOM and the same time units before delivery. In other words, it still has the same flow structure, and the time required to perform the transformation activities remains the same. Arguably, the form has not been postponed. Even so, postponement and preponement are arguably tightly related to the CODP, but it might not be feasible to define postponement and preponement based on it, at least not for the concepts of form and place postponement.

Creating a typology for displacement

Both postponement and preponement have their base in the time dimension, meaning that a time-phased flow structure is a suitable starting point. This also basically means that both terms are based on displacement in relation to a fixed point of reference, which is the flow sink (customer) in this case (see Fig. 1). Regarding form transformation, it CODP is essentially about transforming raw materials into a finished product in terms of physical aspects, such as size, color, shape, and function. As for place transformation, it involves relating identity not only to what (form) but also to where (place), which is the meaning of the term stock-keeping units (SKU), covering both form and place. Place is therefore related to having something at one location (central) or at several locations (local). Another important property of transformation is time (when). One conventional way of combining form and place with time is through a time-phased BOM; for an example, see Fig. 1. In contrast to a traditional BOM, a time-phased BOM is usually illustrated horizontally to clearly illuminate the time dimension of the activities performed. Each segment of the structures in Fig. 1 represents different transformations in terms of form and place. The time phasing means that each transformation step is offset by the corresponding time for each segment, that is, the lead time. Displacement of form and place transformation can be achieved by either creating new flow structures or changing the time required to perform the transformation activities. From a form perspective, this means that through postponement, the form is achieved later in the flow, by modularization, using standardized modules instead of building complete unique products from scratch, for instance. On the contrary, preponement could be used to create variants of a previously standard product.

Postponement in terms of place transformation usually refers to the differentiation in location performed at a later point in time, for instance, by keeping the products in a centralized warehouse instead of distributing the goods to local warehouses. This can be formulated as product proliferation in terms of the place property, which is done at a later point in time. Conversely, preponement means that product proliferation in terms of the place property is done at an earlier point in time to ensure local availability, for instance.

The third dimension is then time. Displacement of time seems that time is counted twice, but here, the term time refers to two different things. Displacement has traditionally referred to the change of the point in time when a transformation is carried out in relation to the requested delivery lead time [see, e.g., 27]. Displacement therefore conforms with the definition of the CODP [START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF]. To summarize, displacement and form, place, and time transformation can be combined in six different ways, as shown in Table 1. Basically, there are two types of changes in state. For form and place, the change is related to flow proliferation, with an 'absolute' displacement since it is made in time units. Time is then related to the flow driver and a relative displacement since it pertains to the delivery lead time. This also means that for form and place, the points of departure are the individual points of transformation in the flow, but for time, the point of departure is the CODP, which has its starting point in the delivery lead time. The relevant literature provides a vast array of examples of postponement as used in practice. One of the more famous and disseminated examples is the case of Benetton [START_REF] Yang | Implications of postponement for the supply chain[END_REF][START_REF] Andries | Time-based manufacturing logistics[END_REF][START_REF] Lee | Postponement for mass customization: satisfying customer demands for tailormade products[END_REF]. The new typology for displacement is here applied to this example to illustrate how the transformation process is changed in terms of the three dimensions. Benetton's market strategy and product diversity are essentially based on colors [START_REF] Andries | Time-based manufacturing logistics[END_REF]. In fact, the company is currently known as the United Colors of Benetton (UCB). In its initial manufacturing process, the company dyed its yarn in different colors before knitting it into finished garments [START_REF] Yang | Implications of postponement for the supply chain[END_REF]. According to the company, this process resulted in too many garments in different colors that the customers did not want, whereas the garments that the customers wanted were sold out [START_REF] Yang | Implications of postponement for the supply chain[END_REF][START_REF] Lee | Postponement for mass customization: satisfying customer demands for tailormade products[END_REF]. UCB has since then changed the order of its dying and knitting process, using bleached yarn for knitting its garments and only dying them at a later stage when UCB either has received an order or has a better idea of which colors are selling [START_REF] Yang | Implications of postponement for the supply chain[END_REF]. Fig. 2 presents the UCB application of displacement. Note that the activities presented in the figure are based on the information provided by Yang and Burns [START_REF] Yang | Implications of postponement for the supply chain[END_REF]. However, the lead times and the time-phased BOMs are conceptually developed and merely used as illustrative examples when discussing the displacement in terms of the three dimensions. Note that each segment of the time-phased BOM represents the process to be performed and positioned as ending at the required point in time. In actual execution, different processes could be aggregated and performed simultaneously. For example, the two Qs at the very left of the 'before BOM' in Fig. 2 could be purchased at the same time despite having different requirement dates. By changing the order of knitting and dying, the point at which the garments are dyed is postponed, that is, form postponement. Obviously, the garments are dyed later but probably done in the same factory as before and sold in the same retail stores as before. As such, there is no place postponement. However, it is not clear if the CODP has been shifted in time. According to Lee [START_REF] Lee | Postponement for mass customization: satisfying customer demands for tailormade products[END_REF], in its before state, UCB used a make-to-stock strategy, manufacturing its finished products to stock, whereas in its after state, UCB now applies a build-to-order mode. As such, it could be argued that the delivery lead time has been extended (see Fig. 2), also including the dying of the garments. Thus, there is a time preponement. However, considering individual cases, in the new production process, some customers probably do not have to wait for garments that have As such, it could be argued that the delivery reliability has increased, in line with the reported improvement in customer service [START_REF] Lee | Postponement for mass customization: satisfying customer demands for tailormade products[END_REF].

Discussion, conclusions, and further research

The introduction states that there are mixed answers about the definition of postponement. In numerous scientific and managerial documents, postponement has been discussed as the delaying of activities, especially transformation activities associated with the differentiation of goods, and these activities should be postponed as long as possible [START_REF] Aviv | Design for postponement: a comprehensive characterization of its benefits under unknown demand distributions[END_REF][START_REF] Lee | Modelling the costs and benefits of delayed product differentiation[END_REF][START_REF] Li | Analysis of postponement strategy for perishable items by EOQ-based models[END_REF][START_REF] Yang | Management of uncertainty through postponement[END_REF][START_REF] Swaminathan | Managing broader product lines through delayed differentiation using vanilla boxes[END_REF][START_REF] García-Dastugue | Interorganizational time-based postponement in the supply chain[END_REF], closer to the CODP [START_REF] Cooper | Logistics strategies for global businesses[END_REF][START_REF] Mikkola | Supply-chain integration: implications for mass customization, modularization and postponement strategies[END_REF] or even to or downstream of the CODP [START_REF] Wang | Postponement in mass customization: a literature review[END_REF][START_REF] Yang | Implications of postponement for the supply chain[END_REF][START_REF] Pagh | Supply chain postponement and speculation strategies: how to choose the right strategy[END_REF][START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF][START_REF] Van Hoek | The thesis of leagility revisited[END_REF][START_REF] Zinn | Planning physical distribution with the principle of postponement[END_REF][START_REF] Yang | The application of postponement in industry[END_REF][START_REF] Van Hoek | Logistics and virtual integration: postponement, outsourcing and the flow of information[END_REF][START_REF] Bowersox | Logistical management: the integrated supply chain process[END_REF][START_REF] Waller | Postponement, product customization, and market-oriented supply chain management[END_REF]. Moreover, this way of perceiving postponement is a bit onedimensional; thus, some researchers have discussed postponement by using one or more of the three dimensions, that is, form, place, and time [e.g., [START_REF]World class logistics: the challenge of managing continuous change. 1st ed[END_REF][START_REF] Van Hoek | The rediscovery of postponement: a literature review and directions for research[END_REF][START_REF] Bowersox | Logistical management: the integrated supply chain process[END_REF][START_REF] Forza | Form postponement effects on operational performance: a typological theory[END_REF][START_REF] Aitken | Designing and managing multiple pipelines[END_REF]. By using these three dimensions of postponement and including preponement, a more nuanced understanding of displacement is gained, creating the ability to compare different cases of postponement and their operational benefits. As shown in the illustrative postponement example of UCB, even though the company has achieved form postponement, there is no place displacement, and the delivery lead time has even been somewhat extended, resulting in time preponement. Furthermore, when creating the typology presented in this paper, the meanings of the words prepone and postpone are used as starting points. As such, the point of reference is changed from the CODP to the initial time at which a transformation activity is conducted in terms of form or place. This way, form postponement or form preponement cannot be achieved simply by repositioning the CODP upstream and downstream of the flow, respectively, for instance. However, the CODP is still used in terms of time postponement and time preponement. As such, the form and the place dimensions are related to whether a transformation activity is conducted based on speculation or on a commitment from a customer. In this way, the operational results of postponement and preponement can be taken into consideration, for instance, if an activity after the displacement can be performed based on a commitment from a customer, rather than on speculation. As such, the typology relates back to Alderson's [START_REF] Alderson | Marketing efficiency and the principle of postponement[END_REF] novel view of postponement as illuminating the risks and the uncertainties of carrying out certain activities based on speculation.

To conclude, the typology presented here uses the existing dimensions of postponement, that is, form, place, and time. However, form and place are not defined using the CODP, thereby excluding the view of merely repositioning the CODP as being the postponement of form and place. The typology also includes preponement, realizing that an activity can be preponed. As such, a typology for displacement (i.e., postponement and preponement) has been established. By utilizing flow thinking [START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF] and the CODP, a better understanding of the operational effects of a preponement or a postponement decision is also obtained. The established typology for displacement thus complements the literature on postponement and offers a more nuanced understanding of it. In turn, the typology can help managers make more nuanced displacement decisions by more clearly distinguishing between flow driver and flow differentiation in terms of form and place.

However, further research could include specific studies on customization and variety in combination with preponement and postponement. Here, flow thinking, specifically the customer adaptation decoupling point and the system lead time [see, e.g., [START_REF] Wikner | An ontology for flow thinking based on decoupling pointsunravelling a control logic for lean thinking[END_REF][START_REF] Wikner | On decoupling points and decoupling zones[END_REF], could assist in providing a better understanding of the operational implications of preponing or postponing a customization activity upstream or downstream of the flow, as well as whether a delivery-unique variant can be offered.

Fig. 1 .

 1 Fig. 1. Before versus after displacement, shifting the CODP upstream [based on 34].

  transformation activity performed earlier in the flow structure A form transformation activity performed later in the flow structure Place A place transformation activity performed earlier in the flow structure A place transformation activity performed later in the flow structure Time CODP earlier in the flow structure CODP later in the flow structure 4 Applying the typology for displacement

Fig. 2 .

 2 Fig. 2. Before versus after displacement carried out at UCB.

  yarn U = Dye yarn/garments V = Finish yarn/garments W and X = Manufacture garment parts Y = Join parts Z

Table 1 .

 1 Combinations of displacement and form, place, and time properties.

Acknowledgement

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


Masoud Zafarzadeh
email: masoudz@kth.se

Jannicke Baalsrud Hauge

Magnus Wiktorsson

Ida Hedman

Jasmin Bahtijarevic

Real-time data sharing in production logistics: Exploring use cases by an industrial study

Keywords: Production logistics, Real-time data, Efficiency

Production logistics systems consist often of a number of low valueadded activities combined with a high degree of manual work. Therefore, increasing effectivity and responsiveness has always been a target for production logistics systems. Sharing data in real-time may have a considerable potential to increase effectivity and responsiveness. The first step to realise real-time data sharing is to have a clear understanding of current state of PL systems and their requirements. This work is performed an 'as-is' situation analysis of an industrial case aiming at identifying which areas and applications would benefit most from real-time data sharing. The findings take a step closer to have a better understanding of CPS and Industry4.0.

Introduction

Production logistics (PL) systems comprises often a number of low value-added activities combined with a high degree of manual work [START_REF] Granlund | Automation in internal logistics: strategic and operational challenges[END_REF]. To improve this situation, automating the operations and streamlining the processes is put on the agenda for companies [START_REF] Granlund | Automation in internal logistics: strategic and operational challenges[END_REF]. This transition may often be stepwise, and thus it is not unusual that companies have inhomogeneous technologies within logistics systems, which again may reduce the overall effect of the implementation of the technology component. Typically, problems arising are incompatibility and lack of interoperability across the system leading to low information visibility causing long response time and low efficiency in PL systems [START_REF] Khurana | Barriers to Information Sharing in Supply Chain of Manufacturing Industries[END_REF]. High information visibility allows an improved decision making process and is therefore an important factor for improving the value of PL systems for stakeholders [START_REF] Brahim-Djelloul | Real-time information management in supply chain modelling tools[END_REF]. Furthermore, it can mitigate information sharing challenges caused by implementing inhomogeneous technologies. One of the main items facilitating effective information sharing is real-time data sharing [START_REF] Kagermann | Recommendations for implementing the strategic initiative INDUSTRIE 4.0[END_REF]. Defining real-time information is a matter of conflict but Brahim et al. [START_REF] Brahim-Djelloul | Real-time information management in supply chain modelling tools[END_REF] defined it as "information that constantly allows action on the system in order to react rapidly and in suitable ways with respect to environment dynamics". In line with this definition, it is argued that the system is considered realtime when the information is still valid and relevant after collection and processing [START_REF] Brahim-Djelloul | Real-time information management in supply chain modelling tools[END_REF].

Reviewing the literature shows that using information flow in real-time has some benefits for PL systems in terms of integrated end-to-end delivery planning, more accurate material delivery and increased flexibility [START_REF] Hoffman | Industry 4.0 and the current status as well as future prospects on logistics[END_REF]. Real-time data enables the PL systems to have agile and swift reaction toward changes and unplanned events and can help to improve aligned decision-making among different stakeholders [START_REF] Ward | Impact of Information Technology Integration and Lean/Just-In-Time Practices on Lead-Time Performance*[END_REF][START_REF] Cantor | Maximizing the Potential of Contemporary Workplace Monitoring[END_REF]. Access to realtime data contributes to a reduction of bullwhip effects, reduction of misplacement and theft, reduction of identification errors, better replenishment policies, better scheduling, securing dangerous goods and temperature collected goods, improved traceability of products in the routing and improved the distribution planning are some other examples of these benefits [START_REF] Brahim-Djelloul | Real-time information management in supply chain modelling tools[END_REF]. Besides all these, real-time data is an imperative for any CPS (cyber-physical system) and Industry4.0 concept [START_REF] Kagermann | Recommendations for implementing the strategic initiative INDUSTRIE 4.0[END_REF][START_REF] Obitko | Big data semantics in industry 4.0. industrial applications of holonic and multi-agent systems, Holomas[END_REF]. So far, researches have tried to clarify these concepts and their applications for PL systems. For example, Kagermann et al. [START_REF] Kagermann | Recommendations for implementing the strategic initiative INDUSTRIE 4.0[END_REF] have depicted a stepwise digitalisation model for companies where collecting data in real-time is necessary for successful transition toward Industry4.0. [START_REF] Lee | The Internet of Things (IoT): Applications, investments, and challenges for enterprises[END_REF]. Besides, cloud computing plays an important role to use IoT technologies more efficient in order to create CPS systems [START_REF] Wang | Cloud-Based Cyber-Physical Systems in Manufacturing[END_REF]. PL systems are complex and have high dynamics [START_REF] Granlund | Automation in internal logistics: strategic and operational challenges[END_REF][START_REF] Brahim-Djelloul | Real-time information management in supply chain modelling tools[END_REF] and introducing new concepts and technologies that enable real-time data sharing require clear understanding of their existing situation. Authors have found out most of the existing researches in this area, are still on conceptual level and there are less empirical study. To be able to realise these concepts in real PL systems, first it is essential to understand what areas and processes in PL systems need more attention and can benefit most from real-time data sharing. Therefore, the aim of this paper is to identify real-time data sharing use cases in PL systems by analysing the current state of a case study in production logistics. At the end, some suggestions regarding enabling technologies are shortly discussed.

Research methodology and approach

According to Yin [START_REF] Yin | Case study research : design and methods[END_REF] case study has the potential to investigate a phenomenon in its context. It seems the number of empirical researches in this area is rather scarce [START_REF] Ruiz | The Internet of Things, Factory of Things and Industry 4.0 in Manufacturing : Current and Future Implementations[END_REF], and it is still needed to find out solutions on individual level. Besides, because of the complex nature of this phenomenon, case study can be an appropriate method to shed a light on some specific problems. This might even help to address more generic solutions based on this case study. The case company has been visited several times, and experts are interviewed to provide the current state regarding PL processes as well as describing the company's vision regarding PL. In addition, a group of managers in the case company has been participated in some meetings with the authors in order to clarify and confirm the case company's current and future plans. Some other data regarding logistical routines and procedures are reviewed by the authors to find a more detailed picture.

Case study

AstraZeneca is an international producer of highly advance pharmaceutical, has almost 61100 employees in 18 production sites and supplies to over 100 different markets.

They pay enormous attention to product and process quality in all parts of their supply chain. AstraZeneca has recognized that their logistics processes can be improved, that there is a low degree of information transparency and that they have inhomogeneous technologies in terms of both interoperability and maturity. Therefore, AstraZeneca is a good representative of an industry in digital transition facing the problems described above, and thus very suitable as a case study. One of the major production sites located in Sweden consists of central warehouse, high-bay, fixed bearing, cold chain and picking storages for warehousing system. Today in average, each item needs 20 touches to be able to be transported from initial stages to the end of process. AstraZeneca envision an "one-touch" strategy in order to have more efficient and responsive process flow within its production logistics with inhomogeneous level of automation and interconnectivity. The research presented here is partly carried out within the project 'DIGILOG' [START_REF]Digilog[END_REF] and partly as a practical case study for undergraduate students at KTH University. The next section will first present the AS-IS analysis including an analysis of current problems, real-time data sharing use cases and some possible technical solutions to overcome the challenges.

Production logistics at AstraZeneca

The focus of the study is the analysis of the two areas 'Goods receiving' and 'internal material handling' at AstraZeneca. Figure 1 shows the material flow in the case company. The figure is numbered 1 to 4 for sake of easier read. Each number represents one sub-process and contains relevant activities. For internal logistics, after unloading from trucks (1), the pallets go through quality control [START_REF] Khurana | Barriers to Information Sharing in Supply Chain of Manufacturing Industries[END_REF]. Then, after barcode scanning, they will be transferred to pallet exchange zone since some of the wooden pallets need to be changed to hygiene pallets (3). Next, the pallets will be send to the transit hall to be shipped to 'fixed bearing' storage, semi-finished storage, cold storage or directly to the production area (4). Material movement happens in several floors by means of trucks and by using elevators. Before and after storing the items as well as quality control, the ERP (Enterprise Resource Planning) system must be updated by the operators.

Real-time data sharing use cases at AstraZeneca

AstraZeneca's strategy to automate some of the PL processes as well as existing issues in PL, are two major forces motivate real-time data sharing in order to meet one-touch vision objectives. Issues mentioned here are those that are somehow affected by data sharing among different stakeholders. This is reflected in figure 2. Each of these driving forces are more discussed in the next step, which lead to find use cases for each of these categories.

Fig. 2. Real-time data sharing in the context of production logistics at AstraZeneca

A. Use cases to address existing issues. After reviewing the PL process, interviewing experts at AstraZeneca and analysing the results of student projects, following issues are identified. 1. For goods receiving, there is no information regarding the arrival time, type of items and the exact quantity of the ordered items from the central warehouse. Logistics personnel become aware when trucks arrive to the loading docks.

2. Similar issue exist when parts are shipped from transit hall to the production areas, which do not receive information regarding type, quantity and arrival time. Logistics personnel act based on their experience to collect received items.

3. Transportation of wastes is logistics duty. Logistics personnel move around regularly and remove waste bins. Sometimes there is no waste to be removed and it has already caused unnecessary movement for the personnel because of lack of information.

4. Destinations information are stored in barcodes, labeled on each of the pallets. Operators use laser scanners to read the tags on the pallets making sure items are delivering to the right destination. Sometimes labels have poor condition for scanning. The information visibility is rather low and manual scanning can be considered as low values-added activity.

5. Storing of materials into picking storage has no specific order and there are no information on what is the exact address of each pallet and can be placed in any available storage position. After placement, the storage address is updated in the system for later retrieval.

6. Transporting pallets trough elevators handled by trucks. Since there is no information regarding the availability of the elevators, sometimes truck drivers have to wait to access an available elevator or even deal with unavailable elevators. In general, the elevators have no means of connection and information sharing with the world around.

7. The ERP system needs to be updated in several stages like storing the items, retrieval of the items and registering quality inspection results. All of these happens manually creating low value-added activities for the operators.

B. Use cases causes by planned automation.

AstraZeneca wishes to automate some of the existing processes. Following describes what information is required to have effective implementation of automation.

1. Automated material transportationvertically and horizontally. In this case, these data should be shared in real-time: location, estimated travel time, operational status and destination. To be able to use elevators: elevators position, operational data, maintenance data, estimated travel time and capacity of the elevator should be shared in real-time with the automated transportation system.

2. Automated visual quality inspection. Quality inspection does not only concern finding defects but also identifying the correct item. Identification data, quantity of items and picking location needs to be shared in real-time with both transportation system and the ERP system.

3. Automated storing and retrieval of parts from picking storage. Inventory level of each item needs to be shared with the ERP system in real-time. To be able to make the automated storing and retrieval system effective: location, estimated travel time, operational status and destination needs to be shared among the system agents. This enables the system to plan and schedule dynamically and avoid any probable collision.

4. Automated communication between conveyor and transportation system. Data regarding the location and positioning of items placed on the conveyor belts need to be communicated with both the conveyor system and the transportation system.

Real-time data sharing and enabling technologies

Based upon the analysis of which information required to overcome the challenges identified in section 3.2 it can be summarized that the following information needs to be provided in real-time: In the next step, a brief explanation regarding available technologies and their use in PL processes is presented. Types and numbers of existing technologies in this area varies. To decide what technology might be suitable, communication capability, flexibility, technology maturity and spatial constraints considered. However, some other criteria such as cost and AstraZeneca's competence to embrace these technologies have not been considered either because there were no information available or because it could severely limit the study. Means for truck unloading can varies from using AGV's, conveyor belt or crane system. Even though a more detailed study is needed to find the correct technology for implementation, but considering flexibility, implementation effort and compatibility with other technologies, using AGV seems more reasonable at this stage. Types, variations, and navigation techniques can be different: some follow magnets on the floor while some of the other types use radio waves or lasers. 'Toyota' [17] and 'Rocla' [18] are two examples of AGVs that use laser for navigation [START_REF] Feledy | A State of the Art Map of the AGVS Technology and a Guideline for How and Where to Use It[END_REF]. Another example is 'MIR', which scan the area and then use camera for navigation. The advantage of the solutions similar to MIR is the 'fleet management system', which enables the logistics system to plan and schedule multiple transportation simultaneously [20]. Besides, WSN can support material delivery to and from the conveyor belt system to identify when items should be delivered and identify any probable stoppage in the line. For pallet exchanging, automated pallet exchange machines or automated load transfer systems can be used. An example can be load transfer system from "Cherry's Industrial Equipment" [21]. To make material movement through elevators more efficient, connecting WSN and RTLS with AGV fleet management can ensure that items will be transported vertically fully automated with possibly shorter delivery time. Types of sensors, interconnection methods need further analysis. Regarding the quality control process, in addition to using RFID and industrial cameras, using dimension-measuring technologies might be necessary. An example can be VIPAC D3 [22] uses infrared laser scanners. AS/RS solutions such as Autostore [START_REF]Swisslog hoepage[END_REF] can be considered for automating the process. Implementing AS/RS system needs heavy investment, which makes this suggestion not critical for the first step.

Discussion and conclusion

This paper has identifies use cases for real-time data sharing in PL by investigating an industrial case study at AstraZeneca. Finding shows, PL systems should have access to data within a specific time farm to make the PL system effective and responsive. Otherwise, data might only have value for historical analysis and not for immediate use of other stakeholders such as production line units. Goods receiving, delivery to production lines, waste transportation, storing and retrieval from storages, transportation through elevators and updating ERP system will benefit most from sharing data in realtime. Findings of this case study reveals that two major issues exist in the case company. The first issue is lack of logistical data such as arrival time, shipments quantity, shipments location and transportation equipment position. The second issue is the difficulty to retrieve the data such as difficulty in reading barcodes, difficulty to find storage address and difficulty to update the ERP system in several occasions. In order to deal with these issues, sharing data in real-time is demanded. At first, some of the processes need to be automated not only to facilitate real-time data sharing but also to increase effectivity and make the system less vulnerable against problems causes by human factors. Some automation suggestions are presented in section 3. It should be noted that automation create some new requirements for data sharing which are discussed in the form of use cases in section 3. Further research can focus on evaluating the results of real-time data sharing considering enabling technologies.

Fig. 1 .

 1 Fig. 1. Schematic flow of production logistics-AstraZeneca

( 1 )

 1 Location and position; (2) Operational data (Type of mission, availability for the next mission); (3) Time (estimated arrival time); (4) Items identification (type and quantity of the data); (5) Maintenance data (service time, service appointment etc.); (6) Quality inspection results (Ok, not Ok); (7) Speed (to be used for fleet management); (8) Parts temperature. Next step therefore is a suggestion to map what technologies exist that can ensure real-time data sharing for AstraZeneca.

Table1.

  Technologies ensuring real-time data sharing in AstraZeneca PL processes. Technology Data Process AGV fleet management (Automated Guided Vehicle) Location, Operational data, Time, Maintenance data, Speed -Unloading from the trucks -MM (Material movement) between quality zone and the conveyer system -MM from and to the drop-zone -MM through the elevators -MM from the warehouse to the production RTLS (Real-time Locating System) Location -MM between quality zone and the conveyer system -MM from and to the drop-zone -MM through the elevators -MM from the warehouse to the production RFID control -MM through the elevators -Drop-zone AS/RS (Automated storage and retrieval system) Location, Operational data, Time, Maintenance data, Speed, Items identification -Storage and retrieval from the picking storage -Updating the ERP system

  between physical world and cyber space. Tu et al.[START_REF] Tu | IoT-based production logistics and supply chain system -Part 2[END_REF] have developed an emulation testbed in order to evaluate the effects of implementing IoT based CPS on PL activities. Most of the activates in their testbed are heavily depend on the real-time data, collects by mean of IoT technologies. Means of implementation are different but some examples can be categorized as IoT technologies like RFID, Real-time Locating System (RTLS) and Wireless Sensor Networks (WSN)

Qu et al.

[START_REF] Qu | A case of implementing RFID-based realtime shop-floor material management for household electrical appliance manufacturers[END_REF]

have discussed AUTOM infrastructure, which facilitates real-time communication and helps to have a better resource management in production logistics. AUTOM infrastructure explains a general process of creating Radio Frequency Identification (RFID enabled shop floor. Lee et al.

[START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems[END_REF]

have proposed 5C architecture for CPS implementation, which ensures real-time data circulation

Table 1

 1 listed these technologies what data they should share in real-time to support PL processes in AstraZeneca. Part of this work has been carried out by KTH students at ML1136 in January and February 2018.

Acknowledgment. The authors would like to acknowledge the financial support from Vinnova and Produktion2030 to the project DigiLog.